2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.patch_embed.proj.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.patch_embed.proj.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.patch_embed.proj.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.patch_embed.proj.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.patch_embed.norm.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.patch_embed.norm.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.patch_embed.norm.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.patch_embed.norm.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm1.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm1.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.relative_position_bias_table: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.qkv.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.qkv.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.qkv.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.qkv.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.proj.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.proj.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.proj.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.proj.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm2.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm2.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm2.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc1.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc1.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc2.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc2.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc2.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm1.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm1.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.relative_position_bias_table: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.qkv.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.qkv.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.qkv.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.qkv.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.proj.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.proj.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.proj.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.proj.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm2.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm2.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm2.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc1.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc1.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc2.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc2.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc2.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.downsample.reduction.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.downsample.reduction.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.downsample.norm.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.downsample.norm.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.downsample.norm.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.0.downsample.norm.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm1.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm1.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.relative_position_bias_table: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.qkv.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.qkv.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.qkv.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.qkv.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.proj.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.proj.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.proj.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.proj.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm2.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm2.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm2.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc1.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc1.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc2.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc2.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc2.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm1.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm1.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.relative_position_bias_table: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.qkv.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.qkv.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.qkv.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.qkv.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.proj.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.proj.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.proj.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.proj.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm2.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm2.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm2.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc1.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc1.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc2.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc2.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc2.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.downsample.reduction.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.downsample.reduction.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.downsample.norm.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.downsample.norm.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.downsample.norm.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.1.downsample.norm.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm1.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm1.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.relative_position_bias_table: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.qkv.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.qkv.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.qkv.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.qkv.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.proj.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.proj.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.proj.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.proj.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm2.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm2.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm2.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc1.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc1.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc2.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc2.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc2.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm1.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm1.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.relative_position_bias_table: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.qkv.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.qkv.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.qkv.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.qkv.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.proj.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.proj.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.proj.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.proj.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm2.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm2.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm2.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc1.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc1.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc2.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc2.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc2.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.norm1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.norm1.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.norm1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.norm1.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.relative_position_bias_table: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.qkv.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - 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mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.norm2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.norm2.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc1.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc1.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc2.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc2.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc2.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.norm1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.norm1.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.norm1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.norm1.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.relative_position_bias_table: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.qkv.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - 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mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.norm2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.norm2.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc1.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc1.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc2.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc2.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc2.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.norm1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.norm1.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.norm1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.norm1.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.relative_position_bias_table: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.qkv.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - 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mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.norm2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.norm2.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc1.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc1.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc2.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc2.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc2.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm1.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm1.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.relative_position_bias_table: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.qkv.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.qkv.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.qkv.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.qkv.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.proj.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.proj.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.proj.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.proj.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm2.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm2.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm2.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc1.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc1.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc2.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc2.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc2.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.downsample.reduction.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.downsample.reduction.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.downsample.norm.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.downsample.norm.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.downsample.norm.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.2.downsample.norm.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.norm1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.norm1.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.norm1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.norm1.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.relative_position_bias_table: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.qkv.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.qkv.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.qkv.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.qkv.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - 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mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc1.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc1.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc2.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc2.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc2.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm1.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm1.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.relative_position_bias_table: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.qkv.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.qkv.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.qkv.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.qkv.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.proj.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.proj.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.proj.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.proj.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm2.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm2.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm2.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc1.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc1.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc1.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc1.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc2.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc2.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc2.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc2.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.norm.weight: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.norm.weight: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.norm.bias: lr = 0.0001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- backbone.norm.bias: weight_decay = 0.0 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- cls_head.fc_cls.weight: lr = 0.001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- cls_head.fc_cls.weight: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- cls_head.fc_cls.bias: lr = 0.001 2022/07/31 11:34:45 - mmengine - INFO - paramwise_options -- cls_head.fc_cls.bias: weight_decay = 0.02 2022/07/31 11:34:45 - mmengine - INFO - load model from: https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth 2022/07/31 11:34:47 - 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.3.blocks.0.attn.relative_position_index', 'layers.3.blocks.1.attn.relative_position_index'], unexpected_keys=['head.weight', 'head.bias']) 2022/07/31 11:34:47 - mmengine - INFO - => loaded successfully 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' 2022/07/31 11:34:47 - mmengine - INFO - Checkpoints will be saved to /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/swin-t-2 by HardDiskBackend. 2022/07/31 11:37:35 - mmengine - INFO - Epoch(train) [1][100/3757] lr: 1.0949e-05 eta: 2 days, 4:31:07 time: 0.3748 data_time: 0.0126 memory: 21072 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 5.8829 loss: 5.8829 2022/07/31 11:38:13 - mmengine - INFO - Epoch(train) [1][200/3757] lr: 1.1907e-05 eta: 1 day, 8:06:19 time: 0.3700 data_time: 0.0127 memory: 21072 top1_acc: 0.0000 top5_acc: 0.1250 loss_cls: 5.3112 loss: 5.3112 2022/07/31 11:38:50 - mmengine - INFO - Epoch(train) [1][300/3757] lr: 1.2865e-05 eta: 1 day, 1:16:25 time: 0.3738 data_time: 0.0128 memory: 21072 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 4.5156 loss: 4.5156 2022/07/31 11:39:28 - mmengine - INFO - Epoch(train) [1][400/3757] lr: 1.3824e-05 eta: 21:51:20 time: 0.3715 data_time: 0.0129 memory: 21072 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 4.2753 loss: 4.2753 2022/07/31 11:40:05 - mmengine - INFO - Epoch(train) [1][500/3757] lr: 1.4782e-05 eta: 19:47:59 time: 0.3726 data_time: 0.0125 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 3.7562 loss: 3.7562 2022/07/31 11:40:43 - mmengine - INFO - Epoch(train) [1][600/3757] lr: 1.5740e-05 eta: 18:25:39 time: 0.3718 data_time: 0.0138 memory: 21072 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 3.6080 loss: 3.6080 2022/07/31 11:41:20 - mmengine - INFO - Epoch(train) [1][700/3757] lr: 1.6699e-05 eta: 17:26:52 time: 0.3738 data_time: 0.0135 memory: 21072 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.4771 loss: 3.4771 2022/07/31 11:41:58 - mmengine - INFO - Epoch(train) [1][800/3757] lr: 1.7657e-05 eta: 16:42:43 time: 0.3728 data_time: 0.0135 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 3.4654 loss: 3.4654 2022/07/31 11:42:35 - mmengine - INFO - Epoch(train) [1][900/3757] lr: 1.8615e-05 eta: 16:08:12 time: 0.3729 data_time: 0.0136 memory: 21072 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 3.4257 loss: 3.4257 2022/07/31 11:43:13 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 11:43:13 - mmengine - INFO - Epoch(train) [1][1000/3757] lr: 1.9574e-05 eta: 15:40:28 time: 0.3714 data_time: 0.0144 memory: 21072 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 3.4145 loss: 3.4145 2022/07/31 11:43:50 - mmengine - INFO - Epoch(train) [1][1100/3757] lr: 2.0532e-05 eta: 15:17:55 time: 0.3728 data_time: 0.0144 memory: 21072 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.1279 loss: 3.1279 2022/07/31 11:44:28 - mmengine - INFO - Epoch(train) [1][1200/3757] lr: 2.1490e-05 eta: 14:58:57 time: 0.3717 data_time: 0.0136 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 3.0850 loss: 3.0850 2022/07/31 11:45:05 - mmengine - INFO - Epoch(train) [1][1300/3757] lr: 2.2448e-05 eta: 14:42:33 time: 0.3743 data_time: 0.0136 memory: 21072 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 3.1501 loss: 3.1501 2022/07/31 11:45:43 - mmengine - INFO - Epoch(train) [1][1400/3757] lr: 2.3407e-05 eta: 14:28:30 time: 0.3740 data_time: 0.0144 memory: 21072 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.8903 loss: 2.8903 2022/07/31 11:46:21 - mmengine - INFO - Epoch(train) [1][1500/3757] lr: 2.4365e-05 eta: 14:16:34 time: 0.3808 data_time: 0.0166 memory: 21072 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.9960 loss: 2.9960 2022/07/31 11:46:59 - mmengine - INFO - Epoch(train) [1][1600/3757] lr: 2.5323e-05 eta: 14:06:17 time: 0.3807 data_time: 0.0150 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.9542 loss: 2.9542 2022/07/31 11:47:36 - mmengine - INFO - Epoch(train) [1][1700/3757] lr: 2.6282e-05 eta: 13:56:51 time: 0.3850 data_time: 0.0159 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.6595 loss: 2.6595 2022/07/31 11:48:14 - mmengine - INFO - Epoch(train) [1][1800/3757] lr: 2.7240e-05 eta: 13:48:25 time: 0.3740 data_time: 0.0131 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.5788 loss: 2.5788 2022/07/31 11:48:52 - mmengine - INFO - Epoch(train) [1][1900/3757] lr: 2.8198e-05 eta: 13:40:49 time: 0.3729 data_time: 0.0141 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 3.1463 loss: 3.1463 2022/07/31 11:49:30 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 11:49:30 - mmengine - INFO - Epoch(train) [1][2000/3757] lr: 2.9157e-05 eta: 13:33:48 time: 0.3720 data_time: 0.0141 memory: 21072 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.8614 loss: 2.8614 2022/07/31 11:50:08 - mmengine - INFO - Epoch(train) [1][2100/3757] lr: 3.0115e-05 eta: 13:27:38 time: 0.3742 data_time: 0.0141 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.8227 loss: 2.8227 2022/07/31 11:50:47 - mmengine - INFO - Epoch(train) [1][2200/3757] lr: 3.1073e-05 eta: 13:23:15 time: 0.3758 data_time: 0.0154 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.6729 loss: 2.6729 2022/07/31 11:51:25 - mmengine - INFO - Epoch(train) [1][2300/3757] lr: 3.2032e-05 eta: 13:18:25 time: 0.3756 data_time: 0.0170 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.7100 loss: 2.7100 2022/07/31 11:52:05 - mmengine - INFO - Epoch(train) [1][2400/3757] lr: 3.2990e-05 eta: 13:14:31 time: 0.4197 data_time: 0.0136 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.6450 loss: 2.6450 2022/07/31 11:52:43 - mmengine - INFO - Epoch(train) [1][2500/3757] lr: 3.3948e-05 eta: 13:09:55 time: 0.3762 data_time: 0.0158 memory: 21072 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.7067 loss: 2.7067 2022/07/31 11:53:21 - mmengine - INFO - Epoch(train) [1][2600/3757] lr: 3.4907e-05 eta: 13:05:47 time: 0.3863 data_time: 0.0176 memory: 21072 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.6589 loss: 2.6589 2022/07/31 11:53:59 - mmengine - INFO - Epoch(train) [1][2700/3757] lr: 3.5865e-05 eta: 13:01:39 time: 0.3794 data_time: 0.0160 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.5427 loss: 2.5427 2022/07/31 11:54:37 - mmengine - INFO - Epoch(train) [1][2800/3757] lr: 3.6823e-05 eta: 12:57:55 time: 0.3841 data_time: 0.0170 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.7198 loss: 2.7198 2022/07/31 11:55:15 - mmengine - INFO - Epoch(train) [1][2900/3757] lr: 3.7782e-05 eta: 12:54:45 time: 0.4175 data_time: 0.0151 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.6085 loss: 2.6085 2022/07/31 11:55:58 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 11:55:58 - mmengine - INFO - Epoch(train) [1][3000/3757] lr: 3.8740e-05 eta: 12:54:13 time: 0.4251 data_time: 0.0147 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4506 loss: 2.4506 2022/07/31 11:56:39 - mmengine - INFO - Epoch(train) [1][3100/3757] lr: 3.9698e-05 eta: 12:52:57 time: 0.3899 data_time: 0.0178 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4284 loss: 2.4284 2022/07/31 11:57:23 - mmengine - INFO - Epoch(train) [1][3200/3757] lr: 4.0656e-05 eta: 12:53:05 time: 0.3834 data_time: 0.0172 memory: 21072 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.6513 loss: 2.6513 2022/07/31 11:58:01 - mmengine - INFO - Epoch(train) [1][3300/3757] lr: 4.1615e-05 eta: 12:50:00 time: 0.3849 data_time: 0.0169 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.6947 loss: 2.6947 2022/07/31 11:58:39 - mmengine - INFO - Epoch(train) [1][3400/3757] lr: 4.2573e-05 eta: 12:47:03 time: 0.3814 data_time: 0.0169 memory: 21072 top1_acc: 0.0000 top5_acc: 0.2500 loss_cls: 2.6941 loss: 2.6941 2022/07/31 11:59:17 - mmengine - INFO - Epoch(train) [1][3500/3757] lr: 4.3531e-05 eta: 12:44:23 time: 0.3875 data_time: 0.0159 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.9728 loss: 2.9728 2022/07/31 11:59:55 - mmengine - INFO - Epoch(train) [1][3600/3757] lr: 4.4490e-05 eta: 12:41:32 time: 0.3757 data_time: 0.0162 memory: 21072 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.8403 loss: 2.8403 2022/07/31 12:00:36 - mmengine - INFO - Epoch(train) [1][3700/3757] lr: 4.5448e-05 eta: 12:40:35 time: 0.3914 data_time: 0.0177 memory: 21072 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.5515 loss: 2.5515 2022/07/31 12:00:58 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 12:00:58 - mmengine - INFO - Epoch(train) [1][3757/3757] lr: 4.5994e-05 eta: 12:39:31 time: 0.3709 data_time: 0.0156 memory: 21072 top1_acc: 0.2857 top5_acc: 0.4286 loss_cls: 2.7063 loss: 2.7063 2022/07/31 12:01:38 - mmengine - INFO - Epoch(train) [2][100/3757] lr: 4.6824e-05 eta: 12:34:31 time: 0.3795 data_time: 0.0163 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 2.5083 loss: 2.5083 2022/07/31 12:02:16 - mmengine - INFO - Epoch(train) [2][200/3757] lr: 4.7780e-05 eta: 12:32:19 time: 0.3899 data_time: 0.0178 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.5030 loss: 2.5030 2022/07/31 12:02:33 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 12:02:54 - mmengine - INFO - Epoch(train) [2][300/3757] lr: 4.8735e-05 eta: 12:30:01 time: 0.3759 data_time: 0.0160 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.3323 loss: 2.3323 2022/07/31 12:03:32 - mmengine - INFO - Epoch(train) [2][400/3757] lr: 4.9691e-05 eta: 12:27:53 time: 0.3787 data_time: 0.0161 memory: 21072 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.3108 loss: 2.3108 2022/07/31 12:04:10 - mmengine - INFO - Epoch(train) [2][500/3757] lr: 5.0647e-05 eta: 12:25:46 time: 0.3785 data_time: 0.0170 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1369 loss: 2.1369 2022/07/31 12:04:48 - mmengine - INFO - Epoch(train) [2][600/3757] lr: 5.1602e-05 eta: 12:23:43 time: 0.3771 data_time: 0.0166 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2247 loss: 2.2247 2022/07/31 12:05:27 - mmengine - INFO - Epoch(train) [2][700/3757] lr: 5.2558e-05 eta: 12:21:50 time: 0.3822 data_time: 0.0156 memory: 21072 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.4929 loss: 2.4929 2022/07/31 12:06:05 - mmengine - INFO - Epoch(train) [2][800/3757] lr: 5.3514e-05 eta: 12:20:06 time: 0.3774 data_time: 0.0164 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.3723 loss: 2.3723 2022/07/31 12:06:43 - mmengine - INFO - Epoch(train) [2][900/3757] lr: 5.4469e-05 eta: 12:18:16 time: 0.3860 data_time: 0.0167 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.7149 loss: 2.7149 2022/07/31 12:07:21 - mmengine - INFO - Epoch(train) [2][1000/3757] lr: 5.5425e-05 eta: 12:16:26 time: 0.3822 data_time: 0.0169 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.5294 loss: 2.5294 2022/07/31 12:07:59 - mmengine - INFO - Epoch(train) [2][1100/3757] lr: 5.6381e-05 eta: 12:14:39 time: 0.3791 data_time: 0.0167 memory: 21072 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.5078 loss: 2.5078 2022/07/31 12:08:37 - mmengine - INFO - Epoch(train) [2][1200/3757] lr: 5.7337e-05 eta: 12:13:01 time: 0.3906 data_time: 0.0177 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4059 loss: 2.4059 2022/07/31 12:08:54 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 12:09:16 - mmengine - INFO - Epoch(train) [2][1300/3757] lr: 5.8292e-05 eta: 12:11:40 time: 0.3742 data_time: 0.0122 memory: 21072 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 3.0368 loss: 3.0368 2022/07/31 12:09:54 - mmengine - INFO - Epoch(train) [2][1400/3757] lr: 5.9248e-05 eta: 12:10:01 time: 0.3777 data_time: 0.0166 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.4055 loss: 2.4055 2022/07/31 12:10:32 - mmengine - INFO - Epoch(train) [2][1500/3757] lr: 6.0204e-05 eta: 12:08:25 time: 0.3773 data_time: 0.0164 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3730 loss: 2.3730 2022/07/31 12:11:10 - mmengine - INFO - Epoch(train) [2][1600/3757] lr: 6.1159e-05 eta: 12:06:54 time: 0.3806 data_time: 0.0161 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1863 loss: 2.1863 2022/07/31 12:11:48 - mmengine - INFO - Epoch(train) [2][1700/3757] lr: 6.2115e-05 eta: 12:05:21 time: 0.3780 data_time: 0.0170 memory: 21072 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.3752 loss: 2.3752 2022/07/31 12:12:26 - mmengine - INFO - Epoch(train) [2][1800/3757] lr: 6.3071e-05 eta: 12:03:52 time: 0.3771 data_time: 0.0165 memory: 21072 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 2.1167 loss: 2.1167 2022/07/31 12:13:05 - mmengine - INFO - Epoch(train) [2][1900/3757] lr: 6.4026e-05 eta: 12:02:30 time: 0.3759 data_time: 0.0157 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4243 loss: 2.4243 2022/07/31 12:13:43 - mmengine - INFO - Epoch(train) [2][2000/3757] lr: 6.4982e-05 eta: 12:01:04 time: 0.3817 data_time: 0.0175 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4553 loss: 2.4553 2022/07/31 12:14:21 - mmengine - INFO - Epoch(train) [2][2100/3757] lr: 6.5938e-05 eta: 11:59:39 time: 0.3800 data_time: 0.0174 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1097 loss: 2.1097 2022/07/31 12:14:59 - mmengine - INFO - Epoch(train) [2][2200/3757] lr: 6.6893e-05 eta: 11:58:16 time: 0.3845 data_time: 0.0160 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 2.3209 loss: 2.3209 2022/07/31 12:15:15 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 12:15:37 - mmengine - INFO - Epoch(train) [2][2300/3757] lr: 6.7849e-05 eta: 11:56:52 time: 0.3772 data_time: 0.0169 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2097 loss: 2.2097 2022/07/31 12:16:15 - mmengine - INFO - Epoch(train) [2][2400/3757] lr: 6.8805e-05 eta: 11:55:31 time: 0.3784 data_time: 0.0170 memory: 21072 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.4782 loss: 2.4782 2022/07/31 12:16:53 - mmengine - INFO - Epoch(train) [2][2500/3757] lr: 6.9760e-05 eta: 11:54:14 time: 0.3799 data_time: 0.0161 memory: 21072 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 2.3863 loss: 2.3863 2022/07/31 12:17:31 - mmengine - INFO - Epoch(train) [2][2600/3757] lr: 7.0716e-05 eta: 11:52:56 time: 0.3788 data_time: 0.0154 memory: 21072 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.5316 loss: 2.5316 2022/07/31 12:18:09 - mmengine - INFO - Epoch(train) [2][2700/3757] lr: 7.1672e-05 eta: 11:51:40 time: 0.3779 data_time: 0.0158 memory: 21072 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.4942 loss: 2.4942 2022/07/31 12:18:47 - mmengine - INFO - Epoch(train) [2][2800/3757] lr: 7.2628e-05 eta: 11:50:25 time: 0.3767 data_time: 0.0167 memory: 21072 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.2138 loss: 2.2138 2022/07/31 12:19:25 - mmengine - INFO - Epoch(train) [2][2900/3757] lr: 7.3583e-05 eta: 11:49:17 time: 0.3830 data_time: 0.0178 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 2.4140 loss: 2.4140 2022/07/31 12:20:04 - mmengine - INFO - Epoch(train) [2][3000/3757] lr: 7.4539e-05 eta: 11:48:08 time: 0.3889 data_time: 0.0177 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.3745 loss: 2.3745 2022/07/31 12:20:42 - mmengine - INFO - Epoch(train) [2][3100/3757] lr: 7.5495e-05 eta: 11:47:02 time: 0.4009 data_time: 0.0179 memory: 21072 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.3747 loss: 2.3747 2022/07/31 12:21:21 - mmengine - INFO - Epoch(train) [2][3200/3757] lr: 7.6450e-05 eta: 11:45:57 time: 0.3949 data_time: 0.0178 memory: 21072 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.5544 loss: 2.5544 2022/07/31 12:21:37 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 12:21:59 - mmengine - INFO - Epoch(train) [2][3300/3757] lr: 7.7406e-05 eta: 11:44:47 time: 0.3784 data_time: 0.0171 memory: 21072 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.1979 loss: 2.1979 2022/07/31 12:22:36 - mmengine - INFO - Epoch(train) [2][3400/3757] lr: 7.8362e-05 eta: 11:43:35 time: 0.3770 data_time: 0.0163 memory: 21072 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 2.4361 loss: 2.4361 2022/07/31 12:23:15 - mmengine - INFO - Epoch(train) [2][3500/3757] lr: 7.9317e-05 eta: 11:42:28 time: 0.3835 data_time: 0.0168 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2391 loss: 2.2391 2022/07/31 12:23:53 - mmengine - INFO - Epoch(train) [2][3600/3757] lr: 8.0273e-05 eta: 11:41:22 time: 0.3863 data_time: 0.0179 memory: 21072 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.4434 loss: 2.4434 2022/07/31 12:24:31 - mmengine - INFO - Epoch(train) [2][3700/3757] lr: 8.1229e-05 eta: 11:40:18 time: 0.3853 data_time: 0.0170 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3661 loss: 2.3661 2022/07/31 12:24:52 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 12:24:52 - mmengine - INFO - Epoch(train) [2][3757/3757] lr: 8.1773e-05 eta: 11:39:50 time: 0.3685 data_time: 0.0158 memory: 21072 top1_acc: 0.4286 top5_acc: 0.5714 loss_cls: 1.9824 loss: 1.9824 2022/07/31 12:25:33 - mmengine - INFO - Epoch(train) [3][100/3757] lr: 8.2050e-05 eta: 11:37:36 time: 0.3822 data_time: 0.0168 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3077 loss: 2.3077 2022/07/31 12:26:11 - mmengine - INFO - Epoch(train) [3][200/3757] lr: 8.2998e-05 eta: 11:36:32 time: 0.3852 data_time: 0.0176 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.6055 loss: 2.6055 2022/07/31 12:26:49 - mmengine - INFO - Epoch(train) [3][300/3757] lr: 8.3946e-05 eta: 11:35:30 time: 0.3826 data_time: 0.0172 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3177 loss: 2.3177 2022/07/31 12:27:27 - mmengine - INFO - Epoch(train) [3][400/3757] lr: 8.4894e-05 eta: 11:34:27 time: 0.3774 data_time: 0.0169 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.5745 loss: 2.5745 2022/07/31 12:28:00 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 12:28:05 - mmengine - INFO - Epoch(train) [3][500/3757] lr: 8.5841e-05 eta: 11:33:27 time: 0.3840 data_time: 0.0165 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2240 loss: 2.2240 2022/07/31 12:28:43 - mmengine - INFO - Epoch(train) [3][600/3757] lr: 8.6789e-05 eta: 11:32:25 time: 0.3804 data_time: 0.0170 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3606 loss: 2.3606 2022/07/31 12:29:21 - mmengine - INFO - Epoch(train) [3][700/3757] lr: 8.7737e-05 eta: 11:31:26 time: 0.3878 data_time: 0.0174 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.3122 loss: 2.3122 2022/07/31 12:29:59 - mmengine - INFO - Epoch(train) [3][800/3757] lr: 8.8685e-05 eta: 11:30:24 time: 0.3838 data_time: 0.0164 memory: 21072 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 2.5700 loss: 2.5700 2022/07/31 12:30:37 - mmengine - INFO - Epoch(train) [3][900/3757] lr: 8.9633e-05 eta: 11:29:23 time: 0.3778 data_time: 0.0166 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.4862 loss: 2.4862 2022/07/31 12:31:15 - mmengine - INFO - Epoch(train) [3][1000/3757] lr: 9.0581e-05 eta: 11:28:24 time: 0.3787 data_time: 0.0179 memory: 21072 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.5514 loss: 2.5514 2022/07/31 12:31:54 - mmengine - INFO - Epoch(train) [3][1100/3757] lr: 9.1528e-05 eta: 11:27:26 time: 0.3781 data_time: 0.0167 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.5786 loss: 2.5786 2022/07/31 12:32:32 - mmengine - INFO - Epoch(train) [3][1200/3757] lr: 9.2476e-05 eta: 11:26:28 time: 0.3788 data_time: 0.0171 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2233 loss: 2.2233 2022/07/31 12:33:10 - mmengine - INFO - Epoch(train) [3][1300/3757] lr: 9.3424e-05 eta: 11:25:32 time: 0.3847 data_time: 0.0174 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 2.0946 loss: 2.0946 2022/07/31 12:33:48 - mmengine - INFO - Epoch(train) [3][1400/3757] lr: 9.4372e-05 eta: 11:24:35 time: 0.3780 data_time: 0.0164 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.3946 loss: 2.3946 2022/07/31 12:34:21 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 12:34:26 - mmengine - INFO - Epoch(train) [3][1500/3757] lr: 9.5320e-05 eta: 11:23:40 time: 0.3839 data_time: 0.0158 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2227 loss: 2.2227 2022/07/31 12:35:04 - mmengine - INFO - Epoch(train) [3][1600/3757] lr: 9.6268e-05 eta: 11:22:45 time: 0.3862 data_time: 0.0158 memory: 21072 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.4949 loss: 2.4949 2022/07/31 12:35:43 - mmengine - INFO - Epoch(train) [3][1700/3757] lr: 9.7215e-05 eta: 11:21:49 time: 0.3798 data_time: 0.0167 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.0080 loss: 2.0080 2022/07/31 12:36:21 - mmengine - INFO - Epoch(train) [3][1800/3757] lr: 9.8163e-05 eta: 11:20:55 time: 0.3806 data_time: 0.0168 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2849 loss: 2.2849 2022/07/31 12:37:01 - mmengine - INFO - Epoch(train) [3][1900/3757] lr: 9.8912e-05 eta: 11:20:25 time: 0.3773 data_time: 0.0165 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1028 loss: 2.1028 2022/07/31 12:37:39 - mmengine - INFO - Epoch(train) [3][2000/3757] lr: 9.8912e-05 eta: 11:19:29 time: 0.3767 data_time: 0.0162 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.1582 loss: 2.1582 2022/07/31 12:38:18 - mmengine - INFO - Epoch(train) [3][2100/3757] lr: 9.8912e-05 eta: 11:18:46 time: 0.3773 data_time: 0.0166 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0816 loss: 2.0816 2022/07/31 12:38:56 - mmengine - INFO - Epoch(train) [3][2200/3757] lr: 9.8912e-05 eta: 11:17:52 time: 0.3872 data_time: 0.0173 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 2.3203 loss: 2.3203 2022/07/31 12:39:37 - mmengine - INFO - Epoch(train) [3][2300/3757] lr: 9.8912e-05 eta: 11:17:19 time: 0.4800 data_time: 0.0213 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.1820 loss: 2.1820 2022/07/31 12:40:15 - mmengine - INFO - Epoch(train) [3][2400/3757] lr: 9.8912e-05 eta: 11:16:28 time: 0.3840 data_time: 0.0167 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.3943 loss: 2.3943 2022/07/31 12:40:48 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 12:40:53 - mmengine - INFO - Epoch(train) [3][2500/3757] lr: 9.8912e-05 eta: 11:15:34 time: 0.3831 data_time: 0.0179 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0993 loss: 2.0993 2022/07/31 12:41:32 - mmengine - INFO - Epoch(train) [3][2600/3757] lr: 9.8912e-05 eta: 11:14:44 time: 0.3869 data_time: 0.0173 memory: 21072 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.4913 loss: 2.4913 2022/07/31 12:42:10 - mmengine - INFO - Epoch(train) [3][2700/3757] lr: 9.8912e-05 eta: 11:13:51 time: 0.3779 data_time: 0.0160 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9411 loss: 1.9411 2022/07/31 12:42:48 - mmengine - INFO - Epoch(train) [3][2800/3757] lr: 9.8912e-05 eta: 11:12:58 time: 0.3764 data_time: 0.0167 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4155 loss: 2.4155 2022/07/31 12:43:26 - mmengine - INFO - Epoch(train) [3][2900/3757] lr: 9.8912e-05 eta: 11:12:05 time: 0.3763 data_time: 0.0154 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.9412 loss: 1.9412 2022/07/31 12:44:04 - mmengine - INFO - Epoch(train) [3][3000/3757] lr: 9.8912e-05 eta: 11:11:13 time: 0.3862 data_time: 0.0182 memory: 21072 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.0223 loss: 2.0223 2022/07/31 12:44:42 - mmengine - INFO - Epoch(train) [3][3100/3757] lr: 9.8912e-05 eta: 11:10:22 time: 0.3776 data_time: 0.0162 memory: 21072 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.1422 loss: 2.1422 2022/07/31 12:45:20 - mmengine - INFO - Epoch(train) [3][3200/3757] lr: 9.8912e-05 eta: 11:09:31 time: 0.3793 data_time: 0.0170 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0970 loss: 2.0970 2022/07/31 12:45:58 - mmengine - INFO - Epoch(train) [3][3300/3757] lr: 9.8912e-05 eta: 11:08:40 time: 0.3775 data_time: 0.0158 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3150 loss: 2.3150 2022/07/31 12:46:37 - mmengine - INFO - Epoch(train) [3][3400/3757] lr: 9.8912e-05 eta: 11:07:52 time: 0.3914 data_time: 0.0175 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.4293 loss: 2.4293 2022/07/31 12:47:10 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 12:47:16 - mmengine - INFO - Epoch(train) [3][3500/3757] lr: 9.8912e-05 eta: 11:07:06 time: 0.4091 data_time: 0.0181 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0707 loss: 2.0707 2022/07/31 12:47:54 - mmengine - INFO - Epoch(train) [3][3600/3757] lr: 9.8912e-05 eta: 11:06:20 time: 0.3871 data_time: 0.0173 memory: 21072 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.2975 loss: 2.2975 2022/07/31 12:48:33 - mmengine - INFO - Epoch(train) [3][3700/3757] lr: 9.8912e-05 eta: 11:05:31 time: 0.3826 data_time: 0.0172 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.1574 loss: 2.1574 2022/07/31 12:48:54 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 12:48:54 - mmengine - INFO - Epoch(train) [3][3757/3757] lr: 9.8912e-05 eta: 11:05:11 time: 0.3690 data_time: 0.0150 memory: 21072 top1_acc: 0.4286 top5_acc: 0.8571 loss_cls: 2.0127 loss: 2.0127 2022/07/31 12:48:54 - mmengine - INFO - Saving checkpoint at 3 epochs 2022/07/31 12:49:58 - mmengine - INFO - Epoch(val) [3][100/310] eta: 0:00:43 time: 0.2075 data_time: 0.0687 memory: 5891 2022/07/31 12:50:19 - mmengine - INFO - Epoch(val) [3][200/310] eta: 0:00:19 time: 0.1803 data_time: 0.0414 memory: 5891 2022/07/31 12:50:41 - mmengine - INFO - Epoch(val) [3][300/310] eta: 0:00:02 time: 0.2078 data_time: 0.0691 memory: 5891 2022/07/31 12:50:46 - mmengine - INFO - Epoch(val) [3][310/310] acc/top1: 0.5816 acc/top5: 0.8198 acc/mean1: 0.5814 2022/07/31 12:50:47 - mmengine - INFO - The best checkpoint with 0.5816 acc/top1 at 4 epoch is saved to best_acc/top1_epoch_4.pth. 2022/07/31 12:51:26 - mmengine - INFO - Epoch(train) [4][100/3757] lr: 9.7558e-05 eta: 11:03:24 time: 0.3820 data_time: 0.0163 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2300 loss: 2.2300 2022/07/31 12:52:04 - mmengine - INFO - Epoch(train) [4][200/3757] lr: 9.7558e-05 eta: 11:02:32 time: 0.3788 data_time: 0.0162 memory: 21072 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.2068 loss: 2.2068 2022/07/31 12:52:42 - mmengine - INFO - Epoch(train) [4][300/3757] lr: 9.7558e-05 eta: 11:01:43 time: 0.3830 data_time: 0.0177 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1299 loss: 2.1299 2022/07/31 12:53:20 - mmengine - INFO - Epoch(train) [4][400/3757] lr: 9.7558e-05 eta: 11:00:52 time: 0.3776 data_time: 0.0171 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0777 loss: 2.0777 2022/07/31 12:53:58 - mmengine - INFO - Epoch(train) [4][500/3757] lr: 9.7558e-05 eta: 11:00:01 time: 0.3782 data_time: 0.0163 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2985 loss: 2.2985 2022/07/31 12:54:36 - mmengine - INFO - Epoch(train) [4][600/3757] lr: 9.7558e-05 eta: 10:59:11 time: 0.3778 data_time: 0.0166 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.3233 loss: 2.3233 2022/07/31 12:55:18 - mmengine - INFO - Epoch(train) [4][700/3757] lr: 9.7558e-05 eta: 10:58:53 time: 0.3774 data_time: 0.0164 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1427 loss: 2.1427 2022/07/31 12:55:29 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 12:55:56 - mmengine - INFO - Epoch(train) [4][800/3757] lr: 9.7558e-05 eta: 10:58:07 time: 0.3758 data_time: 0.0154 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2587 loss: 2.2587 2022/07/31 12:56:37 - mmengine - INFO - Epoch(train) [4][900/3757] lr: 9.7558e-05 eta: 10:57:45 time: 0.3766 data_time: 0.0154 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.1044 loss: 2.1044 2022/07/31 12:57:16 - mmengine - INFO - Epoch(train) [4][1000/3757] lr: 9.7558e-05 eta: 10:56:57 time: 0.3773 data_time: 0.0165 memory: 21072 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.2896 loss: 2.2896 2022/07/31 12:57:54 - mmengine - INFO - Epoch(train) [4][1100/3757] lr: 9.7558e-05 eta: 10:56:10 time: 0.3767 data_time: 0.0173 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2177 loss: 2.2177 2022/07/31 12:58:32 - mmengine - INFO - Epoch(train) [4][1200/3757] lr: 9.7558e-05 eta: 10:55:26 time: 0.4032 data_time: 0.0179 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3649 loss: 2.3649 2022/07/31 12:59:10 - mmengine - INFO - Epoch(train) [4][1300/3757] lr: 9.7558e-05 eta: 10:54:36 time: 0.3771 data_time: 0.0166 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.3117 loss: 2.3117 2022/07/31 12:59:48 - mmengine - INFO - Epoch(train) [4][1400/3757] lr: 9.7558e-05 eta: 10:53:47 time: 0.3831 data_time: 0.0155 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2585 loss: 2.2585 2022/07/31 13:00:27 - mmengine - INFO - Epoch(train) [4][1500/3757] lr: 9.7558e-05 eta: 10:52:59 time: 0.3786 data_time: 0.0143 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1215 loss: 2.1215 2022/07/31 13:01:04 - mmengine - INFO - Epoch(train) [4][1600/3757] lr: 9.7558e-05 eta: 10:52:10 time: 0.3822 data_time: 0.0150 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2159 loss: 2.2159 2022/07/31 13:01:42 - mmengine - INFO - Epoch(train) [4][1700/3757] lr: 9.7558e-05 eta: 10:51:21 time: 0.3797 data_time: 0.0150 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 2.2800 loss: 2.2800 2022/07/31 13:01:53 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 13:02:20 - mmengine - INFO - Epoch(train) [4][1800/3757] lr: 9.7558e-05 eta: 10:50:30 time: 0.3751 data_time: 0.0148 memory: 21072 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.6796 loss: 2.6796 2022/07/31 13:02:58 - mmengine - INFO - Epoch(train) [4][1900/3757] lr: 9.7558e-05 eta: 10:49:41 time: 0.3805 data_time: 0.0151 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1581 loss: 2.1581 2022/07/31 13:03:36 - mmengine - INFO - Epoch(train) [4][2000/3757] lr: 9.7558e-05 eta: 10:48:53 time: 0.3776 data_time: 0.0145 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.0353 loss: 2.0353 2022/07/31 13:04:14 - mmengine - INFO - Epoch(train) [4][2100/3757] lr: 9.7558e-05 eta: 10:48:03 time: 0.3788 data_time: 0.0172 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7847 loss: 1.7847 2022/07/31 13:04:52 - mmengine - INFO - Epoch(train) [4][2200/3757] lr: 9.7558e-05 eta: 10:47:18 time: 0.3953 data_time: 0.0149 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1503 loss: 2.1503 2022/07/31 13:05:30 - mmengine - INFO - Epoch(train) [4][2300/3757] lr: 9.7558e-05 eta: 10:46:29 time: 0.3741 data_time: 0.0147 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2415 loss: 2.2415 2022/07/31 13:06:08 - mmengine - INFO - Epoch(train) [4][2400/3757] lr: 9.7558e-05 eta: 10:45:45 time: 0.3803 data_time: 0.0167 memory: 21072 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.2359 loss: 2.2359 2022/07/31 13:06:46 - mmengine - INFO - Epoch(train) [4][2500/3757] lr: 9.7558e-05 eta: 10:44:58 time: 0.3786 data_time: 0.0157 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.5954 loss: 2.5954 2022/07/31 13:07:24 - mmengine - INFO - Epoch(train) [4][2600/3757] lr: 9.7558e-05 eta: 10:44:10 time: 0.3785 data_time: 0.0171 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1026 loss: 2.1026 2022/07/31 13:08:02 - mmengine - INFO - Epoch(train) [4][2700/3757] lr: 9.7558e-05 eta: 10:43:23 time: 0.3770 data_time: 0.0161 memory: 21072 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.3492 loss: 2.3492 2022/07/31 13:08:13 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 13:08:40 - mmengine - INFO - Epoch(train) [4][2800/3757] lr: 9.7558e-05 eta: 10:42:36 time: 0.3770 data_time: 0.0169 memory: 21072 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.1697 loss: 2.1697 2022/07/31 13:09:18 - mmengine - INFO - Epoch(train) [4][2900/3757] lr: 9.7558e-05 eta: 10:41:50 time: 0.3813 data_time: 0.0175 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.9768 loss: 1.9768 2022/07/31 13:09:56 - mmengine - INFO - Epoch(train) [4][3000/3757] lr: 9.7558e-05 eta: 10:41:04 time: 0.3787 data_time: 0.0172 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.9682 loss: 1.9682 2022/07/31 13:10:34 - mmengine - INFO - Epoch(train) [4][3100/3757] lr: 9.7558e-05 eta: 10:40:18 time: 0.3835 data_time: 0.0176 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0502 loss: 2.0502 2022/07/31 13:11:12 - mmengine - INFO - Epoch(train) [4][3200/3757] lr: 9.7558e-05 eta: 10:39:33 time: 0.3794 data_time: 0.0169 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1634 loss: 2.1634 2022/07/31 13:11:50 - mmengine - INFO - Epoch(train) [4][3300/3757] lr: 9.7558e-05 eta: 10:38:47 time: 0.3779 data_time: 0.0171 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.2338 loss: 2.2338 2022/07/31 13:12:31 - mmengine - INFO - Epoch(train) [4][3400/3757] lr: 9.7558e-05 eta: 10:38:16 time: 0.3766 data_time: 0.0166 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.2248 loss: 2.2248 2022/07/31 13:13:09 - mmengine - INFO - Epoch(train) [4][3500/3757] lr: 9.7558e-05 eta: 10:37:31 time: 0.3784 data_time: 0.0161 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0574 loss: 2.0574 2022/07/31 13:13:47 - mmengine - INFO - Epoch(train) [4][3600/3757] lr: 9.7558e-05 eta: 10:36:46 time: 0.3824 data_time: 0.0178 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2620 loss: 2.2620 2022/07/31 13:14:25 - mmengine - INFO - Epoch(train) [4][3700/3757] lr: 9.7558e-05 eta: 10:36:00 time: 0.3780 data_time: 0.0175 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.2217 loss: 2.2217 2022/07/31 13:14:36 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 13:14:50 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 13:14:50 - mmengine - INFO - Epoch(train) [4][3757/3757] lr: 9.7558e-05 eta: 10:35:45 time: 0.5316 data_time: 0.1015 memory: 21072 top1_acc: 0.2857 top5_acc: 0.8571 loss_cls: 2.2441 loss: 2.2441 2022/07/31 13:15:30 - mmengine - INFO - Epoch(train) [5][100/3757] lr: 9.5682e-05 eta: 10:34:23 time: 0.3814 data_time: 0.0176 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8669 loss: 1.8669 2022/07/31 13:16:08 - mmengine - INFO - Epoch(train) [5][200/3757] lr: 9.5682e-05 eta: 10:33:37 time: 0.3784 data_time: 0.0165 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.0981 loss: 2.0981 2022/07/31 13:16:46 - mmengine - INFO - Epoch(train) [5][300/3757] lr: 9.5682e-05 eta: 10:32:53 time: 0.3826 data_time: 0.0175 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6800 loss: 1.6800 2022/07/31 13:17:26 - mmengine - INFO - Epoch(train) [5][400/3757] lr: 9.5682e-05 eta: 10:32:20 time: 0.3802 data_time: 0.0166 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6576 loss: 1.6576 2022/07/31 13:18:06 - mmengine - INFO - Epoch(train) [5][500/3757] lr: 9.5682e-05 eta: 10:31:49 time: 0.3808 data_time: 0.0172 memory: 21072 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.0979 loss: 2.0979 2022/07/31 13:18:45 - mmengine - INFO - Epoch(train) [5][600/3757] lr: 9.5682e-05 eta: 10:31:05 time: 0.3813 data_time: 0.0179 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1140 loss: 2.1140 2022/07/31 13:19:22 - mmengine - INFO - Epoch(train) [5][700/3757] lr: 9.5682e-05 eta: 10:30:19 time: 0.3770 data_time: 0.0163 memory: 21072 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.3560 loss: 2.3560 2022/07/31 13:20:01 - mmengine - INFO - Epoch(train) [5][800/3757] lr: 9.5682e-05 eta: 10:29:34 time: 0.3796 data_time: 0.0172 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8643 loss: 1.8643 2022/07/31 13:20:39 - mmengine - INFO - Epoch(train) [5][900/3757] lr: 9.5682e-05 eta: 10:28:50 time: 0.3799 data_time: 0.0172 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8861 loss: 1.8861 2022/07/31 13:21:06 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 13:21:17 - mmengine - INFO - Epoch(train) [5][1000/3757] lr: 9.5682e-05 eta: 10:28:06 time: 0.3776 data_time: 0.0173 memory: 21072 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.1379 loss: 2.1379 2022/07/31 13:21:55 - mmengine - INFO - Epoch(train) [5][1100/3757] lr: 9.5682e-05 eta: 10:27:22 time: 0.3786 data_time: 0.0162 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2607 loss: 2.2607 2022/07/31 13:22:33 - mmengine - INFO - Epoch(train) [5][1200/3757] lr: 9.5682e-05 eta: 10:26:37 time: 0.3771 data_time: 0.0166 memory: 21072 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.0387 loss: 2.0387 2022/07/31 13:23:11 - mmengine - INFO - Epoch(train) [5][1300/3757] lr: 9.5682e-05 eta: 10:25:53 time: 0.3839 data_time: 0.0176 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.3871 loss: 2.3871 2022/07/31 13:23:49 - mmengine - INFO - Epoch(train) [5][1400/3757] lr: 9.5682e-05 eta: 10:25:09 time: 0.3852 data_time: 0.0179 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1466 loss: 2.1466 2022/07/31 13:24:27 - mmengine - INFO - Epoch(train) [5][1500/3757] lr: 9.5682e-05 eta: 10:24:25 time: 0.3805 data_time: 0.0167 memory: 21072 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 1.9185 loss: 1.9185 2022/07/31 13:25:05 - mmengine - INFO - Epoch(train) [5][1600/3757] lr: 9.5682e-05 eta: 10:23:41 time: 0.3833 data_time: 0.0177 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2260 loss: 2.2260 2022/07/31 13:25:43 - mmengine - INFO - Epoch(train) [5][1700/3757] lr: 9.5682e-05 eta: 10:22:56 time: 0.3777 data_time: 0.0168 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9218 loss: 1.9218 2022/07/31 13:26:21 - mmengine - INFO - Epoch(train) [5][1800/3757] lr: 9.5682e-05 eta: 10:22:13 time: 0.3812 data_time: 0.0175 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.0563 loss: 2.0563 2022/07/31 13:26:59 - mmengine - INFO - Epoch(train) [5][1900/3757] lr: 9.5682e-05 eta: 10:21:29 time: 0.3820 data_time: 0.0169 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1659 loss: 2.1659 2022/07/31 13:27:27 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 13:27:37 - mmengine - INFO - Epoch(train) [5][2000/3757] lr: 9.5682e-05 eta: 10:20:44 time: 0.3827 data_time: 0.0181 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0091 loss: 2.0091 2022/07/31 13:28:15 - mmengine - INFO - Epoch(train) [5][2100/3757] lr: 9.5682e-05 eta: 10:20:00 time: 0.3828 data_time: 0.0181 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9631 loss: 1.9631 2022/07/31 13:28:53 - mmengine - INFO - Epoch(train) [5][2200/3757] lr: 9.5682e-05 eta: 10:19:16 time: 0.3781 data_time: 0.0162 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 2.1509 loss: 2.1509 2022/07/31 13:29:31 - mmengine - INFO - Epoch(train) [5][2300/3757] lr: 9.5682e-05 eta: 10:18:32 time: 0.3790 data_time: 0.0179 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.9176 loss: 1.9176 2022/07/31 13:30:10 - mmengine - INFO - Epoch(train) [5][2400/3757] lr: 9.5682e-05 eta: 10:17:49 time: 0.3815 data_time: 0.0174 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1133 loss: 2.1133 2022/07/31 13:30:48 - mmengine - INFO - Epoch(train) [5][2500/3757] lr: 9.5682e-05 eta: 10:17:05 time: 0.3814 data_time: 0.0182 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1801 loss: 2.1801 2022/07/31 13:31:26 - mmengine - INFO - Epoch(train) [5][2600/3757] lr: 9.5682e-05 eta: 10:16:22 time: 0.3826 data_time: 0.0173 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.9793 loss: 1.9793 2022/07/31 13:32:04 - mmengine - INFO - Epoch(train) [5][2700/3757] lr: 9.5682e-05 eta: 10:15:37 time: 0.3775 data_time: 0.0174 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8632 loss: 1.8632 2022/07/31 13:32:42 - mmengine - INFO - Epoch(train) [5][2800/3757] lr: 9.5682e-05 eta: 10:14:54 time: 0.3791 data_time: 0.0164 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.6711 loss: 1.6711 2022/07/31 13:33:20 - mmengine - INFO - Epoch(train) [5][2900/3757] lr: 9.5682e-05 eta: 10:14:11 time: 0.3808 data_time: 0.0175 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7811 loss: 1.7811 2022/07/31 13:33:47 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 13:33:58 - mmengine - INFO - Epoch(train) [5][3000/3757] lr: 9.5682e-05 eta: 10:13:27 time: 0.3817 data_time: 0.0180 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.7962 loss: 1.7962 2022/07/31 13:34:36 - mmengine - INFO - Epoch(train) [5][3100/3757] lr: 9.5682e-05 eta: 10:12:45 time: 0.3845 data_time: 0.0171 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2828 loss: 2.2828 2022/07/31 13:35:14 - mmengine - INFO - Epoch(train) [5][3200/3757] lr: 9.5682e-05 eta: 10:12:01 time: 0.3779 data_time: 0.0165 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2404 loss: 2.2404 2022/07/31 13:35:52 - mmengine - INFO - Epoch(train) [5][3300/3757] lr: 9.5682e-05 eta: 10:11:18 time: 0.3791 data_time: 0.0177 memory: 21072 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3029 loss: 2.3029 2022/07/31 13:36:30 - mmengine - INFO - Epoch(train) [5][3400/3757] lr: 9.5682e-05 eta: 10:10:35 time: 0.3856 data_time: 0.0177 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1674 loss: 2.1674 2022/07/31 13:37:08 - mmengine - INFO - Epoch(train) [5][3500/3757] lr: 9.5682e-05 eta: 10:09:53 time: 0.3822 data_time: 0.0165 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3281 loss: 2.3281 2022/07/31 13:37:46 - mmengine - INFO - Epoch(train) [5][3600/3757] lr: 9.5682e-05 eta: 10:09:10 time: 0.3806 data_time: 0.0175 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0634 loss: 2.0634 2022/07/31 13:38:24 - mmengine - INFO - Epoch(train) [5][3700/3757] lr: 9.5682e-05 eta: 10:08:26 time: 0.3772 data_time: 0.0159 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.8304 loss: 1.8304 2022/07/31 13:38:46 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 13:38:46 - mmengine - INFO - Epoch(train) [5][3757/3757] lr: 9.5682e-05 eta: 10:08:10 time: 0.3673 data_time: 0.0151 memory: 21072 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 2.1926 loss: 2.1926 2022/07/31 13:39:25 - mmengine - INFO - Epoch(train) [6][100/3757] lr: 9.3306e-05 eta: 10:06:55 time: 0.3795 data_time: 0.0167 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4658 loss: 1.4658 2022/07/31 13:40:05 - mmengine - INFO - Epoch(train) [6][200/3757] lr: 9.3306e-05 eta: 10:06:20 time: 0.3782 data_time: 0.0166 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9073 loss: 1.9073 2022/07/31 13:40:11 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 13:40:43 - mmengine - INFO - Epoch(train) [6][300/3757] lr: 9.3306e-05 eta: 10:05:38 time: 0.3798 data_time: 0.0172 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0790 loss: 2.0790 2022/07/31 13:41:21 - mmengine - INFO - Epoch(train) [6][400/3757] lr: 9.3306e-05 eta: 10:04:56 time: 0.3778 data_time: 0.0173 memory: 21072 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.0299 loss: 2.0299 2022/07/31 13:41:59 - mmengine - INFO - Epoch(train) [6][500/3757] lr: 9.3306e-05 eta: 10:04:14 time: 0.3789 data_time: 0.0172 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8964 loss: 1.8964 2022/07/31 13:42:38 - mmengine - INFO - Epoch(train) [6][600/3757] lr: 9.3306e-05 eta: 10:03:32 time: 0.3774 data_time: 0.0160 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2065 loss: 2.2065 2022/07/31 13:43:16 - mmengine - INFO - Epoch(train) [6][700/3757] lr: 9.3306e-05 eta: 10:02:49 time: 0.3785 data_time: 0.0161 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9097 loss: 1.9097 2022/07/31 13:43:54 - mmengine - INFO - Epoch(train) [6][800/3757] lr: 9.3306e-05 eta: 10:02:07 time: 0.3778 data_time: 0.0166 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.7590 loss: 1.7590 2022/07/31 13:44:32 - mmengine - INFO - Epoch(train) [6][900/3757] lr: 9.3306e-05 eta: 10:01:24 time: 0.3775 data_time: 0.0169 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9853 loss: 1.9853 2022/07/31 13:45:15 - mmengine - INFO - Epoch(train) [6][1000/3757] lr: 9.3306e-05 eta: 10:01:06 time: 0.5314 data_time: 0.0166 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.9783 loss: 1.9783 2022/07/31 13:46:10 - mmengine - INFO - Epoch(train) [6][1100/3757] lr: 9.3306e-05 eta: 10:01:43 time: 0.4947 data_time: 0.0171 memory: 21072 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.9691 loss: 1.9691 2022/07/31 13:47:09 - mmengine - INFO - Epoch(train) [6][1200/3757] lr: 9.3306e-05 eta: 10:02:38 time: 0.5962 data_time: 0.1044 memory: 21072 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 1.7860 loss: 1.7860 2022/07/31 13:47:17 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 13:48:02 - mmengine - INFO - Epoch(train) [6][1300/3757] lr: 9.3306e-05 eta: 10:03:02 time: 0.4991 data_time: 0.0151 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.9491 loss: 1.9491 2022/07/31 13:48:51 - mmengine - INFO - Epoch(train) [6][1400/3757] lr: 9.3306e-05 eta: 10:03:10 time: 0.3740 data_time: 0.0145 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9701 loss: 1.9701 2022/07/31 13:49:46 - mmengine - INFO - Epoch(train) [6][1500/3757] lr: 9.3306e-05 eta: 10:03:43 time: 0.4813 data_time: 0.0161 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.3467 loss: 2.3467 2022/07/31 13:50:30 - mmengine - INFO - Epoch(train) [6][1600/3757] lr: 9.3306e-05 eta: 10:03:27 time: 0.3834 data_time: 0.0138 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8545 loss: 1.8545 2022/07/31 13:51:12 - mmengine - INFO - Epoch(train) [6][1700/3757] lr: 9.3306e-05 eta: 10:03:01 time: 0.3834 data_time: 0.0160 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7030 loss: 1.7030 2022/07/31 13:51:50 - mmengine - INFO - Epoch(train) [6][1800/3757] lr: 9.3306e-05 eta: 10:02:16 time: 0.3788 data_time: 0.0168 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.9687 loss: 1.9687 2022/07/31 13:52:29 - mmengine - INFO - Epoch(train) [6][1900/3757] lr: 9.3306e-05 eta: 10:01:32 time: 0.3815 data_time: 0.0163 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9720 loss: 1.9720 2022/07/31 13:53:07 - mmengine - INFO - Epoch(train) [6][2000/3757] lr: 9.3306e-05 eta: 10:00:48 time: 0.3839 data_time: 0.0171 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7954 loss: 1.7954 2022/07/31 13:53:45 - mmengine - INFO - Epoch(train) [6][2100/3757] lr: 9.3306e-05 eta: 10:00:04 time: 0.3817 data_time: 0.0172 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3217 loss: 1.3217 2022/07/31 13:54:23 - mmengine - INFO - Epoch(train) [6][2200/3757] lr: 9.3306e-05 eta: 9:59:19 time: 0.3812 data_time: 0.0177 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1502 loss: 2.1502 2022/07/31 13:54:29 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 13:55:01 - mmengine - INFO - Epoch(train) [6][2300/3757] lr: 9.3306e-05 eta: 9:58:35 time: 0.3802 data_time: 0.0182 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0316 loss: 2.0316 2022/07/31 13:55:39 - mmengine - INFO - Epoch(train) [6][2400/3757] lr: 9.3306e-05 eta: 9:57:51 time: 0.3815 data_time: 0.0166 memory: 21072 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.3375 loss: 2.3375 2022/07/31 13:56:26 - mmengine - INFO - Epoch(train) [6][2500/3757] lr: 9.3306e-05 eta: 9:57:44 time: 0.5463 data_time: 0.0150 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8204 loss: 1.8204 2022/07/31 13:57:08 - mmengine - INFO - Epoch(train) [6][2600/3757] lr: 9.3306e-05 eta: 9:57:18 time: 0.3823 data_time: 0.0173 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.8167 loss: 1.8167 2022/07/31 13:57:49 - mmengine - INFO - Epoch(train) [6][2700/3757] lr: 9.3306e-05 eta: 9:56:45 time: 0.3812 data_time: 0.0138 memory: 21072 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 2.1068 loss: 2.1068 2022/07/31 13:58:27 - mmengine - INFO - Epoch(train) [6][2800/3757] lr: 9.3306e-05 eta: 9:56:02 time: 0.3770 data_time: 0.0150 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5863 loss: 1.5863 2022/07/31 13:59:05 - mmengine - INFO - Epoch(train) [6][2900/3757] lr: 9.3306e-05 eta: 9:55:17 time: 0.3756 data_time: 0.0141 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.8693 loss: 1.8693 2022/07/31 13:59:43 - mmengine - INFO - Epoch(train) [6][3000/3757] lr: 9.3306e-05 eta: 9:54:32 time: 0.3805 data_time: 0.0131 memory: 21072 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 2.0627 loss: 2.0627 2022/07/31 14:00:22 - mmengine - INFO - Epoch(train) [6][3100/3757] lr: 9.3306e-05 eta: 9:53:52 time: 0.3764 data_time: 0.0158 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9268 loss: 1.9268 2022/07/31 14:01:00 - mmengine - INFO - Epoch(train) [6][3200/3757] lr: 9.3306e-05 eta: 9:53:07 time: 0.3799 data_time: 0.0164 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.0265 loss: 2.0265 2022/07/31 14:01:05 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 14:01:38 - mmengine - INFO - Epoch(train) [6][3300/3757] lr: 9.3306e-05 eta: 9:52:23 time: 0.3791 data_time: 0.0174 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6623 loss: 1.6623 2022/07/31 14:02:16 - mmengine - INFO - Epoch(train) [6][3400/3757] lr: 9.3306e-05 eta: 9:51:38 time: 0.3773 data_time: 0.0168 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0590 loss: 2.0590 2022/07/31 14:02:54 - mmengine - INFO - Epoch(train) [6][3500/3757] lr: 9.3306e-05 eta: 9:50:54 time: 0.3817 data_time: 0.0177 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.8427 loss: 1.8427 2022/07/31 14:03:32 - mmengine - INFO - Epoch(train) [6][3600/3757] lr: 9.3306e-05 eta: 9:50:11 time: 0.3807 data_time: 0.0187 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9196 loss: 1.9196 2022/07/31 14:04:10 - mmengine - INFO - Epoch(train) [6][3700/3757] lr: 9.3306e-05 eta: 9:49:27 time: 0.3800 data_time: 0.0180 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7265 loss: 1.7265 2022/07/31 14:04:32 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 14:04:32 - mmengine - INFO - Epoch(train) [6][3757/3757] lr: 9.3306e-05 eta: 9:49:10 time: 0.3719 data_time: 0.0162 memory: 21072 top1_acc: 0.7143 top5_acc: 1.0000 loss_cls: 1.9314 loss: 1.9314 2022/07/31 14:04:32 - mmengine - INFO - Saving checkpoint at 6 epochs 2022/07/31 14:04:56 - mmengine - INFO - Epoch(val) [6][100/310] eta: 0:00:47 time: 0.2258 data_time: 0.0826 memory: 5891 2022/07/31 14:05:18 - mmengine - INFO - Epoch(val) [6][200/310] eta: 0:00:20 time: 0.1877 data_time: 0.0502 memory: 5891 2022/07/31 14:05:39 - mmengine - INFO - Epoch(val) [6][300/310] eta: 0:00:02 time: 0.2049 data_time: 0.0670 memory: 5891 2022/07/31 14:05:42 - mmengine - INFO - Epoch(val) [6][310/310] acc/top1: 0.6337 acc/top5: 0.8524 acc/mean1: 0.6336 2022/07/31 14:05:42 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/swin-t-2/best_acc/top1_epoch_4.pth is removed 2022/07/31 14:05:43 - mmengine - INFO - The best checkpoint with 0.6337 acc/top1 at 7 epoch is saved to best_acc/top1_epoch_7.pth. 2022/07/31 14:06:23 - mmengine - INFO - Epoch(train) [7][100/3757] lr: 9.0455e-05 eta: 9:48:00 time: 0.3833 data_time: 0.0172 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.8780 loss: 1.8780 2022/07/31 14:07:01 - mmengine - INFO - Epoch(train) [7][200/3757] lr: 9.0455e-05 eta: 9:47:16 time: 0.3856 data_time: 0.0179 memory: 21072 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 1.9025 loss: 1.9025 2022/07/31 14:07:39 - mmengine - INFO - Epoch(train) [7][300/3757] lr: 9.0455e-05 eta: 9:46:32 time: 0.3799 data_time: 0.0169 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8521 loss: 1.8521 2022/07/31 14:08:17 - mmengine - INFO - Epoch(train) [7][400/3757] lr: 9.0455e-05 eta: 9:45:49 time: 0.3790 data_time: 0.0169 memory: 21072 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 1.9001 loss: 1.9001 2022/07/31 14:08:39 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 14:08:55 - mmengine - INFO - Epoch(train) [7][500/3757] lr: 9.0455e-05 eta: 9:45:05 time: 0.3784 data_time: 0.0167 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0106 loss: 2.0106 2022/07/31 14:09:33 - mmengine - INFO - Epoch(train) [7][600/3757] lr: 9.0455e-05 eta: 9:44:22 time: 0.3796 data_time: 0.0173 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7775 loss: 1.7775 2022/07/31 14:10:11 - mmengine - INFO - Epoch(train) [7][700/3757] lr: 9.0455e-05 eta: 9:43:39 time: 0.3821 data_time: 0.0176 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.9137 loss: 1.9137 2022/07/31 14:10:49 - mmengine - INFO - Epoch(train) [7][800/3757] lr: 9.0455e-05 eta: 9:42:55 time: 0.3802 data_time: 0.0172 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.9489 loss: 1.9489 2022/07/31 14:11:27 - mmengine - INFO - Epoch(train) [7][900/3757] lr: 9.0455e-05 eta: 9:42:11 time: 0.3772 data_time: 0.0157 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9182 loss: 1.9182 2022/07/31 14:12:05 - mmengine - INFO - Epoch(train) [7][1000/3757] lr: 9.0455e-05 eta: 9:41:28 time: 0.3843 data_time: 0.0175 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9450 loss: 1.9450 2022/07/31 14:12:43 - mmengine - INFO - Epoch(train) [7][1100/3757] lr: 9.0455e-05 eta: 9:40:46 time: 0.3859 data_time: 0.0179 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7911 loss: 1.7911 2022/07/31 14:13:21 - mmengine - INFO - Epoch(train) [7][1200/3757] lr: 9.0455e-05 eta: 9:40:03 time: 0.3845 data_time: 0.0180 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5841 loss: 1.5841 2022/07/31 14:13:59 - mmengine - INFO - Epoch(train) [7][1300/3757] lr: 9.0455e-05 eta: 9:39:19 time: 0.3822 data_time: 0.0177 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6895 loss: 1.6895 2022/07/31 14:14:38 - mmengine - INFO - Epoch(train) [7][1400/3757] lr: 9.0455e-05 eta: 9:38:37 time: 0.3779 data_time: 0.0166 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.8993 loss: 1.8993 2022/07/31 14:15:00 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 14:15:16 - mmengine - INFO - Epoch(train) [7][1500/3757] lr: 9.0455e-05 eta: 9:37:54 time: 0.3791 data_time: 0.0161 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.8963 loss: 1.8963 2022/07/31 14:15:54 - mmengine - INFO - Epoch(train) [7][1600/3757] lr: 9.0455e-05 eta: 9:37:11 time: 0.3778 data_time: 0.0164 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.8218 loss: 1.8218 2022/07/31 14:16:32 - mmengine - INFO - Epoch(train) [7][1700/3757] lr: 9.0455e-05 eta: 9:36:28 time: 0.3804 data_time: 0.0180 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8286 loss: 1.8286 2022/07/31 14:17:10 - mmengine - INFO - Epoch(train) [7][1800/3757] lr: 9.0455e-05 eta: 9:35:45 time: 0.3784 data_time: 0.0169 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.8482 loss: 1.8482 2022/07/31 14:17:48 - mmengine - INFO - Epoch(train) [7][1900/3757] lr: 9.0455e-05 eta: 9:35:02 time: 0.3777 data_time: 0.0160 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.2228 loss: 2.2228 2022/07/31 14:18:26 - mmengine - INFO - Epoch(train) [7][2000/3757] lr: 9.0455e-05 eta: 9:34:19 time: 0.3813 data_time: 0.0166 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 2.1493 loss: 2.1493 2022/07/31 14:19:04 - mmengine - INFO - Epoch(train) [7][2100/3757] lr: 9.0455e-05 eta: 9:33:37 time: 0.3806 data_time: 0.0173 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.6858 loss: 1.6858 2022/07/31 14:19:42 - mmengine - INFO - Epoch(train) [7][2200/3757] lr: 9.0455e-05 eta: 9:32:55 time: 0.3861 data_time: 0.0156 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9982 loss: 1.9982 2022/07/31 14:20:20 - mmengine - INFO - Epoch(train) [7][2300/3757] lr: 9.0455e-05 eta: 9:32:12 time: 0.3785 data_time: 0.0172 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9314 loss: 1.9314 2022/07/31 14:20:58 - mmengine - INFO - Epoch(train) [7][2400/3757] lr: 9.0455e-05 eta: 9:31:28 time: 0.3768 data_time: 0.0170 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6184 loss: 1.6184 2022/07/31 14:21:20 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 14:21:36 - mmengine - INFO - Epoch(train) [7][2500/3757] lr: 9.0455e-05 eta: 9:30:46 time: 0.3771 data_time: 0.0172 memory: 21072 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.7561 loss: 1.7561 2022/07/31 14:22:14 - mmengine - INFO - Epoch(train) [7][2600/3757] lr: 9.0455e-05 eta: 9:30:03 time: 0.3791 data_time: 0.0174 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.8315 loss: 1.8315 2022/07/31 14:22:52 - mmengine - INFO - Epoch(train) [7][2700/3757] lr: 9.0455e-05 eta: 9:29:20 time: 0.3826 data_time: 0.0172 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0192 loss: 2.0192 2022/07/31 14:23:30 - mmengine - INFO - Epoch(train) [7][2800/3757] lr: 9.0455e-05 eta: 9:28:38 time: 0.3792 data_time: 0.0175 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.1407 loss: 2.1407 2022/07/31 14:24:08 - mmengine - INFO - Epoch(train) [7][2900/3757] lr: 9.0455e-05 eta: 9:27:55 time: 0.3792 data_time: 0.0165 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7299 loss: 1.7299 2022/07/31 14:24:47 - mmengine - INFO - Epoch(train) [7][3000/3757] lr: 9.0455e-05 eta: 9:27:13 time: 0.3836 data_time: 0.0161 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9936 loss: 1.9936 2022/07/31 14:25:25 - mmengine - INFO - Epoch(train) [7][3100/3757] lr: 9.0455e-05 eta: 9:26:31 time: 0.3890 data_time: 0.0169 memory: 21072 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.9901 loss: 1.9901 2022/07/31 14:26:03 - mmengine - INFO - Epoch(train) [7][3200/3757] lr: 9.0455e-05 eta: 9:25:49 time: 0.3829 data_time: 0.0176 memory: 21072 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.8350 loss: 1.8350 2022/07/31 14:26:41 - mmengine - INFO - Epoch(train) [7][3300/3757] lr: 9.0455e-05 eta: 9:25:08 time: 0.3986 data_time: 0.0177 memory: 21072 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.0159 loss: 2.0159 2022/07/31 14:27:20 - mmengine - INFO - Epoch(train) [7][3400/3757] lr: 9.0455e-05 eta: 9:24:27 time: 0.3814 data_time: 0.0161 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.2527 loss: 2.2527 2022/07/31 14:27:42 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 14:27:58 - mmengine - INFO - Epoch(train) [7][3500/3757] lr: 9.0455e-05 eta: 9:23:45 time: 0.3780 data_time: 0.0147 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.7829 loss: 1.7829 2022/07/31 14:28:36 - mmengine - INFO - Epoch(train) [7][3600/3757] lr: 9.0455e-05 eta: 9:23:03 time: 0.3793 data_time: 0.0168 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.6527 loss: 1.6527 2022/07/31 14:29:14 - mmengine - INFO - Epoch(train) [7][3700/3757] lr: 9.0455e-05 eta: 9:22:21 time: 0.3784 data_time: 0.0170 memory: 21072 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.9716 loss: 1.9716 2022/07/31 14:29:36 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 14:29:36 - mmengine - INFO - Epoch(train) [7][3757/3757] lr: 9.0455e-05 eta: 9:22:04 time: 0.3693 data_time: 0.0162 memory: 21072 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 1.9494 loss: 1.9494 2022/07/31 14:30:16 - mmengine - INFO - Epoch(train) [8][100/3757] lr: 8.7161e-05 eta: 9:21:01 time: 0.3802 data_time: 0.0173 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.6403 loss: 1.6403 2022/07/31 14:30:54 - mmengine - INFO - Epoch(train) [8][200/3757] lr: 8.7161e-05 eta: 9:20:19 time: 0.3778 data_time: 0.0181 memory: 21072 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 1.8668 loss: 1.8668 2022/07/31 14:31:32 - mmengine - INFO - Epoch(train) [8][300/3757] lr: 8.7161e-05 eta: 9:19:38 time: 0.3836 data_time: 0.0173 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.5688 loss: 1.5688 2022/07/31 14:32:11 - mmengine - INFO - Epoch(train) [8][400/3757] lr: 8.7161e-05 eta: 9:18:56 time: 0.3850 data_time: 0.0168 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2240 loss: 2.2240 2022/07/31 14:32:49 - mmengine - INFO - Epoch(train) [8][500/3757] lr: 8.7161e-05 eta: 9:18:13 time: 0.3786 data_time: 0.0162 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6211 loss: 1.6211 2022/07/31 14:33:27 - mmengine - INFO - Epoch(train) [8][600/3757] lr: 8.7161e-05 eta: 9:17:32 time: 0.3798 data_time: 0.0174 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9584 loss: 1.9584 2022/07/31 14:34:05 - mmengine - INFO - Epoch(train) [8][700/3757] lr: 8.7161e-05 eta: 9:16:50 time: 0.3796 data_time: 0.0165 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.9025 loss: 1.9025 2022/07/31 14:34:05 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 14:34:43 - mmengine - INFO - Epoch(train) [8][800/3757] lr: 8.7161e-05 eta: 9:16:08 time: 0.3857 data_time: 0.0178 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7912 loss: 1.7912 2022/07/31 14:35:21 - mmengine - INFO - Epoch(train) [8][900/3757] lr: 8.7161e-05 eta: 9:15:26 time: 0.3778 data_time: 0.0173 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5463 loss: 1.5463 2022/07/31 14:35:59 - mmengine - INFO - Epoch(train) [8][1000/3757] lr: 8.7161e-05 eta: 9:14:44 time: 0.3777 data_time: 0.0160 memory: 21072 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.6471 loss: 1.6471 2022/07/31 14:36:37 - mmengine - INFO - Epoch(train) [8][1100/3757] lr: 8.7161e-05 eta: 9:14:03 time: 0.3859 data_time: 0.0160 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5565 loss: 1.5565 2022/07/31 14:37:15 - mmengine - INFO - Epoch(train) [8][1200/3757] lr: 8.7161e-05 eta: 9:13:21 time: 0.3793 data_time: 0.0181 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.7337 loss: 1.7337 2022/07/31 14:37:53 - mmengine - INFO - Epoch(train) [8][1300/3757] lr: 8.7161e-05 eta: 9:12:39 time: 0.3823 data_time: 0.0173 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8592 loss: 1.8592 2022/07/31 14:38:31 - mmengine - INFO - Epoch(train) [8][1400/3757] lr: 8.7161e-05 eta: 9:11:57 time: 0.3788 data_time: 0.0164 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8279 loss: 1.8279 2022/07/31 14:39:09 - mmengine - INFO - Epoch(train) [8][1500/3757] lr: 8.7161e-05 eta: 9:11:16 time: 0.3775 data_time: 0.0170 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8738 loss: 1.8738 2022/07/31 14:39:48 - mmengine - INFO - Epoch(train) [8][1600/3757] lr: 8.7161e-05 eta: 9:10:34 time: 0.3812 data_time: 0.0188 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6977 loss: 1.6977 2022/07/31 14:40:26 - mmengine - INFO - Epoch(train) [8][1700/3757] lr: 8.7161e-05 eta: 9:09:53 time: 0.3788 data_time: 0.0175 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7374 loss: 1.7374 2022/07/31 14:40:26 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 14:41:04 - mmengine - INFO - Epoch(train) [8][1800/3757] lr: 8.7161e-05 eta: 9:09:11 time: 0.3789 data_time: 0.0177 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9495 loss: 1.9495 2022/07/31 14:41:42 - mmengine - INFO - Epoch(train) [8][1900/3757] lr: 8.7161e-05 eta: 9:08:30 time: 0.3804 data_time: 0.0179 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.9558 loss: 1.9558 2022/07/31 14:42:20 - mmengine - INFO - Epoch(train) [8][2000/3757] lr: 8.7161e-05 eta: 9:07:48 time: 0.3773 data_time: 0.0160 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9294 loss: 1.9294 2022/07/31 14:42:58 - mmengine - INFO - Epoch(train) [8][2100/3757] lr: 8.7161e-05 eta: 9:07:06 time: 0.3800 data_time: 0.0161 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9418 loss: 1.9418 2022/07/31 14:43:36 - mmengine - INFO - Epoch(train) [8][2200/3757] lr: 8.7161e-05 eta: 9:06:25 time: 0.3797 data_time: 0.0171 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4775 loss: 1.4775 2022/07/31 14:44:14 - mmengine - INFO - Epoch(train) [8][2300/3757] lr: 8.7161e-05 eta: 9:05:44 time: 0.3799 data_time: 0.0166 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8756 loss: 1.8756 2022/07/31 14:44:52 - mmengine - INFO - Epoch(train) [8][2400/3757] lr: 8.7161e-05 eta: 9:05:02 time: 0.3798 data_time: 0.0163 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7214 loss: 1.7214 2022/07/31 14:45:31 - mmengine - INFO - Epoch(train) [8][2500/3757] lr: 8.7161e-05 eta: 9:04:23 time: 0.3796 data_time: 0.0168 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.4822 loss: 1.4822 2022/07/31 14:46:09 - mmengine - INFO - Epoch(train) [8][2600/3757] lr: 8.7161e-05 eta: 9:03:42 time: 0.3766 data_time: 0.0168 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.9448 loss: 1.9448 2022/07/31 14:46:48 - mmengine - INFO - Epoch(train) [8][2700/3757] lr: 8.7161e-05 eta: 9:03:01 time: 0.3870 data_time: 0.0176 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7986 loss: 1.7986 2022/07/31 14:46:48 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 14:47:26 - mmengine - INFO - Epoch(train) [8][2800/3757] lr: 8.7161e-05 eta: 9:02:20 time: 0.3863 data_time: 0.0176 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7407 loss: 1.7407 2022/07/31 14:48:04 - mmengine - INFO - Epoch(train) [8][2900/3757] lr: 8.7161e-05 eta: 9:01:39 time: 0.3802 data_time: 0.0178 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.7231 loss: 1.7231 2022/07/31 14:48:42 - mmengine - INFO - Epoch(train) [8][3000/3757] lr: 8.7161e-05 eta: 9:00:58 time: 0.3798 data_time: 0.0166 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.6838 loss: 1.6838 2022/07/31 14:49:20 - mmengine - INFO - Epoch(train) [8][3100/3757] lr: 8.7161e-05 eta: 9:00:16 time: 0.3778 data_time: 0.0158 memory: 21072 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.9880 loss: 1.9880 2022/07/31 14:49:58 - mmengine - INFO - Epoch(train) [8][3200/3757] lr: 8.7161e-05 eta: 8:59:36 time: 0.3875 data_time: 0.0166 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.6695 loss: 1.6695 2022/07/31 14:50:37 - mmengine - INFO - Epoch(train) [8][3300/3757] lr: 8.7161e-05 eta: 8:58:55 time: 0.3928 data_time: 0.0177 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.9529 loss: 1.9529 2022/07/31 14:51:15 - mmengine - INFO - Epoch(train) [8][3400/3757] lr: 8.7161e-05 eta: 8:58:15 time: 0.3993 data_time: 0.0159 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7726 loss: 1.7726 2022/07/31 14:51:54 - mmengine - INFO - Epoch(train) [8][3500/3757] lr: 8.7161e-05 eta: 8:57:36 time: 0.4031 data_time: 0.0174 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.6638 loss: 1.6638 2022/07/31 14:52:33 - mmengine - INFO - Epoch(train) [8][3600/3757] lr: 8.7161e-05 eta: 8:56:56 time: 0.3833 data_time: 0.0181 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6863 loss: 1.6863 2022/07/31 14:53:11 - mmengine - INFO - Epoch(train) [8][3700/3757] lr: 8.7161e-05 eta: 8:56:15 time: 0.3788 data_time: 0.0166 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6413 loss: 1.6413 2022/07/31 14:53:11 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 14:53:33 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 14:53:33 - mmengine - INFO - Epoch(train) [8][3757/3757] lr: 8.7161e-05 eta: 8:55:59 time: 0.3749 data_time: 0.0153 memory: 21072 top1_acc: 0.7143 top5_acc: 0.7143 loss_cls: 1.9435 loss: 1.9435 2022/07/31 14:54:13 - mmengine - INFO - Epoch(train) [9][100/3757] lr: 8.3461e-05 eta: 8:54:59 time: 0.3867 data_time: 0.0173 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9241 loss: 1.9241 2022/07/31 14:54:51 - mmengine - INFO - Epoch(train) [9][200/3757] lr: 8.3461e-05 eta: 8:54:17 time: 0.3782 data_time: 0.0173 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.6857 loss: 1.6857 2022/07/31 14:55:29 - mmengine - INFO - Epoch(train) [9][300/3757] lr: 8.3461e-05 eta: 8:53:37 time: 0.3786 data_time: 0.0169 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6191 loss: 1.6191 2022/07/31 14:56:07 - mmengine - INFO - Epoch(train) [9][400/3757] lr: 8.3461e-05 eta: 8:52:56 time: 0.3794 data_time: 0.0183 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6955 loss: 1.6955 2022/07/31 14:56:45 - mmengine - INFO - Epoch(train) [9][500/3757] lr: 8.3461e-05 eta: 8:52:15 time: 0.3797 data_time: 0.0175 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.7139 loss: 1.7139 2022/07/31 14:57:24 - mmengine - INFO - Epoch(train) [9][600/3757] lr: 8.3461e-05 eta: 8:51:35 time: 0.3786 data_time: 0.0169 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3912 loss: 1.3912 2022/07/31 14:58:02 - mmengine - INFO - Epoch(train) [9][700/3757] lr: 8.3461e-05 eta: 8:50:54 time: 0.3778 data_time: 0.0163 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5562 loss: 1.5562 2022/07/31 14:58:40 - mmengine - INFO - Epoch(train) [9][800/3757] lr: 8.3461e-05 eta: 8:50:14 time: 0.3872 data_time: 0.0187 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.5857 loss: 1.5857 2022/07/31 14:59:19 - mmengine - INFO - Epoch(train) [9][900/3757] lr: 8.3461e-05 eta: 8:49:33 time: 0.3890 data_time: 0.0166 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.5999 loss: 1.5999 2022/07/31 14:59:35 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 14:59:57 - mmengine - INFO - Epoch(train) [9][1000/3757] lr: 8.3461e-05 eta: 8:48:53 time: 0.3848 data_time: 0.0162 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0386 loss: 2.0386 2022/07/31 15:00:35 - mmengine - INFO - Epoch(train) [9][1100/3757] lr: 8.3461e-05 eta: 8:48:12 time: 0.3872 data_time: 0.0169 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.6312 loss: 1.6312 2022/07/31 15:01:13 - mmengine - INFO - Epoch(train) [9][1200/3757] lr: 8.3461e-05 eta: 8:47:32 time: 0.3810 data_time: 0.0163 memory: 21072 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.9337 loss: 1.9337 2022/07/31 15:01:51 - mmengine - INFO - Epoch(train) [9][1300/3757] lr: 8.3461e-05 eta: 8:46:51 time: 0.3783 data_time: 0.0164 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9456 loss: 1.9456 2022/07/31 15:02:37 - mmengine - INFO - Epoch(train) [9][1400/3757] lr: 8.3461e-05 eta: 8:46:29 time: 0.4312 data_time: 0.0161 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.7704 loss: 1.7704 2022/07/31 15:03:15 - mmengine - INFO - Epoch(train) [9][1500/3757] lr: 8.3461e-05 eta: 8:45:48 time: 0.3831 data_time: 0.0173 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4471 loss: 1.4471 2022/07/31 15:03:53 - mmengine - INFO - Epoch(train) [9][1600/3757] lr: 8.3461e-05 eta: 8:45:07 time: 0.3766 data_time: 0.0151 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0774 loss: 2.0774 2022/07/31 15:04:31 - mmengine - INFO - Epoch(train) [9][1700/3757] lr: 8.3461e-05 eta: 8:44:26 time: 0.3792 data_time: 0.0170 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8319 loss: 1.8319 2022/07/31 15:05:09 - mmengine - INFO - Epoch(train) [9][1800/3757] lr: 8.3461e-05 eta: 8:43:45 time: 0.3813 data_time: 0.0164 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8594 loss: 1.8594 2022/07/31 15:05:47 - mmengine - INFO - Epoch(train) [9][1900/3757] lr: 8.3461e-05 eta: 8:43:05 time: 0.3799 data_time: 0.0182 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0091 loss: 2.0091 2022/07/31 15:06:04 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 15:06:30 - mmengine - INFO - Epoch(train) [9][2000/3757] lr: 8.3461e-05 eta: 8:42:34 time: 0.5891 data_time: 0.0519 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9187 loss: 1.9187 2022/07/31 15:07:31 - mmengine - INFO - Epoch(train) [9][2100/3757] lr: 8.3461e-05 eta: 8:42:52 time: 0.5811 data_time: 0.0126 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6644 loss: 1.6644 2022/07/31 15:08:29 - mmengine - INFO - Epoch(train) [9][2200/3757] lr: 8.3461e-05 eta: 8:43:00 time: 0.5051 data_time: 0.0132 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7721 loss: 1.7721 2022/07/31 15:09:18 - mmengine - INFO - Epoch(train) [9][2300/3757] lr: 8.3461e-05 eta: 8:42:46 time: 0.3809 data_time: 0.0173 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 2.0935 loss: 2.0935 2022/07/31 15:09:56 - mmengine - INFO - Epoch(train) [9][2400/3757] lr: 8.3461e-05 eta: 8:42:04 time: 0.3793 data_time: 0.0180 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.7172 loss: 1.7172 2022/07/31 15:10:34 - mmengine - INFO - Epoch(train) [9][2500/3757] lr: 8.3461e-05 eta: 8:41:23 time: 0.3778 data_time: 0.0162 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.8040 loss: 1.8040 2022/07/31 15:11:12 - mmengine - INFO - Epoch(train) [9][2600/3757] lr: 8.3461e-05 eta: 8:40:41 time: 0.3788 data_time: 0.0170 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9912 loss: 1.9912 2022/07/31 15:11:50 - mmengine - INFO - Epoch(train) [9][2700/3757] lr: 8.3461e-05 eta: 8:40:00 time: 0.3787 data_time: 0.0164 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1313 loss: 2.1313 2022/07/31 15:12:29 - mmengine - INFO - Epoch(train) [9][2800/3757] lr: 8.3461e-05 eta: 8:39:20 time: 0.4092 data_time: 0.0168 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6787 loss: 1.6787 2022/07/31 15:13:07 - mmengine - INFO - Epoch(train) [9][2900/3757] lr: 8.3461e-05 eta: 8:38:39 time: 0.3838 data_time: 0.0177 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7454 loss: 1.7454 2022/07/31 15:13:24 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 15:13:47 - mmengine - INFO - Epoch(train) [9][3000/3757] lr: 8.3461e-05 eta: 8:38:02 time: 0.4637 data_time: 0.0149 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7480 loss: 1.7480 2022/07/31 15:14:39 - mmengine - INFO - Epoch(train) [9][3100/3757] lr: 8.3461e-05 eta: 8:37:53 time: 0.5161 data_time: 0.0163 memory: 21072 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.6250 loss: 1.6250 2022/07/31 15:15:29 - mmengine - INFO - Epoch(train) [9][3200/3757] lr: 8.3461e-05 eta: 8:37:41 time: 0.3793 data_time: 0.0175 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.5610 loss: 1.5610 2022/07/31 15:16:08 - mmengine - INFO - Epoch(train) [9][3300/3757] lr: 8.3461e-05 eta: 8:37:03 time: 0.3817 data_time: 0.0136 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.7252 loss: 1.7252 2022/07/31 15:16:46 - mmengine - INFO - Epoch(train) [9][3400/3757] lr: 8.3461e-05 eta: 8:36:21 time: 0.3825 data_time: 0.0170 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4612 loss: 1.4612 2022/07/31 15:17:24 - mmengine - INFO - Epoch(train) [9][3500/3757] lr: 8.3461e-05 eta: 8:35:39 time: 0.3781 data_time: 0.0169 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.7069 loss: 1.7069 2022/07/31 15:18:02 - mmengine - INFO - Epoch(train) [9][3600/3757] lr: 8.3461e-05 eta: 8:34:58 time: 0.3820 data_time: 0.0176 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6427 loss: 1.6427 2022/07/31 15:18:40 - mmengine - INFO - Epoch(train) [9][3700/3757] lr: 8.3461e-05 eta: 8:34:16 time: 0.3866 data_time: 0.0175 memory: 21072 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.4750 loss: 1.4750 2022/07/31 15:19:02 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 15:19:02 - mmengine - INFO - Epoch(train) [9][3757/3757] lr: 8.3461e-05 eta: 8:34:00 time: 0.3723 data_time: 0.0162 memory: 21072 top1_acc: 0.7143 top5_acc: 1.0000 loss_cls: 1.5524 loss: 1.5524 2022/07/31 15:19:02 - mmengine - INFO - Saving checkpoint at 9 epochs 2022/07/31 15:19:27 - mmengine - INFO - Epoch(val) [9][100/310] eta: 0:00:43 time: 0.2094 data_time: 0.0688 memory: 5891 2022/07/31 15:19:49 - mmengine - INFO - Epoch(val) [9][200/310] eta: 0:00:21 time: 0.1969 data_time: 0.0586 memory: 5891 2022/07/31 15:20:09 - mmengine - INFO - Epoch(val) [9][300/310] eta: 0:00:01 time: 0.1789 data_time: 0.0489 memory: 5891 2022/07/31 15:20:12 - mmengine - INFO - Epoch(val) [9][310/310] acc/top1: 0.6629 acc/top5: 0.8684 acc/mean1: 0.6627 2022/07/31 15:20:12 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/swin-t-2/best_acc/top1_epoch_7.pth is removed 2022/07/31 15:20:13 - mmengine - INFO - The best checkpoint with 0.6629 acc/top1 at 10 epoch is saved to best_acc/top1_epoch_10.pth. 2022/07/31 15:20:53 - mmengine - INFO - Epoch(train) [10][100/3757] lr: 7.9393e-05 eta: 8:33:00 time: 0.3785 data_time: 0.0172 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.7105 loss: 1.7105 2022/07/31 15:21:26 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 15:21:31 - mmengine - INFO - Epoch(train) [10][200/3757] lr: 7.9393e-05 eta: 8:32:19 time: 0.3836 data_time: 0.0171 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7503 loss: 1.7503 2022/07/31 15:22:09 - mmengine - INFO - Epoch(train) [10][300/3757] lr: 7.9393e-05 eta: 8:31:38 time: 0.3848 data_time: 0.0169 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.8159 loss: 1.8159 2022/07/31 15:22:47 - mmengine - INFO - Epoch(train) [10][400/3757] lr: 7.9393e-05 eta: 8:30:56 time: 0.3834 data_time: 0.0183 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8267 loss: 1.8267 2022/07/31 15:23:25 - mmengine - INFO - Epoch(train) [10][500/3757] lr: 7.9393e-05 eta: 8:30:15 time: 0.3803 data_time: 0.0171 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8068 loss: 1.8068 2022/07/31 15:24:03 - mmengine - INFO - Epoch(train) [10][600/3757] lr: 7.9393e-05 eta: 8:29:33 time: 0.3783 data_time: 0.0163 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.6549 loss: 1.6549 2022/07/31 15:24:41 - mmengine - INFO - Epoch(train) [10][700/3757] lr: 7.9393e-05 eta: 8:28:52 time: 0.3790 data_time: 0.0173 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.8181 loss: 1.8181 2022/07/31 15:25:19 - mmengine - INFO - Epoch(train) [10][800/3757] lr: 7.9393e-05 eta: 8:28:11 time: 0.3790 data_time: 0.0170 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.6865 loss: 1.6865 2022/07/31 15:25:58 - mmengine - INFO - Epoch(train) [10][900/3757] lr: 7.9393e-05 eta: 8:27:30 time: 0.3790 data_time: 0.0177 memory: 21072 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.0082 loss: 2.0082 2022/07/31 15:26:36 - mmengine - INFO - Epoch(train) [10][1000/3757] lr: 7.9393e-05 eta: 8:26:49 time: 0.3767 data_time: 0.0165 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8448 loss: 1.8448 2022/07/31 15:27:14 - mmengine - INFO - Epoch(train) [10][1100/3757] lr: 7.9393e-05 eta: 8:26:09 time: 0.3789 data_time: 0.0170 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7619 loss: 1.7619 2022/07/31 15:27:48 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 15:27:53 - mmengine - INFO - Epoch(train) [10][1200/3757] lr: 7.9393e-05 eta: 8:25:28 time: 0.3897 data_time: 0.0176 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6978 loss: 1.6978 2022/07/31 15:28:31 - mmengine - INFO - Epoch(train) [10][1300/3757] lr: 7.9393e-05 eta: 8:24:47 time: 0.3827 data_time: 0.0167 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6796 loss: 1.6796 2022/07/31 15:29:09 - mmengine - INFO - Epoch(train) [10][1400/3757] lr: 7.9393e-05 eta: 8:24:06 time: 0.3793 data_time: 0.0168 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4215 loss: 1.4215 2022/07/31 15:29:47 - mmengine - INFO - Epoch(train) [10][1500/3757] lr: 7.9393e-05 eta: 8:23:25 time: 0.3888 data_time: 0.0159 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7519 loss: 1.7519 2022/07/31 15:30:25 - mmengine - INFO - Epoch(train) [10][1600/3757] lr: 7.9393e-05 eta: 8:22:44 time: 0.3789 data_time: 0.0166 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6973 loss: 1.6973 2022/07/31 15:31:03 - mmengine - INFO - Epoch(train) [10][1700/3757] lr: 7.9393e-05 eta: 8:22:03 time: 0.3833 data_time: 0.0159 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5441 loss: 1.5441 2022/07/31 15:31:46 - mmengine - INFO - Epoch(train) [10][1800/3757] lr: 7.9393e-05 eta: 8:21:31 time: 0.3778 data_time: 0.0158 memory: 21072 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.8311 loss: 1.8311 2022/07/31 15:32:24 - mmengine - INFO - Epoch(train) [10][1900/3757] lr: 7.9393e-05 eta: 8:20:50 time: 0.3792 data_time: 0.0166 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5844 loss: 1.5844 2022/07/31 15:33:02 - mmengine - INFO - Epoch(train) [10][2000/3757] lr: 7.9393e-05 eta: 8:20:09 time: 0.3795 data_time: 0.0165 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.8621 loss: 1.8621 2022/07/31 15:33:40 - mmengine - INFO - Epoch(train) [10][2100/3757] lr: 7.9393e-05 eta: 8:19:28 time: 0.3800 data_time: 0.0176 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.5382 loss: 1.5382 2022/07/31 15:34:13 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 15:34:18 - mmengine - INFO - Epoch(train) [10][2200/3757] lr: 7.9393e-05 eta: 8:18:47 time: 0.3779 data_time: 0.0166 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3915 loss: 1.3915 2022/07/31 15:34:56 - mmengine - INFO - Epoch(train) [10][2300/3757] lr: 7.9393e-05 eta: 8:18:06 time: 0.3792 data_time: 0.0166 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.6861 loss: 1.6861 2022/07/31 15:35:35 - mmengine - INFO - Epoch(train) [10][2400/3757] lr: 7.9393e-05 eta: 8:17:26 time: 0.3788 data_time: 0.0172 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0213 loss: 2.0213 2022/07/31 15:36:13 - mmengine - INFO - Epoch(train) [10][2500/3757] lr: 7.9393e-05 eta: 8:16:45 time: 0.3862 data_time: 0.0179 memory: 21072 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.7949 loss: 1.7949 2022/07/31 15:36:51 - mmengine - INFO - Epoch(train) [10][2600/3757] lr: 7.9393e-05 eta: 8:16:04 time: 0.3832 data_time: 0.0182 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.8052 loss: 1.8052 2022/07/31 15:37:29 - mmengine - INFO - Epoch(train) [10][2700/3757] lr: 7.9393e-05 eta: 8:15:23 time: 0.3838 data_time: 0.0173 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.7113 loss: 1.7113 2022/07/31 15:38:07 - mmengine - INFO - Epoch(train) [10][2800/3757] lr: 7.9393e-05 eta: 8:14:43 time: 0.3890 data_time: 0.0171 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6771 loss: 1.6771 2022/07/31 15:38:45 - mmengine - INFO - Epoch(train) [10][2900/3757] lr: 7.9393e-05 eta: 8:14:02 time: 0.3785 data_time: 0.0167 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.6682 loss: 1.6682 2022/07/31 15:39:24 - mmengine - INFO - Epoch(train) [10][3000/3757] lr: 7.9393e-05 eta: 8:13:21 time: 0.3805 data_time: 0.0173 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.7896 loss: 1.7896 2022/07/31 15:40:02 - mmengine - INFO - Epoch(train) [10][3100/3757] lr: 7.9393e-05 eta: 8:12:41 time: 0.3815 data_time: 0.0171 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.4918 loss: 1.4918 2022/07/31 15:40:35 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 15:40:40 - mmengine - INFO - Epoch(train) [10][3200/3757] lr: 7.9393e-05 eta: 8:12:00 time: 0.3849 data_time: 0.0172 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7237 loss: 1.7237 2022/07/31 15:41:18 - mmengine - INFO - Epoch(train) [10][3300/3757] lr: 7.9393e-05 eta: 8:11:19 time: 0.3841 data_time: 0.0179 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3236 loss: 1.3236 2022/07/31 15:41:56 - mmengine - INFO - Epoch(train) [10][3400/3757] lr: 7.9393e-05 eta: 8:10:39 time: 0.3777 data_time: 0.0164 memory: 21072 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.9394 loss: 1.9394 2022/07/31 15:42:35 - mmengine - INFO - Epoch(train) [10][3500/3757] lr: 7.9393e-05 eta: 8:09:58 time: 0.3779 data_time: 0.0168 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8241 loss: 1.8241 2022/07/31 15:43:13 - mmengine - INFO - Epoch(train) [10][3600/3757] lr: 7.9393e-05 eta: 8:09:17 time: 0.3782 data_time: 0.0168 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7531 loss: 1.7531 2022/07/31 15:43:51 - mmengine - INFO - Epoch(train) [10][3700/3757] lr: 7.9393e-05 eta: 8:08:37 time: 0.3828 data_time: 0.0185 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6345 loss: 1.6345 2022/07/31 15:44:13 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 15:44:13 - mmengine - INFO - Epoch(train) [10][3757/3757] lr: 7.9393e-05 eta: 8:08:21 time: 0.3715 data_time: 0.0167 memory: 21072 top1_acc: 0.4286 top5_acc: 0.7143 loss_cls: 2.0947 loss: 2.0947 2022/07/31 15:44:53 - mmengine - INFO - Epoch(train) [11][100/3757] lr: 7.5004e-05 eta: 8:07:24 time: 0.3826 data_time: 0.0181 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4427 loss: 1.4427 2022/07/31 15:45:31 - mmengine - INFO - Epoch(train) [11][200/3757] lr: 7.5004e-05 eta: 8:06:43 time: 0.3880 data_time: 0.0180 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7346 loss: 1.7346 2022/07/31 15:46:09 - mmengine - INFO - Epoch(train) [11][300/3757] lr: 7.5004e-05 eta: 8:06:03 time: 0.3797 data_time: 0.0165 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5038 loss: 1.5038 2022/07/31 15:46:47 - mmengine - INFO - Epoch(train) [11][400/3757] lr: 7.5004e-05 eta: 8:05:22 time: 0.3783 data_time: 0.0162 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.5882 loss: 1.5882 2022/07/31 15:46:59 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 15:47:26 - mmengine - INFO - Epoch(train) [11][500/3757] lr: 7.5004e-05 eta: 8:04:42 time: 0.3798 data_time: 0.0171 memory: 21072 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.5800 loss: 1.5800 2022/07/31 15:48:04 - mmengine - INFO - Epoch(train) [11][600/3757] lr: 7.5004e-05 eta: 8:04:01 time: 0.3786 data_time: 0.0157 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7751 loss: 1.7751 2022/07/31 15:48:42 - mmengine - INFO - Epoch(train) [11][700/3757] lr: 7.5004e-05 eta: 8:03:20 time: 0.3805 data_time: 0.0160 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.7943 loss: 1.7943 2022/07/31 15:49:20 - mmengine - INFO - Epoch(train) [11][800/3757] lr: 7.5004e-05 eta: 8:02:40 time: 0.3798 data_time: 0.0167 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.6143 loss: 1.6143 2022/07/31 15:49:58 - mmengine - INFO - Epoch(train) [11][900/3757] lr: 7.5004e-05 eta: 8:02:00 time: 0.3840 data_time: 0.0187 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9547 loss: 1.9547 2022/07/31 15:50:36 - mmengine - INFO - Epoch(train) [11][1000/3757] lr: 7.5004e-05 eta: 8:01:19 time: 0.3778 data_time: 0.0166 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5315 loss: 1.5315 2022/07/31 15:51:15 - mmengine - INFO - Epoch(train) [11][1100/3757] lr: 7.5004e-05 eta: 8:00:39 time: 0.3777 data_time: 0.0164 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.5218 loss: 1.5218 2022/07/31 15:51:53 - mmengine - INFO - Epoch(train) [11][1200/3757] lr: 7.5004e-05 eta: 7:59:58 time: 0.3797 data_time: 0.0171 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3164 loss: 1.3164 2022/07/31 15:52:31 - mmengine - INFO - Epoch(train) [11][1300/3757] lr: 7.5004e-05 eta: 7:59:18 time: 0.3782 data_time: 0.0177 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6339 loss: 1.6339 2022/07/31 15:53:09 - mmengine - INFO - Epoch(train) [11][1400/3757] lr: 7.5004e-05 eta: 7:58:38 time: 0.3814 data_time: 0.0170 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3866 loss: 1.3866 2022/07/31 15:53:21 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 15:53:47 - mmengine - INFO - Epoch(train) [11][1500/3757] lr: 7.5004e-05 eta: 7:57:57 time: 0.3811 data_time: 0.0163 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.7655 loss: 1.7655 2022/07/31 15:54:26 - mmengine - INFO - Epoch(train) [11][1600/3757] lr: 7.5004e-05 eta: 7:57:17 time: 0.3801 data_time: 0.0174 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5364 loss: 1.5364 2022/07/31 15:55:04 - mmengine - INFO - Epoch(train) [11][1700/3757] lr: 7.5004e-05 eta: 7:56:36 time: 0.3843 data_time: 0.0168 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.6977 loss: 1.6977 2022/07/31 15:55:42 - mmengine - INFO - Epoch(train) [11][1800/3757] lr: 7.5004e-05 eta: 7:55:56 time: 0.3839 data_time: 0.0180 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4591 loss: 1.4591 2022/07/31 15:56:20 - mmengine - INFO - Epoch(train) [11][1900/3757] lr: 7.5004e-05 eta: 7:55:15 time: 0.3802 data_time: 0.0171 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.6657 loss: 1.6657 2022/07/31 15:56:58 - mmengine - INFO - Epoch(train) [11][2000/3757] lr: 7.5004e-05 eta: 7:54:34 time: 0.3775 data_time: 0.0158 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.5859 loss: 1.5859 2022/07/31 15:57:36 - mmengine - INFO - Epoch(train) [11][2100/3757] lr: 7.5004e-05 eta: 7:53:55 time: 0.3813 data_time: 0.0182 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5379 loss: 1.5379 2022/07/31 15:58:14 - mmengine - INFO - Epoch(train) [11][2200/3757] lr: 7.5004e-05 eta: 7:53:14 time: 0.3811 data_time: 0.0176 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.7787 loss: 1.7787 2022/07/31 15:58:52 - mmengine - INFO - Epoch(train) [11][2300/3757] lr: 7.5004e-05 eta: 7:52:33 time: 0.3781 data_time: 0.0167 memory: 21072 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.6140 loss: 1.6140 2022/07/31 15:59:31 - mmengine - INFO - Epoch(train) [11][2400/3757] lr: 7.5004e-05 eta: 7:51:53 time: 0.3787 data_time: 0.0165 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.9212 loss: 1.9212 2022/07/31 15:59:42 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 16:00:09 - mmengine - INFO - Epoch(train) [11][2500/3757] lr: 7.5004e-05 eta: 7:51:13 time: 0.3832 data_time: 0.0161 memory: 21072 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.5990 loss: 1.5990 2022/07/31 16:00:47 - mmengine - INFO - Epoch(train) [11][2600/3757] lr: 7.5004e-05 eta: 7:50:32 time: 0.3778 data_time: 0.0161 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5675 loss: 1.5675 2022/07/31 16:01:25 - mmengine - INFO - Epoch(train) [11][2700/3757] lr: 7.5004e-05 eta: 7:49:52 time: 0.3799 data_time: 0.0158 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.5434 loss: 1.5434 2022/07/31 16:02:03 - mmengine - INFO - Epoch(train) [11][2800/3757] lr: 7.5004e-05 eta: 7:49:12 time: 0.3785 data_time: 0.0166 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.7222 loss: 1.7222 2022/07/31 16:02:42 - mmengine - INFO - Epoch(train) [11][2900/3757] lr: 7.5004e-05 eta: 7:48:32 time: 0.3789 data_time: 0.0173 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.8098 loss: 1.8098 2022/07/31 16:03:20 - mmengine - INFO - Epoch(train) [11][3000/3757] lr: 7.5004e-05 eta: 7:47:51 time: 0.3775 data_time: 0.0163 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.5701 loss: 1.5701 2022/07/31 16:03:58 - mmengine - INFO - Epoch(train) [11][3100/3757] lr: 7.5004e-05 eta: 7:47:11 time: 0.3788 data_time: 0.0161 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5891 loss: 1.5891 2022/07/31 16:04:36 - mmengine - INFO - Epoch(train) [11][3200/3757] lr: 7.5004e-05 eta: 7:46:31 time: 0.3834 data_time: 0.0169 memory: 21072 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 1.5959 loss: 1.5959 2022/07/31 16:05:14 - mmengine - INFO - Epoch(train) [11][3300/3757] lr: 7.5004e-05 eta: 7:45:51 time: 0.3796 data_time: 0.0167 memory: 21072 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.7207 loss: 1.7207 2022/07/31 16:05:52 - mmengine - INFO - Epoch(train) [11][3400/3757] lr: 7.5004e-05 eta: 7:45:10 time: 0.3779 data_time: 0.0166 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8911 loss: 1.8911 2022/07/31 16:06:04 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 16:06:36 - mmengine - INFO - Epoch(train) [11][3500/3757] lr: 7.5004e-05 eta: 7:44:39 time: 0.3793 data_time: 0.0125 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6926 loss: 1.6926 2022/07/31 16:07:15 - mmengine - INFO - Epoch(train) [11][3600/3757] lr: 7.5004e-05 eta: 7:44:01 time: 0.3773 data_time: 0.0159 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5395 loss: 1.5395 2022/07/31 16:08:00 - mmengine - INFO - Epoch(train) [11][3700/3757] lr: 7.5004e-05 eta: 7:43:33 time: 0.3801 data_time: 0.0145 memory: 21072 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 1.8300 loss: 1.8300 2022/07/31 16:08:24 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 16:08:24 - mmengine - INFO - Epoch(train) [11][3757/3757] lr: 7.5004e-05 eta: 7:43:17 time: 0.4944 data_time: 0.0133 memory: 21072 top1_acc: 0.7143 top5_acc: 0.7143 loss_cls: 1.7211 loss: 1.7211 2022/07/31 16:09:08 - mmengine - INFO - Epoch(train) [12][100/3757] lr: 7.0340e-05 eta: 7:42:27 time: 0.3749 data_time: 0.0151 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7881 loss: 1.7881 2022/07/31 16:09:46 - mmengine - INFO - Epoch(train) [12][200/3757] lr: 7.0340e-05 eta: 7:41:47 time: 0.3741 data_time: 0.0158 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5779 loss: 1.5779 2022/07/31 16:10:25 - mmengine - INFO - Epoch(train) [12][300/3757] lr: 7.0340e-05 eta: 7:41:09 time: 0.4117 data_time: 0.0144 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5382 loss: 1.5382 2022/07/31 16:11:03 - mmengine - INFO - Epoch(train) [12][400/3757] lr: 7.0340e-05 eta: 7:40:28 time: 0.3787 data_time: 0.0154 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6722 loss: 1.6722 2022/07/31 16:11:41 - mmengine - INFO - Epoch(train) [12][500/3757] lr: 7.0340e-05 eta: 7:39:48 time: 0.3808 data_time: 0.0164 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4985 loss: 1.4985 2022/07/31 16:12:19 - mmengine - INFO - Epoch(train) [12][600/3757] lr: 7.0340e-05 eta: 7:39:07 time: 0.3773 data_time: 0.0162 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6734 loss: 1.6734 2022/07/31 16:12:47 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 16:12:57 - mmengine - INFO - Epoch(train) [12][700/3757] lr: 7.0340e-05 eta: 7:38:26 time: 0.3767 data_time: 0.0152 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8455 loss: 1.8455 2022/07/31 16:13:35 - mmengine - INFO - Epoch(train) [12][800/3757] lr: 7.0340e-05 eta: 7:37:47 time: 0.3761 data_time: 0.0152 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.6309 loss: 1.6309 2022/07/31 16:14:15 - mmengine - INFO - Epoch(train) [12][900/3757] lr: 7.0340e-05 eta: 7:37:09 time: 0.3826 data_time: 0.0172 memory: 21072 top1_acc: 0.2500 top5_acc: 0.2500 loss_cls: 1.8322 loss: 1.8322 2022/07/31 16:14:53 - mmengine - INFO - Epoch(train) [12][1000/3757] lr: 7.0340e-05 eta: 7:36:28 time: 0.3811 data_time: 0.0174 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4349 loss: 1.4349 2022/07/31 16:15:31 - mmengine - INFO - Epoch(train) [12][1100/3757] lr: 7.0340e-05 eta: 7:35:48 time: 0.3788 data_time: 0.0162 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.4701 loss: 1.4701 2022/07/31 16:16:09 - mmengine - INFO - Epoch(train) [12][1200/3757] lr: 7.0340e-05 eta: 7:35:08 time: 0.3784 data_time: 0.0166 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6321 loss: 1.6321 2022/07/31 16:16:47 - mmengine - INFO - Epoch(train) [12][1300/3757] lr: 7.0340e-05 eta: 7:34:28 time: 0.3812 data_time: 0.0175 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7527 loss: 1.7527 2022/07/31 16:17:26 - mmengine - INFO - Epoch(train) [12][1400/3757] lr: 7.0340e-05 eta: 7:33:48 time: 0.3821 data_time: 0.0176 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6430 loss: 1.6430 2022/07/31 16:18:04 - mmengine - INFO - Epoch(train) [12][1500/3757] lr: 7.0340e-05 eta: 7:33:08 time: 0.3789 data_time: 0.0160 memory: 21072 top1_acc: 0.1250 top5_acc: 0.8750 loss_cls: 1.8261 loss: 1.8261 2022/07/31 16:18:42 - mmengine - INFO - Epoch(train) [12][1600/3757] lr: 7.0340e-05 eta: 7:32:27 time: 0.3794 data_time: 0.0168 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4431 loss: 1.4431 2022/07/31 16:19:10 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 16:19:20 - mmengine - INFO - Epoch(train) [12][1700/3757] lr: 7.0340e-05 eta: 7:31:47 time: 0.3768 data_time: 0.0171 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.5695 loss: 1.5695 2022/07/31 16:19:58 - mmengine - INFO - Epoch(train) [12][1800/3757] lr: 7.0340e-05 eta: 7:31:07 time: 0.3835 data_time: 0.0170 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.6135 loss: 1.6135 2022/07/31 16:20:36 - mmengine - INFO - Epoch(train) [12][1900/3757] lr: 7.0340e-05 eta: 7:30:27 time: 0.3845 data_time: 0.0170 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.7297 loss: 1.7297 2022/07/31 16:21:15 - mmengine - INFO - Epoch(train) [12][2000/3757] lr: 7.0340e-05 eta: 7:29:47 time: 0.3856 data_time: 0.0156 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7888 loss: 1.7888 2022/07/31 16:21:53 - mmengine - INFO - Epoch(train) [12][2100/3757] lr: 7.0340e-05 eta: 7:29:08 time: 0.3865 data_time: 0.0178 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8341 loss: 1.8341 2022/07/31 16:22:31 - mmengine - INFO - Epoch(train) [12][2200/3757] lr: 7.0340e-05 eta: 7:28:28 time: 0.3865 data_time: 0.0182 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5078 loss: 1.5078 2022/07/31 16:23:09 - mmengine - INFO - Epoch(train) [12][2300/3757] lr: 7.0340e-05 eta: 7:27:47 time: 0.3789 data_time: 0.0159 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.6771 loss: 1.6771 2022/07/31 16:23:48 - mmengine - INFO - Epoch(train) [12][2400/3757] lr: 7.0340e-05 eta: 7:27:08 time: 0.3803 data_time: 0.0172 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6096 loss: 1.6096 2022/07/31 16:24:26 - mmengine - INFO - Epoch(train) [12][2500/3757] lr: 7.0340e-05 eta: 7:26:28 time: 0.3796 data_time: 0.0164 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2669 loss: 1.2669 2022/07/31 16:25:04 - mmengine - INFO - Epoch(train) [12][2600/3757] lr: 7.0340e-05 eta: 7:25:48 time: 0.3796 data_time: 0.0174 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5755 loss: 1.5755 2022/07/31 16:25:32 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 16:25:43 - mmengine - INFO - Epoch(train) [12][2700/3757] lr: 7.0340e-05 eta: 7:25:08 time: 0.3766 data_time: 0.0135 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8173 loss: 1.8173 2022/07/31 16:26:21 - mmengine - INFO - Epoch(train) [12][2800/3757] lr: 7.0340e-05 eta: 7:24:28 time: 0.3789 data_time: 0.0175 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.7044 loss: 1.7044 2022/07/31 16:26:59 - mmengine - INFO - Epoch(train) [12][2900/3757] lr: 7.0340e-05 eta: 7:23:48 time: 0.3789 data_time: 0.0175 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.6085 loss: 1.6085 2022/07/31 16:27:37 - mmengine - INFO - Epoch(train) [12][3000/3757] lr: 7.0340e-05 eta: 7:23:08 time: 0.3832 data_time: 0.0181 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.9393 loss: 1.9393 2022/07/31 16:28:15 - mmengine - INFO - Epoch(train) [12][3100/3757] lr: 7.0340e-05 eta: 7:22:28 time: 0.3806 data_time: 0.0172 memory: 21072 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.5681 loss: 1.5681 2022/07/31 16:28:54 - mmengine - INFO - Epoch(train) [12][3200/3757] lr: 7.0340e-05 eta: 7:21:48 time: 0.3882 data_time: 0.0159 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.5951 loss: 1.5951 2022/07/31 16:29:32 - mmengine - INFO - Epoch(train) [12][3300/3757] lr: 7.0340e-05 eta: 7:21:08 time: 0.3808 data_time: 0.0139 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.7037 loss: 1.7037 2022/07/31 16:30:10 - mmengine - INFO - Epoch(train) [12][3400/3757] lr: 7.0340e-05 eta: 7:20:28 time: 0.3766 data_time: 0.0155 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4827 loss: 1.4827 2022/07/31 16:30:48 - mmengine - INFO - Epoch(train) [12][3500/3757] lr: 7.0340e-05 eta: 7:19:48 time: 0.3808 data_time: 0.0182 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6512 loss: 1.6512 2022/07/31 16:31:26 - mmengine - INFO - Epoch(train) [12][3600/3757] lr: 7.0340e-05 eta: 7:19:08 time: 0.3796 data_time: 0.0170 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.5496 loss: 1.5496 2022/07/31 16:31:54 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 16:32:04 - mmengine - INFO - Epoch(train) [12][3700/3757] lr: 7.0340e-05 eta: 7:18:28 time: 0.3783 data_time: 0.0157 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4122 loss: 1.4122 2022/07/31 16:32:26 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 16:32:26 - mmengine - INFO - Epoch(train) [12][3757/3757] lr: 7.0340e-05 eta: 7:18:12 time: 0.3701 data_time: 0.0152 memory: 21072 top1_acc: 0.8571 top5_acc: 0.8571 loss_cls: 1.3974 loss: 1.3974 2022/07/31 16:32:26 - mmengine - INFO - Saving checkpoint at 12 epochs 2022/07/31 16:32:49 - mmengine - INFO - Epoch(val) [12][100/310] eta: 0:00:44 time: 0.2100 data_time: 0.0702 memory: 5891 2022/07/31 16:33:10 - mmengine - INFO - Epoch(val) [12][200/310] eta: 0:00:20 time: 0.1874 data_time: 0.0423 memory: 5891 2022/07/31 16:33:32 - mmengine - INFO - Epoch(val) [12][300/310] eta: 0:00:01 time: 0.1989 data_time: 0.0589 memory: 5891 2022/07/31 16:33:35 - mmengine - INFO - Epoch(val) [12][310/310] acc/top1: 0.6785 acc/top5: 0.8779 acc/mean1: 0.6785 2022/07/31 16:33:35 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/swin-t-2/best_acc/top1_epoch_10.pth is removed 2022/07/31 16:33:37 - mmengine - INFO - The best checkpoint with 0.6785 acc/top1 at 13 epoch is saved to best_acc/top1_epoch_13.pth. 2022/07/31 16:34:16 - mmengine - INFO - Epoch(train) [13][100/3757] lr: 6.5454e-05 eta: 7:17:18 time: 0.3828 data_time: 0.0168 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5925 loss: 1.5925 2022/07/31 16:34:54 - mmengine - INFO - Epoch(train) [13][200/3757] lr: 6.5454e-05 eta: 7:16:38 time: 0.3865 data_time: 0.0181 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.6051 loss: 1.6051 2022/07/31 16:35:32 - mmengine - INFO - Epoch(train) [13][300/3757] lr: 6.5454e-05 eta: 7:15:58 time: 0.3810 data_time: 0.0171 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8553 loss: 1.8553 2022/07/31 16:36:10 - mmengine - INFO - Epoch(train) [13][400/3757] lr: 6.5454e-05 eta: 7:15:18 time: 0.3770 data_time: 0.0164 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2649 loss: 1.2649 2022/07/31 16:36:51 - mmengine - INFO - Epoch(train) [13][500/3757] lr: 6.5454e-05 eta: 7:14:41 time: 0.4802 data_time: 0.0159 memory: 21072 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 1.8195 loss: 1.8195 2022/07/31 16:37:29 - mmengine - INFO - Epoch(train) [13][600/3757] lr: 6.5454e-05 eta: 7:14:01 time: 0.3783 data_time: 0.0170 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4572 loss: 1.4572 2022/07/31 16:38:07 - mmengine - INFO - Epoch(train) [13][700/3757] lr: 6.5454e-05 eta: 7:13:22 time: 0.3787 data_time: 0.0167 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.5117 loss: 1.5117 2022/07/31 16:38:50 - mmengine - INFO - Epoch(train) [13][800/3757] lr: 6.5454e-05 eta: 7:12:48 time: 0.4867 data_time: 0.0167 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9084 loss: 1.9084 2022/07/31 16:39:44 - mmengine - INFO - Epoch(train) [13][900/3757] lr: 6.5454e-05 eta: 7:12:32 time: 0.5432 data_time: 0.0250 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3075 loss: 1.3075 2022/07/31 16:39:50 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 16:40:22 - mmengine - INFO - Epoch(train) [13][1000/3757] lr: 6.5454e-05 eta: 7:11:52 time: 0.3796 data_time: 0.0178 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.6377 loss: 1.6377 2022/07/31 16:41:00 - mmengine - INFO - Epoch(train) [13][1100/3757] lr: 6.5454e-05 eta: 7:11:12 time: 0.3801 data_time: 0.0177 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.6163 loss: 1.6163 2022/07/31 16:41:39 - mmengine - INFO - Epoch(train) [13][1200/3757] lr: 6.5454e-05 eta: 7:10:32 time: 0.3845 data_time: 0.0178 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.9100 loss: 1.9100 2022/07/31 16:42:17 - mmengine - INFO - Epoch(train) [13][1300/3757] lr: 6.5454e-05 eta: 7:09:52 time: 0.3855 data_time: 0.0183 memory: 21072 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.6135 loss: 1.6135 2022/07/31 16:42:55 - mmengine - INFO - Epoch(train) [13][1400/3757] lr: 6.5454e-05 eta: 7:09:12 time: 0.3872 data_time: 0.0173 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5153 loss: 1.5153 2022/07/31 16:43:34 - mmengine - INFO - Epoch(train) [13][1500/3757] lr: 6.5454e-05 eta: 7:08:33 time: 0.3838 data_time: 0.0172 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.6573 loss: 1.6573 2022/07/31 16:44:12 - mmengine - INFO - Epoch(train) [13][1600/3757] lr: 6.5454e-05 eta: 7:07:52 time: 0.3781 data_time: 0.0167 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5530 loss: 1.5530 2022/07/31 16:44:50 - mmengine - INFO - Epoch(train) [13][1700/3757] lr: 6.5454e-05 eta: 7:07:13 time: 0.3791 data_time: 0.0170 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.6049 loss: 1.6049 2022/07/31 16:45:28 - mmengine - INFO - Epoch(train) [13][1800/3757] lr: 6.5454e-05 eta: 7:06:33 time: 0.3781 data_time: 0.0163 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4568 loss: 1.4568 2022/07/31 16:46:07 - mmengine - INFO - Epoch(train) [13][1900/3757] lr: 6.5454e-05 eta: 7:05:53 time: 0.3775 data_time: 0.0163 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4517 loss: 1.4517 2022/07/31 16:46:13 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 16:46:45 - mmengine - INFO - Epoch(train) [13][2000/3757] lr: 6.5454e-05 eta: 7:05:13 time: 0.3788 data_time: 0.0173 memory: 21072 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.2898 loss: 1.2898 2022/07/31 16:47:23 - mmengine - INFO - Epoch(train) [13][2100/3757] lr: 6.5454e-05 eta: 7:04:34 time: 0.3798 data_time: 0.0159 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1989 loss: 1.1989 2022/07/31 16:48:01 - mmengine - INFO - Epoch(train) [13][2200/3757] lr: 6.5454e-05 eta: 7:03:54 time: 0.3798 data_time: 0.0182 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4994 loss: 1.4994 2022/07/31 16:48:39 - mmengine - INFO - Epoch(train) [13][2300/3757] lr: 6.5454e-05 eta: 7:03:14 time: 0.3833 data_time: 0.0177 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.6450 loss: 1.6450 2022/07/31 16:49:17 - mmengine - INFO - Epoch(train) [13][2400/3757] lr: 6.5454e-05 eta: 7:02:34 time: 0.3828 data_time: 0.0172 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2603 loss: 1.2603 2022/07/31 16:49:55 - mmengine - INFO - Epoch(train) [13][2500/3757] lr: 6.5454e-05 eta: 7:01:54 time: 0.3811 data_time: 0.0179 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4146 loss: 1.4146 2022/07/31 16:50:34 - mmengine - INFO - Epoch(train) [13][2600/3757] lr: 6.5454e-05 eta: 7:01:14 time: 0.3822 data_time: 0.0165 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5391 loss: 1.5391 2022/07/31 16:51:12 - mmengine - INFO - Epoch(train) [13][2700/3757] lr: 6.5454e-05 eta: 7:00:34 time: 0.3784 data_time: 0.0162 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3912 loss: 1.3912 2022/07/31 16:51:50 - mmengine - INFO - Epoch(train) [13][2800/3757] lr: 6.5454e-05 eta: 6:59:54 time: 0.3808 data_time: 0.0185 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4829 loss: 1.4829 2022/07/31 16:52:28 - mmengine - INFO - Epoch(train) [13][2900/3757] lr: 6.5454e-05 eta: 6:59:14 time: 0.3779 data_time: 0.0164 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.7800 loss: 1.7800 2022/07/31 16:52:34 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 16:53:06 - mmengine - INFO - Epoch(train) [13][3000/3757] lr: 6.5454e-05 eta: 6:58:34 time: 0.3786 data_time: 0.0153 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.7080 loss: 1.7080 2022/07/31 16:53:45 - mmengine - INFO - Epoch(train) [13][3100/3757] lr: 6.5454e-05 eta: 6:57:55 time: 0.3810 data_time: 0.0178 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1014 loss: 1.1014 2022/07/31 16:54:23 - mmengine - INFO - Epoch(train) [13][3200/3757] lr: 6.5454e-05 eta: 6:57:15 time: 0.3804 data_time: 0.0161 memory: 21072 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 1.7072 loss: 1.7072 2022/07/31 16:55:01 - mmengine - INFO - Epoch(train) [13][3300/3757] lr: 6.5454e-05 eta: 6:56:35 time: 0.3823 data_time: 0.0173 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4100 loss: 1.4100 2022/07/31 16:55:39 - mmengine - INFO - Epoch(train) [13][3400/3757] lr: 6.5454e-05 eta: 6:55:56 time: 0.3829 data_time: 0.0176 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5254 loss: 1.5254 2022/07/31 16:56:17 - mmengine - INFO - Epoch(train) [13][3500/3757] lr: 6.5454e-05 eta: 6:55:16 time: 0.3871 data_time: 0.0184 memory: 21072 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 1.6102 loss: 1.6102 2022/07/31 16:56:56 - mmengine - INFO - Epoch(train) [13][3600/3757] lr: 6.5454e-05 eta: 6:54:36 time: 0.3848 data_time: 0.0184 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4300 loss: 1.4300 2022/07/31 16:57:35 - mmengine - INFO - Epoch(train) [13][3700/3757] lr: 6.5454e-05 eta: 6:53:58 time: 0.3825 data_time: 0.0173 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.7901 loss: 1.7901 2022/07/31 16:57:56 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 16:57:56 - mmengine - INFO - Epoch(train) [13][3757/3757] lr: 6.5454e-05 eta: 6:53:42 time: 0.3687 data_time: 0.0164 memory: 21072 top1_acc: 0.4286 top5_acc: 0.8571 loss_cls: 1.8953 loss: 1.8953 2022/07/31 16:58:37 - mmengine - INFO - Epoch(train) [14][100/3757] lr: 6.0398e-05 eta: 6:52:50 time: 0.3839 data_time: 0.0183 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5966 loss: 1.5966 2022/07/31 16:58:59 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 16:59:15 - mmengine - INFO - Epoch(train) [14][200/3757] lr: 6.0398e-05 eta: 6:52:10 time: 0.3808 data_time: 0.0166 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2285 loss: 1.2285 2022/07/31 16:59:53 - mmengine - INFO - Epoch(train) [14][300/3757] lr: 6.0398e-05 eta: 6:51:30 time: 0.3778 data_time: 0.0169 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7411 loss: 1.7411 2022/07/31 17:00:31 - mmengine - INFO - Epoch(train) [14][400/3757] lr: 6.0398e-05 eta: 6:50:50 time: 0.3782 data_time: 0.0166 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5761 loss: 1.5761 2022/07/31 17:01:09 - mmengine - INFO - Epoch(train) [14][500/3757] lr: 6.0398e-05 eta: 6:50:11 time: 0.3803 data_time: 0.0172 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.6580 loss: 1.6580 2022/07/31 17:01:48 - mmengine - INFO - Epoch(train) [14][600/3757] lr: 6.0398e-05 eta: 6:49:31 time: 0.3788 data_time: 0.0166 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3693 loss: 1.3693 2022/07/31 17:02:26 - mmengine - INFO - Epoch(train) [14][700/3757] lr: 6.0398e-05 eta: 6:48:51 time: 0.3792 data_time: 0.0174 memory: 21072 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.7418 loss: 1.7418 2022/07/31 17:03:04 - mmengine - INFO - Epoch(train) [14][800/3757] lr: 6.0398e-05 eta: 6:48:11 time: 0.3779 data_time: 0.0173 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.4543 loss: 1.4543 2022/07/31 17:03:42 - mmengine - INFO - Epoch(train) [14][900/3757] lr: 6.0398e-05 eta: 6:47:32 time: 0.3854 data_time: 0.0176 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3156 loss: 1.3156 2022/07/31 17:04:20 - mmengine - INFO - Epoch(train) [14][1000/3757] lr: 6.0398e-05 eta: 6:46:52 time: 0.3867 data_time: 0.0170 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3720 loss: 1.3720 2022/07/31 17:04:59 - mmengine - INFO - Epoch(train) [14][1100/3757] lr: 6.0398e-05 eta: 6:46:13 time: 0.3835 data_time: 0.0185 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4367 loss: 1.4367 2022/07/31 17:05:21 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 17:05:37 - mmengine - INFO - Epoch(train) [14][1200/3757] lr: 6.0398e-05 eta: 6:45:33 time: 0.3791 data_time: 0.0177 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4534 loss: 1.4534 2022/07/31 17:06:15 - mmengine - INFO - Epoch(train) [14][1300/3757] lr: 6.0398e-05 eta: 6:44:53 time: 0.3795 data_time: 0.0176 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3455 loss: 1.3455 2022/07/31 17:06:53 - mmengine - INFO - Epoch(train) [14][1400/3757] lr: 6.0398e-05 eta: 6:44:14 time: 0.3795 data_time: 0.0169 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.6459 loss: 1.6459 2022/07/31 17:07:31 - mmengine - INFO - Epoch(train) [14][1500/3757] lr: 6.0398e-05 eta: 6:43:34 time: 0.3767 data_time: 0.0164 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4668 loss: 1.4668 2022/07/31 17:08:09 - mmengine - INFO - Epoch(train) [14][1600/3757] lr: 6.0398e-05 eta: 6:42:54 time: 0.3790 data_time: 0.0167 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.8783 loss: 1.8783 2022/07/31 17:08:48 - mmengine - INFO - Epoch(train) [14][1700/3757] lr: 6.0398e-05 eta: 6:42:15 time: 0.3787 data_time: 0.0162 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5668 loss: 1.5668 2022/07/31 17:09:26 - mmengine - INFO - Epoch(train) [14][1800/3757] lr: 6.0398e-05 eta: 6:41:36 time: 0.3772 data_time: 0.0162 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4349 loss: 1.4349 2022/07/31 17:10:05 - mmengine - INFO - Epoch(train) [14][1900/3757] lr: 6.0398e-05 eta: 6:40:56 time: 0.3782 data_time: 0.0161 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.4740 loss: 1.4740 2022/07/31 17:10:43 - mmengine - INFO - Epoch(train) [14][2000/3757] lr: 6.0398e-05 eta: 6:40:17 time: 0.3968 data_time: 0.0187 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6492 loss: 1.6492 2022/07/31 17:11:21 - mmengine - INFO - Epoch(train) [14][2100/3757] lr: 6.0398e-05 eta: 6:39:37 time: 0.3832 data_time: 0.0174 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4904 loss: 1.4904 2022/07/31 17:11:44 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 17:11:59 - mmengine - INFO - Epoch(train) [14][2200/3757] lr: 6.0398e-05 eta: 6:38:58 time: 0.3806 data_time: 0.0164 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.3800 loss: 1.3800 2022/07/31 17:12:38 - mmengine - INFO - Epoch(train) [14][2300/3757] lr: 6.0398e-05 eta: 6:38:18 time: 0.3823 data_time: 0.0165 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5128 loss: 1.5128 2022/07/31 17:13:16 - mmengine - INFO - Epoch(train) [14][2400/3757] lr: 6.0398e-05 eta: 6:37:38 time: 0.3799 data_time: 0.0176 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.4873 loss: 1.4873 2022/07/31 17:13:54 - mmengine - INFO - Epoch(train) [14][2500/3757] lr: 6.0398e-05 eta: 6:36:59 time: 0.3790 data_time: 0.0160 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.5910 loss: 1.5910 2022/07/31 17:14:32 - mmengine - INFO - Epoch(train) [14][2600/3757] lr: 6.0398e-05 eta: 6:36:19 time: 0.3797 data_time: 0.0180 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.8213 loss: 1.8213 2022/07/31 17:15:13 - mmengine - INFO - Epoch(train) [14][2700/3757] lr: 6.0398e-05 eta: 6:35:43 time: 0.3815 data_time: 0.0181 memory: 21072 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.4491 loss: 1.4491 2022/07/31 17:15:51 - mmengine - INFO - Epoch(train) [14][2800/3757] lr: 6.0398e-05 eta: 6:35:03 time: 0.3783 data_time: 0.0184 memory: 21072 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.4180 loss: 1.4180 2022/07/31 17:16:29 - mmengine - INFO - Epoch(train) [14][2900/3757] lr: 6.0398e-05 eta: 6:34:24 time: 0.3789 data_time: 0.0162 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6444 loss: 1.6444 2022/07/31 17:17:10 - mmengine - INFO - Epoch(train) [14][3000/3757] lr: 6.0398e-05 eta: 6:33:47 time: 0.4945 data_time: 0.0149 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5757 loss: 1.5757 2022/07/31 17:17:48 - mmengine - INFO - Epoch(train) [14][3100/3757] lr: 6.0398e-05 eta: 6:33:08 time: 0.3786 data_time: 0.0170 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6919 loss: 1.6919 2022/07/31 17:18:11 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 17:18:27 - mmengine - INFO - Epoch(train) [14][3200/3757] lr: 6.0398e-05 eta: 6:32:28 time: 0.3823 data_time: 0.0207 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.5802 loss: 1.5802 2022/07/31 17:19:05 - mmengine - INFO - Epoch(train) [14][3300/3757] lr: 6.0398e-05 eta: 6:31:49 time: 0.3807 data_time: 0.0173 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4582 loss: 1.4582 2022/07/31 17:19:43 - mmengine - INFO - Epoch(train) [14][3400/3757] lr: 6.0398e-05 eta: 6:31:09 time: 0.3913 data_time: 0.0173 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.6185 loss: 1.6185 2022/07/31 17:20:22 - mmengine - INFO - Epoch(train) [14][3500/3757] lr: 6.0398e-05 eta: 6:30:30 time: 0.3931 data_time: 0.0183 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5568 loss: 1.5568 2022/07/31 17:21:00 - mmengine - INFO - Epoch(train) [14][3600/3757] lr: 6.0398e-05 eta: 6:29:51 time: 0.3849 data_time: 0.0180 memory: 21072 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.7057 loss: 1.7057 2022/07/31 17:21:38 - mmengine - INFO - Epoch(train) [14][3700/3757] lr: 6.0398e-05 eta: 6:29:11 time: 0.3914 data_time: 0.0166 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5891 loss: 1.5891 2022/07/31 17:22:00 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 17:22:00 - mmengine - INFO - Epoch(train) [14][3757/3757] lr: 6.0398e-05 eta: 6:28:55 time: 0.3800 data_time: 0.0168 memory: 21072 top1_acc: 0.8571 top5_acc: 0.8571 loss_cls: 1.4471 loss: 1.4471 2022/07/31 17:22:41 - mmengine - INFO - Epoch(train) [15][100/3757] lr: 5.5229e-05 eta: 6:28:05 time: 0.3844 data_time: 0.0179 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.4538 loss: 1.4538 2022/07/31 17:23:19 - mmengine - INFO - Epoch(train) [15][200/3757] lr: 5.5229e-05 eta: 6:27:26 time: 0.3881 data_time: 0.0174 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4439 loss: 1.4439 2022/07/31 17:23:58 - mmengine - INFO - Epoch(train) [15][300/3757] lr: 5.5229e-05 eta: 6:26:46 time: 0.3847 data_time: 0.0178 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2638 loss: 1.2638 2022/07/31 17:24:36 - mmengine - INFO - Epoch(train) [15][400/3757] lr: 5.5229e-05 eta: 6:26:07 time: 0.3791 data_time: 0.0169 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.5539 loss: 1.5539 2022/07/31 17:24:37 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 17:25:15 - mmengine - INFO - Epoch(train) [15][500/3757] lr: 5.5229e-05 eta: 6:25:28 time: 0.3780 data_time: 0.0158 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3812 loss: 1.3812 2022/07/31 17:25:53 - mmengine - INFO - Epoch(train) [15][600/3757] lr: 5.5229e-05 eta: 6:24:48 time: 0.3804 data_time: 0.0172 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.6014 loss: 1.6014 2022/07/31 17:26:31 - mmengine - INFO - Epoch(train) [15][700/3757] lr: 5.5229e-05 eta: 6:24:09 time: 0.3808 data_time: 0.0173 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4647 loss: 1.4647 2022/07/31 17:27:09 - mmengine - INFO - Epoch(train) [15][800/3757] lr: 5.5229e-05 eta: 6:23:30 time: 0.3791 data_time: 0.0170 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.5012 loss: 1.5012 2022/07/31 17:27:50 - mmengine - INFO - Epoch(train) [15][900/3757] lr: 5.5229e-05 eta: 6:22:53 time: 0.4330 data_time: 0.0164 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7470 loss: 1.7470 2022/07/31 17:28:29 - mmengine - INFO - Epoch(train) [15][1000/3757] lr: 5.5229e-05 eta: 6:22:14 time: 0.3776 data_time: 0.0168 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2473 loss: 1.2473 2022/07/31 17:29:07 - mmengine - INFO - Epoch(train) [15][1100/3757] lr: 5.5229e-05 eta: 6:21:35 time: 0.3794 data_time: 0.0172 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.6060 loss: 1.6060 2022/07/31 17:29:45 - mmengine - INFO - Epoch(train) [15][1200/3757] lr: 5.5229e-05 eta: 6:20:55 time: 0.3788 data_time: 0.0172 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5094 loss: 1.5094 2022/07/31 17:30:24 - mmengine - INFO - Epoch(train) [15][1300/3757] lr: 5.5229e-05 eta: 6:20:16 time: 0.3828 data_time: 0.0169 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6132 loss: 1.6132 2022/07/31 17:31:02 - mmengine - INFO - Epoch(train) [15][1400/3757] lr: 5.5229e-05 eta: 6:19:36 time: 0.3832 data_time: 0.0176 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2037 loss: 1.2037 2022/07/31 17:31:03 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 17:31:40 - mmengine - INFO - Epoch(train) [15][1500/3757] lr: 5.5229e-05 eta: 6:18:57 time: 0.3808 data_time: 0.0179 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6092 loss: 1.6092 2022/07/31 17:32:18 - mmengine - INFO - Epoch(train) [15][1600/3757] lr: 5.5229e-05 eta: 6:18:17 time: 0.3847 data_time: 0.0162 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7431 loss: 1.7431 2022/07/31 17:32:56 - mmengine - INFO - Epoch(train) [15][1700/3757] lr: 5.5229e-05 eta: 6:17:38 time: 0.3800 data_time: 0.0172 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3595 loss: 1.3595 2022/07/31 17:33:35 - mmengine - INFO - Epoch(train) [15][1800/3757] lr: 5.5229e-05 eta: 6:16:59 time: 0.3790 data_time: 0.0174 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4717 loss: 1.4717 2022/07/31 17:34:13 - mmengine - INFO - Epoch(train) [15][1900/3757] lr: 5.5229e-05 eta: 6:16:19 time: 0.3838 data_time: 0.0173 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2405 loss: 1.2405 2022/07/31 17:34:52 - mmengine - INFO - Epoch(train) [15][2000/3757] lr: 5.5229e-05 eta: 6:15:40 time: 0.3785 data_time: 0.0162 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5428 loss: 1.5428 2022/07/31 17:35:30 - mmengine - INFO - Epoch(train) [15][2100/3757] lr: 5.5229e-05 eta: 6:15:00 time: 0.3825 data_time: 0.0168 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4377 loss: 1.4377 2022/07/31 17:36:08 - mmengine - INFO - Epoch(train) [15][2200/3757] lr: 5.5229e-05 eta: 6:14:21 time: 0.3801 data_time: 0.0165 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.6671 loss: 1.6671 2022/07/31 17:36:54 - mmengine - INFO - Epoch(train) [15][2300/3757] lr: 5.5229e-05 eta: 6:13:50 time: 0.6951 data_time: 0.0146 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4573 loss: 1.4573 2022/07/31 17:37:45 - mmengine - INFO - Epoch(train) [15][2400/3757] lr: 5.5229e-05 eta: 6:13:24 time: 0.4804 data_time: 0.0148 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3095 loss: 1.3095 2022/07/31 17:37:46 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 17:38:39 - mmengine - INFO - Epoch(train) [15][2500/3757] lr: 5.5229e-05 eta: 6:13:01 time: 0.5447 data_time: 0.0173 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3850 loss: 1.3850 2022/07/31 17:39:21 - mmengine - INFO - Epoch(train) [15][2600/3757] lr: 5.5229e-05 eta: 6:12:25 time: 0.3802 data_time: 0.0170 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7736 loss: 1.7736 2022/07/31 17:40:10 - mmengine - INFO - Epoch(train) [15][2700/3757] lr: 5.5229e-05 eta: 6:11:57 time: 0.6992 data_time: 0.0137 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4820 loss: 1.4820 2022/07/31 17:41:15 - mmengine - INFO - Epoch(train) [15][2800/3757] lr: 5.5229e-05 eta: 6:11:45 time: 0.7441 data_time: 0.0162 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6352 loss: 1.6352 2022/07/31 17:41:53 - mmengine - INFO - Epoch(train) [15][2900/3757] lr: 5.5229e-05 eta: 6:11:05 time: 0.3821 data_time: 0.0180 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3184 loss: 1.3184 2022/07/31 17:42:31 - mmengine - INFO - Epoch(train) [15][3000/3757] lr: 5.5229e-05 eta: 6:10:25 time: 0.3854 data_time: 0.0174 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4775 loss: 1.4775 2022/07/31 17:43:14 - mmengine - INFO - Epoch(train) [15][3100/3757] lr: 5.5229e-05 eta: 6:09:51 time: 0.6326 data_time: 0.0156 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.5327 loss: 1.5327 2022/07/31 17:43:53 - mmengine - INFO - Epoch(train) [15][3200/3757] lr: 5.5229e-05 eta: 6:09:11 time: 0.3847 data_time: 0.0168 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4922 loss: 1.4922 2022/07/31 17:44:31 - mmengine - INFO - Epoch(train) [15][3300/3757] lr: 5.5229e-05 eta: 6:08:32 time: 0.3817 data_time: 0.0174 memory: 21072 top1_acc: 0.3750 top5_acc: 1.0000 loss_cls: 1.2818 loss: 1.2818 2022/07/31 17:45:09 - mmengine - INFO - Epoch(train) [15][3400/3757] lr: 5.5229e-05 eta: 6:07:52 time: 0.3782 data_time: 0.0160 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5560 loss: 1.5560 2022/07/31 17:45:10 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 17:45:48 - mmengine - INFO - Epoch(train) [15][3500/3757] lr: 5.5229e-05 eta: 6:07:13 time: 0.3795 data_time: 0.0163 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.3746 loss: 1.3746 2022/07/31 17:46:26 - mmengine - INFO - Epoch(train) [15][3600/3757] lr: 5.5229e-05 eta: 6:06:33 time: 0.3796 data_time: 0.0164 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2245 loss: 1.2245 2022/07/31 17:47:04 - mmengine - INFO - Epoch(train) [15][3700/3757] lr: 5.5229e-05 eta: 6:05:54 time: 0.3838 data_time: 0.0188 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2146 loss: 1.2146 2022/07/31 17:47:26 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 17:47:26 - mmengine - INFO - Epoch(train) [15][3757/3757] lr: 5.5229e-05 eta: 6:05:38 time: 0.3785 data_time: 0.0156 memory: 21072 top1_acc: 0.2857 top5_acc: 0.7143 loss_cls: 1.5063 loss: 1.5063 2022/07/31 17:47:26 - mmengine - INFO - Saving checkpoint at 15 epochs 2022/07/31 17:47:50 - mmengine - INFO - Epoch(val) [15][100/310] eta: 0:00:43 time: 0.2075 data_time: 0.0691 memory: 5891 2022/07/31 17:48:11 - mmengine - INFO - Epoch(val) [15][200/310] eta: 0:00:22 time: 0.2010 data_time: 0.0601 memory: 5891 2022/07/31 17:48:32 - mmengine - INFO - Epoch(val) [15][300/310] eta: 0:00:01 time: 0.1893 data_time: 0.0507 memory: 5891 2022/07/31 17:48:35 - mmengine - INFO - Epoch(val) [15][310/310] acc/top1: 0.6978 acc/top5: 0.8877 acc/mean1: 0.6977 2022/07/31 17:48:35 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/swin-t-2/best_acc/top1_epoch_13.pth is removed 2022/07/31 17:48:36 - mmengine - INFO - The best checkpoint with 0.6978 acc/top1 at 16 epoch is saved to best_acc/top1_epoch_16.pth. 2022/07/31 17:49:17 - mmengine - INFO - Epoch(train) [16][100/3757] lr: 5.0002e-05 eta: 6:04:47 time: 0.3807 data_time: 0.0163 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2752 loss: 1.2752 2022/07/31 17:49:55 - mmengine - INFO - Epoch(train) [16][200/3757] lr: 5.0002e-05 eta: 6:04:08 time: 0.3793 data_time: 0.0172 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3383 loss: 1.3383 2022/07/31 17:50:33 - mmengine - INFO - Epoch(train) [16][300/3757] lr: 5.0002e-05 eta: 6:03:28 time: 0.3829 data_time: 0.0184 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.5773 loss: 1.5773 2022/07/31 17:51:12 - mmengine - INFO - Epoch(train) [16][400/3757] lr: 5.0002e-05 eta: 6:02:49 time: 0.3822 data_time: 0.0178 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4802 loss: 1.4802 2022/07/31 17:51:50 - mmengine - INFO - Epoch(train) [16][500/3757] lr: 5.0002e-05 eta: 6:02:09 time: 0.3785 data_time: 0.0167 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3097 loss: 1.3097 2022/07/31 17:52:28 - mmengine - INFO - Epoch(train) [16][600/3757] lr: 5.0002e-05 eta: 6:01:29 time: 0.3795 data_time: 0.0166 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1967 loss: 1.1967 2022/07/31 17:52:45 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 17:53:06 - mmengine - INFO - Epoch(train) [16][700/3757] lr: 5.0002e-05 eta: 6:00:50 time: 0.3872 data_time: 0.0163 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6535 loss: 1.6535 2022/07/31 17:53:45 - mmengine - INFO - Epoch(train) [16][800/3757] lr: 5.0002e-05 eta: 6:00:10 time: 0.3830 data_time: 0.0176 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2709 loss: 1.2709 2022/07/31 17:54:23 - mmengine - INFO - Epoch(train) [16][900/3757] lr: 5.0002e-05 eta: 5:59:31 time: 0.3840 data_time: 0.0184 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2793 loss: 1.2793 2022/07/31 17:55:01 - mmengine - INFO - Epoch(train) [16][1000/3757] lr: 5.0002e-05 eta: 5:58:51 time: 0.3843 data_time: 0.0175 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2824 loss: 1.2824 2022/07/31 17:55:39 - mmengine - INFO - Epoch(train) [16][1100/3757] lr: 5.0002e-05 eta: 5:58:12 time: 0.3816 data_time: 0.0168 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.5955 loss: 1.5955 2022/07/31 17:56:18 - mmengine - INFO - Epoch(train) [16][1200/3757] lr: 5.0002e-05 eta: 5:57:32 time: 0.3819 data_time: 0.0168 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4202 loss: 1.4202 2022/07/31 17:56:56 - mmengine - INFO - Epoch(train) [16][1300/3757] lr: 5.0002e-05 eta: 5:56:52 time: 0.3785 data_time: 0.0168 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4103 loss: 1.4103 2022/07/31 17:57:34 - mmengine - INFO - Epoch(train) [16][1400/3757] lr: 5.0002e-05 eta: 5:56:13 time: 0.3866 data_time: 0.0174 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.3757 loss: 1.3757 2022/07/31 17:58:13 - mmengine - INFO - Epoch(train) [16][1500/3757] lr: 5.0002e-05 eta: 5:55:34 time: 0.3793 data_time: 0.0168 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3205 loss: 1.3205 2022/07/31 17:58:52 - mmengine - INFO - Epoch(train) [16][1600/3757] lr: 5.0002e-05 eta: 5:54:55 time: 0.3798 data_time: 0.0169 memory: 21072 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.3295 loss: 1.3295 2022/07/31 17:59:09 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 17:59:30 - mmengine - INFO - Epoch(train) [16][1700/3757] lr: 5.0002e-05 eta: 5:54:15 time: 0.3805 data_time: 0.0175 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3987 loss: 1.3987 2022/07/31 18:00:11 - mmengine - INFO - Epoch(train) [16][1800/3757] lr: 5.0002e-05 eta: 5:53:39 time: 0.5411 data_time: 0.0256 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3960 loss: 1.3960 2022/07/31 18:00:49 - mmengine - INFO - Epoch(train) [16][1900/3757] lr: 5.0002e-05 eta: 5:52:59 time: 0.3795 data_time: 0.0167 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3864 loss: 1.3864 2022/07/31 18:01:28 - mmengine - INFO - Epoch(train) [16][2000/3757] lr: 5.0002e-05 eta: 5:52:20 time: 0.3966 data_time: 0.0183 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.7212 loss: 1.7212 2022/07/31 18:02:06 - mmengine - INFO - Epoch(train) [16][2100/3757] lr: 5.0002e-05 eta: 5:51:41 time: 0.3904 data_time: 0.0175 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3100 loss: 1.3100 2022/07/31 18:02:45 - mmengine - INFO - Epoch(train) [16][2200/3757] lr: 5.0002e-05 eta: 5:51:01 time: 0.3920 data_time: 0.0171 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5076 loss: 1.5076 2022/07/31 18:03:23 - mmengine - INFO - Epoch(train) [16][2300/3757] lr: 5.0002e-05 eta: 5:50:22 time: 0.3803 data_time: 0.0169 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4428 loss: 1.4428 2022/07/31 18:04:01 - mmengine - INFO - Epoch(train) [16][2400/3757] lr: 5.0002e-05 eta: 5:49:42 time: 0.3803 data_time: 0.0175 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3456 loss: 1.3456 2022/07/31 18:04:39 - mmengine - INFO - Epoch(train) [16][2500/3757] lr: 5.0002e-05 eta: 5:49:03 time: 0.3793 data_time: 0.0165 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.3659 loss: 1.3659 2022/07/31 18:05:18 - mmengine - INFO - Epoch(train) [16][2600/3757] lr: 5.0002e-05 eta: 5:48:23 time: 0.3779 data_time: 0.0159 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3568 loss: 1.3568 2022/07/31 18:05:35 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 18:05:56 - mmengine - INFO - Epoch(train) [16][2700/3757] lr: 5.0002e-05 eta: 5:47:44 time: 0.3790 data_time: 0.0167 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6129 loss: 1.6129 2022/07/31 18:06:34 - mmengine - INFO - Epoch(train) [16][2800/3757] lr: 5.0002e-05 eta: 5:47:04 time: 0.3832 data_time: 0.0175 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3004 loss: 1.3004 2022/07/31 18:07:12 - mmengine - INFO - Epoch(train) [16][2900/3757] lr: 5.0002e-05 eta: 5:46:25 time: 0.3833 data_time: 0.0169 memory: 21072 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.5331 loss: 1.5331 2022/07/31 18:07:50 - mmengine - INFO - Epoch(train) [16][3000/3757] lr: 5.0002e-05 eta: 5:45:45 time: 0.3797 data_time: 0.0170 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.6077 loss: 1.6077 2022/07/31 18:08:29 - mmengine - INFO - Epoch(train) [16][3100/3757] lr: 5.0002e-05 eta: 5:45:06 time: 0.3800 data_time: 0.0166 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3273 loss: 1.3273 2022/07/31 18:09:07 - mmengine - INFO - Epoch(train) [16][3200/3757] lr: 5.0002e-05 eta: 5:44:26 time: 0.3790 data_time: 0.0167 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4004 loss: 1.4004 2022/07/31 18:09:45 - mmengine - INFO - Epoch(train) [16][3300/3757] lr: 5.0002e-05 eta: 5:43:47 time: 0.3799 data_time: 0.0177 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1798 loss: 1.1798 2022/07/31 18:10:23 - mmengine - INFO - Epoch(train) [16][3400/3757] lr: 5.0002e-05 eta: 5:43:07 time: 0.3808 data_time: 0.0174 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2850 loss: 1.2850 2022/07/31 18:11:01 - mmengine - INFO - Epoch(train) [16][3500/3757] lr: 5.0002e-05 eta: 5:42:28 time: 0.3798 data_time: 0.0162 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.5588 loss: 1.5588 2022/07/31 18:11:40 - mmengine - INFO - Epoch(train) [16][3600/3757] lr: 5.0002e-05 eta: 5:41:48 time: 0.3780 data_time: 0.0164 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0828 loss: 1.0828 2022/07/31 18:11:57 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 18:12:20 - mmengine - INFO - Epoch(train) [16][3700/3757] lr: 5.0002e-05 eta: 5:41:11 time: 0.4847 data_time: 0.1229 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.5084 loss: 1.5084 2022/07/31 18:12:42 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 18:12:42 - mmengine - INFO - Epoch(train) [16][3757/3757] lr: 5.0002e-05 eta: 5:40:55 time: 0.3727 data_time: 0.0173 memory: 21072 top1_acc: 0.7143 top5_acc: 0.7143 loss_cls: 1.4617 loss: 1.4617 2022/07/31 18:13:22 - mmengine - INFO - Epoch(train) [17][100/3757] lr: 4.4776e-05 eta: 5:40:05 time: 0.3806 data_time: 0.0172 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4482 loss: 1.4482 2022/07/31 18:14:01 - mmengine - INFO - Epoch(train) [17][200/3757] lr: 4.4776e-05 eta: 5:39:26 time: 0.3784 data_time: 0.0167 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5235 loss: 1.5235 2022/07/31 18:14:39 - mmengine - INFO - Epoch(train) [17][300/3757] lr: 4.4776e-05 eta: 5:38:46 time: 0.3767 data_time: 0.0162 memory: 21072 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.4648 loss: 1.4648 2022/07/31 18:15:17 - mmengine - INFO - Epoch(train) [17][400/3757] lr: 4.4776e-05 eta: 5:38:06 time: 0.3808 data_time: 0.0147 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3758 loss: 1.3758 2022/07/31 18:15:55 - mmengine - INFO - Epoch(train) [17][500/3757] lr: 4.4776e-05 eta: 5:37:27 time: 0.3870 data_time: 0.0158 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.6719 loss: 1.6719 2022/07/31 18:16:33 - mmengine - INFO - Epoch(train) [17][600/3757] lr: 4.4776e-05 eta: 5:36:48 time: 0.3809 data_time: 0.0171 memory: 21072 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.4985 loss: 1.4985 2022/07/31 18:17:11 - mmengine - INFO - Epoch(train) [17][700/3757] lr: 4.4776e-05 eta: 5:36:08 time: 0.3788 data_time: 0.0163 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4052 loss: 1.4052 2022/07/31 18:17:50 - mmengine - INFO - Epoch(train) [17][800/3757] lr: 4.4776e-05 eta: 5:35:29 time: 0.3818 data_time: 0.0174 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6289 loss: 1.6289 2022/07/31 18:18:23 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 18:18:28 - mmengine - INFO - Epoch(train) [17][900/3757] lr: 4.4776e-05 eta: 5:34:49 time: 0.3799 data_time: 0.0172 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2308 loss: 1.2308 2022/07/31 18:19:06 - mmengine - INFO - Epoch(train) [17][1000/3757] lr: 4.4776e-05 eta: 5:34:10 time: 0.3797 data_time: 0.0167 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4514 loss: 1.4514 2022/07/31 18:19:45 - mmengine - INFO - Epoch(train) [17][1100/3757] lr: 4.4776e-05 eta: 5:33:31 time: 0.3797 data_time: 0.0167 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.4822 loss: 1.4822 2022/07/31 18:20:23 - mmengine - INFO - Epoch(train) [17][1200/3757] lr: 4.4776e-05 eta: 5:32:51 time: 0.3786 data_time: 0.0168 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1765 loss: 1.1765 2022/07/31 18:21:01 - mmengine - INFO - Epoch(train) [17][1300/3757] lr: 4.4776e-05 eta: 5:32:12 time: 0.3786 data_time: 0.0153 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3355 loss: 1.3355 2022/07/31 18:21:40 - mmengine - INFO - Epoch(train) [17][1400/3757] lr: 4.4776e-05 eta: 5:31:33 time: 0.3843 data_time: 0.0182 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3093 loss: 1.3093 2022/07/31 18:22:18 - mmengine - INFO - Epoch(train) [17][1500/3757] lr: 4.4776e-05 eta: 5:30:53 time: 0.3858 data_time: 0.0172 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6063 loss: 1.6063 2022/07/31 18:22:56 - mmengine - INFO - Epoch(train) [17][1600/3757] lr: 4.4776e-05 eta: 5:30:14 time: 0.3892 data_time: 0.0166 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3208 loss: 1.3208 2022/07/31 18:23:34 - mmengine - INFO - Epoch(train) [17][1700/3757] lr: 4.4776e-05 eta: 5:29:34 time: 0.3887 data_time: 0.0181 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3395 loss: 1.3395 2022/07/31 18:24:12 - mmengine - INFO - Epoch(train) [17][1800/3757] lr: 4.4776e-05 eta: 5:28:55 time: 0.3777 data_time: 0.0163 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.6312 loss: 1.6312 2022/07/31 18:24:46 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 18:24:50 - mmengine - INFO - Epoch(train) [17][1900/3757] lr: 4.4776e-05 eta: 5:28:16 time: 0.3779 data_time: 0.0162 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4380 loss: 1.4380 2022/07/31 18:25:29 - mmengine - INFO - Epoch(train) [17][2000/3757] lr: 4.4776e-05 eta: 5:27:36 time: 0.3789 data_time: 0.0171 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4278 loss: 1.4278 2022/07/31 18:26:07 - mmengine - INFO - Epoch(train) [17][2100/3757] lr: 4.4776e-05 eta: 5:26:57 time: 0.3787 data_time: 0.0179 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4060 loss: 1.4060 2022/07/31 18:26:45 - mmengine - INFO - Epoch(train) [17][2200/3757] lr: 4.4776e-05 eta: 5:26:18 time: 0.3822 data_time: 0.0170 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.6573 loss: 1.6573 2022/07/31 18:27:23 - mmengine - INFO - Epoch(train) [17][2300/3757] lr: 4.4776e-05 eta: 5:25:38 time: 0.3781 data_time: 0.0163 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.4088 loss: 1.4088 2022/07/31 18:28:02 - mmengine - INFO - Epoch(train) [17][2400/3757] lr: 4.4776e-05 eta: 5:24:59 time: 0.3795 data_time: 0.0173 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4820 loss: 1.4820 2022/07/31 18:28:40 - mmengine - INFO - Epoch(train) [17][2500/3757] lr: 4.4776e-05 eta: 5:24:19 time: 0.3838 data_time: 0.0174 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4761 loss: 1.4761 2022/07/31 18:29:18 - mmengine - INFO - Epoch(train) [17][2600/3757] lr: 4.4776e-05 eta: 5:23:40 time: 0.3825 data_time: 0.0176 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5001 loss: 1.5001 2022/07/31 18:29:56 - mmengine - INFO - Epoch(train) [17][2700/3757] lr: 4.4776e-05 eta: 5:23:00 time: 0.3826 data_time: 0.0168 memory: 21072 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.8071 loss: 1.8071 2022/07/31 18:30:34 - mmengine - INFO - Epoch(train) [17][2800/3757] lr: 4.4776e-05 eta: 5:22:21 time: 0.3835 data_time: 0.0174 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1898 loss: 1.1898 2022/07/31 18:31:08 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 18:31:12 - mmengine - INFO - Epoch(train) [17][2900/3757] lr: 4.4776e-05 eta: 5:21:42 time: 0.3796 data_time: 0.0171 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1369 loss: 1.1369 2022/07/31 18:31:51 - mmengine - INFO - Epoch(train) [17][3000/3757] lr: 4.4776e-05 eta: 5:21:02 time: 0.3790 data_time: 0.0171 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5249 loss: 1.5249 2022/07/31 18:32:29 - mmengine - INFO - Epoch(train) [17][3100/3757] lr: 4.4776e-05 eta: 5:20:23 time: 0.3786 data_time: 0.0166 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.6213 loss: 1.6213 2022/07/31 18:33:07 - mmengine - INFO - Epoch(train) [17][3200/3757] lr: 4.4776e-05 eta: 5:19:44 time: 0.3810 data_time: 0.0176 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.3767 loss: 1.3767 2022/07/31 18:33:45 - mmengine - INFO - Epoch(train) [17][3300/3757] lr: 4.4776e-05 eta: 5:19:04 time: 0.3793 data_time: 0.0168 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2808 loss: 1.2808 2022/07/31 18:34:23 - mmengine - INFO - Epoch(train) [17][3400/3757] lr: 4.4776e-05 eta: 5:18:25 time: 0.3791 data_time: 0.0166 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4561 loss: 1.4561 2022/07/31 18:35:01 - mmengine - INFO - Epoch(train) [17][3500/3757] lr: 4.4776e-05 eta: 5:17:46 time: 0.3813 data_time: 0.0185 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.7229 loss: 1.7229 2022/07/31 18:35:40 - mmengine - INFO - Epoch(train) [17][3600/3757] lr: 4.4776e-05 eta: 5:17:06 time: 0.3813 data_time: 0.0176 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4302 loss: 1.4302 2022/07/31 18:36:18 - mmengine - INFO - Epoch(train) [17][3700/3757] lr: 4.4776e-05 eta: 5:16:27 time: 0.3827 data_time: 0.0165 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4811 loss: 1.4811 2022/07/31 18:36:40 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 18:36:40 - mmengine - INFO - Epoch(train) [17][3757/3757] lr: 4.4776e-05 eta: 5:16:11 time: 0.3790 data_time: 0.0164 memory: 21072 top1_acc: 0.2857 top5_acc: 0.5714 loss_cls: 1.8456 loss: 1.8456 2022/07/31 18:37:20 - mmengine - INFO - Epoch(train) [18][100/3757] lr: 3.9606e-05 eta: 5:15:22 time: 0.3863 data_time: 0.0180 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2291 loss: 1.2291 2022/07/31 18:37:32 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 18:37:58 - mmengine - INFO - Epoch(train) [18][200/3757] lr: 3.9606e-05 eta: 5:14:42 time: 0.3885 data_time: 0.0205 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1367 loss: 1.1367 2022/07/31 18:38:46 - mmengine - INFO - Epoch(train) [18][300/3757] lr: 3.9606e-05 eta: 5:14:11 time: 0.4143 data_time: 0.0160 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5117 loss: 1.5117 2022/07/31 18:39:31 - mmengine - INFO - Epoch(train) [18][400/3757] lr: 3.9606e-05 eta: 5:13:36 time: 0.4780 data_time: 0.0164 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4190 loss: 1.4190 2022/07/31 18:40:15 - mmengine - INFO - Epoch(train) [18][500/3757] lr: 3.9606e-05 eta: 5:13:01 time: 0.5654 data_time: 0.1880 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2298 loss: 1.2298 2022/07/31 18:40:54 - mmengine - INFO - Epoch(train) [18][600/3757] lr: 3.9606e-05 eta: 5:12:23 time: 0.3802 data_time: 0.0165 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.5580 loss: 1.5580 2022/07/31 18:41:33 - mmengine - INFO - Epoch(train) [18][700/3757] lr: 3.9606e-05 eta: 5:11:44 time: 0.3749 data_time: 0.0152 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5334 loss: 1.5334 2022/07/31 18:42:13 - mmengine - INFO - Epoch(train) [18][800/3757] lr: 3.9606e-05 eta: 5:11:06 time: 0.3794 data_time: 0.0164 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4805 loss: 1.4805 2022/07/31 18:42:54 - mmengine - INFO - Epoch(train) [18][900/3757] lr: 3.9606e-05 eta: 5:10:28 time: 0.3770 data_time: 0.0155 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4085 loss: 1.4085 2022/07/31 18:43:32 - mmengine - INFO - Epoch(train) [18][1000/3757] lr: 3.9606e-05 eta: 5:09:49 time: 0.3788 data_time: 0.0169 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.4689 loss: 1.4689 2022/07/31 18:44:11 - mmengine - INFO - Epoch(train) [18][1100/3757] lr: 3.9606e-05 eta: 5:09:10 time: 0.3882 data_time: 0.0174 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4241 loss: 1.4241 2022/07/31 18:44:23 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 18:44:49 - mmengine - INFO - Epoch(train) [18][1200/3757] lr: 3.9606e-05 eta: 5:08:31 time: 0.3800 data_time: 0.0175 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1116 loss: 1.1116 2022/07/31 18:45:27 - mmengine - INFO - Epoch(train) [18][1300/3757] lr: 3.9606e-05 eta: 5:07:51 time: 0.3798 data_time: 0.0173 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4667 loss: 1.4667 2022/07/31 18:46:06 - mmengine - INFO - Epoch(train) [18][1400/3757] lr: 3.9606e-05 eta: 5:07:12 time: 0.3804 data_time: 0.0178 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7675 loss: 1.7675 2022/07/31 18:46:44 - mmengine - INFO - Epoch(train) [18][1500/3757] lr: 3.9606e-05 eta: 5:06:33 time: 0.3794 data_time: 0.0170 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4330 loss: 1.4330 2022/07/31 18:47:22 - mmengine - INFO - Epoch(train) [18][1600/3757] lr: 3.9606e-05 eta: 5:05:54 time: 0.3872 data_time: 0.0168 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1123 loss: 1.1123 2022/07/31 18:48:00 - mmengine - INFO - Epoch(train) [18][1700/3757] lr: 3.9606e-05 eta: 5:05:14 time: 0.3792 data_time: 0.0171 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0911 loss: 1.0911 2022/07/31 18:48:39 - mmengine - INFO - Epoch(train) [18][1800/3757] lr: 3.9606e-05 eta: 5:04:35 time: 0.3833 data_time: 0.0181 memory: 21072 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 1.1689 loss: 1.1689 2022/07/31 18:49:17 - mmengine - INFO - Epoch(train) [18][1900/3757] lr: 3.9606e-05 eta: 5:03:55 time: 0.3826 data_time: 0.0171 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4138 loss: 1.4138 2022/07/31 18:49:55 - mmengine - INFO - Epoch(train) [18][2000/3757] lr: 3.9606e-05 eta: 5:03:16 time: 0.3794 data_time: 0.0168 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5650 loss: 1.5650 2022/07/31 18:50:33 - mmengine - INFO - Epoch(train) [18][2100/3757] lr: 3.9606e-05 eta: 5:02:37 time: 0.3787 data_time: 0.0169 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4996 loss: 1.4996 2022/07/31 18:50:45 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 18:51:11 - mmengine - INFO - Epoch(train) [18][2200/3757] lr: 3.9606e-05 eta: 5:01:58 time: 0.3903 data_time: 0.0180 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3106 loss: 1.3106 2022/07/31 18:51:50 - mmengine - INFO - Epoch(train) [18][2300/3757] lr: 3.9606e-05 eta: 5:01:18 time: 0.3804 data_time: 0.0174 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8344 loss: 0.8344 2022/07/31 18:52:28 - mmengine - INFO - Epoch(train) [18][2400/3757] lr: 3.9606e-05 eta: 5:00:39 time: 0.3804 data_time: 0.0175 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6027 loss: 1.6027 2022/07/31 18:53:06 - mmengine - INFO - Epoch(train) [18][2500/3757] lr: 3.9606e-05 eta: 5:00:00 time: 0.3808 data_time: 0.0172 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4633 loss: 1.4633 2022/07/31 18:53:44 - mmengine - INFO - Epoch(train) [18][2600/3757] lr: 3.9606e-05 eta: 4:59:20 time: 0.3782 data_time: 0.0168 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.4286 loss: 1.4286 2022/07/31 18:54:22 - mmengine - INFO - Epoch(train) [18][2700/3757] lr: 3.9606e-05 eta: 4:58:41 time: 0.3845 data_time: 0.0175 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0191 loss: 1.0191 2022/07/31 18:55:01 - mmengine - INFO - Epoch(train) [18][2800/3757] lr: 3.9606e-05 eta: 4:58:02 time: 0.3829 data_time: 0.0150 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1983 loss: 1.1983 2022/07/31 18:55:39 - mmengine - INFO - Epoch(train) [18][2900/3757] lr: 3.9606e-05 eta: 4:57:23 time: 0.3917 data_time: 0.0181 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5582 loss: 1.5582 2022/07/31 18:56:17 - mmengine - INFO - Epoch(train) [18][3000/3757] lr: 3.9606e-05 eta: 4:56:43 time: 0.3820 data_time: 0.0160 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2945 loss: 1.2945 2022/07/31 18:56:56 - mmengine - INFO - Epoch(train) [18][3100/3757] lr: 3.9606e-05 eta: 4:56:04 time: 0.3803 data_time: 0.0171 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3309 loss: 1.3309 2022/07/31 18:57:08 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 18:57:34 - mmengine - INFO - Epoch(train) [18][3200/3757] lr: 3.9606e-05 eta: 4:55:25 time: 0.3788 data_time: 0.0171 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7043 loss: 1.7043 2022/07/31 18:58:12 - mmengine - INFO - Epoch(train) [18][3300/3757] lr: 3.9606e-05 eta: 4:54:45 time: 0.3804 data_time: 0.0175 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7908 loss: 1.7908 2022/07/31 18:58:50 - mmengine - INFO - Epoch(train) [18][3400/3757] lr: 3.9606e-05 eta: 4:54:06 time: 0.3792 data_time: 0.0164 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.6894 loss: 1.6894 2022/07/31 18:59:28 - mmengine - INFO - Epoch(train) [18][3500/3757] lr: 3.9606e-05 eta: 4:53:27 time: 0.3783 data_time: 0.0161 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.4195 loss: 1.4195 2022/07/31 19:00:07 - mmengine - INFO - Epoch(train) [18][3600/3757] lr: 3.9606e-05 eta: 4:52:48 time: 0.3782 data_time: 0.0161 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4821 loss: 1.4821 2022/07/31 19:00:45 - mmengine - INFO - Epoch(train) [18][3700/3757] lr: 3.9606e-05 eta: 4:52:09 time: 0.3794 data_time: 0.0159 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3718 loss: 1.3718 2022/07/31 19:01:07 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 19:01:07 - mmengine - INFO - Epoch(train) [18][3757/3757] lr: 3.9606e-05 eta: 4:51:53 time: 0.3676 data_time: 0.0159 memory: 21072 top1_acc: 0.7143 top5_acc: 1.0000 loss_cls: 1.4621 loss: 1.4621 2022/07/31 19:01:07 - mmengine - INFO - Saving checkpoint at 18 epochs 2022/07/31 19:01:35 - mmengine - INFO - Epoch(val) [18][100/310] eta: 0:00:45 time: 0.2147 data_time: 0.0740 memory: 5891 2022/07/31 19:01:56 - mmengine - INFO - Epoch(val) [18][200/310] eta: 0:00:20 time: 0.1887 data_time: 0.0491 memory: 5891 2022/07/31 19:02:15 - mmengine - INFO - Epoch(val) [18][300/310] eta: 0:00:01 time: 0.1416 data_time: 0.0169 memory: 5891 2022/07/31 19:02:17 - mmengine - INFO - Epoch(val) [18][310/310] acc/top1: 0.7083 acc/top5: 0.8944 acc/mean1: 0.7081 2022/07/31 19:02:18 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/swin-t-2/best_acc/top1_epoch_16.pth is removed 2022/07/31 19:02:19 - mmengine - INFO - The best checkpoint with 0.7083 acc/top1 at 19 epoch is saved to best_acc/top1_epoch_19.pth. 2022/07/31 19:02:59 - mmengine - INFO - Epoch(train) [19][100/3757] lr: 3.4551e-05 eta: 4:51:04 time: 0.3816 data_time: 0.0169 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1183 loss: 1.1183 2022/07/31 19:03:37 - mmengine - INFO - Epoch(train) [19][200/3757] lr: 3.4551e-05 eta: 4:50:24 time: 0.3848 data_time: 0.0176 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2910 loss: 1.2910 2022/07/31 19:04:15 - mmengine - INFO - Epoch(train) [19][300/3757] lr: 3.4551e-05 eta: 4:49:45 time: 0.3789 data_time: 0.0165 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4219 loss: 1.4219 2022/07/31 19:04:43 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 19:04:53 - mmengine - INFO - Epoch(train) [19][400/3757] lr: 3.4551e-05 eta: 4:49:06 time: 0.3777 data_time: 0.0160 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.0834 loss: 1.0834 2022/07/31 19:05:32 - mmengine - INFO - Epoch(train) [19][500/3757] lr: 3.4551e-05 eta: 4:48:27 time: 0.3802 data_time: 0.0168 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2858 loss: 1.2858 2022/07/31 19:06:10 - mmengine - INFO - Epoch(train) [19][600/3757] lr: 3.4551e-05 eta: 4:47:47 time: 0.3788 data_time: 0.0166 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0949 loss: 1.0949 2022/07/31 19:06:48 - mmengine - INFO - Epoch(train) [19][700/3757] lr: 3.4551e-05 eta: 4:47:08 time: 0.3802 data_time: 0.0174 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.2275 loss: 1.2275 2022/07/31 19:07:26 - mmengine - INFO - Epoch(train) [19][800/3757] lr: 3.4551e-05 eta: 4:46:29 time: 0.3798 data_time: 0.0161 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2442 loss: 1.2442 2022/07/31 19:08:05 - mmengine - INFO - Epoch(train) [19][900/3757] lr: 3.4551e-05 eta: 4:45:50 time: 0.3808 data_time: 0.0172 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3983 loss: 1.3983 2022/07/31 19:08:53 - mmengine - INFO - Epoch(train) [19][1000/3757] lr: 3.4551e-05 eta: 4:45:17 time: 0.5688 data_time: 0.0145 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.5890 loss: 1.5890 2022/07/31 19:10:04 - mmengine - INFO - Epoch(train) [19][1100/3757] lr: 3.4551e-05 eta: 4:44:59 time: 0.7776 data_time: 0.0148 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2998 loss: 1.2998 2022/07/31 19:10:45 - mmengine - INFO - Epoch(train) [19][1200/3757] lr: 3.4551e-05 eta: 4:44:21 time: 0.3977 data_time: 0.0360 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0311 loss: 1.0311 2022/07/31 19:11:23 - mmengine - INFO - Epoch(train) [19][1300/3757] lr: 3.4551e-05 eta: 4:43:42 time: 0.3813 data_time: 0.0172 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2882 loss: 1.2882 2022/07/31 19:11:51 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 19:12:01 - mmengine - INFO - Epoch(train) [19][1400/3757] lr: 3.4551e-05 eta: 4:43:03 time: 0.3836 data_time: 0.0173 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0814 loss: 1.0814 2022/07/31 19:12:39 - mmengine - INFO - Epoch(train) [19][1500/3757] lr: 3.4551e-05 eta: 4:42:23 time: 0.3812 data_time: 0.0170 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3234 loss: 1.3234 2022/07/31 19:13:18 - mmengine - INFO - Epoch(train) [19][1600/3757] lr: 3.4551e-05 eta: 4:41:44 time: 0.3816 data_time: 0.0170 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.4685 loss: 1.4685 2022/07/31 19:13:56 - mmengine - INFO - Epoch(train) [19][1700/3757] lr: 3.4551e-05 eta: 4:41:05 time: 0.3798 data_time: 0.0174 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5798 loss: 1.5798 2022/07/31 19:14:34 - mmengine - INFO - Epoch(train) [19][1800/3757] lr: 3.4551e-05 eta: 4:40:25 time: 0.3799 data_time: 0.0182 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0960 loss: 1.0960 2022/07/31 19:15:12 - mmengine - INFO - Epoch(train) [19][1900/3757] lr: 3.4551e-05 eta: 4:39:46 time: 0.3803 data_time: 0.0168 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3242 loss: 1.3242 2022/07/31 19:15:51 - mmengine - INFO - Epoch(train) [19][2000/3757] lr: 3.4551e-05 eta: 4:39:07 time: 0.3844 data_time: 0.0169 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1764 loss: 1.1764 2022/07/31 19:16:29 - mmengine - INFO - Epoch(train) [19][2100/3757] lr: 3.4551e-05 eta: 4:38:28 time: 0.3793 data_time: 0.0173 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2774 loss: 1.2774 2022/07/31 19:17:08 - mmengine - INFO - Epoch(train) [19][2200/3757] lr: 3.4551e-05 eta: 4:37:49 time: 0.3775 data_time: 0.0160 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3825 loss: 1.3825 2022/07/31 19:17:47 - mmengine - INFO - Epoch(train) [19][2300/3757] lr: 3.4551e-05 eta: 4:37:10 time: 0.3795 data_time: 0.0166 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3850 loss: 1.3850 2022/07/31 19:18:15 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 19:18:25 - mmengine - INFO - Epoch(train) [19][2400/3757] lr: 3.4551e-05 eta: 4:36:30 time: 0.3824 data_time: 0.0173 memory: 21072 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.3916 loss: 1.3916 2022/07/31 19:19:03 - mmengine - INFO - Epoch(train) [19][2500/3757] lr: 3.4551e-05 eta: 4:35:51 time: 0.3815 data_time: 0.0181 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2474 loss: 1.2474 2022/07/31 19:19:41 - mmengine - INFO - Epoch(train) [19][2600/3757] lr: 3.4551e-05 eta: 4:35:12 time: 0.3805 data_time: 0.0173 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3366 loss: 1.3366 2022/07/31 19:20:19 - mmengine - INFO - Epoch(train) [19][2700/3757] lr: 3.4551e-05 eta: 4:34:33 time: 0.3835 data_time: 0.0184 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.5865 loss: 1.5865 2022/07/31 19:20:58 - mmengine - INFO - Epoch(train) [19][2800/3757] lr: 3.4551e-05 eta: 4:33:53 time: 0.3787 data_time: 0.0169 memory: 21072 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.2888 loss: 1.2888 2022/07/31 19:21:36 - mmengine - INFO - Epoch(train) [19][2900/3757] lr: 3.4551e-05 eta: 4:33:14 time: 0.3801 data_time: 0.0164 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4278 loss: 1.4278 2022/07/31 19:22:14 - mmengine - INFO - Epoch(train) [19][3000/3757] lr: 3.4551e-05 eta: 4:32:35 time: 0.3800 data_time: 0.0170 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.6955 loss: 1.6955 2022/07/31 19:22:52 - mmengine - INFO - Epoch(train) [19][3100/3757] lr: 3.4551e-05 eta: 4:31:55 time: 0.3815 data_time: 0.0173 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.2442 loss: 1.2442 2022/07/31 19:23:30 - mmengine - INFO - Epoch(train) [19][3200/3757] lr: 3.4551e-05 eta: 4:31:16 time: 0.3799 data_time: 0.0169 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2313 loss: 1.2313 2022/07/31 19:24:09 - mmengine - INFO - Epoch(train) [19][3300/3757] lr: 3.4551e-05 eta: 4:30:37 time: 0.3785 data_time: 0.0169 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3957 loss: 1.3957 2022/07/31 19:24:37 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 19:24:47 - mmengine - INFO - Epoch(train) [19][3400/3757] lr: 3.4551e-05 eta: 4:29:58 time: 0.3784 data_time: 0.0163 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4394 loss: 1.4394 2022/07/31 19:25:25 - mmengine - INFO - Epoch(train) [19][3500/3757] lr: 3.4551e-05 eta: 4:29:19 time: 0.3816 data_time: 0.0172 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4564 loss: 1.4564 2022/07/31 19:26:04 - mmengine - INFO - Epoch(train) [19][3600/3757] lr: 3.4551e-05 eta: 4:28:39 time: 0.3816 data_time: 0.0168 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.3766 loss: 1.3766 2022/07/31 19:26:42 - mmengine - INFO - Epoch(train) [19][3700/3757] lr: 3.4551e-05 eta: 4:28:00 time: 0.3821 data_time: 0.0171 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1341 loss: 1.1341 2022/07/31 19:27:03 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 19:27:03 - mmengine - INFO - Epoch(train) [19][3757/3757] lr: 3.4551e-05 eta: 4:27:44 time: 0.3695 data_time: 0.0167 memory: 21072 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 1.3059 loss: 1.3059 2022/07/31 19:27:44 - mmengine - INFO - Epoch(train) [20][100/3757] lr: 2.9665e-05 eta: 4:26:56 time: 0.3828 data_time: 0.0177 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0101 loss: 1.0101 2022/07/31 19:28:22 - mmengine - INFO - Epoch(train) [20][200/3757] lr: 2.9665e-05 eta: 4:26:17 time: 0.3813 data_time: 0.0169 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.3917 loss: 1.3917 2022/07/31 19:29:00 - mmengine - INFO - Epoch(train) [20][300/3757] lr: 2.9665e-05 eta: 4:25:38 time: 0.3797 data_time: 0.0167 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2592 loss: 1.2592 2022/07/31 19:29:39 - mmengine - INFO - Epoch(train) [20][400/3757] lr: 2.9665e-05 eta: 4:24:58 time: 0.3795 data_time: 0.0169 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8225 loss: 0.8225 2022/07/31 19:30:17 - mmengine - INFO - Epoch(train) [20][500/3757] lr: 2.9665e-05 eta: 4:24:19 time: 0.3789 data_time: 0.0173 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2159 loss: 1.2159 2022/07/31 19:30:55 - mmengine - INFO - Epoch(train) [20][600/3757] lr: 2.9665e-05 eta: 4:23:40 time: 0.3810 data_time: 0.0163 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0660 loss: 1.0660 2022/07/31 19:31:02 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 19:31:33 - mmengine - INFO - Epoch(train) [20][700/3757] lr: 2.9665e-05 eta: 4:23:01 time: 0.3801 data_time: 0.0174 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.3543 loss: 1.3543 2022/07/31 19:32:12 - mmengine - INFO - Epoch(train) [20][800/3757] lr: 2.9665e-05 eta: 4:22:22 time: 0.3819 data_time: 0.0170 memory: 21072 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.2417 loss: 1.2417 2022/07/31 19:32:50 - mmengine - INFO - Epoch(train) [20][900/3757] lr: 2.9665e-05 eta: 4:21:43 time: 0.3832 data_time: 0.0178 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1825 loss: 1.1825 2022/07/31 19:33:28 - mmengine - INFO - Epoch(train) [20][1000/3757] lr: 2.9665e-05 eta: 4:21:03 time: 0.3788 data_time: 0.0173 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3429 loss: 1.3429 2022/07/31 19:34:06 - mmengine - INFO - Epoch(train) [20][1100/3757] lr: 2.9665e-05 eta: 4:20:24 time: 0.3813 data_time: 0.0162 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.4439 loss: 1.4439 2022/07/31 19:34:45 - mmengine - INFO - Epoch(train) [20][1200/3757] lr: 2.9665e-05 eta: 4:19:45 time: 0.3826 data_time: 0.0166 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0830 loss: 1.0830 2022/07/31 19:35:23 - mmengine - INFO - Epoch(train) [20][1300/3757] lr: 2.9665e-05 eta: 4:19:06 time: 0.3838 data_time: 0.0182 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1759 loss: 1.1759 2022/07/31 19:36:01 - mmengine - INFO - Epoch(train) [20][1400/3757] lr: 2.9665e-05 eta: 4:18:27 time: 0.3814 data_time: 0.0164 memory: 21072 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 1.0986 loss: 1.0986 2022/07/31 19:36:39 - mmengine - INFO - Epoch(train) [20][1500/3757] lr: 2.9665e-05 eta: 4:17:47 time: 0.3799 data_time: 0.0171 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9645 loss: 0.9645 2022/07/31 19:37:18 - mmengine - INFO - Epoch(train) [20][1600/3757] lr: 2.9665e-05 eta: 4:17:08 time: 0.3793 data_time: 0.0163 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2580 loss: 1.2580 2022/07/31 19:37:24 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 19:37:56 - mmengine - INFO - Epoch(train) [20][1700/3757] lr: 2.9665e-05 eta: 4:16:29 time: 0.3812 data_time: 0.0177 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0724 loss: 1.0724 2022/07/31 19:38:34 - mmengine - INFO - Epoch(train) [20][1800/3757] lr: 2.9665e-05 eta: 4:15:50 time: 0.3788 data_time: 0.0171 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1984 loss: 1.1984 2022/07/31 19:39:13 - mmengine - INFO - Epoch(train) [20][1900/3757] lr: 2.9665e-05 eta: 4:15:11 time: 0.3754 data_time: 0.0148 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2857 loss: 1.2857 2022/07/31 19:39:51 - mmengine - INFO - Epoch(train) [20][2000/3757] lr: 2.9665e-05 eta: 4:14:32 time: 0.3789 data_time: 0.0168 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3240 loss: 1.3240 2022/07/31 19:40:30 - mmengine - INFO - Epoch(train) [20][2100/3757] lr: 2.9665e-05 eta: 4:13:53 time: 0.3775 data_time: 0.0157 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3345 loss: 1.3345 2022/07/31 19:41:08 - mmengine - INFO - Epoch(train) [20][2200/3757] lr: 2.9665e-05 eta: 4:13:14 time: 0.3782 data_time: 0.0177 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3532 loss: 1.3532 2022/07/31 19:41:46 - mmengine - INFO - Epoch(train) [20][2300/3757] lr: 2.9665e-05 eta: 4:12:35 time: 0.3908 data_time: 0.0179 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3307 loss: 1.3307 2022/07/31 19:42:24 - mmengine - INFO - Epoch(train) [20][2400/3757] lr: 2.9665e-05 eta: 4:11:55 time: 0.3786 data_time: 0.0162 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2381 loss: 1.2381 2022/07/31 19:43:03 - mmengine - INFO - Epoch(train) [20][2500/3757] lr: 2.9665e-05 eta: 4:11:16 time: 0.3899 data_time: 0.0179 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2950 loss: 1.2950 2022/07/31 19:43:41 - mmengine - INFO - Epoch(train) [20][2600/3757] lr: 2.9665e-05 eta: 4:10:37 time: 0.3798 data_time: 0.0204 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3636 loss: 1.3636 2022/07/31 19:43:47 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 19:44:19 - mmengine - INFO - Epoch(train) [20][2700/3757] lr: 2.9665e-05 eta: 4:09:58 time: 0.3779 data_time: 0.0153 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4636 loss: 1.4636 2022/07/31 19:44:57 - mmengine - INFO - Epoch(train) [20][2800/3757] lr: 2.9665e-05 eta: 4:09:18 time: 0.3776 data_time: 0.0152 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5652 loss: 1.5652 2022/07/31 19:45:35 - mmengine - INFO - Epoch(train) [20][2900/3757] lr: 2.9665e-05 eta: 4:08:39 time: 0.3792 data_time: 0.0162 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2064 loss: 1.2064 2022/07/31 19:46:13 - mmengine - INFO - Epoch(train) [20][3000/3757] lr: 2.9665e-05 eta: 4:08:00 time: 0.3806 data_time: 0.0174 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2561 loss: 1.2561 2022/07/31 19:46:52 - mmengine - INFO - Epoch(train) [20][3100/3757] lr: 2.9665e-05 eta: 4:07:21 time: 0.3814 data_time: 0.0174 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4076 loss: 1.4076 2022/07/31 19:47:30 - mmengine - INFO - Epoch(train) [20][3200/3757] lr: 2.9665e-05 eta: 4:06:42 time: 0.3785 data_time: 0.0169 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1012 loss: 1.1012 2022/07/31 19:48:08 - mmengine - INFO - Epoch(train) [20][3300/3757] lr: 2.9665e-05 eta: 4:06:03 time: 0.3833 data_time: 0.0192 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1232 loss: 1.1232 2022/07/31 19:48:46 - mmengine - INFO - Epoch(train) [20][3400/3757] lr: 2.9665e-05 eta: 4:05:24 time: 0.3838 data_time: 0.0182 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2289 loss: 1.2289 2022/07/31 19:49:25 - mmengine - INFO - Epoch(train) [20][3500/3757] lr: 2.9665e-05 eta: 4:04:44 time: 0.3830 data_time: 0.0171 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2963 loss: 1.2963 2022/07/31 19:50:03 - mmengine - INFO - Epoch(train) [20][3600/3757] lr: 2.9665e-05 eta: 4:04:05 time: 0.3826 data_time: 0.0172 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1816 loss: 1.1816 2022/07/31 19:50:09 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 19:50:41 - mmengine - INFO - Epoch(train) [20][3700/3757] lr: 2.9665e-05 eta: 4:03:26 time: 0.3802 data_time: 0.0170 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3666 loss: 1.3666 2022/07/31 19:51:03 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 19:51:03 - mmengine - INFO - Epoch(train) [20][3757/3757] lr: 2.9665e-05 eta: 4:03:11 time: 0.4018 data_time: 0.0182 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.5166 loss: 1.5166 2022/07/31 19:51:44 - mmengine - INFO - Epoch(train) [21][100/3757] lr: 2.5001e-05 eta: 4:02:23 time: 0.3828 data_time: 0.0173 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9579 loss: 0.9579 2022/07/31 19:52:22 - mmengine - INFO - Epoch(train) [21][200/3757] lr: 2.5001e-05 eta: 4:01:44 time: 0.3902 data_time: 0.0179 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0507 loss: 1.0507 2022/07/31 19:53:00 - mmengine - INFO - Epoch(train) [21][300/3757] lr: 2.5001e-05 eta: 4:01:04 time: 0.3790 data_time: 0.0171 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4297 loss: 1.4297 2022/07/31 19:53:39 - mmengine - INFO - Epoch(train) [21][400/3757] lr: 2.5001e-05 eta: 4:00:25 time: 0.3790 data_time: 0.0162 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5271 loss: 1.5271 2022/07/31 19:54:17 - mmengine - INFO - Epoch(train) [21][500/3757] lr: 2.5001e-05 eta: 3:59:46 time: 0.3800 data_time: 0.0167 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1121 loss: 1.1121 2022/07/31 19:54:56 - mmengine - INFO - Epoch(train) [21][600/3757] lr: 2.5001e-05 eta: 3:59:07 time: 0.3810 data_time: 0.0172 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4575 loss: 1.4575 2022/07/31 19:55:34 - mmengine - INFO - Epoch(train) [21][700/3757] lr: 2.5001e-05 eta: 3:58:28 time: 0.3812 data_time: 0.0168 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4201 loss: 1.4201 2022/07/31 19:56:13 - mmengine - INFO - Epoch(train) [21][800/3757] lr: 2.5001e-05 eta: 3:57:50 time: 0.3798 data_time: 0.0166 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4497 loss: 1.4497 2022/07/31 19:56:36 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 19:56:51 - mmengine - INFO - Epoch(train) [21][900/3757] lr: 2.5001e-05 eta: 3:57:11 time: 0.3782 data_time: 0.0162 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.0958 loss: 1.0958 2022/07/31 19:57:30 - mmengine - INFO - Epoch(train) [21][1000/3757] lr: 2.5001e-05 eta: 3:56:32 time: 0.3792 data_time: 0.0170 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1661 loss: 1.1661 2022/07/31 19:58:08 - mmengine - INFO - Epoch(train) [21][1100/3757] lr: 2.5001e-05 eta: 3:55:53 time: 0.3867 data_time: 0.0165 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4185 loss: 1.4185 2022/07/31 19:58:47 - mmengine - INFO - Epoch(train) [21][1200/3757] lr: 2.5001e-05 eta: 3:55:14 time: 0.3912 data_time: 0.0170 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1358 loss: 1.1358 2022/07/31 19:59:25 - mmengine - INFO - Epoch(train) [21][1300/3757] lr: 2.5001e-05 eta: 3:54:35 time: 0.3816 data_time: 0.0166 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2267 loss: 1.2267 2022/07/31 20:00:03 - mmengine - INFO - Epoch(train) [21][1400/3757] lr: 2.5001e-05 eta: 3:53:56 time: 0.3822 data_time: 0.0167 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.4485 loss: 1.4485 2022/07/31 20:00:41 - mmengine - INFO - Epoch(train) [21][1500/3757] lr: 2.5001e-05 eta: 3:53:16 time: 0.3789 data_time: 0.0169 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3321 loss: 1.3321 2022/07/31 20:01:20 - mmengine - INFO - Epoch(train) [21][1600/3757] lr: 2.5001e-05 eta: 3:52:37 time: 0.3801 data_time: 0.0170 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0924 loss: 1.0924 2022/07/31 20:01:59 - mmengine - INFO - Epoch(train) [21][1700/3757] lr: 2.5001e-05 eta: 3:51:59 time: 0.3797 data_time: 0.0163 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2148 loss: 1.2148 2022/07/31 20:02:37 - mmengine - INFO - Epoch(train) [21][1800/3757] lr: 2.5001e-05 eta: 3:51:20 time: 0.3844 data_time: 0.0176 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1503 loss: 1.1503 2022/07/31 20:03:00 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 20:03:16 - mmengine - INFO - Epoch(train) [21][1900/3757] lr: 2.5001e-05 eta: 3:50:41 time: 0.3857 data_time: 0.0184 memory: 21072 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 1.3787 loss: 1.3787 2022/07/31 20:03:54 - mmengine - INFO - Epoch(train) [21][2000/3757] lr: 2.5001e-05 eta: 3:50:02 time: 0.3872 data_time: 0.0172 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.1211 loss: 1.1211 2022/07/31 20:04:32 - mmengine - INFO - Epoch(train) [21][2100/3757] lr: 2.5001e-05 eta: 3:49:22 time: 0.3784 data_time: 0.0171 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.3596 loss: 1.3596 2022/07/31 20:05:10 - mmengine - INFO - Epoch(train) [21][2200/3757] lr: 2.5001e-05 eta: 3:48:43 time: 0.3780 data_time: 0.0160 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3725 loss: 1.3725 2022/07/31 20:05:48 - mmengine - INFO - Epoch(train) [21][2300/3757] lr: 2.5001e-05 eta: 3:48:04 time: 0.3850 data_time: 0.0173 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2758 loss: 1.2758 2022/07/31 20:06:26 - mmengine - INFO - Epoch(train) [21][2400/3757] lr: 2.5001e-05 eta: 3:47:25 time: 0.3807 data_time: 0.0164 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2415 loss: 1.2415 2022/07/31 20:07:05 - mmengine - INFO - Epoch(train) [21][2500/3757] lr: 2.5001e-05 eta: 3:46:46 time: 0.3865 data_time: 0.0185 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2322 loss: 1.2322 2022/07/31 20:07:43 - mmengine - INFO - Epoch(train) [21][2600/3757] lr: 2.5001e-05 eta: 3:46:07 time: 0.3889 data_time: 0.0194 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1361 loss: 1.1361 2022/07/31 20:08:21 - mmengine - INFO - Epoch(train) [21][2700/3757] lr: 2.5001e-05 eta: 3:45:28 time: 0.3792 data_time: 0.0163 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.3751 loss: 1.3751 2022/07/31 20:09:00 - mmengine - INFO - Epoch(train) [21][2800/3757] lr: 2.5001e-05 eta: 3:44:49 time: 0.3788 data_time: 0.0171 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1110 loss: 1.1110 2022/07/31 20:09:23 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 20:09:38 - mmengine - INFO - Epoch(train) [21][2900/3757] lr: 2.5001e-05 eta: 3:44:10 time: 0.3797 data_time: 0.0163 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2008 loss: 1.2008 2022/07/31 20:10:16 - mmengine - INFO - Epoch(train) [21][3000/3757] lr: 2.5001e-05 eta: 3:43:31 time: 0.3792 data_time: 0.0171 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3745 loss: 1.3745 2022/07/31 20:10:54 - mmengine - INFO - Epoch(train) [21][3100/3757] lr: 2.5001e-05 eta: 3:42:52 time: 0.3799 data_time: 0.0170 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2215 loss: 1.2215 2022/07/31 20:11:33 - mmengine - INFO - Epoch(train) [21][3200/3757] lr: 2.5001e-05 eta: 3:42:13 time: 0.3784 data_time: 0.0160 memory: 21072 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.1157 loss: 1.1157 2022/07/31 20:12:11 - mmengine - INFO - Epoch(train) [21][3300/3757] lr: 2.5001e-05 eta: 3:41:34 time: 0.3798 data_time: 0.0172 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2037 loss: 1.2037 2022/07/31 20:12:49 - mmengine - INFO - Epoch(train) [21][3400/3757] lr: 2.5001e-05 eta: 3:40:55 time: 0.3820 data_time: 0.0174 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0605 loss: 1.0605 2022/07/31 20:13:28 - mmengine - INFO - Epoch(train) [21][3500/3757] lr: 2.5001e-05 eta: 3:40:16 time: 0.3786 data_time: 0.0164 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1579 loss: 1.1579 2022/07/31 20:14:06 - mmengine - INFO - Epoch(train) [21][3600/3757] lr: 2.5001e-05 eta: 3:39:37 time: 0.3832 data_time: 0.0175 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3430 loss: 1.3430 2022/07/31 20:14:44 - mmengine - INFO - Epoch(train) [21][3700/3757] lr: 2.5001e-05 eta: 3:38:58 time: 0.3801 data_time: 0.0171 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2347 loss: 1.2347 2022/07/31 20:15:06 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 20:15:06 - mmengine - INFO - Epoch(train) [21][3757/3757] lr: 2.5001e-05 eta: 3:38:42 time: 0.3678 data_time: 0.0158 memory: 21072 top1_acc: 0.4286 top5_acc: 0.8571 loss_cls: 0.9166 loss: 0.9166 2022/07/31 20:15:06 - mmengine - INFO - Saving checkpoint at 21 epochs 2022/07/31 20:15:30 - mmengine - INFO - Epoch(val) [21][100/310] eta: 0:00:40 time: 0.1943 data_time: 0.0522 memory: 5891 2022/07/31 20:15:51 - mmengine - INFO - Epoch(val) [21][200/310] eta: 0:00:21 time: 0.1980 data_time: 0.0581 memory: 5891 2022/07/31 20:16:13 - mmengine - INFO - Epoch(val) [21][300/310] eta: 0:00:01 time: 0.1965 data_time: 0.0610 memory: 5891 2022/07/31 20:16:16 - mmengine - INFO - Epoch(val) [21][310/310] acc/top1: 0.7155 acc/top5: 0.8982 acc/mean1: 0.7154 2022/07/31 20:16:16 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/swin-t-2/best_acc/top1_epoch_19.pth is removed 2022/07/31 20:16:17 - mmengine - INFO - The best checkpoint with 0.7155 acc/top1 at 22 epoch is saved to best_acc/top1_epoch_22.pth. 2022/07/31 20:17:00 - mmengine - INFO - Epoch(train) [22][100/3757] lr: 2.0612e-05 eta: 3:37:56 time: 0.3778 data_time: 0.0155 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4140 loss: 1.4140 2022/07/31 20:17:01 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 20:17:38 - mmengine - INFO - Epoch(train) [22][200/3757] lr: 2.0612e-05 eta: 3:37:16 time: 0.3835 data_time: 0.0181 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1502 loss: 1.1502 2022/07/31 20:18:16 - mmengine - INFO - Epoch(train) [22][300/3757] lr: 2.0612e-05 eta: 3:36:37 time: 0.3801 data_time: 0.0163 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1204 loss: 1.1204 2022/07/31 20:18:54 - mmengine - INFO - Epoch(train) [22][400/3757] lr: 2.0612e-05 eta: 3:35:58 time: 0.3798 data_time: 0.0174 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9948 loss: 0.9948 2022/07/31 20:19:33 - mmengine - INFO - Epoch(train) [22][500/3757] lr: 2.0612e-05 eta: 3:35:19 time: 0.3789 data_time: 0.0169 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9217 loss: 0.9217 2022/07/31 20:20:11 - mmengine - INFO - Epoch(train) [22][600/3757] lr: 2.0612e-05 eta: 3:34:40 time: 0.3820 data_time: 0.0178 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0056 loss: 1.0056 2022/07/31 20:20:49 - mmengine - INFO - Epoch(train) [22][700/3757] lr: 2.0612e-05 eta: 3:34:01 time: 0.3817 data_time: 0.0167 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0261 loss: 1.0261 2022/07/31 20:21:27 - mmengine - INFO - Epoch(train) [22][800/3757] lr: 2.0612e-05 eta: 3:33:22 time: 0.3831 data_time: 0.0177 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2524 loss: 1.2524 2022/07/31 20:22:06 - mmengine - INFO - Epoch(train) [22][900/3757] lr: 2.0612e-05 eta: 3:32:43 time: 0.3880 data_time: 0.0173 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4923 loss: 1.4923 2022/07/31 20:22:44 - mmengine - INFO - Epoch(train) [22][1000/3757] lr: 2.0612e-05 eta: 3:32:04 time: 0.3802 data_time: 0.0171 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2704 loss: 1.2704 2022/07/31 20:23:22 - mmengine - INFO - Epoch(train) [22][1100/3757] lr: 2.0612e-05 eta: 3:31:25 time: 0.3802 data_time: 0.0171 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5677 loss: 1.5677 2022/07/31 20:23:23 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 20:24:00 - mmengine - INFO - Epoch(train) [22][1200/3757] lr: 2.0612e-05 eta: 3:30:46 time: 0.3800 data_time: 0.0178 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1266 loss: 1.1266 2022/07/31 20:24:39 - mmengine - INFO - Epoch(train) [22][1300/3757] lr: 2.0612e-05 eta: 3:30:07 time: 0.3817 data_time: 0.0185 memory: 21072 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 1.5001 loss: 1.5001 2022/07/31 20:25:17 - mmengine - INFO - Epoch(train) [22][1400/3757] lr: 2.0612e-05 eta: 3:29:28 time: 0.3814 data_time: 0.0183 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3483 loss: 1.3483 2022/07/31 20:25:55 - mmengine - INFO - Epoch(train) [22][1500/3757] lr: 2.0612e-05 eta: 3:28:49 time: 0.3816 data_time: 0.0169 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5397 loss: 1.5397 2022/07/31 20:26:33 - mmengine - INFO - Epoch(train) [22][1600/3757] lr: 2.0612e-05 eta: 3:28:10 time: 0.3798 data_time: 0.0174 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4800 loss: 1.4800 2022/07/31 20:27:11 - mmengine - INFO - Epoch(train) [22][1700/3757] lr: 2.0612e-05 eta: 3:27:31 time: 0.3834 data_time: 0.0181 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0672 loss: 1.0672 2022/07/31 20:27:50 - mmengine - INFO - Epoch(train) [22][1800/3757] lr: 2.0612e-05 eta: 3:26:52 time: 0.3849 data_time: 0.0198 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0213 loss: 1.0213 2022/07/31 20:28:28 - mmengine - INFO - Epoch(train) [22][1900/3757] lr: 2.0612e-05 eta: 3:26:13 time: 0.3848 data_time: 0.0165 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3568 loss: 1.3568 2022/07/31 20:29:06 - mmengine - INFO - Epoch(train) [22][2000/3757] lr: 2.0612e-05 eta: 3:25:34 time: 0.3914 data_time: 0.0182 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9615 loss: 0.9615 2022/07/31 20:29:45 - mmengine - INFO - Epoch(train) [22][2100/3757] lr: 2.0612e-05 eta: 3:24:55 time: 0.3852 data_time: 0.0177 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3089 loss: 1.3089 2022/07/31 20:29:46 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 20:30:23 - mmengine - INFO - Epoch(train) [22][2200/3757] lr: 2.0612e-05 eta: 3:24:16 time: 0.3787 data_time: 0.0163 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1520 loss: 1.1520 2022/07/31 20:31:01 - mmengine - INFO - Epoch(train) [22][2300/3757] lr: 2.0612e-05 eta: 3:23:37 time: 0.3844 data_time: 0.0172 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1285 loss: 1.1285 2022/07/31 20:31:40 - mmengine - INFO - Epoch(train) [22][2400/3757] lr: 2.0612e-05 eta: 3:22:58 time: 0.3835 data_time: 0.0176 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.2423 loss: 1.2423 2022/07/31 20:32:18 - mmengine - INFO - Epoch(train) [22][2500/3757] lr: 2.0612e-05 eta: 3:22:19 time: 0.3837 data_time: 0.0171 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2083 loss: 1.2083 2022/07/31 20:32:56 - mmengine - INFO - Epoch(train) [22][2600/3757] lr: 2.0612e-05 eta: 3:21:40 time: 0.3862 data_time: 0.0173 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3356 loss: 1.3356 2022/07/31 20:33:35 - mmengine - INFO - Epoch(train) [22][2700/3757] lr: 2.0612e-05 eta: 3:21:02 time: 0.3777 data_time: 0.0143 memory: 21072 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.2428 loss: 1.2428 2022/07/31 20:34:14 - mmengine - INFO - Epoch(train) [22][2800/3757] lr: 2.0612e-05 eta: 3:20:23 time: 0.4007 data_time: 0.0158 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3236 loss: 1.3236 2022/07/31 20:34:52 - mmengine - INFO - Epoch(train) [22][2900/3757] lr: 2.0612e-05 eta: 3:19:44 time: 0.3829 data_time: 0.0159 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4210 loss: 1.4210 2022/07/31 20:35:30 - mmengine - INFO - Epoch(train) [22][3000/3757] lr: 2.0612e-05 eta: 3:19:05 time: 0.3774 data_time: 0.0148 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1447 loss: 1.1447 2022/07/31 20:36:09 - mmengine - INFO - Epoch(train) [22][3100/3757] lr: 2.0612e-05 eta: 3:18:26 time: 0.4158 data_time: 0.0147 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3151 loss: 1.3151 2022/07/31 20:36:10 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 20:36:47 - mmengine - INFO - Epoch(train) [22][3200/3757] lr: 2.0612e-05 eta: 3:17:47 time: 0.3792 data_time: 0.0143 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.2802 loss: 1.2802 2022/07/31 20:37:25 - mmengine - INFO - Epoch(train) [22][3300/3757] lr: 2.0612e-05 eta: 3:17:08 time: 0.3807 data_time: 0.0158 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1265 loss: 1.1265 2022/07/31 20:38:03 - mmengine - INFO - Epoch(train) [22][3400/3757] lr: 2.0612e-05 eta: 3:16:29 time: 0.3791 data_time: 0.0152 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0501 loss: 1.0501 2022/07/31 20:38:41 - mmengine - INFO - Epoch(train) [22][3500/3757] lr: 2.0612e-05 eta: 3:15:50 time: 0.4002 data_time: 0.0151 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0961 loss: 1.0961 2022/07/31 20:39:21 - mmengine - INFO - Epoch(train) [22][3600/3757] lr: 2.0612e-05 eta: 3:15:11 time: 0.3789 data_time: 0.0168 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1541 loss: 1.1541 2022/07/31 20:39:59 - mmengine - INFO - Epoch(train) [22][3700/3757] lr: 2.0612e-05 eta: 3:14:33 time: 0.3788 data_time: 0.0168 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1478 loss: 1.1478 2022/07/31 20:40:20 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 20:40:20 - mmengine - INFO - Epoch(train) [22][3757/3757] lr: 2.0612e-05 eta: 3:14:17 time: 0.3720 data_time: 0.0160 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1862 loss: 1.1862 2022/07/31 20:41:03 - mmengine - INFO - Epoch(train) [23][100/3757] lr: 1.6544e-05 eta: 3:13:30 time: 0.4815 data_time: 0.0172 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0539 loss: 1.0539 2022/07/31 20:42:02 - mmengine - INFO - Epoch(train) [23][200/3757] lr: 1.6544e-05 eta: 3:12:59 time: 0.5802 data_time: 0.0151 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1695 loss: 1.1695 2022/07/31 20:42:46 - mmengine - INFO - Epoch(train) [23][300/3757] lr: 1.6544e-05 eta: 3:12:22 time: 0.3832 data_time: 0.0176 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 0.9329 loss: 0.9329 2022/07/31 20:43:04 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 20:43:25 - mmengine - INFO - Epoch(train) [23][400/3757] lr: 1.6544e-05 eta: 3:11:43 time: 0.3789 data_time: 0.0164 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9568 loss: 0.9568 2022/07/31 20:44:03 - mmengine - INFO - Epoch(train) [23][500/3757] lr: 1.6544e-05 eta: 3:11:04 time: 0.3816 data_time: 0.0178 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1051 loss: 1.1051 2022/07/31 20:44:41 - mmengine - INFO - Epoch(train) [23][600/3757] lr: 1.6544e-05 eta: 3:10:25 time: 0.3809 data_time: 0.0182 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1088 loss: 1.1088 2022/07/31 20:45:19 - mmengine - INFO - Epoch(train) [23][700/3757] lr: 1.6544e-05 eta: 3:09:46 time: 0.3809 data_time: 0.0172 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.3310 loss: 1.3310 2022/07/31 20:45:58 - mmengine - INFO - Epoch(train) [23][800/3757] lr: 1.6544e-05 eta: 3:09:07 time: 0.3840 data_time: 0.0176 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4237 loss: 1.4237 2022/07/31 20:46:36 - mmengine - INFO - Epoch(train) [23][900/3757] lr: 1.6544e-05 eta: 3:08:28 time: 0.3794 data_time: 0.0168 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.3240 loss: 1.3240 2022/07/31 20:47:14 - mmengine - INFO - Epoch(train) [23][1000/3757] lr: 1.6544e-05 eta: 3:07:49 time: 0.3799 data_time: 0.0167 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1141 loss: 1.1141 2022/07/31 20:47:53 - mmengine - INFO - Epoch(train) [23][1100/3757] lr: 1.6544e-05 eta: 3:07:10 time: 0.3823 data_time: 0.0167 memory: 21072 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 0.9147 loss: 0.9147 2022/07/31 20:48:31 - mmengine - INFO - Epoch(train) [23][1200/3757] lr: 1.6544e-05 eta: 3:06:32 time: 0.3862 data_time: 0.0168 memory: 21072 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.1973 loss: 1.1973 2022/07/31 20:49:09 - mmengine - INFO - Epoch(train) [23][1300/3757] lr: 1.6544e-05 eta: 3:05:53 time: 0.3848 data_time: 0.0167 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1224 loss: 1.1224 2022/07/31 20:49:27 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 20:49:49 - mmengine - INFO - Epoch(train) [23][1400/3757] lr: 1.6544e-05 eta: 3:05:14 time: 0.4229 data_time: 0.0161 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1754 loss: 1.1754 2022/07/31 20:50:30 - mmengine - INFO - Epoch(train) [23][1500/3757] lr: 1.6544e-05 eta: 3:04:36 time: 0.3810 data_time: 0.0159 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1885 loss: 1.1885 2022/07/31 20:51:08 - mmengine - INFO - Epoch(train) [23][1600/3757] lr: 1.6544e-05 eta: 3:03:57 time: 0.3849 data_time: 0.0177 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2730 loss: 1.2730 2022/07/31 20:51:46 - mmengine - INFO - Epoch(train) [23][1700/3757] lr: 1.6544e-05 eta: 3:03:18 time: 0.3789 data_time: 0.0172 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9115 loss: 0.9115 2022/07/31 20:52:25 - mmengine - INFO - Epoch(train) [23][1800/3757] lr: 1.6544e-05 eta: 3:02:39 time: 0.3802 data_time: 0.0169 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5199 loss: 1.5199 2022/07/31 20:53:03 - mmengine - INFO - Epoch(train) [23][1900/3757] lr: 1.6544e-05 eta: 3:02:00 time: 0.3866 data_time: 0.0162 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1892 loss: 1.1892 2022/07/31 20:53:41 - mmengine - INFO - Epoch(train) [23][2000/3757] lr: 1.6544e-05 eta: 3:01:21 time: 0.3809 data_time: 0.0182 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2176 loss: 1.2176 2022/07/31 20:54:20 - mmengine - INFO - Epoch(train) [23][2100/3757] lr: 1.6544e-05 eta: 3:00:42 time: 0.3828 data_time: 0.0177 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2223 loss: 1.2223 2022/07/31 20:54:58 - mmengine - INFO - Epoch(train) [23][2200/3757] lr: 1.6544e-05 eta: 3:00:03 time: 0.3855 data_time: 0.0173 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.3171 loss: 1.3171 2022/07/31 20:55:36 - mmengine - INFO - Epoch(train) [23][2300/3757] lr: 1.6544e-05 eta: 2:59:24 time: 0.3801 data_time: 0.0174 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5247 loss: 1.5247 2022/07/31 20:55:54 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 20:56:15 - mmengine - INFO - Epoch(train) [23][2400/3757] lr: 1.6544e-05 eta: 2:58:46 time: 0.3868 data_time: 0.0182 memory: 21072 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.0445 loss: 1.0445 2022/07/31 20:56:53 - mmengine - INFO - Epoch(train) [23][2500/3757] lr: 1.6544e-05 eta: 2:58:07 time: 0.3823 data_time: 0.0177 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3507 loss: 1.3507 2022/07/31 20:57:32 - mmengine - INFO - Epoch(train) [23][2600/3757] lr: 1.6544e-05 eta: 2:57:28 time: 0.3839 data_time: 0.0168 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2334 loss: 1.2334 2022/07/31 20:58:10 - mmengine - INFO - Epoch(train) [23][2700/3757] lr: 1.6544e-05 eta: 2:56:49 time: 0.3888 data_time: 0.0179 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0200 loss: 1.0200 2022/07/31 20:58:48 - mmengine - INFO - Epoch(train) [23][2800/3757] lr: 1.6544e-05 eta: 2:56:10 time: 0.3847 data_time: 0.0164 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6500 loss: 1.6500 2022/07/31 20:59:26 - mmengine - INFO - Epoch(train) [23][2900/3757] lr: 1.6544e-05 eta: 2:55:31 time: 0.3791 data_time: 0.0175 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2823 loss: 1.2823 2022/07/31 21:00:05 - mmengine - INFO - Epoch(train) [23][3000/3757] lr: 1.6544e-05 eta: 2:54:52 time: 0.3827 data_time: 0.0169 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2687 loss: 1.2687 2022/07/31 21:00:43 - mmengine - INFO - Epoch(train) [23][3100/3757] lr: 1.6544e-05 eta: 2:54:13 time: 0.3820 data_time: 0.0177 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0080 loss: 1.0080 2022/07/31 21:01:22 - mmengine - INFO - Epoch(train) [23][3200/3757] lr: 1.6544e-05 eta: 2:53:34 time: 0.3889 data_time: 0.0172 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.2523 loss: 1.2523 2022/07/31 21:02:00 - mmengine - INFO - Epoch(train) [23][3300/3757] lr: 1.6544e-05 eta: 2:52:55 time: 0.3866 data_time: 0.0177 memory: 21072 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.2871 loss: 1.2871 2022/07/31 21:02:18 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 21:02:39 - mmengine - INFO - Epoch(train) [23][3400/3757] lr: 1.6544e-05 eta: 2:52:16 time: 0.3848 data_time: 0.0180 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0994 loss: 1.0994 2022/07/31 21:03:17 - mmengine - INFO - Epoch(train) [23][3500/3757] lr: 1.6544e-05 eta: 2:51:37 time: 0.3796 data_time: 0.0172 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3506 loss: 1.3506 2022/07/31 21:03:56 - mmengine - INFO - Epoch(train) [23][3600/3757] lr: 1.6544e-05 eta: 2:50:59 time: 0.3820 data_time: 0.0180 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1824 loss: 1.1824 2022/07/31 21:04:34 - mmengine - INFO - Epoch(train) [23][3700/3757] lr: 1.6544e-05 eta: 2:50:20 time: 0.3865 data_time: 0.0185 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2373 loss: 1.2373 2022/07/31 21:04:56 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 21:04:56 - mmengine - INFO - Epoch(train) [23][3757/3757] lr: 1.6544e-05 eta: 2:50:04 time: 0.3675 data_time: 0.0160 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1005 loss: 1.1005 2022/07/31 21:05:38 - mmengine - INFO - Epoch(train) [24][100/3757] lr: 1.2843e-05 eta: 2:49:18 time: 0.3792 data_time: 0.0170 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0972 loss: 1.0972 2022/07/31 21:06:16 - mmengine - INFO - Epoch(train) [24][200/3757] lr: 1.2843e-05 eta: 2:48:39 time: 0.3808 data_time: 0.0163 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.4031 loss: 1.4031 2022/07/31 21:06:54 - mmengine - INFO - Epoch(train) [24][300/3757] lr: 1.2843e-05 eta: 2:48:00 time: 0.3847 data_time: 0.0165 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2301 loss: 1.2301 2022/07/31 21:07:33 - mmengine - INFO - Epoch(train) [24][400/3757] lr: 1.2843e-05 eta: 2:47:21 time: 0.3885 data_time: 0.0172 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0509 loss: 1.0509 2022/07/31 21:08:11 - mmengine - INFO - Epoch(train) [24][500/3757] lr: 1.2843e-05 eta: 2:46:42 time: 0.3839 data_time: 0.0175 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.0046 loss: 1.0046 2022/07/31 21:08:46 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 21:08:50 - mmengine - INFO - Epoch(train) [24][600/3757] lr: 1.2843e-05 eta: 2:46:03 time: 0.3857 data_time: 0.0176 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.9796 loss: 0.9796 2022/07/31 21:09:29 - mmengine - INFO - Epoch(train) [24][700/3757] lr: 1.2843e-05 eta: 2:45:24 time: 0.3813 data_time: 0.0172 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.0711 loss: 1.0711 2022/07/31 21:10:07 - mmengine - INFO - Epoch(train) [24][800/3757] lr: 1.2843e-05 eta: 2:44:46 time: 0.3795 data_time: 0.0169 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.3323 loss: 1.3323 2022/07/31 21:10:45 - mmengine - INFO - Epoch(train) [24][900/3757] lr: 1.2843e-05 eta: 2:44:07 time: 0.3808 data_time: 0.0169 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2517 loss: 1.2517 2022/07/31 21:11:23 - mmengine - INFO - Epoch(train) [24][1000/3757] lr: 1.2843e-05 eta: 2:43:28 time: 0.3799 data_time: 0.0167 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0280 loss: 1.0280 2022/07/31 21:12:01 - mmengine - INFO - Epoch(train) [24][1100/3757] lr: 1.2843e-05 eta: 2:42:49 time: 0.3853 data_time: 0.0190 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1215 loss: 1.1215 2022/07/31 21:12:40 - mmengine - INFO - Epoch(train) [24][1200/3757] lr: 1.2843e-05 eta: 2:42:10 time: 0.3804 data_time: 0.0170 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1792 loss: 1.1792 2022/07/31 21:13:18 - mmengine - INFO - Epoch(train) [24][1300/3757] lr: 1.2843e-05 eta: 2:41:31 time: 0.3791 data_time: 0.0167 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0141 loss: 1.0141 2022/07/31 21:13:56 - mmengine - INFO - Epoch(train) [24][1400/3757] lr: 1.2843e-05 eta: 2:40:52 time: 0.3855 data_time: 0.0180 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1060 loss: 1.1060 2022/07/31 21:14:35 - mmengine - INFO - Epoch(train) [24][1500/3757] lr: 1.2843e-05 eta: 2:40:13 time: 0.3840 data_time: 0.0175 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3305 loss: 1.3305 2022/07/31 21:15:09 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 21:15:13 - mmengine - INFO - Epoch(train) [24][1600/3757] lr: 1.2843e-05 eta: 2:39:34 time: 0.3853 data_time: 0.0177 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.3645 loss: 1.3645 2022/07/31 21:15:51 - mmengine - INFO - Epoch(train) [24][1700/3757] lr: 1.2843e-05 eta: 2:38:55 time: 0.3877 data_time: 0.0182 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1567 loss: 1.1567 2022/07/31 21:16:30 - mmengine - INFO - Epoch(train) [24][1800/3757] lr: 1.2843e-05 eta: 2:38:16 time: 0.3850 data_time: 0.0182 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.3361 loss: 1.3361 2022/07/31 21:17:08 - mmengine - INFO - Epoch(train) [24][1900/3757] lr: 1.2843e-05 eta: 2:37:38 time: 0.3787 data_time: 0.0167 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.3121 loss: 1.3121 2022/07/31 21:17:47 - mmengine - INFO - Epoch(train) [24][2000/3757] lr: 1.2843e-05 eta: 2:36:59 time: 0.3866 data_time: 0.0179 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2750 loss: 1.2750 2022/07/31 21:18:25 - mmengine - INFO - Epoch(train) [24][2100/3757] lr: 1.2843e-05 eta: 2:36:20 time: 0.3808 data_time: 0.0171 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2155 loss: 1.2155 2022/07/31 21:19:03 - mmengine - INFO - Epoch(train) [24][2200/3757] lr: 1.2843e-05 eta: 2:35:41 time: 0.3811 data_time: 0.0174 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1040 loss: 1.1040 2022/07/31 21:19:42 - mmengine - INFO - Epoch(train) [24][2300/3757] lr: 1.2843e-05 eta: 2:35:02 time: 0.3813 data_time: 0.0181 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0645 loss: 1.0645 2022/07/31 21:20:20 - mmengine - INFO - Epoch(train) [24][2400/3757] lr: 1.2843e-05 eta: 2:34:23 time: 0.3810 data_time: 0.0165 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3539 loss: 1.3539 2022/07/31 21:20:59 - mmengine - INFO - Epoch(train) [24][2500/3757] lr: 1.2843e-05 eta: 2:33:44 time: 0.3796 data_time: 0.0170 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3479 loss: 1.3479 2022/07/31 21:21:33 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 21:21:37 - mmengine - INFO - Epoch(train) [24][2600/3757] lr: 1.2843e-05 eta: 2:33:06 time: 0.3963 data_time: 0.0173 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2181 loss: 1.2181 2022/07/31 21:22:16 - mmengine - INFO - Epoch(train) [24][2700/3757] lr: 1.2843e-05 eta: 2:32:27 time: 0.3997 data_time: 0.0173 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0764 loss: 1.0764 2022/07/31 21:22:55 - mmengine - INFO - Epoch(train) [24][2800/3757] lr: 1.2843e-05 eta: 2:31:48 time: 0.3858 data_time: 0.0165 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2537 loss: 1.2537 2022/07/31 21:23:33 - mmengine - INFO - Epoch(train) [24][2900/3757] lr: 1.2843e-05 eta: 2:31:09 time: 0.3861 data_time: 0.0181 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.0904 loss: 1.0904 2022/07/31 21:24:11 - mmengine - INFO - Epoch(train) [24][3000/3757] lr: 1.2843e-05 eta: 2:30:30 time: 0.3929 data_time: 0.0174 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3231 loss: 1.3231 2022/07/31 21:24:50 - mmengine - INFO - Epoch(train) [24][3100/3757] lr: 1.2843e-05 eta: 2:29:51 time: 0.3800 data_time: 0.0171 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9861 loss: 0.9861 2022/07/31 21:25:28 - mmengine - INFO - Epoch(train) [24][3200/3757] lr: 1.2843e-05 eta: 2:29:12 time: 0.3802 data_time: 0.0175 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2636 loss: 1.2636 2022/07/31 21:26:07 - mmengine - INFO - Epoch(train) [24][3300/3757] lr: 1.2843e-05 eta: 2:28:34 time: 0.3823 data_time: 0.0172 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2161 loss: 1.2161 2022/07/31 21:26:45 - mmengine - INFO - Epoch(train) [24][3400/3757] lr: 1.2843e-05 eta: 2:27:55 time: 0.3805 data_time: 0.0178 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1554 loss: 1.1554 2022/07/31 21:27:23 - mmengine - INFO - Epoch(train) [24][3500/3757] lr: 1.2843e-05 eta: 2:27:16 time: 0.3824 data_time: 0.0178 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1871 loss: 1.1871 2022/07/31 21:27:57 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 21:28:02 - mmengine - INFO - Epoch(train) [24][3600/3757] lr: 1.2843e-05 eta: 2:26:37 time: 0.3820 data_time: 0.0182 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2582 loss: 1.2582 2022/07/31 21:28:40 - mmengine - INFO - Epoch(train) [24][3700/3757] lr: 1.2843e-05 eta: 2:25:58 time: 0.3800 data_time: 0.0176 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4703 loss: 1.4703 2022/07/31 21:29:02 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 21:29:02 - mmengine - INFO - Epoch(train) [24][3757/3757] lr: 1.2843e-05 eta: 2:25:43 time: 0.3709 data_time: 0.0174 memory: 21072 top1_acc: 0.8571 top5_acc: 0.8571 loss_cls: 1.1933 loss: 1.1933 2022/07/31 21:29:02 - mmengine - INFO - Saving checkpoint at 24 epochs 2022/07/31 21:29:27 - mmengine - INFO - Epoch(val) [24][100/310] eta: 0:00:46 time: 0.2204 data_time: 0.0829 memory: 5891 2022/07/31 21:29:48 - mmengine - INFO - Epoch(val) [24][200/310] eta: 0:00:20 time: 0.1856 data_time: 0.0438 memory: 5891 2022/07/31 21:30:09 - mmengine - INFO - Epoch(val) [24][300/310] eta: 0:00:01 time: 0.1858 data_time: 0.0525 memory: 5891 2022/07/31 21:30:11 - mmengine - INFO - Epoch(val) [24][310/310] acc/top1: 0.7242 acc/top5: 0.9015 acc/mean1: 0.7240 2022/07/31 21:30:12 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/swin-t-2/best_acc/top1_epoch_22.pth is removed 2022/07/31 21:30:13 - mmengine - INFO - The best checkpoint with 0.7242 acc/top1 at 25 epoch is saved to best_acc/top1_epoch_25.pth. 2022/07/31 21:30:53 - mmengine - INFO - Epoch(train) [25][100/3757] lr: 9.5496e-06 eta: 2:24:56 time: 0.3842 data_time: 0.0174 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2238 loss: 1.2238 2022/07/31 21:31:31 - mmengine - INFO - Epoch(train) [25][200/3757] lr: 9.5496e-06 eta: 2:24:17 time: 0.3807 data_time: 0.0159 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.0811 loss: 1.0811 2022/07/31 21:32:09 - mmengine - INFO - Epoch(train) [25][300/3757] lr: 9.5496e-06 eta: 2:23:38 time: 0.3791 data_time: 0.0175 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3129 loss: 1.3129 2022/07/31 21:32:47 - mmengine - INFO - Epoch(train) [25][400/3757] lr: 9.5496e-06 eta: 2:22:59 time: 0.3794 data_time: 0.0167 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9191 loss: 0.9191 2022/07/31 21:33:26 - mmengine - INFO - Epoch(train) [25][500/3757] lr: 9.5496e-06 eta: 2:22:20 time: 0.3836 data_time: 0.0160 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1756 loss: 1.1756 2022/07/31 21:34:04 - mmengine - INFO - Epoch(train) [25][600/3757] lr: 9.5496e-06 eta: 2:21:41 time: 0.3803 data_time: 0.0173 memory: 21072 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.0784 loss: 1.0784 2022/07/31 21:34:42 - mmengine - INFO - Epoch(train) [25][700/3757] lr: 9.5496e-06 eta: 2:21:02 time: 0.3792 data_time: 0.0177 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.0767 loss: 1.0767 2022/07/31 21:35:20 - mmengine - INFO - Epoch(train) [25][800/3757] lr: 9.5496e-06 eta: 2:20:24 time: 0.3793 data_time: 0.0158 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8338 loss: 0.8338 2022/07/31 21:35:32 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 21:35:58 - mmengine - INFO - Epoch(train) [25][900/3757] lr: 9.5496e-06 eta: 2:19:45 time: 0.3848 data_time: 0.0158 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1739 loss: 1.1739 2022/07/31 21:36:36 - mmengine - INFO - Epoch(train) [25][1000/3757] lr: 9.5496e-06 eta: 2:19:06 time: 0.3821 data_time: 0.0174 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1285 loss: 1.1285 2022/07/31 21:37:15 - mmengine - INFO - Epoch(train) [25][1100/3757] lr: 9.5496e-06 eta: 2:18:27 time: 0.3818 data_time: 0.0165 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.1251 loss: 1.1251 2022/07/31 21:37:53 - mmengine - INFO - Epoch(train) [25][1200/3757] lr: 9.5496e-06 eta: 2:17:48 time: 0.3842 data_time: 0.0163 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1011 loss: 1.1011 2022/07/31 21:38:31 - mmengine - INFO - Epoch(train) [25][1300/3757] lr: 9.5496e-06 eta: 2:17:09 time: 0.3775 data_time: 0.0162 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1987 loss: 1.1987 2022/07/31 21:39:09 - mmengine - INFO - Epoch(train) [25][1400/3757] lr: 9.5496e-06 eta: 2:16:30 time: 0.3756 data_time: 0.0154 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9652 loss: 0.9652 2022/07/31 21:39:48 - mmengine - INFO - Epoch(train) [25][1500/3757] lr: 9.5496e-06 eta: 2:15:52 time: 0.4048 data_time: 0.0167 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2067 loss: 1.2067 2022/07/31 21:40:27 - mmengine - INFO - Epoch(train) [25][1600/3757] lr: 9.5496e-06 eta: 2:15:13 time: 0.4059 data_time: 0.0178 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0527 loss: 1.0527 2022/07/31 21:41:05 - mmengine - INFO - Epoch(train) [25][1700/3757] lr: 9.5496e-06 eta: 2:14:34 time: 0.3817 data_time: 0.0159 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0568 loss: 1.0568 2022/07/31 21:41:43 - mmengine - INFO - Epoch(train) [25][1800/3757] lr: 9.5496e-06 eta: 2:13:55 time: 0.3818 data_time: 0.0168 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0973 loss: 1.0973 2022/07/31 21:41:55 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 21:42:21 - mmengine - INFO - Epoch(train) [25][1900/3757] lr: 9.5496e-06 eta: 2:13:16 time: 0.3777 data_time: 0.0158 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9568 loss: 0.9568 2022/07/31 21:42:59 - mmengine - INFO - Epoch(train) [25][2000/3757] lr: 9.5496e-06 eta: 2:12:37 time: 0.3793 data_time: 0.0159 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3973 loss: 1.3973 2022/07/31 21:43:37 - mmengine - INFO - Epoch(train) [25][2100/3757] lr: 9.5496e-06 eta: 2:11:58 time: 0.3778 data_time: 0.0144 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.0904 loss: 1.0904 2022/07/31 21:44:15 - mmengine - INFO - Epoch(train) [25][2200/3757] lr: 9.5496e-06 eta: 2:11:19 time: 0.3773 data_time: 0.0160 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.0714 loss: 1.0714 2022/07/31 21:44:54 - mmengine - INFO - Epoch(train) [25][2300/3757] lr: 9.5496e-06 eta: 2:10:41 time: 0.3784 data_time: 0.0159 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3278 loss: 1.3278 2022/07/31 21:45:32 - mmengine - INFO - Epoch(train) [25][2400/3757] lr: 9.5496e-06 eta: 2:10:02 time: 0.3782 data_time: 0.0158 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2658 loss: 1.2658 2022/07/31 21:46:10 - mmengine - INFO - Epoch(train) [25][2500/3757] lr: 9.5496e-06 eta: 2:09:23 time: 0.3787 data_time: 0.0160 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2668 loss: 1.2668 2022/07/31 21:46:48 - mmengine - INFO - Epoch(train) [25][2600/3757] lr: 9.5496e-06 eta: 2:08:44 time: 0.3786 data_time: 0.0153 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9222 loss: 0.9222 2022/07/31 21:47:26 - mmengine - INFO - Epoch(train) [25][2700/3757] lr: 9.5496e-06 eta: 2:08:05 time: 0.3773 data_time: 0.0162 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1377 loss: 1.1377 2022/07/31 21:48:05 - mmengine - INFO - Epoch(train) [25][2800/3757] lr: 9.5496e-06 eta: 2:07:26 time: 0.3881 data_time: 0.0185 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1211 loss: 1.1211 2022/07/31 21:48:17 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 21:48:43 - mmengine - INFO - Epoch(train) [25][2900/3757] lr: 9.5496e-06 eta: 2:06:47 time: 0.3787 data_time: 0.0159 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2284 loss: 1.2284 2022/07/31 21:49:21 - mmengine - INFO - Epoch(train) [25][3000/3757] lr: 9.5496e-06 eta: 2:06:09 time: 0.3776 data_time: 0.0169 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0566 loss: 1.0566 2022/07/31 21:49:59 - mmengine - INFO - Epoch(train) [25][3100/3757] lr: 9.5496e-06 eta: 2:05:30 time: 0.3808 data_time: 0.0147 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0593 loss: 1.0593 2022/07/31 21:50:37 - mmengine - INFO - Epoch(train) [25][3200/3757] lr: 9.5496e-06 eta: 2:04:51 time: 0.3824 data_time: 0.0159 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2327 loss: 1.2327 2022/07/31 21:51:15 - mmengine - INFO - Epoch(train) [25][3300/3757] lr: 9.5496e-06 eta: 2:04:12 time: 0.3788 data_time: 0.0158 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2619 loss: 1.2619 2022/07/31 21:51:52 - mmengine - INFO - Epoch(train) [25][3400/3757] lr: 9.5496e-06 eta: 2:03:33 time: 0.3761 data_time: 0.0159 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1738 loss: 1.1738 2022/07/31 21:52:31 - mmengine - INFO - Epoch(train) [25][3500/3757] lr: 9.5496e-06 eta: 2:02:54 time: 0.3755 data_time: 0.0153 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0199 loss: 1.0199 2022/07/31 21:53:09 - mmengine - INFO - Epoch(train) [25][3600/3757] lr: 9.5496e-06 eta: 2:02:15 time: 0.3787 data_time: 0.0159 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1870 loss: 1.1870 2022/07/31 21:53:47 - mmengine - INFO - Epoch(train) [25][3700/3757] lr: 9.5496e-06 eta: 2:01:36 time: 0.3776 data_time: 0.0149 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2401 loss: 1.2401 2022/07/31 21:54:08 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 21:54:08 - mmengine - INFO - Epoch(train) [25][3757/3757] lr: 9.5496e-06 eta: 2:01:21 time: 0.3664 data_time: 0.0140 memory: 21072 top1_acc: 0.7143 top5_acc: 0.7143 loss_cls: 1.0622 loss: 1.0622 2022/07/31 21:54:39 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 21:54:48 - mmengine - INFO - Epoch(train) [26][100/3757] lr: 6.6991e-06 eta: 2:00:34 time: 0.3784 data_time: 0.0145 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9944 loss: 0.9944 2022/07/31 21:55:26 - mmengine - INFO - Epoch(train) [26][200/3757] lr: 6.6991e-06 eta: 1:59:56 time: 0.3786 data_time: 0.0154 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1440 loss: 1.1440 2022/07/31 21:56:04 - mmengine - INFO - Epoch(train) [26][300/3757] lr: 6.6991e-06 eta: 1:59:17 time: 0.3831 data_time: 0.0167 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3185 loss: 1.3185 2022/07/31 21:56:42 - mmengine - INFO - Epoch(train) [26][400/3757] lr: 6.6991e-06 eta: 1:58:38 time: 0.3768 data_time: 0.0158 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0186 loss: 1.0186 2022/07/31 21:57:20 - mmengine - INFO - Epoch(train) [26][500/3757] lr: 6.6991e-06 eta: 1:57:59 time: 0.3782 data_time: 0.0178 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2636 loss: 1.2636 2022/07/31 21:57:58 - mmengine - INFO - Epoch(train) [26][600/3757] lr: 6.6991e-06 eta: 1:57:20 time: 0.3773 data_time: 0.0156 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 0.8278 loss: 0.8278 2022/07/31 21:58:36 - mmengine - INFO - Epoch(train) [26][700/3757] lr: 6.6991e-06 eta: 1:56:41 time: 0.3767 data_time: 0.0159 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1439 loss: 1.1439 2022/07/31 21:59:15 - mmengine - INFO - Epoch(train) [26][800/3757] lr: 6.6991e-06 eta: 1:56:02 time: 0.3831 data_time: 0.0156 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0730 loss: 1.0730 2022/07/31 21:59:53 - mmengine - INFO - Epoch(train) [26][900/3757] lr: 6.6991e-06 eta: 1:55:24 time: 0.3767 data_time: 0.0154 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9203 loss: 0.9203 2022/07/31 22:00:31 - mmengine - INFO - Epoch(train) [26][1000/3757] lr: 6.6991e-06 eta: 1:54:45 time: 0.3821 data_time: 0.0171 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0314 loss: 1.0314 2022/07/31 22:00:59 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 22:01:09 - mmengine - INFO - Epoch(train) [26][1100/3757] lr: 6.6991e-06 eta: 1:54:06 time: 0.3785 data_time: 0.0154 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1675 loss: 1.1675 2022/07/31 22:01:47 - mmengine - INFO - Epoch(train) [26][1200/3757] lr: 6.6991e-06 eta: 1:53:27 time: 0.3790 data_time: 0.0150 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1859 loss: 1.1859 2022/07/31 22:02:25 - mmengine - INFO - Epoch(train) [26][1300/3757] lr: 6.6991e-06 eta: 1:52:48 time: 0.3795 data_time: 0.0148 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2077 loss: 1.2077 2022/07/31 22:03:03 - mmengine - INFO - Epoch(train) [26][1400/3757] lr: 6.6991e-06 eta: 1:52:09 time: 0.3761 data_time: 0.0160 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2821 loss: 1.2821 2022/07/31 22:03:41 - mmengine - INFO - Epoch(train) [26][1500/3757] lr: 6.6991e-06 eta: 1:51:31 time: 0.3775 data_time: 0.0150 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1704 loss: 1.1704 2022/07/31 22:04:19 - mmengine - INFO - Epoch(train) [26][1600/3757] lr: 6.6991e-06 eta: 1:50:52 time: 0.3808 data_time: 0.0156 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0787 loss: 1.0787 2022/07/31 22:04:57 - mmengine - INFO - Epoch(train) [26][1700/3757] lr: 6.6991e-06 eta: 1:50:13 time: 0.3799 data_time: 0.0150 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9482 loss: 0.9482 2022/07/31 22:05:35 - mmengine - INFO - Epoch(train) [26][1800/3757] lr: 6.6991e-06 eta: 1:49:34 time: 0.3821 data_time: 0.0163 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.2014 loss: 1.2014 2022/07/31 22:06:13 - mmengine - INFO - Epoch(train) [26][1900/3757] lr: 6.6991e-06 eta: 1:48:55 time: 0.3770 data_time: 0.0151 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8593 loss: 0.8593 2022/07/31 22:06:51 - mmengine - INFO - Epoch(train) [26][2000/3757] lr: 6.6991e-06 eta: 1:48:16 time: 0.3769 data_time: 0.0155 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3924 loss: 1.3924 2022/07/31 22:07:20 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 22:07:29 - mmengine - INFO - Epoch(train) [26][2100/3757] lr: 6.6991e-06 eta: 1:47:38 time: 0.3769 data_time: 0.0148 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2648 loss: 1.2648 2022/07/31 22:08:08 - mmengine - INFO - Epoch(train) [26][2200/3757] lr: 6.6991e-06 eta: 1:46:59 time: 0.3764 data_time: 0.0154 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2092 loss: 1.2092 2022/07/31 22:08:46 - mmengine - INFO - Epoch(train) [26][2300/3757] lr: 6.6991e-06 eta: 1:46:20 time: 0.3877 data_time: 0.0150 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1839 loss: 1.1839 2022/07/31 22:09:26 - mmengine - INFO - Epoch(train) [26][2400/3757] lr: 6.6991e-06 eta: 1:45:41 time: 0.3763 data_time: 0.0159 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1279 loss: 1.1279 2022/07/31 22:10:04 - mmengine - INFO - Epoch(train) [26][2500/3757] lr: 6.6991e-06 eta: 1:45:03 time: 0.3781 data_time: 0.0178 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0191 loss: 1.0191 2022/07/31 22:10:42 - mmengine - INFO - Epoch(train) [26][2600/3757] lr: 6.6991e-06 eta: 1:44:24 time: 0.3783 data_time: 0.0153 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0120 loss: 1.0120 2022/07/31 22:11:20 - mmengine - INFO - Epoch(train) [26][2700/3757] lr: 6.6991e-06 eta: 1:43:45 time: 0.3800 data_time: 0.0181 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4331 loss: 1.4331 2022/07/31 22:12:02 - mmengine - INFO - Epoch(train) [26][2800/3757] lr: 6.6991e-06 eta: 1:43:07 time: 0.4074 data_time: 0.0158 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0943 loss: 1.0943 2022/07/31 22:12:45 - mmengine - INFO - Epoch(train) [26][2900/3757] lr: 6.6991e-06 eta: 1:42:29 time: 0.4079 data_time: 0.0146 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2029 loss: 1.2029 2022/07/31 22:13:24 - mmengine - INFO - Epoch(train) [26][3000/3757] lr: 6.6991e-06 eta: 1:41:50 time: 0.3783 data_time: 0.0144 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9281 loss: 0.9281 2022/07/31 22:13:52 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 22:14:02 - mmengine - INFO - Epoch(train) [26][3100/3757] lr: 6.6991e-06 eta: 1:41:11 time: 0.3912 data_time: 0.0179 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4189 loss: 1.4189 2022/07/31 22:14:40 - mmengine - INFO - Epoch(train) [26][3200/3757] lr: 6.6991e-06 eta: 1:40:33 time: 0.3850 data_time: 0.0190 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0108 loss: 1.0108 2022/07/31 22:15:18 - mmengine - INFO - Epoch(train) [26][3300/3757] lr: 6.6991e-06 eta: 1:39:54 time: 0.3842 data_time: 0.0173 memory: 21072 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.1362 loss: 1.1362 2022/07/31 22:15:57 - mmengine - INFO - Epoch(train) [26][3400/3757] lr: 6.6991e-06 eta: 1:39:15 time: 0.3831 data_time: 0.0173 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1791 loss: 1.1791 2022/07/31 22:16:35 - mmengine - INFO - Epoch(train) [26][3500/3757] lr: 6.6991e-06 eta: 1:38:36 time: 0.3798 data_time: 0.0169 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2430 loss: 1.2430 2022/07/31 22:17:13 - mmengine - INFO - Epoch(train) [26][3600/3757] lr: 6.6991e-06 eta: 1:37:57 time: 0.3792 data_time: 0.0163 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2241 loss: 1.2241 2022/07/31 22:17:52 - mmengine - INFO - Epoch(train) [26][3700/3757] lr: 6.6991e-06 eta: 1:37:19 time: 0.3786 data_time: 0.0166 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0708 loss: 1.0708 2022/07/31 22:18:13 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 22:18:13 - mmengine - INFO - Epoch(train) [26][3757/3757] lr: 6.6991e-06 eta: 1:37:03 time: 0.3651 data_time: 0.0144 memory: 21072 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 0.9164 loss: 0.9164 2022/07/31 22:18:53 - mmengine - INFO - Epoch(train) [27][100/3757] lr: 4.3229e-06 eta: 1:36:17 time: 0.3785 data_time: 0.0166 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.1520 loss: 1.1520 2022/07/31 22:19:32 - mmengine - INFO - Epoch(train) [27][200/3757] lr: 4.3229e-06 eta: 1:35:38 time: 0.3835 data_time: 0.0179 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1274 loss: 1.1274 2022/07/31 22:20:10 - mmengine - INFO - Epoch(train) [27][300/3757] lr: 4.3229e-06 eta: 1:35:00 time: 0.3825 data_time: 0.0174 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9670 loss: 0.9670 2022/07/31 22:20:17 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 22:20:49 - mmengine - INFO - Epoch(train) [27][400/3757] lr: 4.3229e-06 eta: 1:34:21 time: 0.3836 data_time: 0.0173 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0965 loss: 1.0965 2022/07/31 22:21:27 - mmengine - INFO - Epoch(train) [27][500/3757] lr: 4.3229e-06 eta: 1:33:42 time: 0.3817 data_time: 0.0167 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1405 loss: 1.1405 2022/07/31 22:22:05 - mmengine - INFO - Epoch(train) [27][600/3757] lr: 4.3229e-06 eta: 1:33:03 time: 0.3793 data_time: 0.0171 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2551 loss: 1.2551 2022/07/31 22:22:44 - mmengine - INFO - Epoch(train) [27][700/3757] lr: 4.3229e-06 eta: 1:32:25 time: 0.3791 data_time: 0.0164 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2029 loss: 1.2029 2022/07/31 22:23:22 - mmengine - INFO - Epoch(train) [27][800/3757] lr: 4.3229e-06 eta: 1:31:46 time: 0.3904 data_time: 0.0195 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2303 loss: 1.2303 2022/07/31 22:24:01 - mmengine - INFO - Epoch(train) [27][900/3757] lr: 4.3229e-06 eta: 1:31:07 time: 0.3908 data_time: 0.0183 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2354 loss: 1.2354 2022/07/31 22:24:40 - mmengine - INFO - Epoch(train) [27][1000/3757] lr: 4.3229e-06 eta: 1:30:28 time: 0.3993 data_time: 0.0174 memory: 21072 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.3288 loss: 1.3288 2022/07/31 22:25:18 - mmengine - INFO - Epoch(train) [27][1100/3757] lr: 4.3229e-06 eta: 1:29:50 time: 0.3824 data_time: 0.0183 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2648 loss: 1.2648 2022/07/31 22:25:57 - mmengine - INFO - Epoch(train) [27][1200/3757] lr: 4.3229e-06 eta: 1:29:11 time: 0.3869 data_time: 0.0183 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0288 loss: 1.0288 2022/07/31 22:26:35 - mmengine - INFO - Epoch(train) [27][1300/3757] lr: 4.3229e-06 eta: 1:28:32 time: 0.3794 data_time: 0.0166 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1041 loss: 1.1041 2022/07/31 22:26:42 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 22:27:13 - mmengine - INFO - Epoch(train) [27][1400/3757] lr: 4.3229e-06 eta: 1:27:53 time: 0.3810 data_time: 0.0161 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0732 loss: 1.0732 2022/07/31 22:27:52 - mmengine - INFO - Epoch(train) [27][1500/3757] lr: 4.3229e-06 eta: 1:27:15 time: 0.3846 data_time: 0.0166 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1487 loss: 1.1487 2022/07/31 22:28:30 - mmengine - INFO - Epoch(train) [27][1600/3757] lr: 4.3229e-06 eta: 1:26:36 time: 0.3812 data_time: 0.0171 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3559 loss: 1.3559 2022/07/31 22:29:09 - mmengine - INFO - Epoch(train) [27][1700/3757] lr: 4.3229e-06 eta: 1:25:57 time: 0.3843 data_time: 0.0165 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1405 loss: 1.1405 2022/07/31 22:29:47 - mmengine - INFO - Epoch(train) [27][1800/3757] lr: 4.3229e-06 eta: 1:25:18 time: 0.3815 data_time: 0.0167 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0505 loss: 1.0505 2022/07/31 22:30:26 - mmengine - INFO - Epoch(train) [27][1900/3757] lr: 4.3229e-06 eta: 1:24:40 time: 0.3789 data_time: 0.0170 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9303 loss: 0.9303 2022/07/31 22:31:04 - mmengine - INFO - Epoch(train) [27][2000/3757] lr: 4.3229e-06 eta: 1:24:01 time: 0.3854 data_time: 0.0180 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2670 loss: 1.2670 2022/07/31 22:31:43 - mmengine - INFO - Epoch(train) [27][2100/3757] lr: 4.3229e-06 eta: 1:23:22 time: 0.3858 data_time: 0.0176 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2303 loss: 1.2303 2022/07/31 22:32:21 - mmengine - INFO - Epoch(train) [27][2200/3757] lr: 4.3229e-06 eta: 1:22:44 time: 0.3872 data_time: 0.0177 memory: 21072 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 1.3569 loss: 1.3569 2022/07/31 22:33:00 - mmengine - INFO - Epoch(train) [27][2300/3757] lr: 4.3229e-06 eta: 1:22:05 time: 0.3860 data_time: 0.0172 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2078 loss: 1.2078 2022/07/31 22:33:07 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 22:33:38 - mmengine - INFO - Epoch(train) [27][2400/3757] lr: 4.3229e-06 eta: 1:21:26 time: 0.3850 data_time: 0.0170 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 0.9950 loss: 0.9950 2022/07/31 22:34:17 - mmengine - INFO - Epoch(train) [27][2500/3757] lr: 4.3229e-06 eta: 1:20:47 time: 0.3803 data_time: 0.0176 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0199 loss: 1.0199 2022/07/31 22:34:55 - mmengine - INFO - Epoch(train) [27][2600/3757] lr: 4.3229e-06 eta: 1:20:09 time: 0.3807 data_time: 0.0168 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2335 loss: 1.2335 2022/07/31 22:35:33 - mmengine - INFO - Epoch(train) [27][2700/3757] lr: 4.3229e-06 eta: 1:19:30 time: 0.3850 data_time: 0.0183 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9998 loss: 0.9998 2022/07/31 22:36:12 - mmengine - INFO - Epoch(train) [27][2800/3757] lr: 4.3229e-06 eta: 1:18:51 time: 0.3870 data_time: 0.0171 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9924 loss: 0.9924 2022/07/31 22:36:50 - mmengine - INFO - Epoch(train) [27][2900/3757] lr: 4.3229e-06 eta: 1:18:12 time: 0.3848 data_time: 0.0168 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2013 loss: 1.2013 2022/07/31 22:37:29 - mmengine - INFO - Epoch(train) [27][3000/3757] lr: 4.3229e-06 eta: 1:17:34 time: 0.3915 data_time: 0.0178 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0559 loss: 1.0559 2022/07/31 22:38:07 - mmengine - INFO - Epoch(train) [27][3100/3757] lr: 4.3229e-06 eta: 1:16:55 time: 0.3826 data_time: 0.0168 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0424 loss: 1.0424 2022/07/31 22:38:46 - mmengine - INFO - Epoch(train) [27][3200/3757] lr: 4.3229e-06 eta: 1:16:16 time: 0.3794 data_time: 0.0170 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1215 loss: 1.1215 2022/07/31 22:39:24 - mmengine - INFO - Epoch(train) [27][3300/3757] lr: 4.3229e-06 eta: 1:15:38 time: 0.3798 data_time: 0.0175 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1239 loss: 1.1239 2022/07/31 22:39:31 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 22:40:02 - mmengine - INFO - Epoch(train) [27][3400/3757] lr: 4.3229e-06 eta: 1:14:59 time: 0.3787 data_time: 0.0169 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1509 loss: 1.1509 2022/07/31 22:40:41 - mmengine - INFO - Epoch(train) [27][3500/3757] lr: 4.3229e-06 eta: 1:14:20 time: 0.3824 data_time: 0.0181 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3850 loss: 1.3850 2022/07/31 22:41:19 - mmengine - INFO - Epoch(train) [27][3600/3757] lr: 4.3229e-06 eta: 1:13:41 time: 0.3802 data_time: 0.0181 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1465 loss: 1.1465 2022/07/31 22:41:57 - mmengine - INFO - Epoch(train) [27][3700/3757] lr: 4.3229e-06 eta: 1:13:03 time: 0.3788 data_time: 0.0173 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1163 loss: 1.1163 2022/07/31 22:42:19 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 22:42:19 - mmengine - INFO - Epoch(train) [27][3757/3757] lr: 4.3229e-06 eta: 1:12:47 time: 0.3735 data_time: 0.0160 memory: 21072 top1_acc: 0.7143 top5_acc: 0.7143 loss_cls: 1.2131 loss: 1.2131 2022/07/31 22:42:19 - mmengine - INFO - Saving checkpoint at 27 epochs 2022/07/31 22:42:45 - mmengine - INFO - Epoch(val) [27][100/310] eta: 0:00:51 time: 0.2449 data_time: 0.1057 memory: 5891 2022/07/31 22:43:06 - mmengine - INFO - Epoch(val) [27][200/310] eta: 0:00:21 time: 0.1966 data_time: 0.0570 memory: 5891 2022/07/31 22:43:27 - mmengine - INFO - Epoch(val) [27][300/310] eta: 0:00:01 time: 0.1840 data_time: 0.0528 memory: 5891 2022/07/31 22:43:29 - mmengine - INFO - Epoch(val) [27][310/310] acc/top1: 0.7280 acc/top5: 0.9042 acc/mean1: 0.7279 2022/07/31 22:43:29 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/swin-t-2/best_acc/top1_epoch_25.pth is removed 2022/07/31 22:43:31 - mmengine - INFO - The best checkpoint with 0.7280 acc/top1 at 28 epoch is saved to best_acc/top1_epoch_28.pth. 2022/07/31 22:44:10 - mmengine - INFO - Epoch(train) [28][100/3757] lr: 2.4473e-06 eta: 1:12:01 time: 0.3825 data_time: 0.0178 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9971 loss: 0.9971 2022/07/31 22:44:48 - mmengine - INFO - Epoch(train) [28][200/3757] lr: 2.4473e-06 eta: 1:11:22 time: 0.3788 data_time: 0.0172 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2202 loss: 1.2202 2022/07/31 22:45:26 - mmengine - INFO - Epoch(train) [28][300/3757] lr: 2.4473e-06 eta: 1:10:44 time: 0.3776 data_time: 0.0156 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1964 loss: 1.1964 2022/07/31 22:46:05 - mmengine - INFO - Epoch(train) [28][400/3757] lr: 2.4473e-06 eta: 1:10:05 time: 0.3780 data_time: 0.0159 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7053 loss: 0.7053 2022/07/31 22:46:43 - mmengine - INFO - Epoch(train) [28][500/3757] lr: 2.4473e-06 eta: 1:09:26 time: 0.3783 data_time: 0.0159 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9280 loss: 0.9280 2022/07/31 22:47:06 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 22:47:21 - mmengine - INFO - Epoch(train) [28][600/3757] lr: 2.4473e-06 eta: 1:08:47 time: 0.3795 data_time: 0.0169 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1293 loss: 1.1293 2022/07/31 22:47:59 - mmengine - INFO - Epoch(train) [28][700/3757] lr: 2.4473e-06 eta: 1:08:09 time: 0.3812 data_time: 0.0182 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2487 loss: 1.2487 2022/07/31 22:48:37 - mmengine - INFO - Epoch(train) [28][800/3757] lr: 2.4473e-06 eta: 1:07:30 time: 0.3810 data_time: 0.0170 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0285 loss: 1.0285 2022/07/31 22:49:16 - mmengine - INFO - Epoch(train) [28][900/3757] lr: 2.4473e-06 eta: 1:06:51 time: 0.3875 data_time: 0.0191 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1063 loss: 1.1063 2022/07/31 22:49:55 - mmengine - INFO - Epoch(train) [28][1000/3757] lr: 2.4473e-06 eta: 1:06:13 time: 0.3920 data_time: 0.0183 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0910 loss: 1.0910 2022/07/31 22:50:33 - mmengine - INFO - Epoch(train) [28][1100/3757] lr: 2.4473e-06 eta: 1:05:34 time: 0.3882 data_time: 0.0175 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8353 loss: 0.8353 2022/07/31 22:51:11 - mmengine - INFO - Epoch(train) [28][1200/3757] lr: 2.4473e-06 eta: 1:04:55 time: 0.3825 data_time: 0.0153 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4102 loss: 1.4102 2022/07/31 22:51:50 - mmengine - INFO - Epoch(train) [28][1300/3757] lr: 2.4473e-06 eta: 1:04:16 time: 0.3839 data_time: 0.0154 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.8789 loss: 0.8789 2022/07/31 22:52:28 - mmengine - INFO - Epoch(train) [28][1400/3757] lr: 2.4473e-06 eta: 1:03:38 time: 0.3794 data_time: 0.0166 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0101 loss: 1.0101 2022/07/31 22:53:06 - mmengine - INFO - Epoch(train) [28][1500/3757] lr: 2.4473e-06 eta: 1:02:59 time: 0.3808 data_time: 0.0164 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 0.9749 loss: 0.9749 2022/07/31 22:53:30 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 22:53:45 - mmengine - INFO - Epoch(train) [28][1600/3757] lr: 2.4473e-06 eta: 1:02:20 time: 0.3813 data_time: 0.0175 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0360 loss: 1.0360 2022/07/31 22:54:23 - mmengine - INFO - Epoch(train) [28][1700/3757] lr: 2.4473e-06 eta: 1:01:42 time: 0.3796 data_time: 0.0166 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1450 loss: 1.1450 2022/07/31 22:55:01 - mmengine - INFO - Epoch(train) [28][1800/3757] lr: 2.4473e-06 eta: 1:01:03 time: 0.3817 data_time: 0.0170 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1669 loss: 1.1669 2022/07/31 22:55:39 - mmengine - INFO - Epoch(train) [28][1900/3757] lr: 2.4473e-06 eta: 1:00:24 time: 0.3869 data_time: 0.0167 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2196 loss: 1.2196 2022/07/31 22:56:18 - mmengine - INFO - Epoch(train) [28][2000/3757] lr: 2.4473e-06 eta: 0:59:45 time: 0.3805 data_time: 0.0165 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1619 loss: 1.1619 2022/07/31 22:56:56 - mmengine - INFO - Epoch(train) [28][2100/3757] lr: 2.4473e-06 eta: 0:59:07 time: 0.3855 data_time: 0.0178 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1519 loss: 1.1519 2022/07/31 22:57:34 - mmengine - INFO - Epoch(train) [28][2200/3757] lr: 2.4473e-06 eta: 0:58:28 time: 0.3815 data_time: 0.0179 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2287 loss: 1.2287 2022/07/31 22:58:13 - mmengine - INFO - Epoch(train) [28][2300/3757] lr: 2.4473e-06 eta: 0:57:49 time: 0.3872 data_time: 0.0161 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0473 loss: 1.0473 2022/07/31 22:58:51 - mmengine - INFO - Epoch(train) [28][2400/3757] lr: 2.4473e-06 eta: 0:57:11 time: 0.3857 data_time: 0.0175 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9101 loss: 0.9101 2022/07/31 22:59:29 - mmengine - INFO - Epoch(train) [28][2500/3757] lr: 2.4473e-06 eta: 0:56:32 time: 0.3822 data_time: 0.0175 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3185 loss: 1.3185 2022/07/31 22:59:53 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 23:00:08 - mmengine - INFO - Epoch(train) [28][2600/3757] lr: 2.4473e-06 eta: 0:55:53 time: 0.3806 data_time: 0.0172 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0445 loss: 1.0445 2022/07/31 23:00:46 - mmengine - INFO - Epoch(train) [28][2700/3757] lr: 2.4473e-06 eta: 0:55:14 time: 0.3836 data_time: 0.0178 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1927 loss: 1.1927 2022/07/31 23:01:24 - mmengine - INFO - Epoch(train) [28][2800/3757] lr: 2.4473e-06 eta: 0:54:36 time: 0.3831 data_time: 0.0173 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1446 loss: 1.1446 2022/07/31 23:02:03 - mmengine - INFO - Epoch(train) [28][2900/3757] lr: 2.4473e-06 eta: 0:53:57 time: 0.3835 data_time: 0.0176 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0188 loss: 1.0188 2022/07/31 23:02:41 - mmengine - INFO - Epoch(train) [28][3000/3757] lr: 2.4473e-06 eta: 0:53:18 time: 0.3846 data_time: 0.0171 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3407 loss: 1.3407 2022/07/31 23:03:19 - mmengine - INFO - Epoch(train) [28][3100/3757] lr: 2.4473e-06 eta: 0:52:40 time: 0.3768 data_time: 0.0158 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9571 loss: 0.9571 2022/07/31 23:03:57 - mmengine - INFO - Epoch(train) [28][3200/3757] lr: 2.4473e-06 eta: 0:52:01 time: 0.3770 data_time: 0.0147 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1582 loss: 1.1582 2022/07/31 23:04:36 - mmengine - INFO - Epoch(train) [28][3300/3757] lr: 2.4473e-06 eta: 0:51:22 time: 0.3797 data_time: 0.0168 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2249 loss: 1.2249 2022/07/31 23:05:14 - mmengine - INFO - Epoch(train) [28][3400/3757] lr: 2.4473e-06 eta: 0:50:43 time: 0.3817 data_time: 0.0170 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2385 loss: 1.2385 2022/07/31 23:05:52 - mmengine - INFO - Epoch(train) [28][3500/3757] lr: 2.4473e-06 eta: 0:50:05 time: 0.3800 data_time: 0.0175 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1939 loss: 1.1939 2022/07/31 23:06:16 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 23:06:31 - mmengine - INFO - Epoch(train) [28][3600/3757] lr: 2.4473e-06 eta: 0:49:26 time: 0.3778 data_time: 0.0158 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0899 loss: 1.0899 2022/07/31 23:07:12 - mmengine - INFO - Epoch(train) [28][3700/3757] lr: 2.4473e-06 eta: 0:48:48 time: 0.3791 data_time: 0.0171 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0229 loss: 1.0229 2022/07/31 23:07:33 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 23:07:33 - mmengine - INFO - Epoch(train) [28][3757/3757] lr: 2.4473e-06 eta: 0:48:32 time: 0.3670 data_time: 0.0145 memory: 21072 top1_acc: 0.7143 top5_acc: 0.7143 loss_cls: 1.0787 loss: 1.0787 2022/07/31 23:08:15 - mmengine - INFO - Epoch(train) [29][100/3757] lr: 1.0927e-06 eta: 0:47:47 time: 0.3803 data_time: 0.0165 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2415 loss: 1.2415 2022/07/31 23:08:54 - mmengine - INFO - Epoch(train) [29][200/3757] lr: 1.0927e-06 eta: 0:47:08 time: 0.3836 data_time: 0.0159 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3442 loss: 1.3442 2022/07/31 23:09:32 - mmengine - INFO - Epoch(train) [29][300/3757] lr: 1.0927e-06 eta: 0:46:29 time: 0.3794 data_time: 0.0150 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1508 loss: 1.1508 2022/07/31 23:10:10 - mmengine - INFO - Epoch(train) [29][400/3757] lr: 1.0927e-06 eta: 0:45:50 time: 0.3792 data_time: 0.0162 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0019 loss: 1.0019 2022/07/31 23:10:48 - mmengine - INFO - Epoch(train) [29][500/3757] lr: 1.0927e-06 eta: 0:45:12 time: 0.3766 data_time: 0.0152 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0446 loss: 1.0446 2022/07/31 23:11:26 - mmengine - INFO - Epoch(train) [29][600/3757] lr: 1.0927e-06 eta: 0:44:33 time: 0.3827 data_time: 0.0167 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3963 loss: 1.3963 2022/07/31 23:12:05 - mmengine - INFO - Epoch(train) [29][700/3757] lr: 1.0927e-06 eta: 0:43:54 time: 0.3794 data_time: 0.0157 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3449 loss: 1.3449 2022/07/31 23:12:43 - mmengine - INFO - Epoch(train) [29][800/3757] lr: 1.0927e-06 eta: 0:43:16 time: 0.3908 data_time: 0.0169 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2683 loss: 1.2683 2022/07/31 23:12:45 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 23:13:25 - mmengine - INFO - Epoch(train) [29][900/3757] lr: 1.0927e-06 eta: 0:42:37 time: 0.4867 data_time: 0.0153 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1353 loss: 1.1353 2022/07/31 23:14:05 - mmengine - INFO - Epoch(train) [29][1000/3757] lr: 1.0927e-06 eta: 0:41:59 time: 0.3814 data_time: 0.0164 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1889 loss: 1.1889 2022/07/31 23:14:43 - mmengine - INFO - Epoch(train) [29][1100/3757] lr: 1.0927e-06 eta: 0:41:20 time: 0.3787 data_time: 0.0172 memory: 21072 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.0724 loss: 1.0724 2022/07/31 23:15:22 - mmengine - INFO - Epoch(train) [29][1200/3757] lr: 1.0927e-06 eta: 0:40:41 time: 0.3925 data_time: 0.0161 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1238 loss: 1.1238 2022/07/31 23:16:00 - mmengine - INFO - Epoch(train) [29][1300/3757] lr: 1.0927e-06 eta: 0:40:03 time: 0.3774 data_time: 0.0150 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.0032 loss: 1.0032 2022/07/31 23:16:38 - mmengine - INFO - Epoch(train) [29][1400/3757] lr: 1.0927e-06 eta: 0:39:24 time: 0.4157 data_time: 0.0180 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1042 loss: 1.1042 2022/07/31 23:17:16 - mmengine - INFO - Epoch(train) [29][1500/3757] lr: 1.0927e-06 eta: 0:38:45 time: 0.3800 data_time: 0.0165 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3681 loss: 1.3681 2022/07/31 23:17:55 - mmengine - INFO - Epoch(train) [29][1600/3757] lr: 1.0927e-06 eta: 0:38:06 time: 0.3796 data_time: 0.0164 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8576 loss: 0.8576 2022/07/31 23:18:33 - mmengine - INFO - Epoch(train) [29][1700/3757] lr: 1.0927e-06 eta: 0:37:28 time: 0.3862 data_time: 0.0156 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1019 loss: 1.1019 2022/07/31 23:19:11 - mmengine - INFO - Epoch(train) [29][1800/3757] lr: 1.0927e-06 eta: 0:36:49 time: 0.3784 data_time: 0.0145 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1105 loss: 1.1105 2022/07/31 23:19:13 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 23:19:50 - mmengine - INFO - Epoch(train) [29][1900/3757] lr: 1.0927e-06 eta: 0:36:10 time: 0.3774 data_time: 0.0177 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8717 loss: 0.8717 2022/07/31 23:20:31 - mmengine - INFO - Epoch(train) [29][2000/3757] lr: 1.0927e-06 eta: 0:35:32 time: 0.3769 data_time: 0.0151 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0878 loss: 1.0878 2022/07/31 23:21:09 - mmengine - INFO - Epoch(train) [29][2100/3757] lr: 1.0927e-06 eta: 0:34:53 time: 0.3858 data_time: 0.0184 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9652 loss: 0.9652 2022/07/31 23:21:47 - mmengine - INFO - Epoch(train) [29][2200/3757] lr: 1.0927e-06 eta: 0:34:14 time: 0.3779 data_time: 0.0153 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0544 loss: 1.0544 2022/07/31 23:22:25 - mmengine - INFO - Epoch(train) [29][2300/3757] lr: 1.0927e-06 eta: 0:33:36 time: 0.3814 data_time: 0.0157 memory: 21072 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.0196 loss: 1.0196 2022/07/31 23:23:03 - mmengine - INFO - Epoch(train) [29][2400/3757] lr: 1.0927e-06 eta: 0:32:57 time: 0.3842 data_time: 0.0168 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0200 loss: 1.0200 2022/07/31 23:23:41 - mmengine - INFO - Epoch(train) [29][2500/3757] lr: 1.0927e-06 eta: 0:32:18 time: 0.3769 data_time: 0.0148 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.3109 loss: 1.3109 2022/07/31 23:24:19 - mmengine - INFO - Epoch(train) [29][2600/3757] lr: 1.0927e-06 eta: 0:31:40 time: 0.3800 data_time: 0.0182 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9409 loss: 0.9409 2022/07/31 23:24:58 - mmengine - INFO - Epoch(train) [29][2700/3757] lr: 1.0927e-06 eta: 0:31:01 time: 0.3805 data_time: 0.0162 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3340 loss: 1.3340 2022/07/31 23:25:36 - mmengine - INFO - Epoch(train) [29][2800/3757] lr: 1.0927e-06 eta: 0:30:22 time: 0.3871 data_time: 0.0154 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1496 loss: 1.1496 2022/07/31 23:25:37 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 23:26:14 - mmengine - INFO - Epoch(train) [29][2900/3757] lr: 1.0927e-06 eta: 0:29:44 time: 0.3772 data_time: 0.0163 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3091 loss: 1.3091 2022/07/31 23:26:52 - mmengine - INFO - Epoch(train) [29][3000/3757] lr: 1.0927e-06 eta: 0:29:05 time: 0.3778 data_time: 0.0143 memory: 21072 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.0549 loss: 1.0549 2022/07/31 23:27:31 - mmengine - INFO - Epoch(train) [29][3100/3757] lr: 1.0927e-06 eta: 0:28:26 time: 0.3768 data_time: 0.0146 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1461 loss: 1.1461 2022/07/31 23:28:09 - mmengine - INFO - Epoch(train) [29][3200/3757] lr: 1.0927e-06 eta: 0:27:48 time: 0.3793 data_time: 0.0149 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0466 loss: 1.0466 2022/07/31 23:28:47 - mmengine - INFO - Epoch(train) [29][3300/3757] lr: 1.0927e-06 eta: 0:27:09 time: 0.3926 data_time: 0.0158 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2448 loss: 1.2448 2022/07/31 23:29:26 - mmengine - INFO - Epoch(train) [29][3400/3757] lr: 1.0927e-06 eta: 0:26:30 time: 0.3841 data_time: 0.0144 memory: 21072 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0610 loss: 1.0610 2022/07/31 23:30:04 - mmengine - INFO - Epoch(train) [29][3500/3757] lr: 1.0927e-06 eta: 0:25:51 time: 0.3811 data_time: 0.0150 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2445 loss: 1.2445 2022/07/31 23:30:42 - mmengine - INFO - Epoch(train) [29][3600/3757] lr: 1.0927e-06 eta: 0:25:13 time: 0.3784 data_time: 0.0159 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1394 loss: 1.1394 2022/07/31 23:31:21 - mmengine - INFO - Epoch(train) [29][3700/3757] lr: 1.0927e-06 eta: 0:24:34 time: 0.3752 data_time: 0.0156 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9735 loss: 0.9735 2022/07/31 23:31:43 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 23:31:43 - mmengine - INFO - Epoch(train) [29][3757/3757] lr: 1.0927e-06 eta: 0:24:19 time: 0.3712 data_time: 0.0135 memory: 21072 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 1.0594 loss: 1.0594 2022/07/31 23:32:06 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 23:32:26 - mmengine - INFO - Epoch(train) [30][100/3757] lr: 2.7392e-07 eta: 0:23:33 time: 0.3809 data_time: 0.0149 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9596 loss: 0.9596 2022/07/31 23:33:05 - mmengine - INFO - Epoch(train) [30][200/3757] lr: 2.7392e-07 eta: 0:22:55 time: 0.3838 data_time: 0.0166 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1996 loss: 1.1996 2022/07/31 23:33:43 - mmengine - INFO - Epoch(train) [30][300/3757] lr: 2.7392e-07 eta: 0:22:16 time: 0.3827 data_time: 0.0152 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1836 loss: 1.1836 2022/07/31 23:34:24 - mmengine - INFO - Epoch(train) [30][400/3757] lr: 2.7392e-07 eta: 0:21:37 time: 0.3800 data_time: 0.0154 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0257 loss: 1.0257 2022/07/31 23:35:03 - mmengine - INFO - Epoch(train) [30][500/3757] lr: 2.7392e-07 eta: 0:20:59 time: 0.3762 data_time: 0.0167 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0414 loss: 1.0414 2022/07/31 23:35:41 - mmengine - INFO - Epoch(train) [30][600/3757] lr: 2.7392e-07 eta: 0:20:20 time: 0.3774 data_time: 0.0169 memory: 21072 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1992 loss: 1.1992 2022/07/31 23:36:19 - mmengine - INFO - Epoch(train) [30][700/3757] lr: 2.7392e-07 eta: 0:19:41 time: 0.3814 data_time: 0.0180 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8279 loss: 0.8279 2022/07/31 23:36:57 - mmengine - INFO - Epoch(train) [30][800/3757] lr: 2.7392e-07 eta: 0:19:03 time: 0.3795 data_time: 0.0151 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2081 loss: 1.2081 2022/07/31 23:37:37 - mmengine - INFO - Epoch(train) [30][900/3757] lr: 2.7392e-07 eta: 0:18:24 time: 0.3787 data_time: 0.0159 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1426 loss: 1.1426 2022/07/31 23:38:15 - mmengine - INFO - Epoch(train) [30][1000/3757] lr: 2.7392e-07 eta: 0:17:45 time: 0.3831 data_time: 0.0161 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9210 loss: 0.9210 2022/07/31 23:38:33 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 23:38:54 - mmengine - INFO - Epoch(train) [30][1100/3757] lr: 2.7392e-07 eta: 0:17:07 time: 0.3879 data_time: 0.0170 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1732 loss: 1.1732 2022/07/31 23:39:32 - mmengine - INFO - Epoch(train) [30][1200/3757] lr: 2.7392e-07 eta: 0:16:28 time: 0.3790 data_time: 0.0150 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0650 loss: 1.0650 2022/07/31 23:40:11 - mmengine - INFO - Epoch(train) [30][1300/3757] lr: 2.7392e-07 eta: 0:15:49 time: 0.3773 data_time: 0.0149 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0497 loss: 1.0497 2022/07/31 23:40:50 - mmengine - INFO - Epoch(train) [30][1400/3757] lr: 2.7392e-07 eta: 0:15:11 time: 0.3833 data_time: 0.0160 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4089 loss: 1.4089 2022/07/31 23:41:29 - mmengine - INFO - Epoch(train) [30][1500/3757] lr: 2.7392e-07 eta: 0:14:32 time: 0.3813 data_time: 0.0172 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 0.8584 loss: 0.8584 2022/07/31 23:42:07 - mmengine - INFO - Epoch(train) [30][1600/3757] lr: 2.7392e-07 eta: 0:13:53 time: 0.3825 data_time: 0.0168 memory: 21072 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.3330 loss: 1.3330 2022/07/31 23:42:45 - mmengine - INFO - Epoch(train) [30][1700/3757] lr: 2.7392e-07 eta: 0:13:15 time: 0.3793 data_time: 0.0168 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2053 loss: 1.2053 2022/07/31 23:43:24 - mmengine - INFO - Epoch(train) [30][1800/3757] lr: 2.7392e-07 eta: 0:12:36 time: 0.3804 data_time: 0.0173 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3208 loss: 1.3208 2022/07/31 23:44:02 - mmengine - INFO - Epoch(train) [30][1900/3757] lr: 2.7392e-07 eta: 0:11:57 time: 0.3798 data_time: 0.0164 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9679 loss: 0.9679 2022/07/31 23:44:40 - mmengine - INFO - Epoch(train) [30][2000/3757] lr: 2.7392e-07 eta: 0:11:19 time: 0.3890 data_time: 0.0181 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8038 loss: 0.8038 2022/07/31 23:44:58 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 23:45:19 - mmengine - INFO - Epoch(train) [30][2100/3757] lr: 2.7392e-07 eta: 0:10:40 time: 0.3862 data_time: 0.0173 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0678 loss: 1.0678 2022/07/31 23:45:57 - mmengine - INFO - Epoch(train) [30][2200/3757] lr: 2.7392e-07 eta: 0:10:01 time: 0.3835 data_time: 0.0178 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.9079 loss: 0.9079 2022/07/31 23:46:36 - mmengine - INFO - Epoch(train) [30][2300/3757] lr: 2.7392e-07 eta: 0:09:23 time: 0.4134 data_time: 0.0172 memory: 21072 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.9998 loss: 0.9998 2022/07/31 23:47:19 - mmengine - INFO - Epoch(train) [30][2400/3757] lr: 2.7392e-07 eta: 0:08:44 time: 0.3794 data_time: 0.0179 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2616 loss: 1.2616 2022/07/31 23:47:57 - mmengine - INFO - Epoch(train) [30][2500/3757] lr: 2.7392e-07 eta: 0:08:06 time: 0.3817 data_time: 0.0162 memory: 21072 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0517 loss: 1.0517 2022/07/31 23:48:36 - mmengine - INFO - Epoch(train) [30][2600/3757] lr: 2.7392e-07 eta: 0:07:27 time: 0.3825 data_time: 0.0161 memory: 21072 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3164 loss: 1.3164 2022/07/31 23:49:14 - mmengine - INFO - Epoch(train) [30][2700/3757] lr: 2.7392e-07 eta: 0:06:48 time: 0.3802 data_time: 0.0172 memory: 21072 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.0488 loss: 1.0488 2022/07/31 23:49:52 - mmengine - INFO - Epoch(train) [30][2800/3757] lr: 2.7392e-07 eta: 0:06:10 time: 0.3796 data_time: 0.0179 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 0.9829 loss: 0.9829 2022/07/31 23:50:31 - mmengine - INFO - Epoch(train) [30][2900/3757] lr: 2.7392e-07 eta: 0:05:31 time: 0.3823 data_time: 0.0182 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 0.9713 loss: 0.9713 2022/07/31 23:51:09 - mmengine - INFO - Epoch(train) [30][3000/3757] lr: 2.7392e-07 eta: 0:04:52 time: 0.3839 data_time: 0.0182 memory: 21072 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0024 loss: 1.0024 2022/07/31 23:51:27 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 23:51:48 - mmengine - INFO - Epoch(train) [30][3100/3757] lr: 2.7392e-07 eta: 0:04:14 time: 0.3844 data_time: 0.0177 memory: 21072 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2910 loss: 1.2910 2022/07/31 23:52:26 - mmengine - INFO - Epoch(train) [30][3200/3757] lr: 2.7392e-07 eta: 0:03:35 time: 0.3834 data_time: 0.0180 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3184 loss: 1.3184 2022/07/31 23:53:04 - mmengine - INFO - Epoch(train) [30][3300/3757] lr: 2.7392e-07 eta: 0:02:56 time: 0.3809 data_time: 0.0175 memory: 21072 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0108 loss: 1.0108 2022/07/31 23:53:44 - mmengine - INFO - Epoch(train) [30][3400/3757] lr: 2.7392e-07 eta: 0:02:18 time: 0.4717 data_time: 0.0155 memory: 21072 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1209 loss: 1.1209 2022/07/31 23:54:23 - mmengine - INFO - Epoch(train) [30][3500/3757] lr: 2.7392e-07 eta: 0:01:39 time: 0.3786 data_time: 0.0166 memory: 21072 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1253 loss: 1.1253 2022/07/31 23:55:01 - mmengine - INFO - Epoch(train) [30][3600/3757] lr: 2.7392e-07 eta: 0:01:00 time: 0.3866 data_time: 0.0176 memory: 21072 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.1673 loss: 1.1673 2022/07/31 23:55:39 - mmengine - INFO - Epoch(train) [30][3700/3757] lr: 2.7392e-07 eta: 0:00:22 time: 0.3864 data_time: 0.0174 memory: 21072 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 1.3406 loss: 1.3406 2022/07/31 23:56:01 - mmengine - INFO - Exp name: swin_tiny_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220731_113429 2022/07/31 23:56:01 - mmengine - INFO - Epoch(train) [30][3757/3757] lr: 2.7392e-07 eta: 0:00:06 time: 0.3715 data_time: 0.0158 memory: 21072 top1_acc: 0.7143 top5_acc: 1.0000 loss_cls: 1.2838 loss: 1.2838 2022/07/31 23:56:01 - mmengine - INFO - Saving checkpoint at 30 epochs 2022/07/31 23:56:26 - mmengine - INFO - Epoch(val) [30][100/310] eta: 0:00:43 time: 0.2079 data_time: 0.0685 memory: 5891 2022/07/31 23:56:47 - mmengine - INFO - Epoch(val) [30][200/310] eta: 0:00:21 time: 0.1926 data_time: 0.0546 memory: 5891 2022/07/31 23:57:08 - mmengine - INFO - Epoch(val) [30][300/310] eta: 0:00:01 time: 0.1822 data_time: 0.0464 memory: 5891 2022/07/31 23:57:10 - mmengine - INFO - Epoch(val) [30][310/310] acc/top1: 0.7306 acc/top5: 0.9053 acc/mean1: 0.7305 2022/07/31 23:57:10 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/swin-t-2/best_acc/top1_epoch_28.pth is removed 2022/07/31 23:57:13 - mmengine - INFO - The best checkpoint with 0.7306 acc/top1 at 31 epoch is saved to best_acc/top1_epoch_31.pth.