2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.patch_embed.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.patch_embed.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.patch_embed.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.patch_embed.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.patch_embed.norm.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.patch_embed.norm.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.patch_embed.norm.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.patch_embed.norm.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.downsample.reduction.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.downsample.reduction.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.downsample.norm.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.downsample.norm.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.downsample.norm.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.0.downsample.norm.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.downsample.reduction.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.downsample.reduction.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.downsample.norm.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.downsample.norm.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.downsample.norm.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.1.downsample.norm.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.downsample.reduction.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.downsample.reduction.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.downsample.norm.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.downsample.norm.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.downsample.norm.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.2.downsample.norm.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm1.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm1.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.relative_position_bias_table: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.relative_position_bias_table: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.qkv.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.qkv.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.qkv.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.qkv.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.proj.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.proj.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.proj.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.proj.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm2.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm2.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc1.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc1.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc1.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc1.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc2.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc2.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc2.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc2.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.norm.weight: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.norm.weight: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.norm.bias: lr = 0.0001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- backbone.norm.bias: weight_decay = 0.0 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- cls_head.fc_cls.weight: lr = 0.001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- cls_head.fc_cls.weight: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- cls_head.fc_cls.bias: lr = 0.001 2022/07/30 13:03:43 - mmengine - INFO - paramwise_options -- cls_head.fc_cls.bias: weight_decay = 0.02 2022/07/30 13:03:43 - mmengine - INFO - load model from: https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth 2022/07/30 13:04:09 - mmengine - INFO - _IncompatibleKeys(missing_keys=['layers.0.blocks.0.attn.relative_position_index', 'layers.0.blocks.1.attn.relative_position_index', 'layers.1.blocks.0.attn.relative_position_index', 'layers.1.blocks.1.attn.relative_position_index', 'layers.2.blocks.0.attn.relative_position_index', 'layers.2.blocks.1.attn.relative_position_index', 'layers.2.blocks.2.attn.relative_position_index', 'layers.2.blocks.3.attn.relative_position_index', 'layers.2.blocks.4.attn.relative_position_index', 'layers.2.blocks.5.attn.relative_position_index', 'layers.2.blocks.6.attn.relative_position_index', 'layers.2.blocks.7.attn.relative_position_index', 'layers.2.blocks.8.attn.relative_position_index', 'layers.2.blocks.9.attn.relative_position_index', 'layers.2.blocks.10.attn.relative_position_index', 'layers.2.blocks.11.attn.relative_position_index', 'layers.2.blocks.12.attn.relative_position_index', 'layers.2.blocks.13.attn.relative_position_index', 'layers.2.blocks.14.attn.relative_position_index', 'layers.2.blocks.15.attn.relative_position_index', 'layers.2.blocks.16.attn.relative_position_index', 'layers.2.blocks.17.attn.relative_position_index', 'layers.3.blocks.0.attn.relative_position_index', 'layers.3.blocks.1.attn.relative_position_index'], unexpected_keys=['head.weight', 'head.bias']) 2022/07/30 13:04:09 - mmengine - INFO - => loaded successfully 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth' 2022/07/30 13:04:09 - mmengine - INFO - Checkpoints will be saved to /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb by HardDiskBackend. 2022/07/30 13:06:09 - mmengine - INFO - Epoch(train) [1][100/3757] lr: 1.0949e-05 eta: 1 day, 13:40:38 time: 0.5454 data_time: 0.0112 memory: 33632 top1_acc: 0.0000 top5_acc: 0.0000 loss_cls: 5.8218 loss: 5.8218 2022/07/30 13:07:04 - mmengine - INFO - Epoch(train) [1][200/3757] lr: 1.1907e-05 eta: 1 day, 3:22:03 time: 0.5429 data_time: 0.0106 memory: 33632 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 5.2550 loss: 5.2550 2022/07/30 13:12:59 - mmengine - INFO - Epoch(train) [1][300/3757] lr: 1.2865e-05 eta: 2 days, 7:09:29 time: 0.5620 data_time: 0.0121 memory: 33632 top1_acc: 0.1250 top5_acc: 0.2500 loss_cls: 4.4659 loss: 4.4659 2022/07/30 13:15:41 - mmengine - INFO - Epoch(train) [1][400/3757] lr: 1.3824e-05 eta: 2 days, 5:59:54 time: 3.2364 data_time: 0.0114 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 4.0262 loss: 4.0262 2022/07/30 13:16:36 - mmengine - INFO - Epoch(train) [1][500/3757] lr: 1.4782e-05 eta: 1 day, 22:35:00 time: 0.5459 data_time: 0.0126 memory: 33632 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 3.6663 loss: 3.6663 2022/07/30 13:18:25 - mmengine - INFO - Epoch(train) [1][600/3757] lr: 1.5740e-05 eta: 1 day, 20:26:24 time: 0.5440 data_time: 0.0109 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 3.5687 loss: 3.5687 2022/07/30 13:19:20 - mmengine - INFO - Epoch(train) [1][700/3757] lr: 1.6699e-05 eta: 1 day, 16:30:42 time: 0.5468 data_time: 0.0127 memory: 33632 top1_acc: 0.0000 top5_acc: 0.3750 loss_cls: 3.5587 loss: 3.5587 2022/07/30 13:20:16 - mmengine - INFO - Epoch(train) [1][800/3757] lr: 1.7657e-05 eta: 1 day, 13:34:22 time: 0.5479 data_time: 0.0125 memory: 33632 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 3.4526 loss: 3.4526 2022/07/30 13:21:11 - mmengine - INFO - Epoch(train) [1][900/3757] lr: 1.8615e-05 eta: 1 day, 11:16:36 time: 0.5462 data_time: 0.0122 memory: 33632 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 3.1693 loss: 3.1693 2022/07/30 13:22:06 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 13:22:06 - mmengine - INFO - Epoch(train) [1][1000/3757] lr: 1.9574e-05 eta: 1 day, 9:25:41 time: 0.5539 data_time: 0.0134 memory: 33632 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 3.2033 loss: 3.2033 2022/07/30 13:23:02 - mmengine - INFO - Epoch(train) [1][1100/3757] lr: 2.0532e-05 eta: 1 day, 7:55:22 time: 0.5561 data_time: 0.0128 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.9931 loss: 2.9931 2022/07/30 13:24:50 - mmengine - INFO - Epoch(train) [1][1200/3757] lr: 2.1490e-05 eta: 1 day, 8:02:15 time: 0.5538 data_time: 0.0133 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 3.0520 loss: 3.0520 2022/07/30 13:25:45 - mmengine - INFO - Epoch(train) [1][1300/3757] lr: 2.2448e-05 eta: 1 day, 6:51:40 time: 0.5459 data_time: 0.0121 memory: 33632 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.5997 loss: 2.5997 2022/07/30 13:27:35 - mmengine - INFO - Epoch(train) [1][1400/3757] lr: 2.3407e-05 eta: 1 day, 7:03:11 time: 0.5569 data_time: 0.0142 memory: 33632 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.7165 loss: 2.7165 2022/07/30 13:28:31 - mmengine - INFO - Epoch(train) [1][1500/3757] lr: 2.4365e-05 eta: 1 day, 6:06:15 time: 0.5564 data_time: 0.0124 memory: 33632 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.9457 loss: 2.9457 2022/07/30 13:29:26 - mmengine - INFO - Epoch(train) [1][1600/3757] lr: 2.5323e-05 eta: 1 day, 5:15:44 time: 0.5482 data_time: 0.0127 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 2.8179 loss: 2.8179 2022/07/30 13:30:22 - mmengine - INFO - Epoch(train) [1][1700/3757] lr: 2.6282e-05 eta: 1 day, 4:31:24 time: 0.5590 data_time: 0.0141 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.8695 loss: 2.8695 2022/07/30 13:31:17 - mmengine - INFO - Epoch(train) [1][1800/3757] lr: 2.7240e-05 eta: 1 day, 3:51:31 time: 0.5508 data_time: 0.0134 memory: 33632 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.6922 loss: 2.6922 2022/07/30 13:32:12 - mmengine - INFO - Epoch(train) [1][1900/3757] lr: 2.8198e-05 eta: 1 day, 3:16:05 time: 0.5559 data_time: 0.0143 memory: 33632 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.5020 loss: 2.5020 2022/07/30 13:33:07 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 13:33:07 - mmengine - INFO - Epoch(train) [1][2000/3757] lr: 2.9157e-05 eta: 1 day, 2:43:44 time: 0.5489 data_time: 0.0129 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.6983 loss: 2.6983 2022/07/30 13:34:03 - mmengine - INFO - Epoch(train) [1][2100/3757] lr: 3.0115e-05 eta: 1 day, 2:14:42 time: 0.5501 data_time: 0.0140 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.7718 loss: 2.7718 2022/07/30 13:34:58 - mmengine - INFO - Epoch(train) [1][2200/3757] lr: 3.1073e-05 eta: 1 day, 1:47:54 time: 0.5507 data_time: 0.0131 memory: 33632 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.7093 loss: 2.7093 2022/07/30 13:35:53 - mmengine - INFO - Epoch(train) [1][2300/3757] lr: 3.2032e-05 eta: 1 day, 1:23:29 time: 0.5574 data_time: 0.0130 memory: 33632 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.5786 loss: 2.5786 2022/07/30 13:36:48 - mmengine - INFO - Epoch(train) [1][2400/3757] lr: 3.2990e-05 eta: 1 day, 1:00:49 time: 0.5484 data_time: 0.0136 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.7695 loss: 2.7695 2022/07/30 13:37:44 - mmengine - INFO - Epoch(train) [1][2500/3757] lr: 3.3948e-05 eta: 1 day, 0:40:05 time: 0.5493 data_time: 0.0131 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.4594 loss: 2.4594 2022/07/30 13:38:39 - mmengine - INFO - Epoch(train) [1][2600/3757] lr: 3.4907e-05 eta: 1 day, 0:20:51 time: 0.5521 data_time: 0.0121 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.4975 loss: 2.4975 2022/07/30 13:39:34 - mmengine - INFO - Epoch(train) [1][2700/3757] lr: 3.5865e-05 eta: 1 day, 0:03:07 time: 0.5523 data_time: 0.0129 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.5381 loss: 2.5381 2022/07/30 13:40:30 - mmengine - INFO - Epoch(train) [1][2800/3757] lr: 3.6823e-05 eta: 23:46:41 time: 0.5499 data_time: 0.0141 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.4592 loss: 2.4592 2022/07/30 13:41:25 - mmengine - INFO - Epoch(train) [1][2900/3757] lr: 3.7782e-05 eta: 23:31:18 time: 0.5480 data_time: 0.0133 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3774 loss: 2.3774 2022/07/30 13:42:21 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 13:42:21 - mmengine - INFO - Epoch(train) [1][3000/3757] lr: 3.8740e-05 eta: 23:16:47 time: 0.5536 data_time: 0.0131 memory: 33632 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.4387 loss: 2.4387 2022/07/30 13:43:16 - mmengine - INFO - Epoch(train) [1][3100/3757] lr: 3.9698e-05 eta: 23:03:18 time: 0.5499 data_time: 0.0128 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.7120 loss: 2.7120 2022/07/30 13:44:12 - mmengine - INFO - Epoch(train) [1][3200/3757] lr: 4.0656e-05 eta: 22:50:21 time: 0.5536 data_time: 0.0150 memory: 33632 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.4556 loss: 2.4556 2022/07/30 13:45:07 - mmengine - INFO - Epoch(train) [1][3300/3757] lr: 4.1615e-05 eta: 22:38:05 time: 0.5553 data_time: 0.0139 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.5417 loss: 2.5417 2022/07/30 13:46:02 - mmengine - INFO - Epoch(train) [1][3400/3757] lr: 4.2573e-05 eta: 22:26:40 time: 0.5490 data_time: 0.0126 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2128 loss: 2.2128 2022/07/30 13:46:58 - mmengine - INFO - Epoch(train) [1][3500/3757] lr: 4.3531e-05 eta: 22:15:41 time: 0.5558 data_time: 0.0145 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.3063 loss: 2.3063 2022/07/30 13:47:53 - mmengine - INFO - Epoch(train) [1][3600/3757] lr: 4.4490e-05 eta: 22:05:18 time: 0.5494 data_time: 0.0139 memory: 33632 top1_acc: 0.1250 top5_acc: 0.8750 loss_cls: 2.3392 loss: 2.3392 2022/07/30 13:48:48 - mmengine - INFO - Epoch(train) [1][3700/3757] lr: 4.5448e-05 eta: 21:55:38 time: 0.5483 data_time: 0.0134 memory: 33632 top1_acc: 0.3750 top5_acc: 1.0000 loss_cls: 2.3726 loss: 2.3726 2022/07/30 13:49:20 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 13:49:20 - mmengine - INFO - Epoch(train) [1][3757/3757] lr: 4.5994e-05 eta: 21:51:48 time: 0.5507 data_time: 0.0144 memory: 33632 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 2.2493 loss: 2.2493 2022/07/30 13:50:17 - mmengine - INFO - Epoch(train) [2][100/3757] lr: 4.6824e-05 eta: 21:37:37 time: 0.5582 data_time: 0.0135 memory: 33632 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 2.6069 loss: 2.6069 2022/07/30 13:51:14 - mmengine - INFO - Epoch(train) [2][200/3757] lr: 4.7780e-05 eta: 21:29:24 time: 0.5764 data_time: 0.0145 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.5921 loss: 2.5921 2022/07/30 13:51:37 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 13:52:09 - mmengine - INFO - Epoch(train) [2][300/3757] lr: 4.8735e-05 eta: 21:21:12 time: 0.5479 data_time: 0.0139 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1919 loss: 2.1919 2022/07/30 13:53:04 - mmengine - INFO - Epoch(train) [2][400/3757] lr: 4.9691e-05 eta: 21:13:18 time: 0.5497 data_time: 0.0146 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1137 loss: 2.1137 2022/07/30 13:54:00 - mmengine - INFO - Epoch(train) [2][500/3757] lr: 5.0647e-05 eta: 21:05:54 time: 0.5593 data_time: 0.0146 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3282 loss: 2.3282 2022/07/30 13:54:56 - mmengine - INFO - Epoch(train) [2][600/3757] lr: 5.1602e-05 eta: 20:58:44 time: 0.5566 data_time: 0.0150 memory: 33632 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.3831 loss: 2.3831 2022/07/30 13:55:51 - mmengine - INFO - Epoch(train) [2][700/3757] lr: 5.2558e-05 eta: 20:51:53 time: 0.5517 data_time: 0.0141 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3472 loss: 2.3472 2022/07/30 13:56:48 - mmengine - INFO - Epoch(train) [2][800/3757] lr: 5.3514e-05 eta: 20:45:40 time: 0.5500 data_time: 0.0140 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0730 loss: 2.0730 2022/07/30 13:57:43 - mmengine - INFO - Epoch(train) [2][900/3757] lr: 5.4469e-05 eta: 20:39:17 time: 0.5513 data_time: 0.0143 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.5049 loss: 2.5049 2022/07/30 13:58:39 - mmengine - INFO - Epoch(train) [2][1000/3757] lr: 5.5425e-05 eta: 20:33:03 time: 0.5529 data_time: 0.0144 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.5061 loss: 2.5061 2022/07/30 13:59:34 - mmengine - INFO - Epoch(train) [2][1100/3757] lr: 5.6381e-05 eta: 20:27:09 time: 0.5559 data_time: 0.0145 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.4991 loss: 2.4991 2022/07/30 14:00:30 - mmengine - INFO - Epoch(train) [2][1200/3757] lr: 5.7337e-05 eta: 20:21:27 time: 0.5493 data_time: 0.0142 memory: 33632 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.5508 loss: 2.5508 2022/07/30 14:00:54 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 14:01:26 - mmengine - INFO - Epoch(train) [2][1300/3757] lr: 5.8292e-05 eta: 20:15:54 time: 0.5495 data_time: 0.0144 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1674 loss: 2.1674 2022/07/30 14:02:21 - mmengine - INFO - Epoch(train) [2][1400/3757] lr: 5.9248e-05 eta: 20:10:30 time: 0.5553 data_time: 0.0136 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2838 loss: 2.2838 2022/07/30 14:03:16 - mmengine - INFO - Epoch(train) [2][1500/3757] lr: 6.0204e-05 eta: 20:05:13 time: 0.5511 data_time: 0.0144 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2317 loss: 2.2317 2022/07/30 14:04:12 - mmengine - INFO - Epoch(train) [2][1600/3757] lr: 6.1159e-05 eta: 20:00:07 time: 0.5524 data_time: 0.0151 memory: 33632 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.4192 loss: 2.4192 2022/07/30 14:05:07 - mmengine - INFO - Epoch(train) [2][1700/3757] lr: 6.2115e-05 eta: 19:55:06 time: 0.5516 data_time: 0.0144 memory: 33632 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.4263 loss: 2.4263 2022/07/30 14:06:02 - mmengine - INFO - Epoch(train) [2][1800/3757] lr: 6.3071e-05 eta: 19:50:18 time: 0.5572 data_time: 0.0149 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1183 loss: 2.1183 2022/07/30 14:06:58 - mmengine - INFO - Epoch(train) [2][1900/3757] lr: 6.4026e-05 eta: 19:45:39 time: 0.5535 data_time: 0.0144 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1983 loss: 2.1983 2022/07/30 14:07:54 - mmengine - INFO - Epoch(train) [2][2000/3757] lr: 6.4982e-05 eta: 19:41:16 time: 0.5650 data_time: 0.0155 memory: 33632 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.3926 loss: 2.3926 2022/07/30 14:08:49 - mmengine - INFO - Epoch(train) [2][2100/3757] lr: 6.5938e-05 eta: 19:36:51 time: 0.5509 data_time: 0.0152 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4917 loss: 2.4917 2022/07/30 14:09:45 - mmengine - INFO - Epoch(train) [2][2200/3757] lr: 6.6893e-05 eta: 19:32:39 time: 0.5510 data_time: 0.0158 memory: 33632 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.8111 loss: 1.8111 2022/07/30 14:10:09 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 14:10:41 - mmengine - INFO - Epoch(train) [2][2300/3757] lr: 6.7849e-05 eta: 19:28:33 time: 0.5629 data_time: 0.0151 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.3461 loss: 2.3461 2022/07/30 14:11:36 - mmengine - INFO - Epoch(train) [2][2400/3757] lr: 6.8805e-05 eta: 19:24:32 time: 0.5568 data_time: 0.0153 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.3597 loss: 2.3597 2022/07/30 14:12:32 - mmengine - INFO - Epoch(train) [2][2500/3757] lr: 6.9760e-05 eta: 19:20:39 time: 0.5519 data_time: 0.0157 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3315 loss: 2.3315 2022/07/30 14:13:27 - mmengine - INFO - Epoch(train) [2][2600/3757] lr: 7.0716e-05 eta: 19:16:47 time: 0.5523 data_time: 0.0150 memory: 33632 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.2243 loss: 2.2243 2022/07/30 14:14:23 - mmengine - INFO - Epoch(train) [2][2700/3757] lr: 7.1672e-05 eta: 19:13:05 time: 0.5587 data_time: 0.0150 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0265 loss: 2.0265 2022/07/30 14:15:19 - mmengine - INFO - Epoch(train) [2][2800/3757] lr: 7.2628e-05 eta: 19:09:25 time: 0.5563 data_time: 0.0137 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1413 loss: 2.1413 2022/07/30 14:16:15 - mmengine - INFO - Epoch(train) [2][2900/3757] lr: 7.3583e-05 eta: 19:05:56 time: 0.5638 data_time: 0.0148 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3996 loss: 2.3996 2022/07/30 14:17:10 - mmengine - INFO - Epoch(train) [2][3000/3757] lr: 7.4539e-05 eta: 19:02:27 time: 0.5513 data_time: 0.0151 memory: 33632 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 2.2975 loss: 2.2975 2022/07/30 14:18:06 - mmengine - INFO - Epoch(train) [2][3100/3757] lr: 7.5495e-05 eta: 18:59:02 time: 0.5499 data_time: 0.0149 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1844 loss: 2.1844 2022/07/30 14:19:01 - mmengine - INFO - Epoch(train) [2][3200/3757] lr: 7.6450e-05 eta: 18:55:42 time: 0.5670 data_time: 0.0161 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.4654 loss: 2.4654 2022/07/30 14:19:25 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 14:19:57 - mmengine - INFO - Epoch(train) [2][3300/3757] lr: 7.7406e-05 eta: 18:52:25 time: 0.5622 data_time: 0.0141 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 2.3503 loss: 2.3503 2022/07/30 14:20:52 - mmengine - INFO - Epoch(train) [2][3400/3757] lr: 7.8362e-05 eta: 18:49:10 time: 0.5505 data_time: 0.0143 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0254 loss: 2.0254 2022/07/30 14:21:48 - mmengine - INFO - Epoch(train) [2][3500/3757] lr: 7.9317e-05 eta: 18:46:05 time: 0.5499 data_time: 0.0140 memory: 33632 top1_acc: 0.1250 top5_acc: 0.5000 loss_cls: 2.4235 loss: 2.4235 2022/07/30 14:22:44 - mmengine - INFO - Epoch(train) [2][3600/3757] lr: 8.0273e-05 eta: 18:42:57 time: 0.5547 data_time: 0.0144 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.4453 loss: 2.4453 2022/07/30 14:23:40 - mmengine - INFO - Epoch(train) [2][3700/3757] lr: 8.1229e-05 eta: 18:39:58 time: 0.5651 data_time: 0.0145 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.3223 loss: 2.3223 2022/07/30 14:24:11 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 14:24:11 - mmengine - INFO - Epoch(train) [2][3757/3757] lr: 8.1773e-05 eta: 18:38:45 time: 0.5479 data_time: 0.0151 memory: 33632 top1_acc: 0.2857 top5_acc: 0.4286 loss_cls: 2.6765 loss: 2.6765 2022/07/30 14:25:09 - mmengine - INFO - Epoch(train) [3][100/3757] lr: 8.2050e-05 eta: 18:33:38 time: 0.5539 data_time: 0.0135 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1911 loss: 2.1911 2022/07/30 14:26:04 - mmengine - INFO - Epoch(train) [3][200/3757] lr: 8.2998e-05 eta: 18:30:45 time: 0.5527 data_time: 0.0149 memory: 33632 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.0296 loss: 2.0296 2022/07/30 14:27:00 - mmengine - INFO - Epoch(train) [3][300/3757] lr: 8.3946e-05 eta: 18:27:51 time: 0.5495 data_time: 0.0139 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.1261 loss: 2.1261 2022/07/30 14:27:56 - mmengine - INFO - Epoch(train) [3][400/3757] lr: 8.4894e-05 eta: 18:25:07 time: 0.5660 data_time: 0.0164 memory: 33632 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.0919 loss: 2.0919 2022/07/30 14:28:43 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 14:28:51 - mmengine - INFO - Epoch(train) [3][500/3757] lr: 8.5841e-05 eta: 18:22:24 time: 0.5613 data_time: 0.0150 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9739 loss: 1.9739 2022/07/30 14:29:47 - mmengine - INFO - Epoch(train) [3][600/3757] lr: 8.6789e-05 eta: 18:19:44 time: 0.5557 data_time: 0.0152 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2083 loss: 2.2083 2022/07/30 14:30:43 - mmengine - INFO - Epoch(train) [3][700/3757] lr: 8.7737e-05 eta: 18:17:09 time: 0.5574 data_time: 0.0146 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9176 loss: 1.9176 2022/07/30 14:31:39 - mmengine - INFO - Epoch(train) [3][800/3757] lr: 8.8685e-05 eta: 18:14:36 time: 0.5558 data_time: 0.0153 memory: 33632 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.0054 loss: 2.0054 2022/07/30 14:32:34 - mmengine - INFO - Epoch(train) [3][900/3757] lr: 8.9633e-05 eta: 18:12:04 time: 0.5607 data_time: 0.0141 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0774 loss: 2.0774 2022/07/30 14:33:30 - mmengine - INFO - Epoch(train) [3][1000/3757] lr: 9.0581e-05 eta: 18:09:32 time: 0.5542 data_time: 0.0160 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.5535 loss: 2.5535 2022/07/30 14:34:25 - mmengine - INFO - Epoch(train) [3][1100/3757] lr: 9.1528e-05 eta: 18:07:04 time: 0.5534 data_time: 0.0150 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.1809 loss: 2.1809 2022/07/30 14:35:21 - mmengine - INFO - Epoch(train) [3][1200/3757] lr: 9.2476e-05 eta: 18:04:39 time: 0.5606 data_time: 0.0155 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.2321 loss: 2.2321 2022/07/30 14:36:17 - mmengine - INFO - Epoch(train) [3][1300/3757] lr: 9.3424e-05 eta: 18:02:14 time: 0.5602 data_time: 0.0156 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1984 loss: 2.1984 2022/07/30 14:37:12 - mmengine - INFO - Epoch(train) [3][1400/3757] lr: 9.4372e-05 eta: 17:59:51 time: 0.5534 data_time: 0.0146 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.3322 loss: 2.3322 2022/07/30 14:38:00 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 14:38:08 - mmengine - INFO - Epoch(train) [3][1500/3757] lr: 9.5320e-05 eta: 17:57:31 time: 0.5539 data_time: 0.0161 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.1146 loss: 2.1146 2022/07/30 14:39:04 - mmengine - INFO - Epoch(train) [3][1600/3757] lr: 9.6268e-05 eta: 17:55:11 time: 0.5507 data_time: 0.0139 memory: 33632 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.3683 loss: 2.3683 2022/07/30 14:40:00 - mmengine - INFO - Epoch(train) [3][1700/3757] lr: 9.7215e-05 eta: 17:52:57 time: 0.5492 data_time: 0.0145 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8864 loss: 1.8864 2022/07/30 14:40:55 - mmengine - INFO - Epoch(train) [3][1800/3757] lr: 9.8163e-05 eta: 17:50:40 time: 0.5594 data_time: 0.0152 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 2.3297 loss: 2.3297 2022/07/30 14:41:51 - mmengine - INFO - Epoch(train) [3][1900/3757] lr: 9.8912e-05 eta: 17:48:28 time: 0.5653 data_time: 0.0153 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.4160 loss: 2.4160 2022/07/30 14:42:46 - mmengine - INFO - Epoch(train) [3][2000/3757] lr: 9.8912e-05 eta: 17:46:15 time: 0.5518 data_time: 0.0135 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1207 loss: 2.1207 2022/07/30 14:43:42 - mmengine - INFO - Epoch(train) [3][2100/3757] lr: 9.8912e-05 eta: 17:44:03 time: 0.5502 data_time: 0.0142 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.4390 loss: 2.4390 2022/07/30 14:44:38 - mmengine - INFO - Epoch(train) [3][2200/3757] lr: 9.8912e-05 eta: 17:41:57 time: 0.5594 data_time: 0.0156 memory: 33632 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 2.2007 loss: 2.2007 2022/07/30 14:45:33 - mmengine - INFO - Epoch(train) [3][2300/3757] lr: 9.8912e-05 eta: 17:39:50 time: 0.5561 data_time: 0.0144 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1342 loss: 2.1342 2022/07/30 14:46:29 - mmengine - INFO - Epoch(train) [3][2400/3757] lr: 9.8912e-05 eta: 17:37:43 time: 0.5527 data_time: 0.0146 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.1071 loss: 2.1071 2022/07/30 14:47:16 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 14:47:24 - mmengine - INFO - Epoch(train) [3][2500/3757] lr: 9.8912e-05 eta: 17:35:35 time: 0.5492 data_time: 0.0133 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.1544 loss: 2.1544 2022/07/30 14:48:20 - mmengine - INFO - Epoch(train) [3][2600/3757] lr: 9.8912e-05 eta: 17:33:32 time: 0.5507 data_time: 0.0155 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.2026 loss: 2.2026 2022/07/30 14:49:15 - mmengine - INFO - Epoch(train) [3][2700/3757] lr: 9.8912e-05 eta: 17:31:28 time: 0.5545 data_time: 0.0149 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.3187 loss: 2.3187 2022/07/30 14:50:11 - mmengine - INFO - Epoch(train) [3][2800/3757] lr: 9.8912e-05 eta: 17:29:29 time: 0.5709 data_time: 0.0166 memory: 33632 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 2.1841 loss: 2.1841 2022/07/30 14:51:07 - mmengine - INFO - Epoch(train) [3][2900/3757] lr: 9.8912e-05 eta: 17:27:31 time: 0.5506 data_time: 0.0142 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0104 loss: 2.0104 2022/07/30 14:52:02 - mmengine - INFO - Epoch(train) [3][3000/3757] lr: 9.8912e-05 eta: 17:25:32 time: 0.5502 data_time: 0.0139 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.1336 loss: 2.1336 2022/07/30 14:52:58 - mmengine - INFO - Epoch(train) [3][3100/3757] lr: 9.8912e-05 eta: 17:23:36 time: 0.5570 data_time: 0.0156 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.1240 loss: 2.1240 2022/07/30 14:53:54 - mmengine - INFO - Epoch(train) [3][3200/3757] lr: 9.8912e-05 eta: 17:21:43 time: 0.5688 data_time: 0.0150 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.3511 loss: 2.3511 2022/07/30 14:54:50 - mmengine - INFO - Epoch(train) [3][3300/3757] lr: 9.8912e-05 eta: 17:19:52 time: 0.5748 data_time: 0.0149 memory: 33632 top1_acc: 0.3750 top5_acc: 1.0000 loss_cls: 1.8790 loss: 1.8790 2022/07/30 14:55:45 - mmengine - INFO - Epoch(train) [3][3400/3757] lr: 9.8912e-05 eta: 17:17:57 time: 0.5495 data_time: 0.0136 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.7414 loss: 1.7414 2022/07/30 14:56:33 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 14:56:41 - mmengine - INFO - Epoch(train) [3][3500/3757] lr: 9.8912e-05 eta: 17:16:06 time: 0.5511 data_time: 0.0174 memory: 33632 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.2214 loss: 2.2214 2022/07/30 14:57:37 - mmengine - INFO - Epoch(train) [3][3600/3757] lr: 9.8912e-05 eta: 17:14:16 time: 0.5662 data_time: 0.0144 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.4336 loss: 2.4336 2022/07/30 14:58:33 - mmengine - INFO - Epoch(train) [3][3700/3757] lr: 9.8912e-05 eta: 17:12:26 time: 0.5685 data_time: 0.0162 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.1138 loss: 2.1138 2022/07/30 14:59:04 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 14:59:04 - mmengine - INFO - Epoch(train) [3][3757/3757] lr: 9.8912e-05 eta: 17:11:43 time: 0.5403 data_time: 0.0141 memory: 33632 top1_acc: 0.2857 top5_acc: 0.8571 loss_cls: 1.8647 loss: 1.8647 2022/07/30 14:59:04 - mmengine - INFO - Saving checkpoint at 3 epochs 2022/07/30 15:00:38 - mmengine - INFO - Epoch(val) [3][100/310] eta: 0:00:46 time: 0.2211 data_time: 0.0204 memory: 6325 2022/07/30 15:01:00 - mmengine - INFO - Epoch(val) [3][200/310] eta: 0:00:23 time: 0.2164 data_time: 0.0154 memory: 6325 2022/07/30 15:01:22 - mmengine - INFO - Epoch(val) [3][300/310] eta: 0:00:02 time: 0.2156 data_time: 0.0155 memory: 6325 2022/07/30 15:01:25 - mmengine - INFO - Epoch(val) [3][310/310] acc/top1: 0.6199 acc/top5: 0.8466 acc/mean1: 0.6196 2022/07/30 15:01:28 - mmengine - INFO - The best checkpoint with 0.6199 acc/top1 at 4 epoch is saved to best_acc/top1_epoch_4.pth. 2022/07/30 15:02:24 - mmengine - INFO - Epoch(train) [4][100/3757] lr: 9.7558e-05 eta: 17:08:18 time: 0.5493 data_time: 0.0138 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.0011 loss: 2.0011 2022/07/30 15:03:20 - mmengine - INFO - Epoch(train) [4][200/3757] lr: 9.7558e-05 eta: 17:06:30 time: 0.5652 data_time: 0.0156 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.2976 loss: 2.2976 2022/07/30 15:04:15 - mmengine - INFO - Epoch(train) [4][300/3757] lr: 9.7558e-05 eta: 17:04:44 time: 0.5511 data_time: 0.0141 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.9876 loss: 1.9876 2022/07/30 15:05:11 - mmengine - INFO - Epoch(train) [4][400/3757] lr: 9.7558e-05 eta: 17:02:58 time: 0.5559 data_time: 0.0138 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.7520 loss: 1.7520 2022/07/30 15:06:06 - mmengine - INFO - Epoch(train) [4][500/3757] lr: 9.7558e-05 eta: 17:01:12 time: 0.5603 data_time: 0.0144 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8504 loss: 1.8504 2022/07/30 15:07:02 - mmengine - INFO - Epoch(train) [4][600/3757] lr: 9.7558e-05 eta: 16:59:27 time: 0.5514 data_time: 0.0134 memory: 33632 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.0305 loss: 2.0305 2022/07/30 15:07:57 - mmengine - INFO - Epoch(train) [4][700/3757] lr: 9.7558e-05 eta: 16:57:40 time: 0.5512 data_time: 0.0141 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8144 loss: 1.8144 2022/07/30 15:08:13 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 15:08:53 - mmengine - INFO - Epoch(train) [4][800/3757] lr: 9.7558e-05 eta: 16:55:59 time: 0.5504 data_time: 0.0130 memory: 33632 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.0618 loss: 2.0618 2022/07/30 15:09:48 - mmengine - INFO - Epoch(train) [4][900/3757] lr: 9.7558e-05 eta: 16:54:16 time: 0.5528 data_time: 0.0144 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.9306 loss: 1.9306 2022/07/30 15:10:44 - mmengine - INFO - Epoch(train) [4][1000/3757] lr: 9.7558e-05 eta: 16:52:34 time: 0.5533 data_time: 0.0153 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.2045 loss: 2.2045 2022/07/30 15:11:39 - mmengine - INFO - Epoch(train) [4][1100/3757] lr: 9.7558e-05 eta: 16:50:53 time: 0.5518 data_time: 0.0148 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0535 loss: 2.0535 2022/07/30 15:12:35 - mmengine - INFO - Epoch(train) [4][1200/3757] lr: 9.7558e-05 eta: 16:49:15 time: 0.5531 data_time: 0.0157 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9790 loss: 1.9790 2022/07/30 15:13:30 - mmengine - INFO - Epoch(train) [4][1300/3757] lr: 9.7558e-05 eta: 16:47:36 time: 0.5515 data_time: 0.0156 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9461 loss: 1.9461 2022/07/30 15:14:26 - mmengine - INFO - Epoch(train) [4][1400/3757] lr: 9.7558e-05 eta: 16:45:58 time: 0.5632 data_time: 0.0135 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0448 loss: 2.0448 2022/07/30 15:15:22 - mmengine - INFO - Epoch(train) [4][1500/3757] lr: 9.7558e-05 eta: 16:44:21 time: 0.5506 data_time: 0.0151 memory: 33632 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.1254 loss: 2.1254 2022/07/30 15:16:17 - mmengine - INFO - Epoch(train) [4][1600/3757] lr: 9.7558e-05 eta: 16:42:44 time: 0.5507 data_time: 0.0147 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.1206 loss: 2.1206 2022/07/30 15:17:13 - mmengine - INFO - Epoch(train) [4][1700/3757] lr: 9.7558e-05 eta: 16:41:07 time: 0.5533 data_time: 0.0149 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.7769 loss: 1.7769 2022/07/30 15:17:29 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 15:18:08 - mmengine - INFO - Epoch(train) [4][1800/3757] lr: 9.7558e-05 eta: 16:39:29 time: 0.5607 data_time: 0.0147 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.1672 loss: 2.1672 2022/07/30 15:19:04 - mmengine - INFO - Epoch(train) [4][1900/3757] lr: 9.7558e-05 eta: 16:37:54 time: 0.5543 data_time: 0.0164 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8849 loss: 1.8849 2022/07/30 15:19:59 - mmengine - INFO - Epoch(train) [4][2000/3757] lr: 9.7558e-05 eta: 16:36:21 time: 0.5499 data_time: 0.0157 memory: 33632 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 2.0749 loss: 2.0749 2022/07/30 15:20:55 - mmengine - INFO - Epoch(train) [4][2100/3757] lr: 9.7558e-05 eta: 16:34:48 time: 0.5559 data_time: 0.0169 memory: 33632 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 1.8044 loss: 1.8044 2022/07/30 15:21:51 - mmengine - INFO - Epoch(train) [4][2200/3757] lr: 9.7558e-05 eta: 16:33:15 time: 0.5614 data_time: 0.0174 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.0422 loss: 2.0422 2022/07/30 15:22:46 - mmengine - INFO - Epoch(train) [4][2300/3757] lr: 9.7558e-05 eta: 16:31:42 time: 0.5524 data_time: 0.0154 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1354 loss: 2.1354 2022/07/30 15:23:42 - mmengine - INFO - Epoch(train) [4][2400/3757] lr: 9.7558e-05 eta: 16:30:12 time: 0.5507 data_time: 0.0165 memory: 33632 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 2.2841 loss: 2.2841 2022/07/30 15:24:38 - mmengine - INFO - Epoch(train) [4][2500/3757] lr: 9.7558e-05 eta: 16:28:40 time: 0.5565 data_time: 0.0166 memory: 33632 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.7896 loss: 1.7896 2022/07/30 15:25:33 - mmengine - INFO - Epoch(train) [4][2600/3757] lr: 9.7558e-05 eta: 16:27:10 time: 0.5662 data_time: 0.0158 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9525 loss: 1.9525 2022/07/30 15:26:29 - mmengine - INFO - Epoch(train) [4][2700/3757] lr: 9.7558e-05 eta: 16:25:38 time: 0.5541 data_time: 0.0157 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0427 loss: 2.0427 2022/07/30 15:26:48 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 15:27:27 - mmengine - INFO - Epoch(train) [4][2800/3757] lr: 9.7558e-05 eta: 16:24:27 time: 0.5570 data_time: 0.0159 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8498 loss: 1.8498 2022/07/30 15:28:23 - mmengine - INFO - Epoch(train) [4][2900/3757] lr: 9.7558e-05 eta: 16:22:59 time: 0.5509 data_time: 0.0161 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0738 loss: 2.0738 2022/07/30 15:29:19 - mmengine - INFO - Epoch(train) [4][3000/3757] lr: 9.7558e-05 eta: 16:21:30 time: 0.5535 data_time: 0.0158 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.2187 loss: 2.2187 2022/07/30 15:30:14 - mmengine - INFO - Epoch(train) [4][3100/3757] lr: 9.7558e-05 eta: 16:20:01 time: 0.5518 data_time: 0.0162 memory: 33632 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 2.1434 loss: 2.1434 2022/07/30 15:31:10 - mmengine - INFO - Epoch(train) [4][3200/3757] lr: 9.7558e-05 eta: 16:18:33 time: 0.5551 data_time: 0.0154 memory: 33632 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.1055 loss: 2.1055 2022/07/30 15:32:06 - mmengine - INFO - Epoch(train) [4][3300/3757] lr: 9.7558e-05 eta: 16:17:06 time: 0.5619 data_time: 0.0162 memory: 33632 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 2.3139 loss: 2.3139 2022/07/30 15:33:02 - mmengine - INFO - Epoch(train) [4][3400/3757] lr: 9.7558e-05 eta: 16:15:41 time: 0.5614 data_time: 0.0156 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7602 loss: 1.7602 2022/07/30 15:33:57 - mmengine - INFO - Epoch(train) [4][3500/3757] lr: 9.7558e-05 eta: 16:14:13 time: 0.5491 data_time: 0.0158 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.9638 loss: 1.9638 2022/07/30 15:34:53 - mmengine - INFO - Epoch(train) [4][3600/3757] lr: 9.7558e-05 eta: 16:12:46 time: 0.5513 data_time: 0.0166 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9693 loss: 1.9693 2022/07/30 15:35:48 - mmengine - INFO - Epoch(train) [4][3700/3757] lr: 9.7558e-05 eta: 16:11:20 time: 0.5535 data_time: 0.0171 memory: 33632 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 2.0738 loss: 2.0738 2022/07/30 15:36:04 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 15:36:20 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 15:36:20 - mmengine - INFO - Epoch(train) [4][3757/3757] lr: 9.7558e-05 eta: 16:10:47 time: 0.5529 data_time: 0.0151 memory: 33632 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 1.8309 loss: 1.8309 2022/07/30 15:37:17 - mmengine - INFO - Epoch(train) [5][100/3757] lr: 9.5682e-05 eta: 16:08:14 time: 0.5524 data_time: 0.0162 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6081 loss: 1.6081 2022/07/30 15:38:13 - mmengine - INFO - Epoch(train) [5][200/3757] lr: 9.5682e-05 eta: 16:06:50 time: 0.5665 data_time: 0.0169 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.1062 loss: 2.1062 2022/07/30 15:39:09 - mmengine - INFO - Epoch(train) [5][300/3757] lr: 9.5682e-05 eta: 16:05:27 time: 0.5525 data_time: 0.0167 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9514 loss: 1.9514 2022/07/30 15:40:05 - mmengine - INFO - Epoch(train) [5][400/3757] lr: 9.5682e-05 eta: 16:04:05 time: 0.5516 data_time: 0.0168 memory: 33632 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 2.0183 loss: 2.0183 2022/07/30 15:41:00 - mmengine - INFO - Epoch(train) [5][500/3757] lr: 9.5682e-05 eta: 16:02:42 time: 0.5551 data_time: 0.0153 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 2.0062 loss: 2.0062 2022/07/30 15:41:56 - mmengine - INFO - Epoch(train) [5][600/3757] lr: 9.5682e-05 eta: 16:01:19 time: 0.5523 data_time: 0.0166 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7082 loss: 1.7082 2022/07/30 15:42:51 - mmengine - INFO - Epoch(train) [5][700/3757] lr: 9.5682e-05 eta: 15:59:55 time: 0.5559 data_time: 0.0172 memory: 33632 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 2.0524 loss: 2.0524 2022/07/30 15:43:47 - mmengine - INFO - Epoch(train) [5][800/3757] lr: 9.5682e-05 eta: 15:58:33 time: 0.5579 data_time: 0.0168 memory: 33632 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.7250 loss: 1.7250 2022/07/30 15:44:43 - mmengine - INFO - Epoch(train) [5][900/3757] lr: 9.5682e-05 eta: 15:57:13 time: 0.5610 data_time: 0.0165 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9274 loss: 1.9274 2022/07/30 15:45:23 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 15:45:38 - mmengine - INFO - Epoch(train) [5][1000/3757] lr: 9.5682e-05 eta: 15:55:49 time: 0.5500 data_time: 0.0154 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 2.1237 loss: 2.1237 2022/07/30 15:46:34 - mmengine - INFO - Epoch(train) [5][1100/3757] lr: 9.5682e-05 eta: 15:54:28 time: 0.5578 data_time: 0.0176 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7601 loss: 1.7601 2022/07/30 15:47:30 - mmengine - INFO - Epoch(train) [5][1200/3757] lr: 9.5682e-05 eta: 15:53:07 time: 0.5597 data_time: 0.0159 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7751 loss: 1.7751 2022/07/30 15:48:26 - mmengine - INFO - Epoch(train) [5][1300/3757] lr: 9.5682e-05 eta: 15:51:47 time: 0.5617 data_time: 0.0160 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.6756 loss: 1.6756 2022/07/30 15:49:21 - mmengine - INFO - Epoch(train) [5][1400/3757] lr: 9.5682e-05 eta: 15:50:26 time: 0.5507 data_time: 0.0156 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9699 loss: 1.9699 2022/07/30 15:50:17 - mmengine - INFO - Epoch(train) [5][1500/3757] lr: 9.5682e-05 eta: 15:49:07 time: 0.5521 data_time: 0.0150 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8524 loss: 1.8524 2022/07/30 15:51:13 - mmengine - INFO - Epoch(train) [5][1600/3757] lr: 9.5682e-05 eta: 15:47:47 time: 0.5613 data_time: 0.0166 memory: 33632 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 2.0093 loss: 2.0093 2022/07/30 15:52:08 - mmengine - INFO - Epoch(train) [5][1700/3757] lr: 9.5682e-05 eta: 15:46:27 time: 0.5653 data_time: 0.0166 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5623 loss: 1.5623 2022/07/30 15:53:04 - mmengine - INFO - Epoch(train) [5][1800/3757] lr: 9.5682e-05 eta: 15:45:08 time: 0.5529 data_time: 0.0171 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8227 loss: 1.8227 2022/07/30 15:54:00 - mmengine - INFO - Epoch(train) [5][1900/3757] lr: 9.5682e-05 eta: 15:43:50 time: 0.5501 data_time: 0.0152 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.6457 loss: 1.6457 2022/07/30 15:54:40 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 15:54:56 - mmengine - INFO - Epoch(train) [5][2000/3757] lr: 9.5682e-05 eta: 15:42:33 time: 0.5507 data_time: 0.0157 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7730 loss: 1.7730 2022/07/30 15:55:51 - mmengine - INFO - Epoch(train) [5][2100/3757] lr: 9.5682e-05 eta: 15:41:15 time: 0.5641 data_time: 0.0168 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7457 loss: 1.7457 2022/07/30 15:56:47 - mmengine - INFO - Epoch(train) [5][2200/3757] lr: 9.5682e-05 eta: 15:39:57 time: 0.5583 data_time: 0.0169 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.9765 loss: 1.9765 2022/07/30 15:57:42 - mmengine - INFO - Epoch(train) [5][2300/3757] lr: 9.5682e-05 eta: 15:38:38 time: 0.5520 data_time: 0.0161 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.8885 loss: 1.8885 2022/07/30 15:58:38 - mmengine - INFO - Epoch(train) [5][2400/3757] lr: 9.5682e-05 eta: 15:37:21 time: 0.5504 data_time: 0.0167 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.8450 loss: 1.8450 2022/07/30 15:59:34 - mmengine - INFO - Epoch(train) [5][2500/3757] lr: 9.5682e-05 eta: 15:36:04 time: 0.5579 data_time: 0.0167 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8696 loss: 1.8696 2022/07/30 16:00:29 - mmengine - INFO - Epoch(train) [5][2600/3757] lr: 9.5682e-05 eta: 15:34:46 time: 0.5588 data_time: 0.0164 memory: 33632 top1_acc: 0.3750 top5_acc: 1.0000 loss_cls: 2.1616 loss: 2.1616 2022/07/30 16:01:25 - mmengine - INFO - Epoch(train) [5][2700/3757] lr: 9.5682e-05 eta: 15:33:29 time: 0.5619 data_time: 0.0179 memory: 33632 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 1.7299 loss: 1.7299 2022/07/30 16:02:21 - mmengine - INFO - Epoch(train) [5][2800/3757] lr: 9.5682e-05 eta: 15:32:12 time: 0.5511 data_time: 0.0162 memory: 33632 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 1.8013 loss: 1.8013 2022/07/30 16:03:16 - mmengine - INFO - Epoch(train) [5][2900/3757] lr: 9.5682e-05 eta: 15:30:56 time: 0.5545 data_time: 0.0159 memory: 33632 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 1.5892 loss: 1.5892 2022/07/30 16:03:56 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 16:04:12 - mmengine - INFO - Epoch(train) [5][3000/3757] lr: 9.5682e-05 eta: 15:29:40 time: 0.5715 data_time: 0.0153 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0530 loss: 2.0530 2022/07/30 16:05:08 - mmengine - INFO - Epoch(train) [5][3100/3757] lr: 9.5682e-05 eta: 15:28:27 time: 0.5636 data_time: 0.0167 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 2.1308 loss: 2.1308 2022/07/30 16:06:04 - mmengine - INFO - Epoch(train) [5][3200/3757] lr: 9.5682e-05 eta: 15:27:11 time: 0.5518 data_time: 0.0168 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7116 loss: 1.7116 2022/07/30 16:06:59 - mmengine - INFO - Epoch(train) [5][3300/3757] lr: 9.5682e-05 eta: 15:25:55 time: 0.5508 data_time: 0.0157 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8370 loss: 1.8370 2022/07/30 16:07:55 - mmengine - INFO - Epoch(train) [5][3400/3757] lr: 9.5682e-05 eta: 15:24:40 time: 0.5611 data_time: 0.0173 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.7047 loss: 1.7047 2022/07/30 16:08:51 - mmengine - INFO - Epoch(train) [5][3500/3757] lr: 9.5682e-05 eta: 15:23:24 time: 0.5572 data_time: 0.0166 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 2.0128 loss: 2.0128 2022/07/30 16:09:46 - mmengine - INFO - Epoch(train) [5][3600/3757] lr: 9.5682e-05 eta: 15:22:10 time: 0.5555 data_time: 0.0162 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9141 loss: 1.9141 2022/07/30 16:10:42 - mmengine - INFO - Epoch(train) [5][3700/3757] lr: 9.5682e-05 eta: 15:20:54 time: 0.5512 data_time: 0.0163 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.2577 loss: 2.2577 2022/07/30 16:11:13 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 16:11:13 - mmengine - INFO - Epoch(train) [5][3757/3757] lr: 9.5682e-05 eta: 15:20:24 time: 0.5454 data_time: 0.0148 memory: 33632 top1_acc: 0.4286 top5_acc: 0.8571 loss_cls: 1.9368 loss: 1.9368 2022/07/30 16:12:11 - mmengine - INFO - Epoch(train) [6][100/3757] lr: 9.3306e-05 eta: 15:18:20 time: 0.5511 data_time: 0.0158 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.8800 loss: 1.8800 2022/07/30 16:13:07 - mmengine - INFO - Epoch(train) [6][200/3757] lr: 9.3306e-05 eta: 15:17:08 time: 0.5653 data_time: 0.0162 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.7876 loss: 1.7876 2022/07/30 16:13:16 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 16:14:03 - mmengine - INFO - Epoch(train) [6][300/3757] lr: 9.3306e-05 eta: 15:15:54 time: 0.5511 data_time: 0.0169 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9468 loss: 1.9468 2022/07/30 16:14:59 - mmengine - INFO - Epoch(train) [6][400/3757] lr: 9.3306e-05 eta: 15:14:41 time: 0.5520 data_time: 0.0165 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.7535 loss: 1.7535 2022/07/30 16:15:54 - mmengine - INFO - Epoch(train) [6][500/3757] lr: 9.3306e-05 eta: 15:13:29 time: 0.5581 data_time: 0.0161 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.9308 loss: 1.9308 2022/07/30 16:16:50 - mmengine - INFO - Epoch(train) [6][600/3757] lr: 9.3306e-05 eta: 15:12:17 time: 0.5599 data_time: 0.0162 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6489 loss: 1.6489 2022/07/30 16:17:46 - mmengine - INFO - Epoch(train) [6][700/3757] lr: 9.3306e-05 eta: 15:11:04 time: 0.5637 data_time: 0.0163 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4780 loss: 1.4780 2022/07/30 16:18:42 - mmengine - INFO - Epoch(train) [6][800/3757] lr: 9.3306e-05 eta: 15:09:53 time: 0.5519 data_time: 0.0167 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.9971 loss: 1.9971 2022/07/30 16:19:38 - mmengine - INFO - Epoch(train) [6][900/3757] lr: 9.3306e-05 eta: 15:08:41 time: 0.5509 data_time: 0.0152 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.7900 loss: 1.7900 2022/07/30 16:20:33 - mmengine - INFO - Epoch(train) [6][1000/3757] lr: 9.3306e-05 eta: 15:07:28 time: 0.5516 data_time: 0.0168 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.6470 loss: 1.6470 2022/07/30 16:21:29 - mmengine - INFO - Epoch(train) [6][1100/3757] lr: 9.3306e-05 eta: 15:06:16 time: 0.5535 data_time: 0.0166 memory: 33632 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.6833 loss: 1.6833 2022/07/30 16:22:25 - mmengine - INFO - Epoch(train) [6][1200/3757] lr: 9.3306e-05 eta: 15:05:04 time: 0.5515 data_time: 0.0171 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.6777 loss: 1.6777 2022/07/30 16:22:33 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 16:23:21 - mmengine - INFO - Epoch(train) [6][1300/3757] lr: 9.3306e-05 eta: 15:03:53 time: 0.5530 data_time: 0.0177 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5693 loss: 1.5693 2022/07/30 16:24:17 - mmengine - INFO - Epoch(train) [6][1400/3757] lr: 9.3306e-05 eta: 15:02:42 time: 0.5552 data_time: 0.0162 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.0160 loss: 2.0160 2022/07/30 16:25:12 - mmengine - INFO - Epoch(train) [6][1500/3757] lr: 9.3306e-05 eta: 15:01:30 time: 0.5562 data_time: 0.0161 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.9483 loss: 1.9483 2022/07/30 16:26:08 - mmengine - INFO - Epoch(train) [6][1600/3757] lr: 9.3306e-05 eta: 15:00:19 time: 0.5586 data_time: 0.0162 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.8495 loss: 1.8495 2022/07/30 16:27:04 - mmengine - INFO - Epoch(train) [6][1700/3757] lr: 9.3306e-05 eta: 14:59:07 time: 0.5569 data_time: 0.0160 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.9791 loss: 1.9791 2022/07/30 16:27:59 - mmengine - INFO - Epoch(train) [6][1800/3757] lr: 9.3306e-05 eta: 14:57:56 time: 0.5549 data_time: 0.0168 memory: 33632 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.6747 loss: 1.6747 2022/07/30 16:28:55 - mmengine - INFO - Epoch(train) [6][1900/3757] lr: 9.3306e-05 eta: 14:56:45 time: 0.5627 data_time: 0.0165 memory: 33632 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.7188 loss: 1.7188 2022/07/30 16:29:51 - mmengine - INFO - Epoch(train) [6][2000/3757] lr: 9.3306e-05 eta: 14:55:34 time: 0.5626 data_time: 0.0166 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8276 loss: 1.8276 2022/07/30 16:30:46 - mmengine - INFO - Epoch(train) [6][2100/3757] lr: 9.3306e-05 eta: 14:54:23 time: 0.5584 data_time: 0.0166 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.4279 loss: 1.4279 2022/07/30 16:31:41 - mmengine - INFO - Epoch(train) [6][2200/3757] lr: 9.3306e-05 eta: 14:53:11 time: 0.5528 data_time: 0.0158 memory: 33632 top1_acc: 0.1250 top5_acc: 0.3750 loss_cls: 1.7894 loss: 1.7894 2022/07/30 16:31:50 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 16:32:37 - mmengine - INFO - Epoch(train) [6][2300/3757] lr: 9.3306e-05 eta: 14:51:59 time: 0.5567 data_time: 0.0164 memory: 33632 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.8261 loss: 1.8261 2022/07/30 16:33:32 - mmengine - INFO - Epoch(train) [6][2400/3757] lr: 9.3306e-05 eta: 14:50:48 time: 0.5546 data_time: 0.0158 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 2.0647 loss: 2.0647 2022/07/30 16:34:28 - mmengine - INFO - Epoch(train) [6][2500/3757] lr: 9.3306e-05 eta: 14:49:39 time: 0.5591 data_time: 0.0164 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.8028 loss: 1.8028 2022/07/30 16:35:24 - mmengine - INFO - Epoch(train) [6][2600/3757] lr: 9.3306e-05 eta: 14:48:28 time: 0.5531 data_time: 0.0171 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8928 loss: 1.8928 2022/07/30 16:36:19 - mmengine - INFO - Epoch(train) [6][2700/3757] lr: 9.3306e-05 eta: 14:47:18 time: 0.5522 data_time: 0.0166 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.7166 loss: 1.7166 2022/07/30 16:37:15 - mmengine - INFO - Epoch(train) [6][2800/3757] lr: 9.3306e-05 eta: 14:46:08 time: 0.5491 data_time: 0.0153 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.7061 loss: 1.7061 2022/07/30 16:38:11 - mmengine - INFO - Epoch(train) [6][2900/3757] lr: 9.3306e-05 eta: 14:44:59 time: 0.5578 data_time: 0.0163 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.8016 loss: 1.8016 2022/07/30 16:39:06 - mmengine - INFO - Epoch(train) [6][3000/3757] lr: 9.3306e-05 eta: 14:43:49 time: 0.5552 data_time: 0.0166 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 2.0685 loss: 2.0685 2022/07/30 16:40:02 - mmengine - INFO - Epoch(train) [6][3100/3757] lr: 9.3306e-05 eta: 14:42:40 time: 0.5540 data_time: 0.0177 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7558 loss: 1.7558 2022/07/30 16:40:58 - mmengine - INFO - Epoch(train) [6][3200/3757] lr: 9.3306e-05 eta: 14:41:30 time: 0.5573 data_time: 0.0176 memory: 33632 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 2.0164 loss: 2.0164 2022/07/30 16:41:06 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 16:41:53 - mmengine - INFO - Epoch(train) [6][3300/3757] lr: 9.3306e-05 eta: 14:40:21 time: 0.5515 data_time: 0.0148 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.9565 loss: 1.9565 2022/07/30 16:42:49 - mmengine - INFO - Epoch(train) [6][3400/3757] lr: 9.3306e-05 eta: 14:39:11 time: 0.5573 data_time: 0.0154 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.9557 loss: 1.9557 2022/07/30 16:43:44 - mmengine - INFO - Epoch(train) [6][3500/3757] lr: 9.3306e-05 eta: 14:38:02 time: 0.5513 data_time: 0.0165 memory: 33632 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.6888 loss: 1.6888 2022/07/30 16:44:40 - mmengine - INFO - Epoch(train) [6][3600/3757] lr: 9.3306e-05 eta: 14:36:54 time: 0.5660 data_time: 0.0167 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7490 loss: 1.7490 2022/07/30 16:45:36 - mmengine - INFO - Epoch(train) [6][3700/3757] lr: 9.3306e-05 eta: 14:35:45 time: 0.5516 data_time: 0.0166 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.8537 loss: 1.8537 2022/07/30 16:46:07 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 16:46:07 - mmengine - INFO - Epoch(train) [6][3757/3757] lr: 9.3306e-05 eta: 14:35:18 time: 0.5462 data_time: 0.0158 memory: 33632 top1_acc: 0.5714 top5_acc: 0.5714 loss_cls: 1.7169 loss: 1.7169 2022/07/30 16:46:07 - mmengine - INFO - Saving checkpoint at 6 epochs 2022/07/30 16:46:35 - mmengine - INFO - Epoch(val) [6][100/310] eta: 0:00:47 time: 0.2242 data_time: 0.0191 memory: 6325 2022/07/30 16:46:57 - mmengine - INFO - Epoch(val) [6][200/310] eta: 0:00:24 time: 0.2220 data_time: 0.0216 memory: 6325 2022/07/30 16:47:19 - mmengine - INFO - Epoch(val) [6][300/310] eta: 0:00:01 time: 0.1992 data_time: 0.0104 memory: 6325 2022/07/30 16:47:22 - mmengine - INFO - Epoch(val) [6][310/310] acc/top1: 0.6595 acc/top5: 0.8699 acc/mean1: 0.6594 2022/07/30 16:47:22 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb/best_acc/top1_epoch_4.pth is removed 2022/07/30 16:47:24 - mmengine - INFO - The best checkpoint with 0.6595 acc/top1 at 7 epoch is saved to best_acc/top1_epoch_7.pth. 2022/07/30 16:48:21 - mmengine - INFO - Epoch(train) [7][100/3757] lr: 9.0455e-05 eta: 14:33:26 time: 0.5549 data_time: 0.0151 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 2.0722 loss: 2.0722 2022/07/30 16:49:16 - mmengine - INFO - Epoch(train) [7][200/3757] lr: 9.0455e-05 eta: 14:32:16 time: 0.5504 data_time: 0.0147 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6991 loss: 1.6991 2022/07/30 16:50:12 - mmengine - INFO - Epoch(train) [7][300/3757] lr: 9.0455e-05 eta: 14:31:07 time: 0.5520 data_time: 0.0144 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.8535 loss: 1.8535 2022/07/30 16:51:07 - mmengine - INFO - Epoch(train) [7][400/3757] lr: 9.0455e-05 eta: 14:29:58 time: 0.5512 data_time: 0.0156 memory: 33632 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.9674 loss: 1.9674 2022/07/30 16:51:39 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 16:52:03 - mmengine - INFO - Epoch(train) [7][500/3757] lr: 9.0455e-05 eta: 14:28:50 time: 0.5528 data_time: 0.0150 memory: 33632 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 1.9019 loss: 1.9019 2022/07/30 16:52:58 - mmengine - INFO - Epoch(train) [7][600/3757] lr: 9.0455e-05 eta: 14:27:41 time: 0.5517 data_time: 0.0142 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 2.0526 loss: 2.0526 2022/07/30 16:53:54 - mmengine - INFO - Epoch(train) [7][700/3757] lr: 9.0455e-05 eta: 14:26:34 time: 0.5511 data_time: 0.0149 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.9943 loss: 1.9943 2022/07/30 16:54:50 - mmengine - INFO - Epoch(train) [7][800/3757] lr: 9.0455e-05 eta: 14:25:26 time: 0.5593 data_time: 0.0160 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7346 loss: 1.7346 2022/07/30 16:55:45 - mmengine - INFO - Epoch(train) [7][900/3757] lr: 9.0455e-05 eta: 14:24:19 time: 0.5517 data_time: 0.0150 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5402 loss: 1.5402 2022/07/30 16:56:41 - mmengine - INFO - Epoch(train) [7][1000/3757] lr: 9.0455e-05 eta: 14:23:12 time: 0.5555 data_time: 0.0156 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6299 loss: 1.6299 2022/07/30 16:57:37 - mmengine - INFO - Epoch(train) [7][1100/3757] lr: 9.0455e-05 eta: 14:22:05 time: 0.5510 data_time: 0.0139 memory: 33632 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.8089 loss: 1.8089 2022/07/30 16:58:32 - mmengine - INFO - Epoch(train) [7][1200/3757] lr: 9.0455e-05 eta: 14:20:57 time: 0.5507 data_time: 0.0140 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.7796 loss: 1.7796 2022/07/30 16:59:28 - mmengine - INFO - Epoch(train) [7][1300/3757] lr: 9.0455e-05 eta: 14:19:50 time: 0.5572 data_time: 0.0144 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7987 loss: 1.7987 2022/07/30 17:00:24 - mmengine - INFO - Epoch(train) [7][1400/3757] lr: 9.0455e-05 eta: 14:18:43 time: 0.5544 data_time: 0.0152 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7923 loss: 1.7923 2022/07/30 17:00:56 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 17:01:19 - mmengine - INFO - Epoch(train) [7][1500/3757] lr: 9.0455e-05 eta: 14:17:36 time: 0.5506 data_time: 0.0151 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7398 loss: 1.7398 2022/07/30 17:02:15 - mmengine - INFO - Epoch(train) [7][1600/3757] lr: 9.0455e-05 eta: 14:16:29 time: 0.5513 data_time: 0.0150 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.9417 loss: 1.9417 2022/07/30 17:03:11 - mmengine - INFO - Epoch(train) [7][1700/3757] lr: 9.0455e-05 eta: 14:15:23 time: 0.5658 data_time: 0.0149 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.7321 loss: 1.7321 2022/07/30 17:04:06 - mmengine - INFO - Epoch(train) [7][1800/3757] lr: 9.0455e-05 eta: 14:14:17 time: 0.5638 data_time: 0.0152 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5511 loss: 1.5511 2022/07/30 17:05:03 - mmengine - INFO - Epoch(train) [7][1900/3757] lr: 9.0455e-05 eta: 14:13:13 time: 0.5690 data_time: 0.0152 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7018 loss: 1.7018 2022/07/30 17:05:58 - mmengine - INFO - Epoch(train) [7][2000/3757] lr: 9.0455e-05 eta: 14:12:06 time: 0.5517 data_time: 0.0158 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8805 loss: 1.8805 2022/07/30 17:06:54 - mmengine - INFO - Epoch(train) [7][2100/3757] lr: 9.0455e-05 eta: 14:11:00 time: 0.5507 data_time: 0.0148 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.8296 loss: 1.8296 2022/07/30 17:07:50 - mmengine - INFO - Epoch(train) [7][2200/3757] lr: 9.0455e-05 eta: 14:09:54 time: 0.5587 data_time: 0.0144 memory: 33632 top1_acc: 0.3750 top5_acc: 1.0000 loss_cls: 1.7434 loss: 1.7434 2022/07/30 17:08:46 - mmengine - INFO - Epoch(train) [7][2300/3757] lr: 9.0455e-05 eta: 14:08:49 time: 0.5748 data_time: 0.0179 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6572 loss: 1.6572 2022/07/30 17:09:41 - mmengine - INFO - Epoch(train) [7][2400/3757] lr: 9.0455e-05 eta: 14:07:43 time: 0.5660 data_time: 0.0151 memory: 33632 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.7363 loss: 1.7363 2022/07/30 17:10:14 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 17:10:37 - mmengine - INFO - Epoch(train) [7][2500/3757] lr: 9.0455e-05 eta: 14:06:36 time: 0.5504 data_time: 0.0134 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5117 loss: 1.5117 2022/07/30 17:11:32 - mmengine - INFO - Epoch(train) [7][2600/3757] lr: 9.0455e-05 eta: 14:05:30 time: 0.5516 data_time: 0.0146 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6760 loss: 1.6760 2022/07/30 17:12:28 - mmengine - INFO - Epoch(train) [7][2700/3757] lr: 9.0455e-05 eta: 14:04:24 time: 0.5530 data_time: 0.0151 memory: 33632 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.4128 loss: 1.4128 2022/07/30 17:13:24 - mmengine - INFO - Epoch(train) [7][2800/3757] lr: 9.0455e-05 eta: 14:03:18 time: 0.5599 data_time: 0.0157 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.6469 loss: 1.6469 2022/07/30 17:14:19 - mmengine - INFO - Epoch(train) [7][2900/3757] lr: 9.0455e-05 eta: 14:02:12 time: 0.5498 data_time: 0.0144 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.7155 loss: 1.7155 2022/07/30 17:15:15 - mmengine - INFO - Epoch(train) [7][3000/3757] lr: 9.0455e-05 eta: 14:01:07 time: 0.5524 data_time: 0.0142 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.8727 loss: 1.8727 2022/07/30 17:16:11 - mmengine - INFO - Epoch(train) [7][3100/3757] lr: 9.0455e-05 eta: 14:00:01 time: 0.5626 data_time: 0.0147 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5996 loss: 1.5996 2022/07/30 17:17:06 - mmengine - INFO - Epoch(train) [7][3200/3757] lr: 9.0455e-05 eta: 13:58:56 time: 0.5561 data_time: 0.0147 memory: 33632 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.5376 loss: 1.5376 2022/07/30 17:18:02 - mmengine - INFO - Epoch(train) [7][3300/3757] lr: 9.0455e-05 eta: 13:57:50 time: 0.5658 data_time: 0.0142 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.7621 loss: 1.7621 2022/07/30 17:18:58 - mmengine - INFO - Epoch(train) [7][3400/3757] lr: 9.0455e-05 eta: 13:56:45 time: 0.5511 data_time: 0.0150 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 2.2180 loss: 2.2180 2022/07/30 17:19:30 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 17:19:53 - mmengine - INFO - Epoch(train) [7][3500/3757] lr: 9.0455e-05 eta: 13:55:39 time: 0.5505 data_time: 0.0138 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.8657 loss: 1.8657 2022/07/30 17:20:49 - mmengine - INFO - Epoch(train) [7][3600/3757] lr: 9.0455e-05 eta: 13:54:34 time: 0.5522 data_time: 0.0139 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.5012 loss: 1.5012 2022/07/30 17:21:44 - mmengine - INFO - Epoch(train) [7][3700/3757] lr: 9.0455e-05 eta: 13:53:27 time: 0.5529 data_time: 0.0147 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7262 loss: 1.7262 2022/07/30 17:22:15 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 17:22:15 - mmengine - INFO - Epoch(train) [7][3757/3757] lr: 9.0455e-05 eta: 13:53:01 time: 0.5462 data_time: 0.0152 memory: 33632 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 1.4739 loss: 1.4739 2022/07/30 17:23:13 - mmengine - INFO - Epoch(train) [8][100/3757] lr: 8.7161e-05 eta: 13:51:20 time: 0.5515 data_time: 0.0156 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.7388 loss: 1.7388 2022/07/30 17:24:08 - mmengine - INFO - Epoch(train) [8][200/3757] lr: 8.7161e-05 eta: 13:50:15 time: 0.5608 data_time: 0.0173 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7163 loss: 1.7163 2022/07/30 17:25:04 - mmengine - INFO - Epoch(train) [8][300/3757] lr: 8.7161e-05 eta: 13:49:11 time: 0.5687 data_time: 0.0156 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7374 loss: 1.7374 2022/07/30 17:26:00 - mmengine - INFO - Epoch(train) [8][400/3757] lr: 8.7161e-05 eta: 13:48:07 time: 0.5538 data_time: 0.0140 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9530 loss: 1.9530 2022/07/30 17:26:56 - mmengine - INFO - Epoch(train) [8][500/3757] lr: 8.7161e-05 eta: 13:47:03 time: 0.5601 data_time: 0.0149 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9471 loss: 1.9471 2022/07/30 17:27:52 - mmengine - INFO - Epoch(train) [8][600/3757] lr: 8.7161e-05 eta: 13:45:58 time: 0.5507 data_time: 0.0150 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4034 loss: 1.4034 2022/07/30 17:28:47 - mmengine - INFO - Epoch(train) [8][700/3757] lr: 8.7161e-05 eta: 13:44:54 time: 0.5519 data_time: 0.0152 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.7036 loss: 1.7036 2022/07/30 17:28:48 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 17:29:43 - mmengine - INFO - Epoch(train) [8][800/3757] lr: 8.7161e-05 eta: 13:43:50 time: 0.5669 data_time: 0.0156 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5360 loss: 1.5360 2022/07/30 17:30:39 - mmengine - INFO - Epoch(train) [8][900/3757] lr: 8.7161e-05 eta: 13:42:45 time: 0.5511 data_time: 0.0145 memory: 33632 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.5331 loss: 1.5331 2022/07/30 17:31:34 - mmengine - INFO - Epoch(train) [8][1000/3757] lr: 8.7161e-05 eta: 13:41:41 time: 0.5606 data_time: 0.0151 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5838 loss: 1.5838 2022/07/30 17:32:30 - mmengine - INFO - Epoch(train) [8][1100/3757] lr: 8.7161e-05 eta: 13:40:36 time: 0.5519 data_time: 0.0149 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.6537 loss: 1.6537 2022/07/30 17:33:26 - mmengine - INFO - Epoch(train) [8][1200/3757] lr: 8.7161e-05 eta: 13:39:32 time: 0.5507 data_time: 0.0150 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6995 loss: 1.6995 2022/07/30 17:34:21 - mmengine - INFO - Epoch(train) [8][1300/3757] lr: 8.7161e-05 eta: 13:38:28 time: 0.5561 data_time: 0.0148 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7301 loss: 1.7301 2022/07/30 17:35:17 - mmengine - INFO - Epoch(train) [8][1400/3757] lr: 8.7161e-05 eta: 13:37:24 time: 0.5528 data_time: 0.0152 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9894 loss: 1.9894 2022/07/30 17:36:13 - mmengine - INFO - Epoch(train) [8][1500/3757] lr: 8.7161e-05 eta: 13:36:20 time: 0.5513 data_time: 0.0153 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7175 loss: 1.7175 2022/07/30 17:37:09 - mmengine - INFO - Epoch(train) [8][1600/3757] lr: 8.7161e-05 eta: 13:35:18 time: 0.5502 data_time: 0.0143 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.7687 loss: 1.7687 2022/07/30 17:38:04 - mmengine - INFO - Epoch(train) [8][1700/3757] lr: 8.7161e-05 eta: 13:34:14 time: 0.5637 data_time: 0.0158 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8178 loss: 1.8178 2022/07/30 17:38:05 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 17:39:00 - mmengine - INFO - Epoch(train) [8][1800/3757] lr: 8.7161e-05 eta: 13:33:10 time: 0.5568 data_time: 0.0155 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5316 loss: 1.5316 2022/07/30 17:39:56 - mmengine - INFO - Epoch(train) [8][1900/3757] lr: 8.7161e-05 eta: 13:32:07 time: 0.5581 data_time: 0.0162 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6296 loss: 1.6296 2022/07/30 17:40:52 - mmengine - INFO - Epoch(train) [8][2000/3757] lr: 8.7161e-05 eta: 13:31:05 time: 0.5513 data_time: 0.0156 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8045 loss: 1.8045 2022/07/30 17:41:47 - mmengine - INFO - Epoch(train) [8][2100/3757] lr: 8.7161e-05 eta: 13:30:01 time: 0.5570 data_time: 0.0151 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.8904 loss: 1.8904 2022/07/30 17:42:43 - mmengine - INFO - Epoch(train) [8][2200/3757] lr: 8.7161e-05 eta: 13:28:57 time: 0.5598 data_time: 0.0145 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7042 loss: 1.7042 2022/07/30 17:43:39 - mmengine - INFO - Epoch(train) [8][2300/3757] lr: 8.7161e-05 eta: 13:27:54 time: 0.5596 data_time: 0.0152 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6189 loss: 1.6189 2022/07/30 17:44:34 - mmengine - INFO - Epoch(train) [8][2400/3757] lr: 8.7161e-05 eta: 13:26:50 time: 0.5592 data_time: 0.0156 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5727 loss: 1.5727 2022/07/30 17:45:30 - mmengine - INFO - Epoch(train) [8][2500/3757] lr: 8.7161e-05 eta: 13:25:47 time: 0.5533 data_time: 0.0154 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.8258 loss: 1.8258 2022/07/30 17:46:26 - mmengine - INFO - Epoch(train) [8][2600/3757] lr: 8.7161e-05 eta: 13:24:44 time: 0.5657 data_time: 0.0155 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.7085 loss: 1.7085 2022/07/30 17:47:21 - mmengine - INFO - Epoch(train) [8][2700/3757] lr: 8.7161e-05 eta: 13:23:41 time: 0.5503 data_time: 0.0151 memory: 33632 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.9385 loss: 1.9385 2022/07/30 17:47:22 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 17:48:18 - mmengine - INFO - Epoch(train) [8][2800/3757] lr: 8.7161e-05 eta: 13:22:39 time: 0.5676 data_time: 0.0151 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7700 loss: 1.7700 2022/07/30 17:49:13 - mmengine - INFO - Epoch(train) [8][2900/3757] lr: 8.7161e-05 eta: 13:21:35 time: 0.5538 data_time: 0.0161 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.7234 loss: 1.7234 2022/07/30 17:50:09 - mmengine - INFO - Epoch(train) [8][3000/3757] lr: 8.7161e-05 eta: 13:20:31 time: 0.5539 data_time: 0.0160 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7454 loss: 1.7454 2022/07/30 17:51:04 - mmengine - INFO - Epoch(train) [8][3100/3757] lr: 8.7161e-05 eta: 13:19:27 time: 0.5504 data_time: 0.0144 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7995 loss: 1.7995 2022/07/30 17:52:00 - mmengine - INFO - Epoch(train) [8][3200/3757] lr: 8.7161e-05 eta: 13:18:25 time: 0.5529 data_time: 0.0144 memory: 33632 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.5219 loss: 1.5219 2022/07/30 17:52:56 - mmengine - INFO - Epoch(train) [8][3300/3757] lr: 8.7161e-05 eta: 13:17:24 time: 0.6071 data_time: 0.0158 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7960 loss: 1.7960 2022/07/30 17:53:52 - mmengine - INFO - Epoch(train) [8][3400/3757] lr: 8.7161e-05 eta: 13:16:21 time: 0.5600 data_time: 0.0156 memory: 33632 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 1.7095 loss: 1.7095 2022/07/30 17:54:48 - mmengine - INFO - Epoch(train) [8][3500/3757] lr: 8.7161e-05 eta: 13:15:19 time: 0.5574 data_time: 0.0158 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.6885 loss: 1.6885 2022/07/30 17:55:43 - mmengine - INFO - Epoch(train) [8][3600/3757] lr: 8.7161e-05 eta: 13:14:15 time: 0.5520 data_time: 0.0140 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.9521 loss: 1.9521 2022/07/30 17:56:39 - mmengine - INFO - Epoch(train) [8][3700/3757] lr: 8.7161e-05 eta: 13:13:13 time: 0.5518 data_time: 0.0144 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.7106 loss: 1.7106 2022/07/30 17:56:39 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 17:57:11 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 17:57:11 - mmengine - INFO - Epoch(train) [8][3757/3757] lr: 8.7161e-05 eta: 13:12:48 time: 0.5524 data_time: 0.0157 memory: 33632 top1_acc: 0.4286 top5_acc: 0.7143 loss_cls: 1.6776 loss: 1.6776 2022/07/30 17:58:08 - mmengine - INFO - Epoch(train) [9][100/3757] lr: 8.3461e-05 eta: 13:11:14 time: 0.5565 data_time: 0.0155 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6443 loss: 1.6443 2022/07/30 17:59:04 - mmengine - INFO - Epoch(train) [9][200/3757] lr: 8.3461e-05 eta: 13:10:12 time: 0.5502 data_time: 0.0141 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.6253 loss: 1.6253 2022/07/30 18:00:00 - mmengine - INFO - Epoch(train) [9][300/3757] lr: 8.3461e-05 eta: 13:09:10 time: 0.5509 data_time: 0.0148 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7454 loss: 1.7454 2022/07/30 18:00:58 - mmengine - INFO - Epoch(train) [9][400/3757] lr: 8.3461e-05 eta: 13:08:13 time: 0.5586 data_time: 0.0158 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.7487 loss: 1.7487 2022/07/30 18:01:53 - mmengine - INFO - Epoch(train) [9][500/3757] lr: 8.3461e-05 eta: 13:07:10 time: 0.5510 data_time: 0.0141 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6736 loss: 1.6736 2022/07/30 18:02:49 - mmengine - INFO - Epoch(train) [9][600/3757] lr: 8.3461e-05 eta: 13:06:08 time: 0.5574 data_time: 0.0143 memory: 33632 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 1.5841 loss: 1.5841 2022/07/30 18:03:45 - mmengine - INFO - Epoch(train) [9][700/3757] lr: 8.3461e-05 eta: 13:05:05 time: 0.5544 data_time: 0.0146 memory: 33632 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.5885 loss: 1.5885 2022/07/30 18:04:41 - mmengine - INFO - Epoch(train) [9][800/3757] lr: 8.3461e-05 eta: 13:04:04 time: 0.5806 data_time: 0.0169 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6335 loss: 1.6335 2022/07/30 18:05:36 - mmengine - INFO - Epoch(train) [9][900/3757] lr: 8.3461e-05 eta: 13:03:02 time: 0.5582 data_time: 0.0152 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.6147 loss: 1.6147 2022/07/30 18:06:01 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 18:06:32 - mmengine - INFO - Epoch(train) [9][1000/3757] lr: 8.3461e-05 eta: 13:01:59 time: 0.5508 data_time: 0.0146 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5865 loss: 1.5865 2022/07/30 18:07:28 - mmengine - INFO - Epoch(train) [9][1100/3757] lr: 8.3461e-05 eta: 13:00:57 time: 0.5528 data_time: 0.0155 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.6109 loss: 1.6109 2022/07/30 18:08:23 - mmengine - INFO - Epoch(train) [9][1200/3757] lr: 8.3461e-05 eta: 12:59:55 time: 0.5563 data_time: 0.0153 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.8847 loss: 1.8847 2022/07/30 18:09:19 - mmengine - INFO - Epoch(train) [9][1300/3757] lr: 8.3461e-05 eta: 12:58:53 time: 0.5621 data_time: 0.0157 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4377 loss: 1.4377 2022/07/30 18:10:15 - mmengine - INFO - Epoch(train) [9][1400/3757] lr: 8.3461e-05 eta: 12:57:51 time: 0.5520 data_time: 0.0153 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.6151 loss: 1.6151 2022/07/30 18:11:10 - mmengine - INFO - Epoch(train) [9][1500/3757] lr: 8.3461e-05 eta: 12:56:49 time: 0.5512 data_time: 0.0152 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5851 loss: 1.5851 2022/07/30 18:12:06 - mmengine - INFO - Epoch(train) [9][1600/3757] lr: 8.3461e-05 eta: 12:55:47 time: 0.5587 data_time: 0.0145 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6224 loss: 1.6224 2022/07/30 18:13:03 - mmengine - INFO - Epoch(train) [9][1700/3757] lr: 8.3461e-05 eta: 12:54:48 time: 0.5715 data_time: 0.0156 memory: 33632 top1_acc: 0.2500 top5_acc: 0.6250 loss_cls: 1.7836 loss: 1.7836 2022/07/30 18:13:58 - mmengine - INFO - Epoch(train) [9][1800/3757] lr: 8.3461e-05 eta: 12:53:46 time: 0.5623 data_time: 0.0163 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8187 loss: 1.8187 2022/07/30 18:14:54 - mmengine - INFO - Epoch(train) [9][1900/3757] lr: 8.3461e-05 eta: 12:52:44 time: 0.5542 data_time: 0.0140 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.9510 loss: 1.9510 2022/07/30 18:15:18 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 18:15:50 - mmengine - INFO - Epoch(train) [9][2000/3757] lr: 8.3461e-05 eta: 12:51:42 time: 0.5511 data_time: 0.0145 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5094 loss: 1.5094 2022/07/30 18:16:45 - mmengine - INFO - Epoch(train) [9][2100/3757] lr: 8.3461e-05 eta: 12:50:40 time: 0.5520 data_time: 0.0161 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6677 loss: 1.6677 2022/07/30 18:17:41 - mmengine - INFO - Epoch(train) [9][2200/3757] lr: 8.3461e-05 eta: 12:49:38 time: 0.5503 data_time: 0.0137 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5303 loss: 1.5303 2022/07/30 18:18:37 - mmengine - INFO - Epoch(train) [9][2300/3757] lr: 8.3461e-05 eta: 12:48:36 time: 0.5627 data_time: 0.0168 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.7328 loss: 1.7328 2022/07/30 18:19:32 - mmengine - INFO - Epoch(train) [9][2400/3757] lr: 8.3461e-05 eta: 12:47:35 time: 0.5570 data_time: 0.0159 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7979 loss: 1.7979 2022/07/30 18:20:28 - mmengine - INFO - Epoch(train) [9][2500/3757] lr: 8.3461e-05 eta: 12:46:34 time: 0.5562 data_time: 0.0151 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.5954 loss: 1.5954 2022/07/30 18:21:24 - mmengine - INFO - Epoch(train) [9][2600/3757] lr: 8.3461e-05 eta: 12:45:32 time: 0.5650 data_time: 0.0191 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.6512 loss: 1.6512 2022/07/30 18:22:19 - mmengine - INFO - Epoch(train) [9][2700/3757] lr: 8.3461e-05 eta: 12:44:31 time: 0.5568 data_time: 0.0160 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.7810 loss: 1.7810 2022/07/30 18:23:16 - mmengine - INFO - Epoch(train) [9][2800/3757] lr: 8.3461e-05 eta: 12:43:30 time: 0.5511 data_time: 0.0139 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6852 loss: 1.6852 2022/07/30 18:24:12 - mmengine - INFO - Epoch(train) [9][2900/3757] lr: 8.3461e-05 eta: 12:42:29 time: 0.5503 data_time: 0.0139 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.5785 loss: 1.5785 2022/07/30 18:24:36 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 18:25:07 - mmengine - INFO - Epoch(train) [9][3000/3757] lr: 8.3461e-05 eta: 12:41:28 time: 0.5619 data_time: 0.0148 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6720 loss: 1.6720 2022/07/30 18:26:03 - mmengine - INFO - Epoch(train) [9][3100/3757] lr: 8.3461e-05 eta: 12:40:27 time: 0.5642 data_time: 0.0163 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5579 loss: 1.5579 2022/07/30 18:26:59 - mmengine - INFO - Epoch(train) [9][3200/3757] lr: 8.3461e-05 eta: 12:39:25 time: 0.5574 data_time: 0.0158 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8693 loss: 1.8693 2022/07/30 18:27:54 - mmengine - INFO - Epoch(train) [9][3300/3757] lr: 8.3461e-05 eta: 12:38:23 time: 0.5495 data_time: 0.0134 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9122 loss: 1.9122 2022/07/30 18:28:50 - mmengine - INFO - Epoch(train) [9][3400/3757] lr: 8.3461e-05 eta: 12:37:22 time: 0.5508 data_time: 0.0145 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6496 loss: 1.6496 2022/07/30 18:29:46 - mmengine - INFO - Epoch(train) [9][3500/3757] lr: 8.3461e-05 eta: 12:36:22 time: 0.5616 data_time: 0.0152 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5611 loss: 1.5611 2022/07/30 18:30:42 - mmengine - INFO - Epoch(train) [9][3600/3757] lr: 8.3461e-05 eta: 12:35:20 time: 0.5528 data_time: 0.0146 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7593 loss: 1.7593 2022/07/30 18:31:37 - mmengine - INFO - Epoch(train) [9][3700/3757] lr: 8.3461e-05 eta: 12:34:19 time: 0.5659 data_time: 0.0161 memory: 33632 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.6727 loss: 1.6727 2022/07/30 18:32:09 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 18:32:09 - mmengine - INFO - Epoch(train) [9][3757/3757] lr: 8.3461e-05 eta: 12:33:54 time: 0.5610 data_time: 0.0138 memory: 33632 top1_acc: 0.5714 top5_acc: 0.5714 loss_cls: 1.8324 loss: 1.8324 2022/07/30 18:32:09 - mmengine - INFO - Saving checkpoint at 9 epochs 2022/07/30 18:32:36 - mmengine - INFO - Epoch(val) [9][100/310] eta: 0:00:47 time: 0.2272 data_time: 0.0225 memory: 6325 2022/07/30 18:32:59 - mmengine - INFO - Epoch(val) [9][200/310] eta: 0:00:24 time: 0.2223 data_time: 0.0172 memory: 6325 2022/07/30 18:33:20 - mmengine - INFO - Epoch(val) [9][300/310] eta: 0:00:01 time: 0.1974 data_time: 0.0096 memory: 6325 2022/07/30 18:33:23 - mmengine - INFO - Epoch(val) [9][310/310] acc/top1: 0.6823 acc/top5: 0.8849 acc/mean1: 0.6821 2022/07/30 18:33:24 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb/best_acc/top1_epoch_7.pth is removed 2022/07/30 18:33:25 - mmengine - INFO - The best checkpoint with 0.6823 acc/top1 at 10 epoch is saved to best_acc/top1_epoch_10.pth. 2022/07/30 18:34:22 - mmengine - INFO - Epoch(train) [10][100/3757] lr: 7.9393e-05 eta: 12:32:24 time: 0.5557 data_time: 0.0155 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7202 loss: 1.7202 2022/07/30 18:35:10 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 18:35:18 - mmengine - INFO - Epoch(train) [10][200/3757] lr: 7.9393e-05 eta: 12:31:23 time: 0.5570 data_time: 0.0164 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6496 loss: 1.6496 2022/07/30 18:36:13 - mmengine - INFO - Epoch(train) [10][300/3757] lr: 7.9393e-05 eta: 12:30:22 time: 0.5517 data_time: 0.0153 memory: 33632 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.6729 loss: 1.6729 2022/07/30 18:37:09 - mmengine - INFO - Epoch(train) [10][400/3757] lr: 7.9393e-05 eta: 12:29:21 time: 0.5662 data_time: 0.0137 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.7518 loss: 1.7518 2022/07/30 18:38:05 - mmengine - INFO - Epoch(train) [10][500/3757] lr: 7.9393e-05 eta: 12:28:20 time: 0.5612 data_time: 0.0155 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5776 loss: 1.5776 2022/07/30 18:39:00 - mmengine - INFO - Epoch(train) [10][600/3757] lr: 7.9393e-05 eta: 12:27:19 time: 0.5605 data_time: 0.0150 memory: 33632 top1_acc: 0.1250 top5_acc: 0.7500 loss_cls: 1.5663 loss: 1.5663 2022/07/30 18:39:56 - mmengine - INFO - Epoch(train) [10][700/3757] lr: 7.9393e-05 eta: 12:26:18 time: 0.5499 data_time: 0.0135 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.4925 loss: 1.4925 2022/07/30 18:40:51 - mmengine - INFO - Epoch(train) [10][800/3757] lr: 7.9393e-05 eta: 12:25:16 time: 0.5509 data_time: 0.0140 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.5162 loss: 1.5162 2022/07/30 18:41:47 - mmengine - INFO - Epoch(train) [10][900/3757] lr: 7.9393e-05 eta: 12:24:15 time: 0.5624 data_time: 0.0160 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.8886 loss: 1.8886 2022/07/30 18:42:43 - mmengine - INFO - Epoch(train) [10][1000/3757] lr: 7.9393e-05 eta: 12:23:15 time: 0.5633 data_time: 0.0165 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5514 loss: 1.5514 2022/07/30 18:43:38 - mmengine - INFO - Epoch(train) [10][1100/3757] lr: 7.9393e-05 eta: 12:22:14 time: 0.5519 data_time: 0.0150 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.6422 loss: 1.6422 2022/07/30 18:44:27 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 18:44:34 - mmengine - INFO - Epoch(train) [10][1200/3757] lr: 7.9393e-05 eta: 12:21:14 time: 0.5557 data_time: 0.0153 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.7498 loss: 1.7498 2022/07/30 18:45:30 - mmengine - INFO - Epoch(train) [10][1300/3757] lr: 7.9393e-05 eta: 12:20:13 time: 0.5615 data_time: 0.0145 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.6883 loss: 1.6883 2022/07/30 18:46:25 - mmengine - INFO - Epoch(train) [10][1400/3757] lr: 7.9393e-05 eta: 12:19:12 time: 0.5565 data_time: 0.0158 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4879 loss: 1.4879 2022/07/30 18:47:21 - mmengine - INFO - Epoch(train) [10][1500/3757] lr: 7.9393e-05 eta: 12:18:11 time: 0.5613 data_time: 0.0149 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4088 loss: 1.4088 2022/07/30 18:48:16 - mmengine - INFO - Epoch(train) [10][1600/3757] lr: 7.9393e-05 eta: 12:17:10 time: 0.5506 data_time: 0.0148 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.8213 loss: 1.8213 2022/07/30 18:49:12 - mmengine - INFO - Epoch(train) [10][1700/3757] lr: 7.9393e-05 eta: 12:16:10 time: 0.5553 data_time: 0.0146 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.8025 loss: 1.8025 2022/07/30 18:50:08 - mmengine - INFO - Epoch(train) [10][1800/3757] lr: 7.9393e-05 eta: 12:15:09 time: 0.5578 data_time: 0.0159 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5527 loss: 1.5527 2022/07/30 18:51:04 - mmengine - INFO - Epoch(train) [10][1900/3757] lr: 7.9393e-05 eta: 12:14:09 time: 0.5597 data_time: 0.0150 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5534 loss: 1.5534 2022/07/30 18:51:59 - mmengine - INFO - Epoch(train) [10][2000/3757] lr: 7.9393e-05 eta: 12:13:08 time: 0.5493 data_time: 0.0140 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.6587 loss: 1.6587 2022/07/30 18:52:55 - mmengine - INFO - Epoch(train) [10][2100/3757] lr: 7.9393e-05 eta: 12:12:07 time: 0.5518 data_time: 0.0144 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6004 loss: 1.6004 2022/07/30 18:53:44 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 18:53:51 - mmengine - INFO - Epoch(train) [10][2200/3757] lr: 7.9393e-05 eta: 12:11:07 time: 0.5575 data_time: 0.0165 memory: 33632 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.4634 loss: 1.4634 2022/07/30 18:54:47 - mmengine - INFO - Epoch(train) [10][2300/3757] lr: 7.9393e-05 eta: 12:10:07 time: 0.5588 data_time: 0.0150 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5517 loss: 1.5517 2022/07/30 18:55:42 - mmengine - INFO - Epoch(train) [10][2400/3757] lr: 7.9393e-05 eta: 12:09:07 time: 0.5551 data_time: 0.0157 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6025 loss: 1.6025 2022/07/30 18:56:38 - mmengine - INFO - Epoch(train) [10][2500/3757] lr: 7.9393e-05 eta: 12:08:07 time: 0.5506 data_time: 0.0144 memory: 33632 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 1.5762 loss: 1.5762 2022/07/30 18:57:34 - mmengine - INFO - Epoch(train) [10][2600/3757] lr: 7.9393e-05 eta: 12:07:06 time: 0.5521 data_time: 0.0144 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.6067 loss: 1.6067 2022/07/30 18:58:29 - mmengine - INFO - Epoch(train) [10][2700/3757] lr: 7.9393e-05 eta: 12:06:06 time: 0.5517 data_time: 0.0158 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8505 loss: 1.8505 2022/07/30 18:59:25 - mmengine - INFO - Epoch(train) [10][2800/3757] lr: 7.9393e-05 eta: 12:05:05 time: 0.5579 data_time: 0.0158 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 2.0523 loss: 2.0523 2022/07/30 19:00:20 - mmengine - INFO - Epoch(train) [10][2900/3757] lr: 7.9393e-05 eta: 12:04:05 time: 0.5510 data_time: 0.0147 memory: 33632 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.1553 loss: 1.1553 2022/07/30 19:01:16 - mmengine - INFO - Epoch(train) [10][3000/3757] lr: 7.9393e-05 eta: 12:03:04 time: 0.5508 data_time: 0.0145 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4425 loss: 1.4425 2022/07/30 19:02:12 - mmengine - INFO - Epoch(train) [10][3100/3757] lr: 7.9393e-05 eta: 12:02:04 time: 0.5604 data_time: 0.0176 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5581 loss: 1.5581 2022/07/30 19:03:00 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 19:03:07 - mmengine - INFO - Epoch(train) [10][3200/3757] lr: 7.9393e-05 eta: 12:01:04 time: 0.5586 data_time: 0.0155 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5680 loss: 1.5680 2022/07/30 19:04:03 - mmengine - INFO - Epoch(train) [10][3300/3757] lr: 7.9393e-05 eta: 12:00:04 time: 0.5550 data_time: 0.0160 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5708 loss: 1.5708 2022/07/30 19:04:59 - mmengine - INFO - Epoch(train) [10][3400/3757] lr: 7.9393e-05 eta: 11:59:03 time: 0.5530 data_time: 0.0155 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2930 loss: 1.2930 2022/07/30 19:05:54 - mmengine - INFO - Epoch(train) [10][3500/3757] lr: 7.9393e-05 eta: 11:58:03 time: 0.5573 data_time: 0.0159 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6956 loss: 1.6956 2022/07/30 19:06:50 - mmengine - INFO - Epoch(train) [10][3600/3757] lr: 7.9393e-05 eta: 11:57:03 time: 0.5548 data_time: 0.0142 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.3427 loss: 1.3427 2022/07/30 19:07:46 - mmengine - INFO - Epoch(train) [10][3700/3757] lr: 7.9393e-05 eta: 11:56:03 time: 0.5590 data_time: 0.0148 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5536 loss: 1.5536 2022/07/30 19:08:18 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 19:08:18 - mmengine - INFO - Epoch(train) [10][3757/3757] lr: 7.9393e-05 eta: 11:55:39 time: 0.5636 data_time: 0.0164 memory: 33632 top1_acc: 0.4286 top5_acc: 0.8571 loss_cls: 1.8286 loss: 1.8286 2022/07/30 19:09:15 - mmengine - INFO - Epoch(train) [11][100/3757] lr: 7.5004e-05 eta: 11:54:14 time: 0.5620 data_time: 0.0155 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4297 loss: 1.4297 2022/07/30 19:10:11 - mmengine - INFO - Epoch(train) [11][200/3757] lr: 7.5004e-05 eta: 11:53:13 time: 0.5532 data_time: 0.0149 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5523 loss: 1.5523 2022/07/30 19:11:07 - mmengine - INFO - Epoch(train) [11][300/3757] lr: 7.5004e-05 eta: 11:52:13 time: 0.5515 data_time: 0.0142 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.6642 loss: 1.6642 2022/07/30 19:12:02 - mmengine - INFO - Epoch(train) [11][400/3757] lr: 7.5004e-05 eta: 11:51:13 time: 0.5558 data_time: 0.0164 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7750 loss: 1.7750 2022/07/30 19:12:19 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 19:12:58 - mmengine - INFO - Epoch(train) [11][500/3757] lr: 7.5004e-05 eta: 11:50:13 time: 0.5571 data_time: 0.0151 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.4769 loss: 1.4769 2022/07/30 19:13:53 - mmengine - INFO - Epoch(train) [11][600/3757] lr: 7.5004e-05 eta: 11:49:13 time: 0.5561 data_time: 0.0155 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.5384 loss: 1.5384 2022/07/30 19:14:48 - mmengine - INFO - Epoch(train) [11][700/3757] lr: 7.5004e-05 eta: 11:48:12 time: 0.5506 data_time: 0.0143 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5513 loss: 1.5513 2022/07/30 19:15:44 - mmengine - INFO - Epoch(train) [11][800/3757] lr: 7.5004e-05 eta: 11:47:12 time: 0.5523 data_time: 0.0152 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6245 loss: 1.6245 2022/07/30 19:16:39 - mmengine - INFO - Epoch(train) [11][900/3757] lr: 7.5004e-05 eta: 11:46:12 time: 0.5642 data_time: 0.0167 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7347 loss: 1.7347 2022/07/30 19:17:35 - mmengine - INFO - Epoch(train) [11][1000/3757] lr: 7.5004e-05 eta: 11:45:13 time: 0.5596 data_time: 0.0153 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5209 loss: 1.5209 2022/07/30 19:18:31 - mmengine - INFO - Epoch(train) [11][1100/3757] lr: 7.5004e-05 eta: 11:44:12 time: 0.5517 data_time: 0.0147 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6597 loss: 1.6597 2022/07/30 19:19:27 - mmengine - INFO - Epoch(train) [11][1200/3757] lr: 7.5004e-05 eta: 11:43:13 time: 0.5510 data_time: 0.0144 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.7112 loss: 1.7112 2022/07/30 19:20:22 - mmengine - INFO - Epoch(train) [11][1300/3757] lr: 7.5004e-05 eta: 11:42:13 time: 0.5519 data_time: 0.0152 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6379 loss: 1.6379 2022/07/30 19:21:18 - mmengine - INFO - Epoch(train) [11][1400/3757] lr: 7.5004e-05 eta: 11:41:14 time: 0.5663 data_time: 0.0158 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5286 loss: 1.5286 2022/07/30 19:21:35 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 19:22:14 - mmengine - INFO - Epoch(train) [11][1500/3757] lr: 7.5004e-05 eta: 11:40:15 time: 0.5661 data_time: 0.0154 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.6348 loss: 1.6348 2022/07/30 19:23:10 - mmengine - INFO - Epoch(train) [11][1600/3757] lr: 7.5004e-05 eta: 11:39:15 time: 0.5513 data_time: 0.0141 memory: 33632 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 1.6551 loss: 1.6551 2022/07/30 19:24:05 - mmengine - INFO - Epoch(train) [11][1700/3757] lr: 7.5004e-05 eta: 11:38:15 time: 0.5518 data_time: 0.0139 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 2.0043 loss: 2.0043 2022/07/30 19:25:01 - mmengine - INFO - Epoch(train) [11][1800/3757] lr: 7.5004e-05 eta: 11:37:15 time: 0.5572 data_time: 0.0147 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.7264 loss: 1.7264 2022/07/30 19:25:56 - mmengine - INFO - Epoch(train) [11][1900/3757] lr: 7.5004e-05 eta: 11:36:15 time: 0.5585 data_time: 0.0152 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2655 loss: 1.2655 2022/07/30 19:26:53 - mmengine - INFO - Epoch(train) [11][2000/3757] lr: 7.5004e-05 eta: 11:35:17 time: 0.5515 data_time: 0.0147 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6041 loss: 1.6041 2022/07/30 19:27:48 - mmengine - INFO - Epoch(train) [11][2100/3757] lr: 7.5004e-05 eta: 11:34:17 time: 0.5672 data_time: 0.0150 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6670 loss: 1.6670 2022/07/30 19:28:44 - mmengine - INFO - Epoch(train) [11][2200/3757] lr: 7.5004e-05 eta: 11:33:17 time: 0.5556 data_time: 0.0150 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7122 loss: 1.7122 2022/07/30 19:29:39 - mmengine - INFO - Epoch(train) [11][2300/3757] lr: 7.5004e-05 eta: 11:32:17 time: 0.5549 data_time: 0.0157 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6190 loss: 1.6190 2022/07/30 19:30:34 - mmengine - INFO - Epoch(train) [11][2400/3757] lr: 7.5004e-05 eta: 11:31:17 time: 0.5552 data_time: 0.0155 memory: 33632 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.7010 loss: 1.7010 2022/07/30 19:30:51 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 19:31:30 - mmengine - INFO - Epoch(train) [11][2500/3757] lr: 7.5004e-05 eta: 11:30:17 time: 0.5510 data_time: 0.0145 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.8432 loss: 1.8432 2022/07/30 19:32:25 - mmengine - INFO - Epoch(train) [11][2600/3757] lr: 7.5004e-05 eta: 11:29:17 time: 0.5530 data_time: 0.0156 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2213 loss: 1.2213 2022/07/30 19:33:21 - mmengine - INFO - Epoch(train) [11][2700/3757] lr: 7.5004e-05 eta: 11:28:17 time: 0.5579 data_time: 0.0178 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4732 loss: 1.4732 2022/07/30 19:34:23 - mmengine - INFO - Epoch(train) [11][2800/3757] lr: 7.5004e-05 eta: 11:27:30 time: 0.5533 data_time: 0.0150 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.5345 loss: 1.5345 2022/07/30 19:35:19 - mmengine - INFO - Epoch(train) [11][2900/3757] lr: 7.5004e-05 eta: 11:26:30 time: 0.5565 data_time: 0.0151 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.7958 loss: 1.7958 2022/07/30 19:36:14 - mmengine - INFO - Epoch(train) [11][3000/3757] lr: 7.5004e-05 eta: 11:25:30 time: 0.5571 data_time: 0.0144 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.7563 loss: 1.7563 2022/07/30 19:37:10 - mmengine - INFO - Epoch(train) [11][3100/3757] lr: 7.5004e-05 eta: 11:24:31 time: 0.5599 data_time: 0.0152 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5036 loss: 1.5036 2022/07/30 19:38:05 - mmengine - INFO - Epoch(train) [11][3200/3757] lr: 7.5004e-05 eta: 11:23:31 time: 0.5591 data_time: 0.0156 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6680 loss: 1.6680 2022/07/30 19:39:01 - mmengine - INFO - Epoch(train) [11][3300/3757] lr: 7.5004e-05 eta: 11:22:31 time: 0.5504 data_time: 0.0138 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5267 loss: 1.5267 2022/07/30 19:39:56 - mmengine - INFO - Epoch(train) [11][3400/3757] lr: 7.5004e-05 eta: 11:21:31 time: 0.5557 data_time: 0.0153 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2344 loss: 1.2344 2022/07/30 19:40:13 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 19:40:52 - mmengine - INFO - Epoch(train) [11][3500/3757] lr: 7.5004e-05 eta: 11:20:32 time: 0.5597 data_time: 0.0164 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5527 loss: 1.5527 2022/07/30 19:41:47 - mmengine - INFO - Epoch(train) [11][3600/3757] lr: 7.5004e-05 eta: 11:19:33 time: 0.5581 data_time: 0.0156 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4709 loss: 1.4709 2022/07/30 19:42:43 - mmengine - INFO - Epoch(train) [11][3700/3757] lr: 7.5004e-05 eta: 11:18:33 time: 0.5572 data_time: 0.0154 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.6292 loss: 1.6292 2022/07/30 19:43:14 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 19:43:14 - mmengine - INFO - Epoch(train) [11][3757/3757] lr: 7.5004e-05 eta: 11:18:10 time: 0.5414 data_time: 0.0145 memory: 33632 top1_acc: 0.4286 top5_acc: 0.8571 loss_cls: 1.5825 loss: 1.5825 2022/07/30 19:44:12 - mmengine - INFO - Epoch(train) [12][100/3757] lr: 7.0340e-05 eta: 11:16:47 time: 0.5514 data_time: 0.0146 memory: 33632 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.6452 loss: 1.6452 2022/07/30 19:45:07 - mmengine - INFO - Epoch(train) [12][200/3757] lr: 7.0340e-05 eta: 11:15:47 time: 0.5535 data_time: 0.0151 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.9318 loss: 1.9318 2022/07/30 19:46:03 - mmengine - INFO - Epoch(train) [12][300/3757] lr: 7.0340e-05 eta: 11:14:48 time: 0.5527 data_time: 0.0149 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3164 loss: 1.3164 2022/07/30 19:46:58 - mmengine - INFO - Epoch(train) [12][400/3757] lr: 7.0340e-05 eta: 11:13:48 time: 0.5577 data_time: 0.0152 memory: 33632 top1_acc: 0.3750 top5_acc: 0.3750 loss_cls: 1.4562 loss: 1.4562 2022/07/30 19:47:54 - mmengine - INFO - Epoch(train) [12][500/3757] lr: 7.0340e-05 eta: 11:12:49 time: 0.5521 data_time: 0.0151 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3685 loss: 1.3685 2022/07/30 19:48:50 - mmengine - INFO - Epoch(train) [12][600/3757] lr: 7.0340e-05 eta: 11:11:50 time: 0.5502 data_time: 0.0141 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.6431 loss: 1.6431 2022/07/30 19:49:30 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 19:49:45 - mmengine - INFO - Epoch(train) [12][700/3757] lr: 7.0340e-05 eta: 11:10:50 time: 0.5538 data_time: 0.0144 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4765 loss: 1.4765 2022/07/30 19:50:41 - mmengine - INFO - Epoch(train) [12][800/3757] lr: 7.0340e-05 eta: 11:09:51 time: 0.5532 data_time: 0.0166 memory: 33632 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.1329 loss: 1.1329 2022/07/30 19:51:36 - mmengine - INFO - Epoch(train) [12][900/3757] lr: 7.0340e-05 eta: 11:08:52 time: 0.5524 data_time: 0.0153 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.4813 loss: 1.4813 2022/07/30 19:52:32 - mmengine - INFO - Epoch(train) [12][1000/3757] lr: 7.0340e-05 eta: 11:07:53 time: 0.5595 data_time: 0.0162 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5744 loss: 1.5744 2022/07/30 19:53:27 - mmengine - INFO - Epoch(train) [12][1100/3757] lr: 7.0340e-05 eta: 11:06:53 time: 0.5577 data_time: 0.0152 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3492 loss: 1.3492 2022/07/30 19:54:23 - mmengine - INFO - Epoch(train) [12][1200/3757] lr: 7.0340e-05 eta: 11:05:54 time: 0.5518 data_time: 0.0141 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.8398 loss: 1.8398 2022/07/30 19:55:19 - mmengine - INFO - Epoch(train) [12][1300/3757] lr: 7.0340e-05 eta: 11:04:55 time: 0.5622 data_time: 0.0154 memory: 33632 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 1.3890 loss: 1.3890 2022/07/30 19:56:14 - mmengine - INFO - Epoch(train) [12][1400/3757] lr: 7.0340e-05 eta: 11:03:56 time: 0.5509 data_time: 0.0141 memory: 33632 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.5026 loss: 1.5026 2022/07/30 19:57:10 - mmengine - INFO - Epoch(train) [12][1500/3757] lr: 7.0340e-05 eta: 11:02:57 time: 0.5533 data_time: 0.0150 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3781 loss: 1.3781 2022/07/30 19:58:05 - mmengine - INFO - Epoch(train) [12][1600/3757] lr: 7.0340e-05 eta: 11:01:58 time: 0.5602 data_time: 0.0166 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.7799 loss: 1.7799 2022/07/30 19:58:46 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 19:59:01 - mmengine - INFO - Epoch(train) [12][1700/3757] lr: 7.0340e-05 eta: 11:00:58 time: 0.5568 data_time: 0.0158 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.7919 loss: 1.7919 2022/07/30 19:59:56 - mmengine - INFO - Epoch(train) [12][1800/3757] lr: 7.0340e-05 eta: 10:59:59 time: 0.5520 data_time: 0.0147 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6336 loss: 1.6336 2022/07/30 20:00:52 - mmengine - INFO - Epoch(train) [12][1900/3757] lr: 7.0340e-05 eta: 10:59:00 time: 0.5515 data_time: 0.0155 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5054 loss: 1.5054 2022/07/30 20:01:48 - mmengine - INFO - Epoch(train) [12][2000/3757] lr: 7.0340e-05 eta: 10:58:01 time: 0.5593 data_time: 0.0157 memory: 33632 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 1.3900 loss: 1.3900 2022/07/30 20:02:43 - mmengine - INFO - Epoch(train) [12][2100/3757] lr: 7.0340e-05 eta: 10:57:02 time: 0.5522 data_time: 0.0143 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4488 loss: 1.4488 2022/07/30 20:03:39 - mmengine - INFO - Epoch(train) [12][2200/3757] lr: 7.0340e-05 eta: 10:56:03 time: 0.5549 data_time: 0.0147 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.5405 loss: 1.5405 2022/07/30 20:04:34 - mmengine - INFO - Epoch(train) [12][2300/3757] lr: 7.0340e-05 eta: 10:55:04 time: 0.5551 data_time: 0.0148 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4828 loss: 1.4828 2022/07/30 20:05:30 - mmengine - INFO - Epoch(train) [12][2400/3757] lr: 7.0340e-05 eta: 10:54:05 time: 0.5580 data_time: 0.0149 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6395 loss: 1.6395 2022/07/30 20:06:26 - mmengine - INFO - Epoch(train) [12][2500/3757] lr: 7.0340e-05 eta: 10:53:07 time: 0.5533 data_time: 0.0161 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.5183 loss: 1.5183 2022/07/30 20:07:21 - mmengine - INFO - Epoch(train) [12][2600/3757] lr: 7.0340e-05 eta: 10:52:08 time: 0.5518 data_time: 0.0147 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2891 loss: 1.2891 2022/07/30 20:08:02 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 20:08:17 - mmengine - INFO - Epoch(train) [12][2700/3757] lr: 7.0340e-05 eta: 10:51:09 time: 0.5526 data_time: 0.0153 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2243 loss: 1.2243 2022/07/30 20:09:13 - mmengine - INFO - Epoch(train) [12][2800/3757] lr: 7.0340e-05 eta: 10:50:10 time: 0.5545 data_time: 0.0163 memory: 33632 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.5409 loss: 1.5409 2022/07/30 20:10:08 - mmengine - INFO - Epoch(train) [12][2900/3757] lr: 7.0340e-05 eta: 10:49:10 time: 0.5500 data_time: 0.0139 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.6687 loss: 1.6687 2022/07/30 20:11:03 - mmengine - INFO - Epoch(train) [12][3000/3757] lr: 7.0340e-05 eta: 10:48:12 time: 0.5502 data_time: 0.0140 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.6186 loss: 1.6186 2022/07/30 20:11:59 - mmengine - INFO - Epoch(train) [12][3100/3757] lr: 7.0340e-05 eta: 10:47:12 time: 0.5559 data_time: 0.0165 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.6928 loss: 1.6928 2022/07/30 20:12:54 - mmengine - INFO - Epoch(train) [12][3200/3757] lr: 7.0340e-05 eta: 10:46:13 time: 0.5541 data_time: 0.0154 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2268 loss: 1.2268 2022/07/30 20:13:50 - mmengine - INFO - Epoch(train) [12][3300/3757] lr: 7.0340e-05 eta: 10:45:15 time: 0.5567 data_time: 0.0157 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4396 loss: 1.4396 2022/07/30 20:14:45 - mmengine - INFO - Epoch(train) [12][3400/3757] lr: 7.0340e-05 eta: 10:44:16 time: 0.5498 data_time: 0.0148 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4513 loss: 1.4513 2022/07/30 20:15:41 - mmengine - INFO - Epoch(train) [12][3500/3757] lr: 7.0340e-05 eta: 10:43:17 time: 0.5537 data_time: 0.0151 memory: 33632 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 1.5407 loss: 1.5407 2022/07/30 20:16:37 - mmengine - INFO - Epoch(train) [12][3600/3757] lr: 7.0340e-05 eta: 10:42:18 time: 0.5497 data_time: 0.0149 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.6957 loss: 1.6957 2022/07/30 20:17:17 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 20:17:32 - mmengine - INFO - Epoch(train) [12][3700/3757] lr: 7.0340e-05 eta: 10:41:19 time: 0.5569 data_time: 0.0146 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6107 loss: 1.6107 2022/07/30 20:18:04 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 20:18:04 - mmengine - INFO - Epoch(train) [12][3757/3757] lr: 7.0340e-05 eta: 10:40:56 time: 0.5478 data_time: 0.0158 memory: 33632 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 1.5314 loss: 1.5314 2022/07/30 20:18:04 - mmengine - INFO - Saving checkpoint at 12 epochs 2022/07/30 20:18:33 - mmengine - INFO - Epoch(val) [12][100/310] eta: 0:00:45 time: 0.2190 data_time: 0.0157 memory: 6325 2022/07/30 20:18:55 - mmengine - INFO - Epoch(val) [12][200/310] eta: 0:00:23 time: 0.2176 data_time: 0.0145 memory: 6325 2022/07/30 20:19:16 - mmengine - INFO - Epoch(val) [12][300/310] eta: 0:00:01 time: 0.1950 data_time: 0.0085 memory: 6325 2022/07/30 20:19:19 - mmengine - INFO - Epoch(val) [12][310/310] acc/top1: 0.6971 acc/top5: 0.8885 acc/mean1: 0.6969 2022/07/30 20:19:19 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb/best_acc/top1_epoch_10.pth is removed 2022/07/30 20:19:22 - mmengine - INFO - The best checkpoint with 0.6971 acc/top1 at 13 epoch is saved to best_acc/top1_epoch_13.pth. 2022/07/30 20:20:18 - mmengine - INFO - Epoch(train) [13][100/3757] lr: 6.5454e-05 eta: 10:39:34 time: 0.5507 data_time: 0.0143 memory: 33632 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.3266 loss: 1.3266 2022/07/30 20:21:14 - mmengine - INFO - Epoch(train) [13][200/3757] lr: 6.5454e-05 eta: 10:38:36 time: 0.5529 data_time: 0.0145 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5641 loss: 1.5641 2022/07/30 20:22:09 - mmengine - INFO - Epoch(train) [13][300/3757] lr: 6.5454e-05 eta: 10:37:37 time: 0.5590 data_time: 0.0154 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2140 loss: 1.2140 2022/07/30 20:23:05 - mmengine - INFO - Epoch(train) [13][400/3757] lr: 6.5454e-05 eta: 10:36:39 time: 0.5558 data_time: 0.0163 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.5286 loss: 1.5286 2022/07/30 20:24:01 - mmengine - INFO - Epoch(train) [13][500/3757] lr: 6.5454e-05 eta: 10:35:40 time: 0.5512 data_time: 0.0143 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4922 loss: 1.4922 2022/07/30 20:24:56 - mmengine - INFO - Epoch(train) [13][600/3757] lr: 6.5454e-05 eta: 10:34:41 time: 0.5599 data_time: 0.0163 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2165 loss: 1.2165 2022/07/30 20:25:52 - mmengine - INFO - Epoch(train) [13][700/3757] lr: 6.5454e-05 eta: 10:33:43 time: 0.5617 data_time: 0.0158 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4354 loss: 1.4354 2022/07/30 20:26:48 - mmengine - INFO - Epoch(train) [13][800/3757] lr: 6.5454e-05 eta: 10:32:44 time: 0.5600 data_time: 0.0164 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3154 loss: 1.3154 2022/07/30 20:27:43 - mmengine - INFO - Epoch(train) [13][900/3757] lr: 6.5454e-05 eta: 10:31:46 time: 0.5577 data_time: 0.0187 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4864 loss: 1.4864 2022/07/30 20:27:52 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 20:28:39 - mmengine - INFO - Epoch(train) [13][1000/3757] lr: 6.5454e-05 eta: 10:30:47 time: 0.5515 data_time: 0.0141 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4733 loss: 1.4733 2022/07/30 20:29:35 - mmengine - INFO - Epoch(train) [13][1100/3757] lr: 6.5454e-05 eta: 10:29:49 time: 0.5513 data_time: 0.0142 memory: 33632 top1_acc: 0.2500 top5_acc: 0.3750 loss_cls: 1.7053 loss: 1.7053 2022/07/30 20:30:31 - mmengine - INFO - Epoch(train) [13][1200/3757] lr: 6.5454e-05 eta: 10:28:51 time: 0.5559 data_time: 0.0160 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1849 loss: 1.1849 2022/07/30 20:31:26 - mmengine - INFO - Epoch(train) [13][1300/3757] lr: 6.5454e-05 eta: 10:27:52 time: 0.5557 data_time: 0.0158 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3831 loss: 1.3831 2022/07/30 20:32:22 - mmengine - INFO - Epoch(train) [13][1400/3757] lr: 6.5454e-05 eta: 10:26:53 time: 0.5571 data_time: 0.0145 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.5664 loss: 1.5664 2022/07/30 20:33:17 - mmengine - INFO - Epoch(train) [13][1500/3757] lr: 6.5454e-05 eta: 10:25:55 time: 0.5518 data_time: 0.0137 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.5097 loss: 1.5097 2022/07/30 20:34:13 - mmengine - INFO - Epoch(train) [13][1600/3757] lr: 6.5454e-05 eta: 10:24:56 time: 0.5520 data_time: 0.0154 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3354 loss: 1.3354 2022/07/30 20:35:09 - mmengine - INFO - Epoch(train) [13][1700/3757] lr: 6.5454e-05 eta: 10:23:58 time: 0.5564 data_time: 0.0149 memory: 33632 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.6307 loss: 1.6307 2022/07/30 20:36:04 - mmengine - INFO - Epoch(train) [13][1800/3757] lr: 6.5454e-05 eta: 10:23:00 time: 0.5632 data_time: 0.0158 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5063 loss: 1.5063 2022/07/30 20:37:00 - mmengine - INFO - Epoch(train) [13][1900/3757] lr: 6.5454e-05 eta: 10:22:01 time: 0.5537 data_time: 0.0146 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2161 loss: 1.2161 2022/07/30 20:37:09 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 20:37:55 - mmengine - INFO - Epoch(train) [13][2000/3757] lr: 6.5454e-05 eta: 10:21:03 time: 0.5551 data_time: 0.0157 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.3381 loss: 1.3381 2022/07/30 20:38:51 - mmengine - INFO - Epoch(train) [13][2100/3757] lr: 6.5454e-05 eta: 10:20:04 time: 0.5626 data_time: 0.0162 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.4936 loss: 1.4936 2022/07/30 20:39:47 - mmengine - INFO - Epoch(train) [13][2200/3757] lr: 6.5454e-05 eta: 10:19:06 time: 0.5514 data_time: 0.0158 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.6420 loss: 1.6420 2022/07/30 20:40:42 - mmengine - INFO - Epoch(train) [13][2300/3757] lr: 6.5454e-05 eta: 10:18:07 time: 0.5538 data_time: 0.0161 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4833 loss: 1.4833 2022/07/30 20:41:38 - mmengine - INFO - Epoch(train) [13][2400/3757] lr: 6.5454e-05 eta: 10:17:09 time: 0.5544 data_time: 0.0148 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.5584 loss: 1.5584 2022/07/30 20:42:33 - mmengine - INFO - Epoch(train) [13][2500/3757] lr: 6.5454e-05 eta: 10:16:10 time: 0.5521 data_time: 0.0158 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6818 loss: 1.6818 2022/07/30 20:43:29 - mmengine - INFO - Epoch(train) [13][2600/3757] lr: 6.5454e-05 eta: 10:15:12 time: 0.5585 data_time: 0.0154 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4420 loss: 1.4420 2022/07/30 20:44:24 - mmengine - INFO - Epoch(train) [13][2700/3757] lr: 6.5454e-05 eta: 10:14:14 time: 0.5535 data_time: 0.0155 memory: 33632 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.6473 loss: 1.6473 2022/07/30 20:45:20 - mmengine - INFO - Epoch(train) [13][2800/3757] lr: 6.5454e-05 eta: 10:13:15 time: 0.5564 data_time: 0.0136 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6134 loss: 1.6134 2022/07/30 20:46:16 - mmengine - INFO - Epoch(train) [13][2900/3757] lr: 6.5454e-05 eta: 10:12:17 time: 0.5515 data_time: 0.0158 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5524 loss: 1.5524 2022/07/30 20:46:24 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 20:47:11 - mmengine - INFO - Epoch(train) [13][3000/3757] lr: 6.5454e-05 eta: 10:11:19 time: 0.5623 data_time: 0.0149 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.7021 loss: 1.7021 2022/07/30 20:48:09 - mmengine - INFO - Epoch(train) [13][3100/3757] lr: 6.5454e-05 eta: 10:10:24 time: 0.6773 data_time: 0.0171 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2748 loss: 1.2748 2022/07/30 20:49:06 - mmengine - INFO - Epoch(train) [13][3200/3757] lr: 6.5454e-05 eta: 10:09:26 time: 0.5632 data_time: 0.0171 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.7457 loss: 1.7457 2022/07/30 20:50:01 - mmengine - INFO - Epoch(train) [13][3300/3757] lr: 6.5454e-05 eta: 10:08:28 time: 0.5539 data_time: 0.0146 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5050 loss: 1.5050 2022/07/30 20:50:57 - mmengine - INFO - Epoch(train) [13][3400/3757] lr: 6.5454e-05 eta: 10:07:29 time: 0.5511 data_time: 0.0142 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5379 loss: 1.5379 2022/07/30 20:51:52 - mmengine - INFO - Epoch(train) [13][3500/3757] lr: 6.5454e-05 eta: 10:06:31 time: 0.5590 data_time: 0.0155 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.5050 loss: 1.5050 2022/07/30 20:52:48 - mmengine - INFO - Epoch(train) [13][3600/3757] lr: 6.5454e-05 eta: 10:05:33 time: 0.5526 data_time: 0.0147 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3941 loss: 1.3941 2022/07/30 20:53:44 - mmengine - INFO - Epoch(train) [13][3700/3757] lr: 6.5454e-05 eta: 10:04:35 time: 0.5581 data_time: 0.0151 memory: 33632 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.6046 loss: 1.6046 2022/07/30 20:54:15 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 20:54:15 - mmengine - INFO - Epoch(train) [13][3757/3757] lr: 6.5454e-05 eta: 10:04:11 time: 0.5499 data_time: 0.0174 memory: 33632 top1_acc: 0.7143 top5_acc: 0.7143 loss_cls: 1.5706 loss: 1.5706 2022/07/30 20:55:13 - mmengine - INFO - Epoch(train) [14][100/3757] lr: 6.0398e-05 eta: 10:02:53 time: 0.5579 data_time: 0.0155 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3067 loss: 1.3067 2022/07/30 20:55:46 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 20:56:08 - mmengine - INFO - Epoch(train) [14][200/3757] lr: 6.0398e-05 eta: 10:01:55 time: 0.5575 data_time: 0.0142 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.7689 loss: 1.7689 2022/07/30 20:57:04 - mmengine - INFO - Epoch(train) [14][300/3757] lr: 6.0398e-05 eta: 10:00:57 time: 0.5514 data_time: 0.0146 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.6595 loss: 1.6595 2022/07/30 20:58:00 - mmengine - INFO - Epoch(train) [14][400/3757] lr: 6.0398e-05 eta: 9:59:59 time: 0.5567 data_time: 0.0143 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3880 loss: 1.3880 2022/07/30 20:58:55 - mmengine - INFO - Epoch(train) [14][500/3757] lr: 6.0398e-05 eta: 9:59:01 time: 0.5600 data_time: 0.0154 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1645 loss: 1.1645 2022/07/30 20:59:51 - mmengine - INFO - Epoch(train) [14][600/3757] lr: 6.0398e-05 eta: 9:58:02 time: 0.5510 data_time: 0.0143 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4578 loss: 1.4578 2022/07/30 21:00:46 - mmengine - INFO - Epoch(train) [14][700/3757] lr: 6.0398e-05 eta: 9:57:04 time: 0.5545 data_time: 0.0153 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5523 loss: 1.5523 2022/07/30 21:01:42 - mmengine - INFO - Epoch(train) [14][800/3757] lr: 6.0398e-05 eta: 9:56:06 time: 0.5534 data_time: 0.0148 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4702 loss: 1.4702 2022/07/30 21:02:37 - mmengine - INFO - Epoch(train) [14][900/3757] lr: 6.0398e-05 eta: 9:55:07 time: 0.5528 data_time: 0.0156 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.5920 loss: 1.5920 2022/07/30 21:03:33 - mmengine - INFO - Epoch(train) [14][1000/3757] lr: 6.0398e-05 eta: 9:54:09 time: 0.5579 data_time: 0.0153 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5035 loss: 1.5035 2022/07/30 21:04:29 - mmengine - INFO - Epoch(train) [14][1100/3757] lr: 6.0398e-05 eta: 9:53:11 time: 0.5621 data_time: 0.0156 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5659 loss: 1.5659 2022/07/30 21:05:01 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 21:05:24 - mmengine - INFO - Epoch(train) [14][1200/3757] lr: 6.0398e-05 eta: 9:52:13 time: 0.5534 data_time: 0.0156 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2446 loss: 1.2446 2022/07/30 21:06:20 - mmengine - INFO - Epoch(train) [14][1300/3757] lr: 6.0398e-05 eta: 9:51:15 time: 0.5564 data_time: 0.0152 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4749 loss: 1.4749 2022/07/30 21:07:15 - mmengine - INFO - Epoch(train) [14][1400/3757] lr: 6.0398e-05 eta: 9:50:17 time: 0.5550 data_time: 0.0153 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4986 loss: 1.4986 2022/07/30 21:08:11 - mmengine - INFO - Epoch(train) [14][1500/3757] lr: 6.0398e-05 eta: 9:49:19 time: 0.5528 data_time: 0.0143 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5428 loss: 1.5428 2022/07/30 21:09:07 - mmengine - INFO - Epoch(train) [14][1600/3757] lr: 6.0398e-05 eta: 9:48:22 time: 0.5522 data_time: 0.0148 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2984 loss: 1.2984 2022/07/30 21:10:03 - mmengine - INFO - Epoch(train) [14][1700/3757] lr: 6.0398e-05 eta: 9:47:24 time: 0.5542 data_time: 0.0152 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4897 loss: 1.4897 2022/07/30 21:10:58 - mmengine - INFO - Epoch(train) [14][1800/3757] lr: 6.0398e-05 eta: 9:46:25 time: 0.5617 data_time: 0.0156 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2391 loss: 1.2391 2022/07/30 21:11:54 - mmengine - INFO - Epoch(train) [14][1900/3757] lr: 6.0398e-05 eta: 9:45:27 time: 0.5560 data_time: 0.0149 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4506 loss: 1.4506 2022/07/30 21:12:49 - mmengine - INFO - Epoch(train) [14][2000/3757] lr: 6.0398e-05 eta: 9:44:29 time: 0.5511 data_time: 0.0143 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4938 loss: 1.4938 2022/07/30 21:13:45 - mmengine - INFO - Epoch(train) [14][2100/3757] lr: 6.0398e-05 eta: 9:43:32 time: 0.5550 data_time: 0.0162 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.5253 loss: 1.5253 2022/07/30 21:14:18 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 21:14:41 - mmengine - INFO - Epoch(train) [14][2200/3757] lr: 6.0398e-05 eta: 9:42:34 time: 0.5598 data_time: 0.0153 memory: 33632 top1_acc: 0.2500 top5_acc: 0.5000 loss_cls: 1.3451 loss: 1.3451 2022/07/30 21:15:37 - mmengine - INFO - Epoch(train) [14][2300/3757] lr: 6.0398e-05 eta: 9:41:36 time: 0.5642 data_time: 0.0148 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.7059 loss: 1.7059 2022/07/30 21:16:32 - mmengine - INFO - Epoch(train) [14][2400/3757] lr: 6.0398e-05 eta: 9:40:38 time: 0.5515 data_time: 0.0139 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4300 loss: 1.4300 2022/07/30 21:17:28 - mmengine - INFO - Epoch(train) [14][2500/3757] lr: 6.0398e-05 eta: 9:39:40 time: 0.5523 data_time: 0.0153 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0966 loss: 1.0966 2022/07/30 21:18:24 - mmengine - INFO - Epoch(train) [14][2600/3757] lr: 6.0398e-05 eta: 9:38:42 time: 0.5617 data_time: 0.0160 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6806 loss: 1.6806 2022/07/30 21:19:19 - mmengine - INFO - Epoch(train) [14][2700/3757] lr: 6.0398e-05 eta: 9:37:44 time: 0.5550 data_time: 0.0149 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.6681 loss: 1.6681 2022/07/30 21:20:15 - mmengine - INFO - Epoch(train) [14][2800/3757] lr: 6.0398e-05 eta: 9:36:46 time: 0.5610 data_time: 0.0156 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.5918 loss: 1.5918 2022/07/30 21:21:11 - mmengine - INFO - Epoch(train) [14][2900/3757] lr: 6.0398e-05 eta: 9:35:49 time: 0.5561 data_time: 0.0152 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3212 loss: 1.3212 2022/07/30 21:22:06 - mmengine - INFO - Epoch(train) [14][3000/3757] lr: 6.0398e-05 eta: 9:34:51 time: 0.5517 data_time: 0.0157 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.3845 loss: 1.3845 2022/07/30 21:23:02 - mmengine - INFO - Epoch(train) [14][3100/3757] lr: 6.0398e-05 eta: 9:33:53 time: 0.5547 data_time: 0.0145 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2681 loss: 1.2681 2022/07/30 21:23:35 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 21:23:58 - mmengine - INFO - Epoch(train) [14][3200/3757] lr: 6.0398e-05 eta: 9:32:55 time: 0.5591 data_time: 0.0147 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3691 loss: 1.3691 2022/07/30 21:24:53 - mmengine - INFO - Epoch(train) [14][3300/3757] lr: 6.0398e-05 eta: 9:31:57 time: 0.5552 data_time: 0.0155 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4636 loss: 1.4636 2022/07/30 21:25:49 - mmengine - INFO - Epoch(train) [14][3400/3757] lr: 6.0398e-05 eta: 9:30:59 time: 0.5511 data_time: 0.0139 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2029 loss: 1.2029 2022/07/30 21:26:44 - mmengine - INFO - Epoch(train) [14][3500/3757] lr: 6.0398e-05 eta: 9:30:01 time: 0.5573 data_time: 0.0160 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2907 loss: 1.2907 2022/07/30 21:27:40 - mmengine - INFO - Epoch(train) [14][3600/3757] lr: 6.0398e-05 eta: 9:29:04 time: 0.5526 data_time: 0.0149 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3954 loss: 1.3954 2022/07/30 21:28:36 - mmengine - INFO - Epoch(train) [14][3700/3757] lr: 6.0398e-05 eta: 9:28:06 time: 0.5632 data_time: 0.0155 memory: 33632 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.3471 loss: 1.3471 2022/07/30 21:29:07 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 21:29:07 - mmengine - INFO - Epoch(train) [14][3757/3757] lr: 6.0398e-05 eta: 9:27:43 time: 0.5402 data_time: 0.0145 memory: 33632 top1_acc: 0.8571 top5_acc: 0.8571 loss_cls: 1.4089 loss: 1.4089 2022/07/30 21:30:05 - mmengine - INFO - Epoch(train) [15][100/3757] lr: 5.5229e-05 eta: 9:26:26 time: 0.5539 data_time: 0.0156 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2784 loss: 1.2784 2022/07/30 21:31:01 - mmengine - INFO - Epoch(train) [15][200/3757] lr: 5.5229e-05 eta: 9:25:29 time: 0.5569 data_time: 0.0149 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.4111 loss: 1.4111 2022/07/30 21:31:56 - mmengine - INFO - Epoch(train) [15][300/3757] lr: 5.5229e-05 eta: 9:24:31 time: 0.5600 data_time: 0.0166 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4501 loss: 1.4501 2022/07/30 21:32:52 - mmengine - INFO - Epoch(train) [15][400/3757] lr: 5.5229e-05 eta: 9:23:33 time: 0.5510 data_time: 0.0147 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4625 loss: 1.4625 2022/07/30 21:32:53 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 21:33:48 - mmengine - INFO - Epoch(train) [15][500/3757] lr: 5.5229e-05 eta: 9:22:35 time: 0.5573 data_time: 0.0149 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3097 loss: 1.3097 2022/07/30 21:34:43 - mmengine - INFO - Epoch(train) [15][600/3757] lr: 5.5229e-05 eta: 9:21:37 time: 0.5548 data_time: 0.0150 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3912 loss: 1.3912 2022/07/30 21:35:39 - mmengine - INFO - Epoch(train) [15][700/3757] lr: 5.5229e-05 eta: 9:20:40 time: 0.5541 data_time: 0.0155 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4228 loss: 1.4228 2022/07/30 21:36:34 - mmengine - INFO - Epoch(train) [15][800/3757] lr: 5.5229e-05 eta: 9:19:42 time: 0.5546 data_time: 0.0156 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2704 loss: 1.2704 2022/07/30 21:37:30 - mmengine - INFO - Epoch(train) [15][900/3757] lr: 5.5229e-05 eta: 9:18:44 time: 0.5572 data_time: 0.0153 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6438 loss: 1.6438 2022/07/30 21:38:26 - mmengine - INFO - Epoch(train) [15][1000/3757] lr: 5.5229e-05 eta: 9:17:46 time: 0.5524 data_time: 0.0145 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2622 loss: 1.2622 2022/07/30 21:39:21 - mmengine - INFO - Epoch(train) [15][1100/3757] lr: 5.5229e-05 eta: 9:16:49 time: 0.5535 data_time: 0.0153 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.3652 loss: 1.3652 2022/07/30 21:40:17 - mmengine - INFO - Epoch(train) [15][1200/3757] lr: 5.5229e-05 eta: 9:15:51 time: 0.5586 data_time: 0.0156 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.2774 loss: 1.2774 2022/07/30 21:41:12 - mmengine - INFO - Epoch(train) [15][1300/3757] lr: 5.5229e-05 eta: 9:14:53 time: 0.5524 data_time: 0.0154 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4314 loss: 1.4314 2022/07/30 21:42:08 - mmengine - INFO - Epoch(train) [15][1400/3757] lr: 5.5229e-05 eta: 9:13:55 time: 0.5536 data_time: 0.0169 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1904 loss: 1.1904 2022/07/30 21:42:09 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 21:43:03 - mmengine - INFO - Epoch(train) [15][1500/3757] lr: 5.5229e-05 eta: 9:12:57 time: 0.5551 data_time: 0.0152 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1992 loss: 1.1992 2022/07/30 21:43:59 - mmengine - INFO - Epoch(train) [15][1600/3757] lr: 5.5229e-05 eta: 9:12:00 time: 0.5549 data_time: 0.0151 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3924 loss: 1.3924 2022/07/30 21:44:54 - mmengine - INFO - Epoch(train) [15][1700/3757] lr: 5.5229e-05 eta: 9:11:02 time: 0.5520 data_time: 0.0154 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3214 loss: 1.3214 2022/07/30 21:45:50 - mmengine - INFO - Epoch(train) [15][1800/3757] lr: 5.5229e-05 eta: 9:10:04 time: 0.5499 data_time: 0.0141 memory: 33632 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.4266 loss: 1.4266 2022/07/30 21:46:45 - mmengine - INFO - Epoch(train) [15][1900/3757] lr: 5.5229e-05 eta: 9:09:06 time: 0.5587 data_time: 0.0153 memory: 33632 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 1.2969 loss: 1.2969 2022/07/30 21:47:41 - mmengine - INFO - Epoch(train) [15][2000/3757] lr: 5.5229e-05 eta: 9:08:08 time: 0.5556 data_time: 0.0154 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4891 loss: 1.4891 2022/07/30 21:48:36 - mmengine - INFO - Epoch(train) [15][2100/3757] lr: 5.5229e-05 eta: 9:07:10 time: 0.5507 data_time: 0.0141 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4108 loss: 1.4108 2022/07/30 21:49:32 - mmengine - INFO - Epoch(train) [15][2200/3757] lr: 5.5229e-05 eta: 9:06:13 time: 0.5512 data_time: 0.0148 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3674 loss: 1.3674 2022/07/30 21:50:27 - mmengine - INFO - Epoch(train) [15][2300/3757] lr: 5.5229e-05 eta: 9:05:15 time: 0.5584 data_time: 0.0152 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4006 loss: 1.4006 2022/07/30 21:51:23 - mmengine - INFO - Epoch(train) [15][2400/3757] lr: 5.5229e-05 eta: 9:04:18 time: 0.5510 data_time: 0.0156 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2717 loss: 1.2717 2022/07/30 21:51:24 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 21:52:19 - mmengine - INFO - Epoch(train) [15][2500/3757] lr: 5.5229e-05 eta: 9:03:20 time: 0.5571 data_time: 0.0160 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.6030 loss: 1.6030 2022/07/30 21:53:15 - mmengine - INFO - Epoch(train) [15][2600/3757] lr: 5.5229e-05 eta: 9:02:23 time: 0.5510 data_time: 0.0137 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.6046 loss: 1.6046 2022/07/30 21:54:11 - mmengine - INFO - Epoch(train) [15][2700/3757] lr: 5.5229e-05 eta: 9:01:26 time: 0.5516 data_time: 0.0148 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3156 loss: 1.3156 2022/07/30 21:55:06 - mmengine - INFO - Epoch(train) [15][2800/3757] lr: 5.5229e-05 eta: 9:00:28 time: 0.5547 data_time: 0.0150 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4092 loss: 1.4092 2022/07/30 21:56:02 - mmengine - INFO - Epoch(train) [15][2900/3757] lr: 5.5229e-05 eta: 8:59:30 time: 0.5514 data_time: 0.0141 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2558 loss: 1.2558 2022/07/30 21:56:57 - mmengine - INFO - Epoch(train) [15][3000/3757] lr: 5.5229e-05 eta: 8:58:32 time: 0.5532 data_time: 0.0140 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3503 loss: 1.3503 2022/07/30 21:57:53 - mmengine - INFO - Epoch(train) [15][3100/3757] lr: 5.5229e-05 eta: 8:57:35 time: 0.5516 data_time: 0.0139 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.6312 loss: 1.6312 2022/07/30 21:58:48 - mmengine - INFO - Epoch(train) [15][3200/3757] lr: 5.5229e-05 eta: 8:56:37 time: 0.5540 data_time: 0.0149 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.7227 loss: 1.7227 2022/07/30 21:59:44 - mmengine - INFO - Epoch(train) [15][3300/3757] lr: 5.5229e-05 eta: 8:55:40 time: 0.5551 data_time: 0.0153 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0758 loss: 1.0758 2022/07/30 22:00:40 - mmengine - INFO - Epoch(train) [15][3400/3757] lr: 5.5229e-05 eta: 8:54:42 time: 0.5648 data_time: 0.0161 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5621 loss: 1.5621 2022/07/30 22:00:41 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 22:01:35 - mmengine - INFO - Epoch(train) [15][3500/3757] lr: 5.5229e-05 eta: 8:53:44 time: 0.5514 data_time: 0.0153 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.5704 loss: 1.5704 2022/07/30 22:02:31 - mmengine - INFO - Epoch(train) [15][3600/3757] lr: 5.5229e-05 eta: 8:52:47 time: 0.5516 data_time: 0.0147 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2705 loss: 1.2705 2022/07/30 22:03:26 - mmengine - INFO - Epoch(train) [15][3700/3757] lr: 5.5229e-05 eta: 8:51:49 time: 0.5575 data_time: 0.0152 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4559 loss: 1.4559 2022/07/30 22:03:58 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 22:03:58 - mmengine - INFO - Epoch(train) [15][3757/3757] lr: 5.5229e-05 eta: 8:51:27 time: 0.5399 data_time: 0.0141 memory: 33632 top1_acc: 0.7143 top5_acc: 1.0000 loss_cls: 1.7074 loss: 1.7074 2022/07/30 22:03:58 - mmengine - INFO - Saving checkpoint at 15 epochs 2022/07/30 22:04:25 - mmengine - INFO - Epoch(val) [15][100/310] eta: 0:00:46 time: 0.2192 data_time: 0.0184 memory: 6325 2022/07/30 22:04:48 - mmengine - INFO - Epoch(val) [15][200/310] eta: 0:00:24 time: 0.2209 data_time: 0.0188 memory: 6325 2022/07/30 22:05:09 - mmengine - INFO - Epoch(val) [15][300/310] eta: 0:00:01 time: 0.1977 data_time: 0.0100 memory: 6325 2022/07/30 22:05:12 - mmengine - INFO - Epoch(val) [15][310/310] acc/top1: 0.7144 acc/top5: 0.9012 acc/mean1: 0.7144 2022/07/30 22:05:12 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb/best_acc/top1_epoch_13.pth is removed 2022/07/30 22:05:15 - mmengine - INFO - The best checkpoint with 0.7144 acc/top1 at 16 epoch is saved to best_acc/top1_epoch_16.pth. 2022/07/30 22:06:11 - mmengine - INFO - Epoch(train) [16][100/3757] lr: 5.0002e-05 eta: 8:50:11 time: 0.5513 data_time: 0.0155 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.6279 loss: 1.6279 2022/07/30 22:07:07 - mmengine - INFO - Epoch(train) [16][200/3757] lr: 5.0002e-05 eta: 8:49:13 time: 0.5498 data_time: 0.0140 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2202 loss: 1.2202 2022/07/30 22:08:02 - mmengine - INFO - Epoch(train) [16][300/3757] lr: 5.0002e-05 eta: 8:48:16 time: 0.5508 data_time: 0.0159 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0886 loss: 1.0886 2022/07/30 22:08:58 - mmengine - INFO - Epoch(train) [16][400/3757] lr: 5.0002e-05 eta: 8:47:18 time: 0.5521 data_time: 0.0159 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3728 loss: 1.3728 2022/07/30 22:09:53 - mmengine - INFO - Epoch(train) [16][500/3757] lr: 5.0002e-05 eta: 8:46:20 time: 0.5563 data_time: 0.0151 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3095 loss: 1.3095 2022/07/30 22:10:49 - mmengine - INFO - Epoch(train) [16][600/3757] lr: 5.0002e-05 eta: 8:45:23 time: 0.5511 data_time: 0.0143 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3855 loss: 1.3855 2022/07/30 22:11:14 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 22:11:44 - mmengine - INFO - Epoch(train) [16][700/3757] lr: 5.0002e-05 eta: 8:44:25 time: 0.5554 data_time: 0.0154 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1777 loss: 1.1777 2022/07/30 22:12:40 - mmengine - INFO - Epoch(train) [16][800/3757] lr: 5.0002e-05 eta: 8:43:28 time: 0.5618 data_time: 0.0158 memory: 33632 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.5272 loss: 1.5272 2022/07/30 22:13:36 - mmengine - INFO - Epoch(train) [16][900/3757] lr: 5.0002e-05 eta: 8:42:31 time: 0.5545 data_time: 0.0145 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4401 loss: 1.4401 2022/07/30 22:14:31 - mmengine - INFO - Epoch(train) [16][1000/3757] lr: 5.0002e-05 eta: 8:41:33 time: 0.5537 data_time: 0.0148 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1103 loss: 1.1103 2022/07/30 22:15:27 - mmengine - INFO - Epoch(train) [16][1100/3757] lr: 5.0002e-05 eta: 8:40:36 time: 0.5552 data_time: 0.0155 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3954 loss: 1.3954 2022/07/30 22:16:23 - mmengine - INFO - Epoch(train) [16][1200/3757] lr: 5.0002e-05 eta: 8:39:39 time: 0.5559 data_time: 0.0154 memory: 33632 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.1915 loss: 1.1915 2022/07/30 22:17:18 - mmengine - INFO - Epoch(train) [16][1300/3757] lr: 5.0002e-05 eta: 8:38:41 time: 0.5517 data_time: 0.0147 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3908 loss: 1.3908 2022/07/30 22:18:14 - mmengine - INFO - Epoch(train) [16][1400/3757] lr: 5.0002e-05 eta: 8:37:44 time: 0.5535 data_time: 0.0148 memory: 33632 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.3785 loss: 1.3785 2022/07/30 22:19:10 - mmengine - INFO - Epoch(train) [16][1500/3757] lr: 5.0002e-05 eta: 8:36:46 time: 0.5523 data_time: 0.0147 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2534 loss: 1.2534 2022/07/30 22:20:05 - mmengine - INFO - Epoch(train) [16][1600/3757] lr: 5.0002e-05 eta: 8:35:49 time: 0.5602 data_time: 0.0157 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.3925 loss: 1.3925 2022/07/30 22:20:30 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 22:21:01 - mmengine - INFO - Epoch(train) [16][1700/3757] lr: 5.0002e-05 eta: 8:34:51 time: 0.5598 data_time: 0.0150 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.5331 loss: 1.5331 2022/07/30 22:21:57 - mmengine - INFO - Epoch(train) [16][1800/3757] lr: 5.0002e-05 eta: 8:33:54 time: 0.5541 data_time: 0.0146 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2876 loss: 1.2876 2022/07/30 22:22:52 - mmengine - INFO - Epoch(train) [16][1900/3757] lr: 5.0002e-05 eta: 8:32:57 time: 0.5553 data_time: 0.0154 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4110 loss: 1.4110 2022/07/30 22:23:48 - mmengine - INFO - Epoch(train) [16][2000/3757] lr: 5.0002e-05 eta: 8:31:59 time: 0.5536 data_time: 0.0155 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.5369 loss: 1.5369 2022/07/30 22:24:44 - mmengine - INFO - Epoch(train) [16][2100/3757] lr: 5.0002e-05 eta: 8:31:02 time: 0.5662 data_time: 0.0158 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0688 loss: 1.0688 2022/07/30 22:25:39 - mmengine - INFO - Epoch(train) [16][2200/3757] lr: 5.0002e-05 eta: 8:30:05 time: 0.5514 data_time: 0.0149 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4944 loss: 1.4944 2022/07/30 22:26:35 - mmengine - INFO - Epoch(train) [16][2300/3757] lr: 5.0002e-05 eta: 8:29:07 time: 0.5515 data_time: 0.0148 memory: 33632 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 0.9907 loss: 0.9907 2022/07/30 22:27:30 - mmengine - INFO - Epoch(train) [16][2400/3757] lr: 5.0002e-05 eta: 8:28:10 time: 0.5525 data_time: 0.0147 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4202 loss: 1.4202 2022/07/30 22:28:26 - mmengine - INFO - Epoch(train) [16][2500/3757] lr: 5.0002e-05 eta: 8:27:13 time: 0.5563 data_time: 0.0155 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4862 loss: 1.4862 2022/07/30 22:29:22 - mmengine - INFO - Epoch(train) [16][2600/3757] lr: 5.0002e-05 eta: 8:26:16 time: 0.5592 data_time: 0.0172 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1954 loss: 1.1954 2022/07/30 22:29:47 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 22:30:17 - mmengine - INFO - Epoch(train) [16][2700/3757] lr: 5.0002e-05 eta: 8:25:18 time: 0.5519 data_time: 0.0146 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3321 loss: 1.3321 2022/07/30 22:31:13 - mmengine - INFO - Epoch(train) [16][2800/3757] lr: 5.0002e-05 eta: 8:24:21 time: 0.5514 data_time: 0.0142 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1434 loss: 1.1434 2022/07/30 22:32:09 - mmengine - INFO - Epoch(train) [16][2900/3757] lr: 5.0002e-05 eta: 8:23:23 time: 0.5616 data_time: 0.0157 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2949 loss: 1.2949 2022/07/30 22:33:04 - mmengine - INFO - Epoch(train) [16][3000/3757] lr: 5.0002e-05 eta: 8:22:26 time: 0.5555 data_time: 0.0144 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3488 loss: 1.3488 2022/07/30 22:34:00 - mmengine - INFO - Epoch(train) [16][3100/3757] lr: 5.0002e-05 eta: 8:21:29 time: 0.5517 data_time: 0.0143 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3601 loss: 1.3601 2022/07/30 22:34:55 - mmengine - INFO - Epoch(train) [16][3200/3757] lr: 5.0002e-05 eta: 8:20:31 time: 0.5524 data_time: 0.0147 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.6769 loss: 1.6769 2022/07/30 22:35:51 - mmengine - INFO - Epoch(train) [16][3300/3757] lr: 5.0002e-05 eta: 8:19:34 time: 0.5539 data_time: 0.0149 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3751 loss: 1.3751 2022/07/30 22:36:46 - mmengine - INFO - Epoch(train) [16][3400/3757] lr: 5.0002e-05 eta: 8:18:37 time: 0.5561 data_time: 0.0141 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2248 loss: 1.2248 2022/07/30 22:37:42 - mmengine - INFO - Epoch(train) [16][3500/3757] lr: 5.0002e-05 eta: 8:17:39 time: 0.5532 data_time: 0.0146 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3391 loss: 1.3391 2022/07/30 22:38:38 - mmengine - INFO - Epoch(train) [16][3600/3757] lr: 5.0002e-05 eta: 8:16:42 time: 0.5510 data_time: 0.0148 memory: 33632 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.3085 loss: 1.3085 2022/07/30 22:39:03 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 22:39:33 - mmengine - INFO - Epoch(train) [16][3700/3757] lr: 5.0002e-05 eta: 8:15:45 time: 0.5588 data_time: 0.0153 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3909 loss: 1.3909 2022/07/30 22:40:05 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 22:40:05 - mmengine - INFO - Epoch(train) [16][3757/3757] lr: 5.0002e-05 eta: 8:15:22 time: 0.5456 data_time: 0.0157 memory: 33632 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 1.4461 loss: 1.4461 2022/07/30 22:41:02 - mmengine - INFO - Epoch(train) [17][100/3757] lr: 4.4776e-05 eta: 8:14:08 time: 0.5567 data_time: 0.0147 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1675 loss: 1.1675 2022/07/30 22:41:58 - mmengine - INFO - Epoch(train) [17][200/3757] lr: 4.4776e-05 eta: 8:13:11 time: 0.5531 data_time: 0.0144 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2267 loss: 1.2267 2022/07/30 22:42:56 - mmengine - INFO - Epoch(train) [17][300/3757] lr: 4.4776e-05 eta: 8:12:16 time: 0.5603 data_time: 0.0163 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3034 loss: 1.3034 2022/07/30 22:43:52 - mmengine - INFO - Epoch(train) [17][400/3757] lr: 4.4776e-05 eta: 8:11:19 time: 0.5555 data_time: 0.0152 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3290 loss: 1.3290 2022/07/30 22:44:47 - mmengine - INFO - Epoch(train) [17][500/3757] lr: 4.4776e-05 eta: 8:10:21 time: 0.5519 data_time: 0.0162 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4425 loss: 1.4425 2022/07/30 22:45:43 - mmengine - INFO - Epoch(train) [17][600/3757] lr: 4.4776e-05 eta: 8:09:24 time: 0.5559 data_time: 0.0142 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2774 loss: 1.2774 2022/07/30 22:46:38 - mmengine - INFO - Epoch(train) [17][700/3757] lr: 4.4776e-05 eta: 8:08:27 time: 0.5540 data_time: 0.0149 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.5181 loss: 1.5181 2022/07/30 22:47:34 - mmengine - INFO - Epoch(train) [17][800/3757] lr: 4.4776e-05 eta: 8:07:30 time: 0.5606 data_time: 0.0153 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4272 loss: 1.4272 2022/07/30 22:48:23 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 22:48:30 - mmengine - INFO - Epoch(train) [17][900/3757] lr: 4.4776e-05 eta: 8:06:32 time: 0.5519 data_time: 0.0143 memory: 33632 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.4797 loss: 1.4797 2022/07/30 22:49:25 - mmengine - INFO - Epoch(train) [17][1000/3757] lr: 4.4776e-05 eta: 8:05:35 time: 0.5528 data_time: 0.0146 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2847 loss: 1.2847 2022/07/30 22:50:23 - mmengine - INFO - Epoch(train) [17][1100/3757] lr: 4.4776e-05 eta: 8:04:40 time: 0.6821 data_time: 0.0273 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.3878 loss: 1.3878 2022/07/30 22:51:19 - mmengine - INFO - Epoch(train) [17][1200/3757] lr: 4.4776e-05 eta: 8:03:43 time: 0.5541 data_time: 0.0155 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1294 loss: 1.1294 2022/07/30 22:52:14 - mmengine - INFO - Epoch(train) [17][1300/3757] lr: 4.4776e-05 eta: 8:02:46 time: 0.5570 data_time: 0.0149 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3505 loss: 1.3505 2022/07/30 22:53:10 - mmengine - INFO - Epoch(train) [17][1400/3757] lr: 4.4776e-05 eta: 8:01:48 time: 0.5523 data_time: 0.0148 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4116 loss: 1.4116 2022/07/30 22:54:06 - mmengine - INFO - Epoch(train) [17][1500/3757] lr: 4.4776e-05 eta: 8:00:51 time: 0.5520 data_time: 0.0153 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.3330 loss: 1.3330 2022/07/30 22:55:01 - mmengine - INFO - Epoch(train) [17][1600/3757] lr: 4.4776e-05 eta: 7:59:54 time: 0.5522 data_time: 0.0172 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3944 loss: 1.3944 2022/07/30 22:55:57 - mmengine - INFO - Epoch(train) [17][1700/3757] lr: 4.4776e-05 eta: 7:58:57 time: 0.5603 data_time: 0.0164 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0250 loss: 1.0250 2022/07/30 22:56:52 - mmengine - INFO - Epoch(train) [17][1800/3757] lr: 4.4776e-05 eta: 7:58:00 time: 0.5538 data_time: 0.0143 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.4522 loss: 1.4522 2022/07/30 22:57:41 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 22:57:48 - mmengine - INFO - Epoch(train) [17][1900/3757] lr: 4.4776e-05 eta: 7:57:02 time: 0.5531 data_time: 0.0153 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2840 loss: 1.2840 2022/07/30 22:58:43 - mmengine - INFO - Epoch(train) [17][2000/3757] lr: 4.4776e-05 eta: 7:56:05 time: 0.5516 data_time: 0.0151 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2035 loss: 1.2035 2022/07/30 22:59:39 - mmengine - INFO - Epoch(train) [17][2100/3757] lr: 4.4776e-05 eta: 7:55:08 time: 0.5540 data_time: 0.0144 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1875 loss: 1.1875 2022/07/30 23:00:35 - mmengine - INFO - Epoch(train) [17][2200/3757] lr: 4.4776e-05 eta: 7:54:11 time: 0.5552 data_time: 0.0154 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2384 loss: 1.2384 2022/07/30 23:01:30 - mmengine - INFO - Epoch(train) [17][2300/3757] lr: 4.4776e-05 eta: 7:53:14 time: 0.5514 data_time: 0.0139 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4075 loss: 1.4075 2022/07/30 23:02:26 - mmengine - INFO - Epoch(train) [17][2400/3757] lr: 4.4776e-05 eta: 7:52:17 time: 0.5518 data_time: 0.0145 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3423 loss: 1.3423 2022/07/30 23:03:22 - mmengine - INFO - Epoch(train) [17][2500/3757] lr: 4.4776e-05 eta: 7:51:20 time: 0.5585 data_time: 0.0148 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3154 loss: 1.3154 2022/07/30 23:04:18 - mmengine - INFO - Epoch(train) [17][2600/3757] lr: 4.4776e-05 eta: 7:50:23 time: 0.5544 data_time: 0.0154 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2905 loss: 1.2905 2022/07/30 23:05:13 - mmengine - INFO - Epoch(train) [17][2700/3757] lr: 4.4776e-05 eta: 7:49:25 time: 0.5575 data_time: 0.0138 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.5003 loss: 1.5003 2022/07/30 23:06:08 - mmengine - INFO - Epoch(train) [17][2800/3757] lr: 4.4776e-05 eta: 7:48:28 time: 0.5537 data_time: 0.0143 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2950 loss: 1.2950 2022/07/30 23:06:57 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 23:07:04 - mmengine - INFO - Epoch(train) [17][2900/3757] lr: 4.4776e-05 eta: 7:47:31 time: 0.5507 data_time: 0.0155 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4919 loss: 1.4919 2022/07/30 23:07:59 - mmengine - INFO - Epoch(train) [17][3000/3757] lr: 4.4776e-05 eta: 7:46:34 time: 0.5543 data_time: 0.0153 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5548 loss: 1.5548 2022/07/30 23:08:55 - mmengine - INFO - Epoch(train) [17][3100/3757] lr: 4.4776e-05 eta: 7:45:36 time: 0.5585 data_time: 0.0158 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1964 loss: 1.1964 2022/07/30 23:09:53 - mmengine - INFO - Epoch(train) [17][3200/3757] lr: 4.4776e-05 eta: 7:44:41 time: 0.6898 data_time: 0.0343 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2500 loss: 1.2500 2022/07/30 23:10:49 - mmengine - INFO - Epoch(train) [17][3300/3757] lr: 4.4776e-05 eta: 7:43:45 time: 0.5515 data_time: 0.0146 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3112 loss: 1.3112 2022/07/30 23:11:45 - mmengine - INFO - Epoch(train) [17][3400/3757] lr: 4.4776e-05 eta: 7:42:47 time: 0.5541 data_time: 0.0151 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4241 loss: 1.4241 2022/07/30 23:12:41 - mmengine - INFO - Epoch(train) [17][3500/3757] lr: 4.4776e-05 eta: 7:41:51 time: 0.5575 data_time: 0.0159 memory: 33632 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.3281 loss: 1.3281 2022/07/30 23:13:37 - mmengine - INFO - Epoch(train) [17][3600/3757] lr: 4.4776e-05 eta: 7:40:54 time: 0.5596 data_time: 0.0169 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2574 loss: 1.2574 2022/07/30 23:14:32 - mmengine - INFO - Epoch(train) [17][3700/3757] lr: 4.4776e-05 eta: 7:39:57 time: 0.5512 data_time: 0.0147 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2096 loss: 1.2096 2022/07/30 23:15:04 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 23:15:04 - mmengine - INFO - Epoch(train) [17][3757/3757] lr: 4.4776e-05 eta: 7:39:34 time: 0.5555 data_time: 0.0153 memory: 33632 top1_acc: 0.7143 top5_acc: 1.0000 loss_cls: 1.3120 loss: 1.3120 2022/07/30 23:16:01 - mmengine - INFO - Epoch(train) [18][100/3757] lr: 3.9606e-05 eta: 7:38:21 time: 0.5686 data_time: 0.0151 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1562 loss: 1.1562 2022/07/30 23:16:19 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 23:16:57 - mmengine - INFO - Epoch(train) [18][200/3757] lr: 3.9606e-05 eta: 7:37:24 time: 0.5562 data_time: 0.0153 memory: 33632 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.3205 loss: 1.3205 2022/07/30 23:17:52 - mmengine - INFO - Epoch(train) [18][300/3757] lr: 3.9606e-05 eta: 7:36:27 time: 0.5525 data_time: 0.0153 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2979 loss: 1.2979 2022/07/30 23:18:48 - mmengine - INFO - Epoch(train) [18][400/3757] lr: 3.9606e-05 eta: 7:35:30 time: 0.5531 data_time: 0.0157 memory: 33632 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 0.8850 loss: 0.8850 2022/07/30 23:19:43 - mmengine - INFO - Epoch(train) [18][500/3757] lr: 3.9606e-05 eta: 7:34:33 time: 0.5519 data_time: 0.0149 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1503 loss: 1.1503 2022/07/30 23:20:39 - mmengine - INFO - Epoch(train) [18][600/3757] lr: 3.9606e-05 eta: 7:33:36 time: 0.5514 data_time: 0.0142 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3969 loss: 1.3969 2022/07/30 23:21:34 - mmengine - INFO - Epoch(train) [18][700/3757] lr: 3.9606e-05 eta: 7:32:38 time: 0.5565 data_time: 0.0158 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3484 loss: 1.3484 2022/07/30 23:22:30 - mmengine - INFO - Epoch(train) [18][800/3757] lr: 3.9606e-05 eta: 7:31:41 time: 0.5509 data_time: 0.0140 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2477 loss: 1.2477 2022/07/30 23:23:25 - mmengine - INFO - Epoch(train) [18][900/3757] lr: 3.9606e-05 eta: 7:30:44 time: 0.5605 data_time: 0.0160 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2399 loss: 1.2399 2022/07/30 23:24:21 - mmengine - INFO - Epoch(train) [18][1000/3757] lr: 3.9606e-05 eta: 7:29:47 time: 0.5555 data_time: 0.0143 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4708 loss: 1.4708 2022/07/30 23:25:16 - mmengine - INFO - Epoch(train) [18][1100/3757] lr: 3.9606e-05 eta: 7:28:50 time: 0.5540 data_time: 0.0157 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0572 loss: 1.0572 2022/07/30 23:25:34 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 23:26:12 - mmengine - INFO - Epoch(train) [18][1200/3757] lr: 3.9606e-05 eta: 7:27:53 time: 0.5591 data_time: 0.0156 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1213 loss: 1.1213 2022/07/30 23:27:08 - mmengine - INFO - Epoch(train) [18][1300/3757] lr: 3.9606e-05 eta: 7:26:56 time: 0.5597 data_time: 0.0158 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.4592 loss: 1.4592 2022/07/30 23:28:03 - mmengine - INFO - Epoch(train) [18][1400/3757] lr: 3.9606e-05 eta: 7:25:59 time: 0.5516 data_time: 0.0154 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2734 loss: 1.2734 2022/07/30 23:28:59 - mmengine - INFO - Epoch(train) [18][1500/3757] lr: 3.9606e-05 eta: 7:25:02 time: 0.5544 data_time: 0.0141 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.3239 loss: 1.3239 2022/07/30 23:29:55 - mmengine - INFO - Epoch(train) [18][1600/3757] lr: 3.9606e-05 eta: 7:24:05 time: 0.5540 data_time: 0.0149 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.3578 loss: 1.3578 2022/07/30 23:30:50 - mmengine - INFO - Epoch(train) [18][1700/3757] lr: 3.9606e-05 eta: 7:23:08 time: 0.5508 data_time: 0.0146 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.2776 loss: 1.2776 2022/07/30 23:31:45 - mmengine - INFO - Epoch(train) [18][1800/3757] lr: 3.9606e-05 eta: 7:22:11 time: 0.5512 data_time: 0.0151 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1223 loss: 1.1223 2022/07/30 23:32:41 - mmengine - INFO - Epoch(train) [18][1900/3757] lr: 3.9606e-05 eta: 7:21:14 time: 0.5652 data_time: 0.0155 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.6395 loss: 1.6395 2022/07/30 23:33:37 - mmengine - INFO - Epoch(train) [18][2000/3757] lr: 3.9606e-05 eta: 7:20:17 time: 0.5517 data_time: 0.0137 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0780 loss: 1.0780 2022/07/30 23:34:33 - mmengine - INFO - Epoch(train) [18][2100/3757] lr: 3.9606e-05 eta: 7:19:21 time: 0.5523 data_time: 0.0149 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0166 loss: 1.0166 2022/07/30 23:34:50 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 23:35:29 - mmengine - INFO - Epoch(train) [18][2200/3757] lr: 3.9606e-05 eta: 7:18:24 time: 0.5548 data_time: 0.0148 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0845 loss: 1.0845 2022/07/30 23:36:24 - mmengine - INFO - Epoch(train) [18][2300/3757] lr: 3.9606e-05 eta: 7:17:27 time: 0.5529 data_time: 0.0140 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3688 loss: 1.3688 2022/07/30 23:37:20 - mmengine - INFO - Epoch(train) [18][2400/3757] lr: 3.9606e-05 eta: 7:16:30 time: 0.5518 data_time: 0.0137 memory: 33632 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.1245 loss: 1.1245 2022/07/30 23:38:16 - mmengine - INFO - Epoch(train) [18][2500/3757] lr: 3.9606e-05 eta: 7:15:33 time: 0.5541 data_time: 0.0153 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1534 loss: 1.1534 2022/07/30 23:39:11 - mmengine - INFO - Epoch(train) [18][2600/3757] lr: 3.9606e-05 eta: 7:14:36 time: 0.5524 data_time: 0.0153 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.8175 loss: 0.8175 2022/07/30 23:40:07 - mmengine - INFO - Epoch(train) [18][2700/3757] lr: 3.9606e-05 eta: 7:13:39 time: 0.5526 data_time: 0.0147 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3091 loss: 1.3091 2022/07/30 23:41:03 - mmengine - INFO - Epoch(train) [18][2800/3757] lr: 3.9606e-05 eta: 7:12:42 time: 0.5593 data_time: 0.0151 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2376 loss: 1.2376 2022/07/30 23:41:58 - mmengine - INFO - Epoch(train) [18][2900/3757] lr: 3.9606e-05 eta: 7:11:45 time: 0.5586 data_time: 0.0149 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1286 loss: 1.1286 2022/07/30 23:42:54 - mmengine - INFO - Epoch(train) [18][3000/3757] lr: 3.9606e-05 eta: 7:10:48 time: 0.5599 data_time: 0.0154 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2327 loss: 1.2327 2022/07/30 23:43:50 - mmengine - INFO - Epoch(train) [18][3100/3757] lr: 3.9606e-05 eta: 7:09:51 time: 0.5535 data_time: 0.0172 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1513 loss: 1.1513 2022/07/30 23:44:07 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 23:44:45 - mmengine - INFO - Epoch(train) [18][3200/3757] lr: 3.9606e-05 eta: 7:08:54 time: 0.5555 data_time: 0.0170 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1629 loss: 1.1629 2022/07/30 23:45:41 - mmengine - INFO - Epoch(train) [18][3300/3757] lr: 3.9606e-05 eta: 7:07:57 time: 0.5651 data_time: 0.0175 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2926 loss: 1.2926 2022/07/30 23:46:36 - mmengine - INFO - Epoch(train) [18][3400/3757] lr: 3.9606e-05 eta: 7:07:00 time: 0.5543 data_time: 0.0149 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2161 loss: 1.2161 2022/07/30 23:47:32 - mmengine - INFO - Epoch(train) [18][3500/3757] lr: 3.9606e-05 eta: 7:06:04 time: 0.5545 data_time: 0.0153 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.3681 loss: 1.3681 2022/07/30 23:48:28 - mmengine - INFO - Epoch(train) [18][3600/3757] lr: 3.9606e-05 eta: 7:05:07 time: 0.5543 data_time: 0.0142 memory: 33632 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.4977 loss: 1.4977 2022/07/30 23:49:23 - mmengine - INFO - Epoch(train) [18][3700/3757] lr: 3.9606e-05 eta: 7:04:10 time: 0.5573 data_time: 0.0159 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2123 loss: 1.2123 2022/07/30 23:49:55 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 23:49:55 - mmengine - INFO - Epoch(train) [18][3757/3757] lr: 3.9606e-05 eta: 7:03:47 time: 0.5482 data_time: 0.0155 memory: 33632 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.1567 loss: 1.1567 2022/07/30 23:49:55 - mmengine - INFO - Saving checkpoint at 18 epochs 2022/07/30 23:50:22 - mmengine - INFO - Epoch(val) [18][100/310] eta: 0:00:47 time: 0.2245 data_time: 0.0187 memory: 6325 2022/07/30 23:50:44 - mmengine - INFO - Epoch(val) [18][200/310] eta: 0:00:23 time: 0.2175 data_time: 0.0157 memory: 6325 2022/07/30 23:51:06 - mmengine - INFO - Epoch(val) [18][300/310] eta: 0:00:01 time: 0.1999 data_time: 0.0103 memory: 6325 2022/07/30 23:51:09 - mmengine - INFO - Epoch(val) [18][310/310] acc/top1: 0.7295 acc/top5: 0.9064 acc/mean1: 0.7294 2022/07/30 23:51:09 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb/best_acc/top1_epoch_16.pth is removed 2022/07/30 23:51:11 - mmengine - INFO - The best checkpoint with 0.7295 acc/top1 at 19 epoch is saved to best_acc/top1_epoch_19.pth. 2022/07/30 23:52:08 - mmengine - INFO - Epoch(train) [19][100/3757] lr: 3.4551e-05 eta: 7:02:35 time: 0.5516 data_time: 0.0152 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.5500 loss: 1.5500 2022/07/30 23:53:03 - mmengine - INFO - Epoch(train) [19][200/3757] lr: 3.4551e-05 eta: 7:01:38 time: 0.5515 data_time: 0.0150 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2694 loss: 1.2694 2022/07/30 23:53:59 - mmengine - INFO - Epoch(train) [19][300/3757] lr: 3.4551e-05 eta: 7:00:41 time: 0.5546 data_time: 0.0153 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2800 loss: 1.2800 2022/07/30 23:54:40 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/30 23:54:54 - mmengine - INFO - Epoch(train) [19][400/3757] lr: 3.4551e-05 eta: 6:59:44 time: 0.5519 data_time: 0.0148 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4305 loss: 1.4305 2022/07/30 23:55:50 - mmengine - INFO - Epoch(train) [19][500/3757] lr: 3.4551e-05 eta: 6:58:47 time: 0.5603 data_time: 0.0151 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.1941 loss: 1.1941 2022/07/30 23:56:45 - mmengine - INFO - Epoch(train) [19][600/3757] lr: 3.4551e-05 eta: 6:57:50 time: 0.5546 data_time: 0.0151 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3503 loss: 1.3503 2022/07/30 23:57:41 - mmengine - INFO - Epoch(train) [19][700/3757] lr: 3.4551e-05 eta: 6:56:53 time: 0.5666 data_time: 0.0156 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.1722 loss: 1.1722 2022/07/30 23:58:36 - mmengine - INFO - Epoch(train) [19][800/3757] lr: 3.4551e-05 eta: 6:55:56 time: 0.5513 data_time: 0.0143 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2985 loss: 1.2985 2022/07/30 23:59:32 - mmengine - INFO - Epoch(train) [19][900/3757] lr: 3.4551e-05 eta: 6:54:59 time: 0.5516 data_time: 0.0143 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1446 loss: 1.1446 2022/07/31 00:00:30 - mmengine - INFO - Epoch(train) [19][1000/3757] lr: 3.4551e-05 eta: 6:54:04 time: 0.5625 data_time: 0.0154 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.5136 loss: 1.5136 2022/07/31 00:01:26 - mmengine - INFO - Epoch(train) [19][1100/3757] lr: 3.4551e-05 eta: 6:53:07 time: 0.5583 data_time: 0.0161 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0632 loss: 1.0632 2022/07/31 00:02:21 - mmengine - INFO - Epoch(train) [19][1200/3757] lr: 3.4551e-05 eta: 6:52:10 time: 0.5547 data_time: 0.0146 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2578 loss: 1.2578 2022/07/31 00:03:17 - mmengine - INFO - Epoch(train) [19][1300/3757] lr: 3.4551e-05 eta: 6:51:13 time: 0.5516 data_time: 0.0152 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1883 loss: 1.1883 2022/07/31 00:03:58 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 00:04:12 - mmengine - INFO - Epoch(train) [19][1400/3757] lr: 3.4551e-05 eta: 6:50:17 time: 0.5519 data_time: 0.0146 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3597 loss: 1.3597 2022/07/31 00:05:08 - mmengine - INFO - Epoch(train) [19][1500/3757] lr: 3.4551e-05 eta: 6:49:20 time: 0.5544 data_time: 0.0147 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9699 loss: 0.9699 2022/07/31 00:06:04 - mmengine - INFO - Epoch(train) [19][1600/3757] lr: 3.4551e-05 eta: 6:48:23 time: 0.5558 data_time: 0.0155 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9351 loss: 0.9351 2022/07/31 00:06:59 - mmengine - INFO - Epoch(train) [19][1700/3757] lr: 3.4551e-05 eta: 6:47:26 time: 0.5511 data_time: 0.0142 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.1678 loss: 1.1678 2022/07/31 00:07:55 - mmengine - INFO - Epoch(train) [19][1800/3757] lr: 3.4551e-05 eta: 6:46:29 time: 0.5512 data_time: 0.0149 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.0829 loss: 1.0829 2022/07/31 00:08:50 - mmengine - INFO - Epoch(train) [19][1900/3757] lr: 3.4551e-05 eta: 6:45:32 time: 0.5508 data_time: 0.0140 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0821 loss: 1.0821 2022/07/31 00:09:46 - mmengine - INFO - Epoch(train) [19][2000/3757] lr: 3.4551e-05 eta: 6:44:36 time: 0.5490 data_time: 0.0141 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2491 loss: 1.2491 2022/07/31 00:10:41 - mmengine - INFO - Epoch(train) [19][2100/3757] lr: 3.4551e-05 eta: 6:43:39 time: 0.5572 data_time: 0.0151 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.4562 loss: 1.4562 2022/07/31 00:11:37 - mmengine - INFO - Epoch(train) [19][2200/3757] lr: 3.4551e-05 eta: 6:42:42 time: 0.5557 data_time: 0.0155 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1842 loss: 1.1842 2022/07/31 00:12:32 - mmengine - INFO - Epoch(train) [19][2300/3757] lr: 3.4551e-05 eta: 6:41:45 time: 0.5522 data_time: 0.0153 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0660 loss: 1.0660 2022/07/31 00:13:13 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 00:13:28 - mmengine - INFO - Epoch(train) [19][2400/3757] lr: 3.4551e-05 eta: 6:40:48 time: 0.5531 data_time: 0.0144 memory: 33632 top1_acc: 0.3750 top5_acc: 0.5000 loss_cls: 1.1013 loss: 1.1013 2022/07/31 00:14:23 - mmengine - INFO - Epoch(train) [19][2500/3757] lr: 3.4551e-05 eta: 6:39:51 time: 0.5565 data_time: 0.0148 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.5278 loss: 1.5278 2022/07/31 00:15:19 - mmengine - INFO - Epoch(train) [19][2600/3757] lr: 3.4551e-05 eta: 6:38:54 time: 0.5516 data_time: 0.0148 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.1604 loss: 1.1604 2022/07/31 00:16:15 - mmengine - INFO - Epoch(train) [19][2700/3757] lr: 3.4551e-05 eta: 6:37:58 time: 0.5604 data_time: 0.0152 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3262 loss: 1.3262 2022/07/31 00:17:10 - mmengine - INFO - Epoch(train) [19][2800/3757] lr: 3.4551e-05 eta: 6:37:01 time: 0.5518 data_time: 0.0140 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0788 loss: 1.0788 2022/07/31 00:18:05 - mmengine - INFO - Epoch(train) [19][2900/3757] lr: 3.4551e-05 eta: 6:36:04 time: 0.5517 data_time: 0.0143 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0581 loss: 1.0581 2022/07/31 00:19:01 - mmengine - INFO - Epoch(train) [19][3000/3757] lr: 3.4551e-05 eta: 6:35:07 time: 0.5573 data_time: 0.0155 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.5269 loss: 1.5269 2022/07/31 00:19:57 - mmengine - INFO - Epoch(train) [19][3100/3757] lr: 3.4551e-05 eta: 6:34:10 time: 0.5556 data_time: 0.0148 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.1838 loss: 1.1838 2022/07/31 00:20:53 - mmengine - INFO - Epoch(train) [19][3200/3757] lr: 3.4551e-05 eta: 6:33:14 time: 0.5663 data_time: 0.0149 memory: 33632 top1_acc: 0.1250 top5_acc: 0.6250 loss_cls: 1.6680 loss: 1.6680 2022/07/31 00:21:48 - mmengine - INFO - Epoch(train) [19][3300/3757] lr: 3.4551e-05 eta: 6:32:17 time: 0.5514 data_time: 0.0148 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2443 loss: 1.2443 2022/07/31 00:22:29 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 00:22:44 - mmengine - INFO - Epoch(train) [19][3400/3757] lr: 3.4551e-05 eta: 6:31:20 time: 0.5516 data_time: 0.0147 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.3969 loss: 1.3969 2022/07/31 00:23:39 - mmengine - INFO - Epoch(train) [19][3500/3757] lr: 3.4551e-05 eta: 6:30:23 time: 0.5539 data_time: 0.0143 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.0300 loss: 1.0300 2022/07/31 00:24:35 - mmengine - INFO - Epoch(train) [19][3600/3757] lr: 3.4551e-05 eta: 6:29:27 time: 0.5573 data_time: 0.0156 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2901 loss: 1.2901 2022/07/31 00:25:30 - mmengine - INFO - Epoch(train) [19][3700/3757] lr: 3.4551e-05 eta: 6:28:30 time: 0.5530 data_time: 0.0155 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4780 loss: 1.4780 2022/07/31 00:26:02 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 00:26:02 - mmengine - INFO - Epoch(train) [19][3757/3757] lr: 3.4551e-05 eta: 6:28:07 time: 0.5422 data_time: 0.0143 memory: 33632 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 1.1058 loss: 1.1058 2022/07/31 00:26:59 - mmengine - INFO - Epoch(train) [20][100/3757] lr: 2.9665e-05 eta: 6:26:56 time: 0.5516 data_time: 0.0137 memory: 33632 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.1323 loss: 1.1323 2022/07/31 00:27:55 - mmengine - INFO - Epoch(train) [20][200/3757] lr: 2.9665e-05 eta: 6:26:00 time: 0.5661 data_time: 0.0145 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2514 loss: 1.2514 2022/07/31 00:28:51 - mmengine - INFO - Epoch(train) [20][300/3757] lr: 2.9665e-05 eta: 6:25:03 time: 0.5530 data_time: 0.0157 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.4347 loss: 1.4347 2022/07/31 00:29:46 - mmengine - INFO - Epoch(train) [20][400/3757] lr: 2.9665e-05 eta: 6:24:06 time: 0.5538 data_time: 0.0153 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0890 loss: 1.0890 2022/07/31 00:30:42 - mmengine - INFO - Epoch(train) [20][500/3757] lr: 2.9665e-05 eta: 6:23:09 time: 0.5562 data_time: 0.0147 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.4192 loss: 1.4192 2022/07/31 00:31:38 - mmengine - INFO - Epoch(train) [20][600/3757] lr: 2.9665e-05 eta: 6:22:13 time: 0.5660 data_time: 0.0156 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1634 loss: 1.1634 2022/07/31 00:31:47 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 00:32:33 - mmengine - INFO - Epoch(train) [20][700/3757] lr: 2.9665e-05 eta: 6:21:16 time: 0.5566 data_time: 0.0153 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4504 loss: 1.4504 2022/07/31 00:33:29 - mmengine - INFO - Epoch(train) [20][800/3757] lr: 2.9665e-05 eta: 6:20:19 time: 0.5552 data_time: 0.0141 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2778 loss: 1.2778 2022/07/31 00:34:24 - mmengine - INFO - Epoch(train) [20][900/3757] lr: 2.9665e-05 eta: 6:19:22 time: 0.5589 data_time: 0.0150 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1610 loss: 1.1610 2022/07/31 00:35:20 - mmengine - INFO - Epoch(train) [20][1000/3757] lr: 2.9665e-05 eta: 6:18:26 time: 0.5586 data_time: 0.0146 memory: 33632 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.1767 loss: 1.1767 2022/07/31 00:36:16 - mmengine - INFO - Epoch(train) [20][1100/3757] lr: 2.9665e-05 eta: 6:17:29 time: 0.5528 data_time: 0.0151 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2404 loss: 1.2404 2022/07/31 00:37:11 - mmengine - INFO - Epoch(train) [20][1200/3757] lr: 2.9665e-05 eta: 6:16:32 time: 0.5511 data_time: 0.0147 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2024 loss: 1.2024 2022/07/31 00:38:07 - mmengine - INFO - Epoch(train) [20][1300/3757] lr: 2.9665e-05 eta: 6:15:36 time: 0.5524 data_time: 0.0155 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1070 loss: 1.1070 2022/07/31 00:39:02 - mmengine - INFO - Epoch(train) [20][1400/3757] lr: 2.9665e-05 eta: 6:14:39 time: 0.5562 data_time: 0.0151 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1546 loss: 1.1546 2022/07/31 00:39:58 - mmengine - INFO - Epoch(train) [20][1500/3757] lr: 2.9665e-05 eta: 6:13:42 time: 0.5528 data_time: 0.0167 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2575 loss: 1.2575 2022/07/31 00:40:53 - mmengine - INFO - Epoch(train) [20][1600/3757] lr: 2.9665e-05 eta: 6:12:45 time: 0.5525 data_time: 0.0140 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1274 loss: 1.1274 2022/07/31 00:41:03 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 00:41:49 - mmengine - INFO - Epoch(train) [20][1700/3757] lr: 2.9665e-05 eta: 6:11:49 time: 0.5515 data_time: 0.0138 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1047 loss: 1.1047 2022/07/31 00:42:45 - mmengine - INFO - Epoch(train) [20][1800/3757] lr: 2.9665e-05 eta: 6:10:52 time: 0.5526 data_time: 0.0154 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3575 loss: 1.3575 2022/07/31 00:43:40 - mmengine - INFO - Epoch(train) [20][1900/3757] lr: 2.9665e-05 eta: 6:09:55 time: 0.5556 data_time: 0.0147 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3968 loss: 1.3968 2022/07/31 00:44:36 - mmengine - INFO - Epoch(train) [20][2000/3757] lr: 2.9665e-05 eta: 6:08:59 time: 0.5526 data_time: 0.0147 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3959 loss: 1.3959 2022/07/31 00:45:31 - mmengine - INFO - Epoch(train) [20][2100/3757] lr: 2.9665e-05 eta: 6:08:02 time: 0.5518 data_time: 0.0154 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2216 loss: 1.2216 2022/07/31 00:46:27 - mmengine - INFO - Epoch(train) [20][2200/3757] lr: 2.9665e-05 eta: 6:07:05 time: 0.5574 data_time: 0.0150 memory: 33632 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.2618 loss: 1.2618 2022/07/31 00:47:22 - mmengine - INFO - Epoch(train) [20][2300/3757] lr: 2.9665e-05 eta: 6:06:09 time: 0.5588 data_time: 0.0150 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9801 loss: 0.9801 2022/07/31 00:48:19 - mmengine - INFO - Epoch(train) [20][2400/3757] lr: 2.9665e-05 eta: 6:05:12 time: 0.5543 data_time: 0.0168 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.3776 loss: 1.3776 2022/07/31 00:49:15 - mmengine - INFO - Epoch(train) [20][2500/3757] lr: 2.9665e-05 eta: 6:04:16 time: 0.5569 data_time: 0.0149 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.0285 loss: 1.0285 2022/07/31 00:50:12 - mmengine - INFO - Epoch(train) [20][2600/3757] lr: 2.9665e-05 eta: 6:03:20 time: 0.5517 data_time: 0.0146 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.4611 loss: 1.4611 2022/07/31 00:50:22 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 00:51:08 - mmengine - INFO - Epoch(train) [20][2700/3757] lr: 2.9665e-05 eta: 6:02:24 time: 0.5522 data_time: 0.0154 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2032 loss: 1.2032 2022/07/31 00:52:04 - mmengine - INFO - Epoch(train) [20][2800/3757] lr: 2.9665e-05 eta: 6:01:27 time: 0.5562 data_time: 0.0151 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2260 loss: 1.2260 2022/07/31 00:52:59 - mmengine - INFO - Epoch(train) [20][2900/3757] lr: 2.9665e-05 eta: 6:00:30 time: 0.5515 data_time: 0.0148 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1533 loss: 1.1533 2022/07/31 00:53:55 - mmengine - INFO - Epoch(train) [20][3000/3757] lr: 2.9665e-05 eta: 5:59:34 time: 0.5537 data_time: 0.0143 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1668 loss: 1.1668 2022/07/31 00:54:51 - mmengine - INFO - Epoch(train) [20][3100/3757] lr: 2.9665e-05 eta: 5:58:37 time: 0.5548 data_time: 0.0151 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3641 loss: 1.3641 2022/07/31 00:55:46 - mmengine - INFO - Epoch(train) [20][3200/3757] lr: 2.9665e-05 eta: 5:57:40 time: 0.5540 data_time: 0.0153 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0689 loss: 1.0689 2022/07/31 00:56:42 - mmengine - INFO - Epoch(train) [20][3300/3757] lr: 2.9665e-05 eta: 5:56:44 time: 0.5524 data_time: 0.0154 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0038 loss: 1.0038 2022/07/31 00:57:37 - mmengine - INFO - Epoch(train) [20][3400/3757] lr: 2.9665e-05 eta: 5:55:47 time: 0.5575 data_time: 0.0155 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0399 loss: 1.0399 2022/07/31 00:58:33 - mmengine - INFO - Epoch(train) [20][3500/3757] lr: 2.9665e-05 eta: 5:54:50 time: 0.5527 data_time: 0.0151 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.5387 loss: 1.5387 2022/07/31 00:59:28 - mmengine - INFO - Epoch(train) [20][3600/3757] lr: 2.9665e-05 eta: 5:53:54 time: 0.5514 data_time: 0.0144 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3221 loss: 1.3221 2022/07/31 00:59:38 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 01:00:24 - mmengine - INFO - Epoch(train) [20][3700/3757] lr: 2.9665e-05 eta: 5:52:57 time: 0.5526 data_time: 0.0150 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1810 loss: 1.1810 2022/07/31 01:00:56 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 01:00:56 - mmengine - INFO - Epoch(train) [20][3757/3757] lr: 2.9665e-05 eta: 5:52:35 time: 0.5482 data_time: 0.0138 memory: 33632 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.2171 loss: 1.2171 2022/07/31 01:01:54 - mmengine - INFO - Epoch(train) [21][100/3757] lr: 2.5001e-05 eta: 5:51:25 time: 0.5545 data_time: 0.0143 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2032 loss: 1.2032 2022/07/31 01:02:50 - mmengine - INFO - Epoch(train) [21][200/3757] lr: 2.5001e-05 eta: 5:50:28 time: 0.5565 data_time: 0.0142 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1733 loss: 1.1733 2022/07/31 01:03:46 - mmengine - INFO - Epoch(train) [21][300/3757] lr: 2.5001e-05 eta: 5:49:32 time: 0.5656 data_time: 0.0154 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.1827 loss: 1.1827 2022/07/31 01:04:41 - mmengine - INFO - Epoch(train) [21][400/3757] lr: 2.5001e-05 eta: 5:48:35 time: 0.5550 data_time: 0.0149 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3527 loss: 1.3527 2022/07/31 01:05:37 - mmengine - INFO - Epoch(train) [21][500/3757] lr: 2.5001e-05 eta: 5:47:39 time: 0.5630 data_time: 0.0162 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1448 loss: 1.1448 2022/07/31 01:06:32 - mmengine - INFO - Epoch(train) [21][600/3757] lr: 2.5001e-05 eta: 5:46:42 time: 0.5544 data_time: 0.0144 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0180 loss: 1.0180 2022/07/31 01:07:28 - mmengine - INFO - Epoch(train) [21][700/3757] lr: 2.5001e-05 eta: 5:45:45 time: 0.5528 data_time: 0.0160 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3080 loss: 1.3080 2022/07/31 01:08:24 - mmengine - INFO - Epoch(train) [21][800/3757] lr: 2.5001e-05 eta: 5:44:49 time: 0.5578 data_time: 0.0152 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.0733 loss: 1.0733 2022/07/31 01:08:57 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 01:09:19 - mmengine - INFO - Epoch(train) [21][900/3757] lr: 2.5001e-05 eta: 5:43:52 time: 0.5528 data_time: 0.0154 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1797 loss: 1.1797 2022/07/31 01:10:15 - mmengine - INFO - Epoch(train) [21][1000/3757] lr: 2.5001e-05 eta: 5:42:55 time: 0.5538 data_time: 0.0160 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3164 loss: 1.3164 2022/07/31 01:11:10 - mmengine - INFO - Epoch(train) [21][1100/3757] lr: 2.5001e-05 eta: 5:41:59 time: 0.5536 data_time: 0.0151 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2160 loss: 1.2160 2022/07/31 01:12:06 - mmengine - INFO - Epoch(train) [21][1200/3757] lr: 2.5001e-05 eta: 5:41:02 time: 0.5575 data_time: 0.0156 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2063 loss: 1.2063 2022/07/31 01:13:02 - mmengine - INFO - Epoch(train) [21][1300/3757] lr: 2.5001e-05 eta: 5:40:06 time: 0.5569 data_time: 0.0150 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3149 loss: 1.3149 2022/07/31 01:13:57 - mmengine - INFO - Epoch(train) [21][1400/3757] lr: 2.5001e-05 eta: 5:39:09 time: 0.5529 data_time: 0.0150 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2713 loss: 1.2713 2022/07/31 01:14:53 - mmengine - INFO - Epoch(train) [21][1500/3757] lr: 2.5001e-05 eta: 5:38:13 time: 0.5573 data_time: 0.0166 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2627 loss: 1.2627 2022/07/31 01:15:48 - mmengine - INFO - Epoch(train) [21][1600/3757] lr: 2.5001e-05 eta: 5:37:16 time: 0.5524 data_time: 0.0155 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.5926 loss: 1.5926 2022/07/31 01:16:44 - mmengine - INFO - Epoch(train) [21][1700/3757] lr: 2.5001e-05 eta: 5:36:19 time: 0.5515 data_time: 0.0135 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2200 loss: 1.2200 2022/07/31 01:17:40 - mmengine - INFO - Epoch(train) [21][1800/3757] lr: 2.5001e-05 eta: 5:35:23 time: 0.5605 data_time: 0.0156 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1923 loss: 1.1923 2022/07/31 01:18:13 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 01:18:36 - mmengine - INFO - Epoch(train) [21][1900/3757] lr: 2.5001e-05 eta: 5:34:26 time: 0.5569 data_time: 0.0148 memory: 33632 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.4338 loss: 1.4338 2022/07/31 01:19:31 - mmengine - INFO - Epoch(train) [21][2000/3757] lr: 2.5001e-05 eta: 5:33:30 time: 0.5515 data_time: 0.0145 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.0246 loss: 1.0246 2022/07/31 01:20:27 - mmengine - INFO - Epoch(train) [21][2100/3757] lr: 2.5001e-05 eta: 5:32:33 time: 0.5567 data_time: 0.0148 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2796 loss: 1.2796 2022/07/31 01:21:23 - mmengine - INFO - Epoch(train) [21][2200/3757] lr: 2.5001e-05 eta: 5:31:37 time: 0.5554 data_time: 0.0159 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4519 loss: 1.4519 2022/07/31 01:22:18 - mmengine - INFO - Epoch(train) [21][2300/3757] lr: 2.5001e-05 eta: 5:30:40 time: 0.5580 data_time: 0.0159 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2684 loss: 1.2684 2022/07/31 01:23:14 - mmengine - INFO - Epoch(train) [21][2400/3757] lr: 2.5001e-05 eta: 5:29:44 time: 0.5525 data_time: 0.0141 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1198 loss: 1.1198 2022/07/31 01:24:10 - mmengine - INFO - Epoch(train) [21][2500/3757] lr: 2.5001e-05 eta: 5:28:47 time: 0.5536 data_time: 0.0162 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3006 loss: 1.3006 2022/07/31 01:25:05 - mmengine - INFO - Epoch(train) [21][2600/3757] lr: 2.5001e-05 eta: 5:27:51 time: 0.5524 data_time: 0.0146 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1050 loss: 1.1050 2022/07/31 01:26:01 - mmengine - INFO - Epoch(train) [21][2700/3757] lr: 2.5001e-05 eta: 5:26:54 time: 0.5591 data_time: 0.0142 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0633 loss: 1.0633 2022/07/31 01:26:56 - mmengine - INFO - Epoch(train) [21][2800/3757] lr: 2.5001e-05 eta: 5:25:57 time: 0.5524 data_time: 0.0157 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.2745 loss: 1.2745 2022/07/31 01:27:29 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 01:27:52 - mmengine - INFO - Epoch(train) [21][2900/3757] lr: 2.5001e-05 eta: 5:25:01 time: 0.5557 data_time: 0.0148 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2752 loss: 1.2752 2022/07/31 01:28:47 - mmengine - INFO - Epoch(train) [21][3000/3757] lr: 2.5001e-05 eta: 5:24:04 time: 0.5595 data_time: 0.0151 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2455 loss: 1.2455 2022/07/31 01:29:43 - mmengine - INFO - Epoch(train) [21][3100/3757] lr: 2.5001e-05 eta: 5:23:08 time: 0.5564 data_time: 0.0151 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1174 loss: 1.1174 2022/07/31 01:30:39 - mmengine - INFO - Epoch(train) [21][3200/3757] lr: 2.5001e-05 eta: 5:22:11 time: 0.5582 data_time: 0.0143 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2436 loss: 1.2436 2022/07/31 01:31:34 - mmengine - INFO - Epoch(train) [21][3300/3757] lr: 2.5001e-05 eta: 5:21:15 time: 0.5540 data_time: 0.0162 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1737 loss: 1.1737 2022/07/31 01:32:30 - mmengine - INFO - Epoch(train) [21][3400/3757] lr: 2.5001e-05 eta: 5:20:18 time: 0.5593 data_time: 0.0151 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1107 loss: 1.1107 2022/07/31 01:33:25 - mmengine - INFO - Epoch(train) [21][3500/3757] lr: 2.5001e-05 eta: 5:19:21 time: 0.5518 data_time: 0.0144 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0561 loss: 1.0561 2022/07/31 01:34:21 - mmengine - INFO - Epoch(train) [21][3600/3757] lr: 2.5001e-05 eta: 5:18:25 time: 0.5514 data_time: 0.0141 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1995 loss: 1.1995 2022/07/31 01:35:16 - mmengine - INFO - Epoch(train) [21][3700/3757] lr: 2.5001e-05 eta: 5:17:28 time: 0.5549 data_time: 0.0146 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1839 loss: 1.1839 2022/07/31 01:35:48 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 01:35:48 - mmengine - INFO - Epoch(train) [21][3757/3757] lr: 2.5001e-05 eta: 5:17:06 time: 0.5455 data_time: 0.0146 memory: 33632 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.1667 loss: 1.1667 2022/07/31 01:35:48 - mmengine - INFO - Saving checkpoint at 21 epochs 2022/07/31 01:36:17 - mmengine - INFO - Epoch(val) [21][100/310] eta: 0:00:45 time: 0.2181 data_time: 0.0152 memory: 6325 2022/07/31 01:36:39 - mmengine - INFO - Epoch(val) [21][200/310] eta: 0:00:24 time: 0.2191 data_time: 0.0163 memory: 6325 2022/07/31 01:37:00 - mmengine - INFO - Epoch(val) [21][300/310] eta: 0:00:01 time: 0.1963 data_time: 0.0090 memory: 6325 2022/07/31 01:37:03 - mmengine - INFO - Epoch(val) [21][310/310] acc/top1: 0.7374 acc/top5: 0.9098 acc/mean1: 0.7373 2022/07/31 01:37:04 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb/best_acc/top1_epoch_19.pth is removed 2022/07/31 01:37:05 - mmengine - INFO - The best checkpoint with 0.7374 acc/top1 at 22 epoch is saved to best_acc/top1_epoch_22.pth. 2022/07/31 01:38:02 - mmengine - INFO - Epoch(train) [22][100/3757] lr: 2.0612e-05 eta: 5:15:56 time: 0.5582 data_time: 0.0146 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1267 loss: 1.1267 2022/07/31 01:38:04 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 01:38:58 - mmengine - INFO - Epoch(train) [22][200/3757] lr: 2.0612e-05 eta: 5:15:00 time: 0.5549 data_time: 0.0162 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3547 loss: 1.3547 2022/07/31 01:39:53 - mmengine - INFO - Epoch(train) [22][300/3757] lr: 2.0612e-05 eta: 5:14:03 time: 0.5585 data_time: 0.0148 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1648 loss: 1.1648 2022/07/31 01:40:49 - mmengine - INFO - Epoch(train) [22][400/3757] lr: 2.0612e-05 eta: 5:13:07 time: 0.5526 data_time: 0.0146 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9094 loss: 0.9094 2022/07/31 01:41:44 - mmengine - INFO - Epoch(train) [22][500/3757] lr: 2.0612e-05 eta: 5:12:10 time: 0.5543 data_time: 0.0149 memory: 33632 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.1008 loss: 1.1008 2022/07/31 01:42:40 - mmengine - INFO - Epoch(train) [22][600/3757] lr: 2.0612e-05 eta: 5:11:14 time: 0.5682 data_time: 0.0176 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2018 loss: 1.2018 2022/07/31 01:43:36 - mmengine - INFO - Epoch(train) [22][700/3757] lr: 2.0612e-05 eta: 5:10:17 time: 0.5613 data_time: 0.0141 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0747 loss: 1.0747 2022/07/31 01:44:32 - mmengine - INFO - Epoch(train) [22][800/3757] lr: 2.0612e-05 eta: 5:09:21 time: 0.5629 data_time: 0.0150 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1851 loss: 1.1851 2022/07/31 01:45:27 - mmengine - INFO - Epoch(train) [22][900/3757] lr: 2.0612e-05 eta: 5:08:25 time: 0.5529 data_time: 0.0144 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 0.9643 loss: 0.9643 2022/07/31 01:46:24 - mmengine - INFO - Epoch(train) [22][1000/3757] lr: 2.0612e-05 eta: 5:07:28 time: 0.5532 data_time: 0.0164 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1478 loss: 1.1478 2022/07/31 01:47:19 - mmengine - INFO - Epoch(train) [22][1100/3757] lr: 2.0612e-05 eta: 5:06:32 time: 0.5591 data_time: 0.0195 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1370 loss: 1.1370 2022/07/31 01:47:21 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 01:48:15 - mmengine - INFO - Epoch(train) [22][1200/3757] lr: 2.0612e-05 eta: 5:05:35 time: 0.5539 data_time: 0.0149 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9916 loss: 0.9916 2022/07/31 01:49:10 - mmengine - INFO - Epoch(train) [22][1300/3757] lr: 2.0612e-05 eta: 5:04:39 time: 0.5531 data_time: 0.0147 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1533 loss: 1.1533 2022/07/31 01:50:06 - mmengine - INFO - Epoch(train) [22][1400/3757] lr: 2.0612e-05 eta: 5:03:42 time: 0.5524 data_time: 0.0151 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0857 loss: 1.0857 2022/07/31 01:51:02 - mmengine - INFO - Epoch(train) [22][1500/3757] lr: 2.0612e-05 eta: 5:02:46 time: 0.5573 data_time: 0.0155 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1705 loss: 1.1705 2022/07/31 01:51:58 - mmengine - INFO - Epoch(train) [22][1600/3757] lr: 2.0612e-05 eta: 5:01:50 time: 0.5537 data_time: 0.0157 memory: 33632 top1_acc: 0.5000 top5_acc: 0.5000 loss_cls: 1.2589 loss: 1.2589 2022/07/31 01:52:54 - mmengine - INFO - Epoch(train) [22][1700/3757] lr: 2.0612e-05 eta: 5:00:53 time: 0.5559 data_time: 0.0154 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2079 loss: 1.2079 2022/07/31 01:53:49 - mmengine - INFO - Epoch(train) [22][1800/3757] lr: 2.0612e-05 eta: 4:59:57 time: 0.5545 data_time: 0.0161 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1554 loss: 1.1554 2022/07/31 01:54:45 - mmengine - INFO - Epoch(train) [22][1900/3757] lr: 2.0612e-05 eta: 4:59:00 time: 0.5546 data_time: 0.0147 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3097 loss: 1.3097 2022/07/31 01:55:40 - mmengine - INFO - Epoch(train) [22][2000/3757] lr: 2.0612e-05 eta: 4:58:04 time: 0.5519 data_time: 0.0153 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.8645 loss: 0.8645 2022/07/31 01:56:36 - mmengine - INFO - Epoch(train) [22][2100/3757] lr: 2.0612e-05 eta: 4:57:07 time: 0.5526 data_time: 0.0147 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2100 loss: 1.2100 2022/07/31 01:56:38 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 01:57:32 - mmengine - INFO - Epoch(train) [22][2200/3757] lr: 2.0612e-05 eta: 4:56:11 time: 0.5639 data_time: 0.0152 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0453 loss: 1.0453 2022/07/31 01:58:27 - mmengine - INFO - Epoch(train) [22][2300/3757] lr: 2.0612e-05 eta: 4:55:15 time: 0.5540 data_time: 0.0147 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9774 loss: 0.9774 2022/07/31 01:59:23 - mmengine - INFO - Epoch(train) [22][2400/3757] lr: 2.0612e-05 eta: 4:54:18 time: 0.5562 data_time: 0.0147 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2257 loss: 1.2257 2022/07/31 02:00:21 - mmengine - INFO - Epoch(train) [22][2500/3757] lr: 2.0612e-05 eta: 4:53:22 time: 0.6554 data_time: 0.0188 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9609 loss: 0.9609 2022/07/31 02:01:16 - mmengine - INFO - Epoch(train) [22][2600/3757] lr: 2.0612e-05 eta: 4:52:26 time: 0.5519 data_time: 0.0158 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9307 loss: 0.9307 2022/07/31 02:02:12 - mmengine - INFO - Epoch(train) [22][2700/3757] lr: 2.0612e-05 eta: 4:51:29 time: 0.5566 data_time: 0.0153 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.4265 loss: 1.4265 2022/07/31 02:03:07 - mmengine - INFO - Epoch(train) [22][2800/3757] lr: 2.0612e-05 eta: 4:50:33 time: 0.5541 data_time: 0.0149 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.3320 loss: 1.3320 2022/07/31 02:04:03 - mmengine - INFO - Epoch(train) [22][2900/3757] lr: 2.0612e-05 eta: 4:49:37 time: 0.5693 data_time: 0.0161 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2182 loss: 1.2182 2022/07/31 02:04:59 - mmengine - INFO - Epoch(train) [22][3000/3757] lr: 2.0612e-05 eta: 4:48:40 time: 0.5536 data_time: 0.0145 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9519 loss: 0.9519 2022/07/31 02:05:54 - mmengine - INFO - Epoch(train) [22][3100/3757] lr: 2.0612e-05 eta: 4:47:44 time: 0.5621 data_time: 0.0158 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1027 loss: 1.1027 2022/07/31 02:05:56 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 02:06:50 - mmengine - INFO - Epoch(train) [22][3200/3757] lr: 2.0612e-05 eta: 4:46:47 time: 0.5531 data_time: 0.0153 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.2904 loss: 1.2904 2022/07/31 02:07:45 - mmengine - INFO - Epoch(train) [22][3300/3757] lr: 2.0612e-05 eta: 4:45:51 time: 0.5577 data_time: 0.0149 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0127 loss: 1.0127 2022/07/31 02:08:41 - mmengine - INFO - Epoch(train) [22][3400/3757] lr: 2.0612e-05 eta: 4:44:54 time: 0.5518 data_time: 0.0140 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2252 loss: 1.2252 2022/07/31 02:09:37 - mmengine - INFO - Epoch(train) [22][3500/3757] lr: 2.0612e-05 eta: 4:43:58 time: 0.5537 data_time: 0.0149 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3070 loss: 1.3070 2022/07/31 02:10:32 - mmengine - INFO - Epoch(train) [22][3600/3757] lr: 2.0612e-05 eta: 4:43:01 time: 0.5548 data_time: 0.0150 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3120 loss: 1.3120 2022/07/31 02:11:28 - mmengine - INFO - Epoch(train) [22][3700/3757] lr: 2.0612e-05 eta: 4:42:05 time: 0.5563 data_time: 0.0150 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.1952 loss: 1.1952 2022/07/31 02:11:59 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 02:11:59 - mmengine - INFO - Epoch(train) [22][3757/3757] lr: 2.0612e-05 eta: 4:41:42 time: 0.5432 data_time: 0.0149 memory: 33632 top1_acc: 0.8571 top5_acc: 0.8571 loss_cls: 1.0954 loss: 1.0954 2022/07/31 02:12:57 - mmengine - INFO - Epoch(train) [23][100/3757] lr: 1.6544e-05 eta: 4:40:34 time: 0.5554 data_time: 0.0154 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1023 loss: 1.1023 2022/07/31 02:13:53 - mmengine - INFO - Epoch(train) [23][200/3757] lr: 1.6544e-05 eta: 4:39:37 time: 0.5591 data_time: 0.0149 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1574 loss: 1.1574 2022/07/31 02:14:48 - mmengine - INFO - Epoch(train) [23][300/3757] lr: 1.6544e-05 eta: 4:38:41 time: 0.5517 data_time: 0.0153 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1527 loss: 1.1527 2022/07/31 02:15:14 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 02:15:44 - mmengine - INFO - Epoch(train) [23][400/3757] lr: 1.6544e-05 eta: 4:37:44 time: 0.5570 data_time: 0.0158 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0835 loss: 1.0835 2022/07/31 02:16:40 - mmengine - INFO - Epoch(train) [23][500/3757] lr: 1.6544e-05 eta: 4:36:48 time: 0.5578 data_time: 0.0150 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1976 loss: 1.1976 2022/07/31 02:17:35 - mmengine - INFO - Epoch(train) [23][600/3757] lr: 1.6544e-05 eta: 4:35:52 time: 0.5530 data_time: 0.0152 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9005 loss: 0.9005 2022/07/31 02:18:31 - mmengine - INFO - Epoch(train) [23][700/3757] lr: 1.6544e-05 eta: 4:34:55 time: 0.5519 data_time: 0.0153 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1011 loss: 1.1011 2022/07/31 02:19:26 - mmengine - INFO - Epoch(train) [23][800/3757] lr: 1.6544e-05 eta: 4:33:59 time: 0.5524 data_time: 0.0145 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2036 loss: 1.2036 2022/07/31 02:20:22 - mmengine - INFO - Epoch(train) [23][900/3757] lr: 1.6544e-05 eta: 4:33:02 time: 0.5674 data_time: 0.0175 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0489 loss: 1.0489 2022/07/31 02:21:18 - mmengine - INFO - Epoch(train) [23][1000/3757] lr: 1.6544e-05 eta: 4:32:06 time: 0.5547 data_time: 0.0147 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8871 loss: 0.8871 2022/07/31 02:22:13 - mmengine - INFO - Epoch(train) [23][1100/3757] lr: 1.6544e-05 eta: 4:31:10 time: 0.5525 data_time: 0.0150 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9293 loss: 0.9293 2022/07/31 02:23:09 - mmengine - INFO - Epoch(train) [23][1200/3757] lr: 1.6544e-05 eta: 4:30:13 time: 0.5550 data_time: 0.0163 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.4124 loss: 1.4124 2022/07/31 02:24:04 - mmengine - INFO - Epoch(train) [23][1300/3757] lr: 1.6544e-05 eta: 4:29:17 time: 0.5601 data_time: 0.0155 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8767 loss: 0.8767 2022/07/31 02:24:30 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 02:25:00 - mmengine - INFO - Epoch(train) [23][1400/3757] lr: 1.6544e-05 eta: 4:28:20 time: 0.5524 data_time: 0.0136 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.1275 loss: 1.1275 2022/07/31 02:25:56 - mmengine - INFO - Epoch(train) [23][1500/3757] lr: 1.6544e-05 eta: 4:27:24 time: 0.5531 data_time: 0.0154 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0225 loss: 1.0225 2022/07/31 02:26:51 - mmengine - INFO - Epoch(train) [23][1600/3757] lr: 1.6544e-05 eta: 4:26:28 time: 0.5541 data_time: 0.0153 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2428 loss: 1.2428 2022/07/31 02:27:47 - mmengine - INFO - Epoch(train) [23][1700/3757] lr: 1.6544e-05 eta: 4:25:31 time: 0.5530 data_time: 0.0161 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.7174 loss: 0.7174 2022/07/31 02:28:42 - mmengine - INFO - Epoch(train) [23][1800/3757] lr: 1.6544e-05 eta: 4:24:35 time: 0.5548 data_time: 0.0138 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2673 loss: 1.2673 2022/07/31 02:29:38 - mmengine - INFO - Epoch(train) [23][1900/3757] lr: 1.6544e-05 eta: 4:23:38 time: 0.5551 data_time: 0.0149 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.4489 loss: 1.4489 2022/07/31 02:30:34 - mmengine - INFO - Epoch(train) [23][2000/3757] lr: 1.6544e-05 eta: 4:22:42 time: 0.5516 data_time: 0.0147 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1191 loss: 1.1191 2022/07/31 02:31:29 - mmengine - INFO - Epoch(train) [23][2100/3757] lr: 1.6544e-05 eta: 4:21:46 time: 0.5522 data_time: 0.0152 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8701 loss: 0.8701 2022/07/31 02:32:25 - mmengine - INFO - Epoch(train) [23][2200/3757] lr: 1.6544e-05 eta: 4:20:49 time: 0.5574 data_time: 0.0153 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1800 loss: 1.1800 2022/07/31 02:33:20 - mmengine - INFO - Epoch(train) [23][2300/3757] lr: 1.6544e-05 eta: 4:19:53 time: 0.5520 data_time: 0.0145 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.1509 loss: 1.1509 2022/07/31 02:33:46 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 02:34:16 - mmengine - INFO - Epoch(train) [23][2400/3757] lr: 1.6544e-05 eta: 4:18:56 time: 0.5557 data_time: 0.0150 memory: 33632 top1_acc: 0.3750 top5_acc: 0.7500 loss_cls: 1.3915 loss: 1.3915 2022/07/31 02:35:11 - mmengine - INFO - Epoch(train) [23][2500/3757] lr: 1.6544e-05 eta: 4:18:00 time: 0.5539 data_time: 0.0141 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0372 loss: 1.0372 2022/07/31 02:36:07 - mmengine - INFO - Epoch(train) [23][2600/3757] lr: 1.6544e-05 eta: 4:17:04 time: 0.5551 data_time: 0.0151 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3223 loss: 1.3223 2022/07/31 02:37:03 - mmengine - INFO - Epoch(train) [23][2700/3757] lr: 1.6544e-05 eta: 4:16:07 time: 0.5629 data_time: 0.0144 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0135 loss: 1.0135 2022/07/31 02:37:58 - mmengine - INFO - Epoch(train) [23][2800/3757] lr: 1.6544e-05 eta: 4:15:11 time: 0.5534 data_time: 0.0152 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8474 loss: 0.8474 2022/07/31 02:38:54 - mmengine - INFO - Epoch(train) [23][2900/3757] lr: 1.6544e-05 eta: 4:14:15 time: 0.5537 data_time: 0.0147 memory: 33632 top1_acc: 0.3750 top5_acc: 0.8750 loss_cls: 1.1891 loss: 1.1891 2022/07/31 02:39:49 - mmengine - INFO - Epoch(train) [23][3000/3757] lr: 1.6544e-05 eta: 4:13:18 time: 0.5530 data_time: 0.0143 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9669 loss: 0.9669 2022/07/31 02:40:45 - mmengine - INFO - Epoch(train) [23][3100/3757] lr: 1.6544e-05 eta: 4:12:22 time: 0.5529 data_time: 0.0149 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.2937 loss: 1.2937 2022/07/31 02:41:40 - mmengine - INFO - Epoch(train) [23][3200/3757] lr: 1.6544e-05 eta: 4:11:25 time: 0.5593 data_time: 0.0150 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2712 loss: 1.2712 2022/07/31 02:42:36 - mmengine - INFO - Epoch(train) [23][3300/3757] lr: 1.6544e-05 eta: 4:10:29 time: 0.5602 data_time: 0.0156 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2680 loss: 1.2680 2022/07/31 02:43:02 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 02:43:32 - mmengine - INFO - Epoch(train) [23][3400/3757] lr: 1.6544e-05 eta: 4:09:33 time: 0.5530 data_time: 0.0153 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0191 loss: 1.0191 2022/07/31 02:44:27 - mmengine - INFO - Epoch(train) [23][3500/3757] lr: 1.6544e-05 eta: 4:08:36 time: 0.5546 data_time: 0.0159 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0224 loss: 1.0224 2022/07/31 02:45:23 - mmengine - INFO - Epoch(train) [23][3600/3757] lr: 1.6544e-05 eta: 4:07:40 time: 0.5516 data_time: 0.0143 memory: 33632 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.1224 loss: 1.1224 2022/07/31 02:46:19 - mmengine - INFO - Epoch(train) [23][3700/3757] lr: 1.6544e-05 eta: 4:06:44 time: 0.5536 data_time: 0.0151 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3059 loss: 1.3059 2022/07/31 02:46:50 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 02:46:50 - mmengine - INFO - Epoch(train) [23][3757/3757] lr: 1.6544e-05 eta: 4:06:21 time: 0.5478 data_time: 0.0143 memory: 33632 top1_acc: 0.5714 top5_acc: 0.8571 loss_cls: 1.2567 loss: 1.2567 2022/07/31 02:47:48 - mmengine - INFO - Epoch(train) [24][100/3757] lr: 1.2843e-05 eta: 4:05:13 time: 0.5519 data_time: 0.0151 memory: 33632 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.0724 loss: 1.0724 2022/07/31 02:48:43 - mmengine - INFO - Epoch(train) [24][200/3757] lr: 1.2843e-05 eta: 4:04:17 time: 0.5549 data_time: 0.0159 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9047 loss: 0.9047 2022/07/31 02:49:39 - mmengine - INFO - Epoch(train) [24][300/3757] lr: 1.2843e-05 eta: 4:03:20 time: 0.5557 data_time: 0.0152 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0915 loss: 1.0915 2022/07/31 02:50:35 - mmengine - INFO - Epoch(train) [24][400/3757] lr: 1.2843e-05 eta: 4:02:24 time: 0.5572 data_time: 0.0147 memory: 33632 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.0193 loss: 1.0193 2022/07/31 02:51:30 - mmengine - INFO - Epoch(train) [24][500/3757] lr: 1.2843e-05 eta: 4:01:28 time: 0.5529 data_time: 0.0158 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0894 loss: 1.0894 2022/07/31 02:52:20 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 02:52:26 - mmengine - INFO - Epoch(train) [24][600/3757] lr: 1.2843e-05 eta: 4:00:31 time: 0.5531 data_time: 0.0158 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0871 loss: 1.0871 2022/07/31 02:53:21 - mmengine - INFO - Epoch(train) [24][700/3757] lr: 1.2843e-05 eta: 3:59:35 time: 0.5522 data_time: 0.0143 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1643 loss: 1.1643 2022/07/31 02:54:17 - mmengine - INFO - Epoch(train) [24][800/3757] lr: 1.2843e-05 eta: 3:58:39 time: 0.5566 data_time: 0.0157 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.2119 loss: 1.2119 2022/07/31 02:55:12 - mmengine - INFO - Epoch(train) [24][900/3757] lr: 1.2843e-05 eta: 3:57:42 time: 0.5525 data_time: 0.0153 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8408 loss: 0.8408 2022/07/31 02:56:08 - mmengine - INFO - Epoch(train) [24][1000/3757] lr: 1.2843e-05 eta: 3:56:46 time: 0.5591 data_time: 0.0154 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1071 loss: 1.1071 2022/07/31 02:57:04 - mmengine - INFO - Epoch(train) [24][1100/3757] lr: 1.2843e-05 eta: 3:55:50 time: 0.5557 data_time: 0.0152 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0445 loss: 1.0445 2022/07/31 02:57:59 - mmengine - INFO - Epoch(train) [24][1200/3757] lr: 1.2843e-05 eta: 3:54:53 time: 0.5537 data_time: 0.0156 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1808 loss: 1.1808 2022/07/31 02:58:55 - mmengine - INFO - Epoch(train) [24][1300/3757] lr: 1.2843e-05 eta: 3:53:57 time: 0.5511 data_time: 0.0140 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0252 loss: 1.0252 2022/07/31 02:59:50 - mmengine - INFO - Epoch(train) [24][1400/3757] lr: 1.2843e-05 eta: 3:53:01 time: 0.5519 data_time: 0.0139 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0103 loss: 1.0103 2022/07/31 03:00:46 - mmengine - INFO - Epoch(train) [24][1500/3757] lr: 1.2843e-05 eta: 3:52:04 time: 0.5574 data_time: 0.0154 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9487 loss: 0.9487 2022/07/31 03:01:35 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 03:01:42 - mmengine - INFO - Epoch(train) [24][1600/3757] lr: 1.2843e-05 eta: 3:51:08 time: 0.5586 data_time: 0.0151 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7285 loss: 0.7285 2022/07/31 03:02:37 - mmengine - INFO - Epoch(train) [24][1700/3757] lr: 1.2843e-05 eta: 3:50:12 time: 0.5547 data_time: 0.0142 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2607 loss: 1.2607 2022/07/31 03:03:33 - mmengine - INFO - Epoch(train) [24][1800/3757] lr: 1.2843e-05 eta: 3:49:15 time: 0.5549 data_time: 0.0147 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0913 loss: 1.0913 2022/07/31 03:04:28 - mmengine - INFO - Epoch(train) [24][1900/3757] lr: 1.2843e-05 eta: 3:48:19 time: 0.5539 data_time: 0.0156 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.3713 loss: 1.3713 2022/07/31 03:05:24 - mmengine - INFO - Epoch(train) [24][2000/3757] lr: 1.2843e-05 eta: 3:47:23 time: 0.5532 data_time: 0.0149 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9745 loss: 0.9745 2022/07/31 03:06:19 - mmengine - INFO - Epoch(train) [24][2100/3757] lr: 1.2843e-05 eta: 3:46:26 time: 0.5515 data_time: 0.0155 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8736 loss: 0.8736 2022/07/31 03:07:15 - mmengine - INFO - Epoch(train) [24][2200/3757] lr: 1.2843e-05 eta: 3:45:30 time: 0.5576 data_time: 0.0154 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0488 loss: 1.0488 2022/07/31 03:08:11 - mmengine - INFO - Epoch(train) [24][2300/3757] lr: 1.2843e-05 eta: 3:44:34 time: 0.5593 data_time: 0.0162 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2190 loss: 1.2190 2022/07/31 03:09:06 - mmengine - INFO - Epoch(train) [24][2400/3757] lr: 1.2843e-05 eta: 3:43:38 time: 0.5526 data_time: 0.0138 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1045 loss: 1.1045 2022/07/31 03:10:02 - mmengine - INFO - Epoch(train) [24][2500/3757] lr: 1.2843e-05 eta: 3:42:41 time: 0.5527 data_time: 0.0146 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9495 loss: 0.9495 2022/07/31 03:10:52 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 03:10:58 - mmengine - INFO - Epoch(train) [24][2600/3757] lr: 1.2843e-05 eta: 3:41:45 time: 0.5565 data_time: 0.0147 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1098 loss: 1.1098 2022/07/31 03:11:53 - mmengine - INFO - Epoch(train) [24][2700/3757] lr: 1.2843e-05 eta: 3:40:49 time: 0.5570 data_time: 0.0155 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1153 loss: 1.1153 2022/07/31 03:12:49 - mmengine - INFO - Epoch(train) [24][2800/3757] lr: 1.2843e-05 eta: 3:39:52 time: 0.5561 data_time: 0.0161 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0009 loss: 1.0009 2022/07/31 03:13:45 - mmengine - INFO - Epoch(train) [24][2900/3757] lr: 1.2843e-05 eta: 3:38:56 time: 0.5562 data_time: 0.0159 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8921 loss: 0.8921 2022/07/31 03:14:40 - mmengine - INFO - Epoch(train) [24][3000/3757] lr: 1.2843e-05 eta: 3:38:00 time: 0.5624 data_time: 0.0155 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0356 loss: 1.0356 2022/07/31 03:15:36 - mmengine - INFO - Epoch(train) [24][3100/3757] lr: 1.2843e-05 eta: 3:37:04 time: 0.5520 data_time: 0.0157 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9340 loss: 0.9340 2022/07/31 03:16:32 - mmengine - INFO - Epoch(train) [24][3200/3757] lr: 1.2843e-05 eta: 3:36:07 time: 0.5516 data_time: 0.0146 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0989 loss: 1.0989 2022/07/31 03:17:27 - mmengine - INFO - Epoch(train) [24][3300/3757] lr: 1.2843e-05 eta: 3:35:11 time: 0.5541 data_time: 0.0151 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9838 loss: 0.9838 2022/07/31 03:18:23 - mmengine - INFO - Epoch(train) [24][3400/3757] lr: 1.2843e-05 eta: 3:34:15 time: 0.5578 data_time: 0.0151 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0190 loss: 1.0190 2022/07/31 03:19:18 - mmengine - INFO - Epoch(train) [24][3500/3757] lr: 1.2843e-05 eta: 3:33:19 time: 0.5523 data_time: 0.0155 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9488 loss: 0.9488 2022/07/31 03:20:08 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 03:20:14 - mmengine - INFO - Epoch(train) [24][3600/3757] lr: 1.2843e-05 eta: 3:32:22 time: 0.5509 data_time: 0.0145 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8882 loss: 0.8882 2022/07/31 03:21:10 - mmengine - INFO - Epoch(train) [24][3700/3757] lr: 1.2843e-05 eta: 3:31:26 time: 0.5551 data_time: 0.0152 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2024 loss: 1.2024 2022/07/31 03:21:41 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 03:21:41 - mmengine - INFO - Epoch(train) [24][3757/3757] lr: 1.2843e-05 eta: 3:31:03 time: 0.5420 data_time: 0.0139 memory: 33632 top1_acc: 0.7143 top5_acc: 0.7143 loss_cls: 0.7511 loss: 0.7511 2022/07/31 03:21:41 - mmengine - INFO - Saving checkpoint at 24 epochs 2022/07/31 03:22:08 - mmengine - INFO - Epoch(val) [24][100/310] eta: 0:00:47 time: 0.2258 data_time: 0.0213 memory: 6325 2022/07/31 03:22:30 - mmengine - INFO - Epoch(val) [24][200/310] eta: 0:00:24 time: 0.2226 data_time: 0.0168 memory: 6325 2022/07/31 03:22:51 - mmengine - INFO - Epoch(val) [24][300/310] eta: 0:00:02 time: 0.2005 data_time: 0.0100 memory: 6325 2022/07/31 03:22:54 - mmengine - INFO - Epoch(val) [24][310/310] acc/top1: 0.7479 acc/top5: 0.9144 acc/mean1: 0.7478 2022/07/31 03:22:55 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb/best_acc/top1_epoch_22.pth is removed 2022/07/31 03:22:57 - mmengine - INFO - The best checkpoint with 0.7479 acc/top1 at 25 epoch is saved to best_acc/top1_epoch_25.pth. 2022/07/31 03:23:54 - mmengine - INFO - Epoch(train) [25][100/3757] lr: 9.5496e-06 eta: 3:29:56 time: 0.5652 data_time: 0.0165 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 0.8989 loss: 0.8989 2022/07/31 03:24:50 - mmengine - INFO - Epoch(train) [25][200/3757] lr: 9.5496e-06 eta: 3:28:59 time: 0.5599 data_time: 0.0147 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.4008 loss: 1.4008 2022/07/31 03:25:45 - mmengine - INFO - Epoch(train) [25][300/3757] lr: 9.5496e-06 eta: 3:28:03 time: 0.5547 data_time: 0.0140 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1135 loss: 1.1135 2022/07/31 03:26:40 - mmengine - INFO - Epoch(train) [25][400/3757] lr: 9.5496e-06 eta: 3:27:07 time: 0.5560 data_time: 0.0159 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.3349 loss: 1.3349 2022/07/31 03:27:36 - mmengine - INFO - Epoch(train) [25][500/3757] lr: 9.5496e-06 eta: 3:26:10 time: 0.5527 data_time: 0.0155 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1130 loss: 1.1130 2022/07/31 03:28:31 - mmengine - INFO - Epoch(train) [25][600/3757] lr: 9.5496e-06 eta: 3:25:14 time: 0.5533 data_time: 0.0157 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1690 loss: 1.1690 2022/07/31 03:29:27 - mmengine - INFO - Epoch(train) [25][700/3757] lr: 9.5496e-06 eta: 3:24:18 time: 0.5520 data_time: 0.0154 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9138 loss: 0.9138 2022/07/31 03:30:23 - mmengine - INFO - Epoch(train) [25][800/3757] lr: 9.5496e-06 eta: 3:23:22 time: 0.5541 data_time: 0.0143 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0194 loss: 1.0194 2022/07/31 03:30:40 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 03:31:18 - mmengine - INFO - Epoch(train) [25][900/3757] lr: 9.5496e-06 eta: 3:22:25 time: 0.5543 data_time: 0.0152 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9390 loss: 0.9390 2022/07/31 03:32:13 - mmengine - INFO - Epoch(train) [25][1000/3757] lr: 9.5496e-06 eta: 3:21:29 time: 0.5544 data_time: 0.0155 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3926 loss: 1.3926 2022/07/31 03:33:09 - mmengine - INFO - Epoch(train) [25][1100/3757] lr: 9.5496e-06 eta: 3:20:33 time: 0.5551 data_time: 0.0142 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0070 loss: 1.0070 2022/07/31 03:34:05 - mmengine - INFO - Epoch(train) [25][1200/3757] lr: 9.5496e-06 eta: 3:19:37 time: 0.5541 data_time: 0.0143 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2460 loss: 1.2460 2022/07/31 03:35:00 - mmengine - INFO - Epoch(train) [25][1300/3757] lr: 9.5496e-06 eta: 3:18:40 time: 0.5519 data_time: 0.0149 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1051 loss: 1.1051 2022/07/31 03:35:56 - mmengine - INFO - Epoch(train) [25][1400/3757] lr: 9.5496e-06 eta: 3:17:44 time: 0.5587 data_time: 0.0152 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1406 loss: 1.1406 2022/07/31 03:36:51 - mmengine - INFO - Epoch(train) [25][1500/3757] lr: 9.5496e-06 eta: 3:16:48 time: 0.5616 data_time: 0.0147 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0084 loss: 1.0084 2022/07/31 03:37:47 - mmengine - INFO - Epoch(train) [25][1600/3757] lr: 9.5496e-06 eta: 3:15:52 time: 0.5579 data_time: 0.0145 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1436 loss: 1.1436 2022/07/31 03:38:43 - mmengine - INFO - Epoch(train) [25][1700/3757] lr: 9.5496e-06 eta: 3:14:55 time: 0.5527 data_time: 0.0145 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1122 loss: 1.1122 2022/07/31 03:39:38 - mmengine - INFO - Epoch(train) [25][1800/3757] lr: 9.5496e-06 eta: 3:13:59 time: 0.5575 data_time: 0.0152 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1419 loss: 1.1419 2022/07/31 03:39:56 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 03:40:34 - mmengine - INFO - Epoch(train) [25][1900/3757] lr: 9.5496e-06 eta: 3:13:03 time: 0.5616 data_time: 0.0151 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0886 loss: 1.0886 2022/07/31 03:41:30 - mmengine - INFO - Epoch(train) [25][2000/3757] lr: 9.5496e-06 eta: 3:12:07 time: 0.5523 data_time: 0.0151 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2544 loss: 1.2544 2022/07/31 03:42:26 - mmengine - INFO - Epoch(train) [25][2100/3757] lr: 9.5496e-06 eta: 3:11:10 time: 0.5516 data_time: 0.0155 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9236 loss: 0.9236 2022/07/31 03:43:21 - mmengine - INFO - Epoch(train) [25][2200/3757] lr: 9.5496e-06 eta: 3:10:14 time: 0.5558 data_time: 0.0152 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1193 loss: 1.1193 2022/07/31 03:44:17 - mmengine - INFO - Epoch(train) [25][2300/3757] lr: 9.5496e-06 eta: 3:09:18 time: 0.5531 data_time: 0.0157 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.1686 loss: 1.1686 2022/07/31 03:45:12 - mmengine - INFO - Epoch(train) [25][2400/3757] lr: 9.5496e-06 eta: 3:08:22 time: 0.5542 data_time: 0.0150 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0374 loss: 1.0374 2022/07/31 03:46:08 - mmengine - INFO - Epoch(train) [25][2500/3757] lr: 9.5496e-06 eta: 3:07:25 time: 0.5536 data_time: 0.0148 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2761 loss: 1.2761 2022/07/31 03:47:04 - mmengine - INFO - Epoch(train) [25][2600/3757] lr: 9.5496e-06 eta: 3:06:29 time: 0.5714 data_time: 0.0162 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9783 loss: 0.9783 2022/07/31 03:47:59 - mmengine - INFO - Epoch(train) [25][2700/3757] lr: 9.5496e-06 eta: 3:05:33 time: 0.5516 data_time: 0.0143 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0817 loss: 1.0817 2022/07/31 03:48:55 - mmengine - INFO - Epoch(train) [25][2800/3757] lr: 9.5496e-06 eta: 3:04:37 time: 0.5531 data_time: 0.0144 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0729 loss: 1.0729 2022/07/31 03:49:13 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 03:49:50 - mmengine - INFO - Epoch(train) [25][2900/3757] lr: 9.5496e-06 eta: 3:03:41 time: 0.5585 data_time: 0.0151 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8355 loss: 0.8355 2022/07/31 03:50:46 - mmengine - INFO - Epoch(train) [25][3000/3757] lr: 9.5496e-06 eta: 3:02:44 time: 0.5565 data_time: 0.0147 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9219 loss: 0.9219 2022/07/31 03:51:42 - mmengine - INFO - Epoch(train) [25][3100/3757] lr: 9.5496e-06 eta: 3:01:48 time: 0.5526 data_time: 0.0150 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1791 loss: 1.1791 2022/07/31 03:52:37 - mmengine - INFO - Epoch(train) [25][3200/3757] lr: 9.5496e-06 eta: 3:00:52 time: 0.5521 data_time: 0.0153 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1475 loss: 1.1475 2022/07/31 03:53:33 - mmengine - INFO - Epoch(train) [25][3300/3757] lr: 9.5496e-06 eta: 2:59:56 time: 0.5643 data_time: 0.0154 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9174 loss: 0.9174 2022/07/31 03:54:29 - mmengine - INFO - Epoch(train) [25][3400/3757] lr: 9.5496e-06 eta: 2:59:00 time: 0.5566 data_time: 0.0174 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1052 loss: 1.1052 2022/07/31 03:55:24 - mmengine - INFO - Epoch(train) [25][3500/3757] lr: 9.5496e-06 eta: 2:58:03 time: 0.5533 data_time: 0.0154 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0243 loss: 1.0243 2022/07/31 03:56:20 - mmengine - INFO - Epoch(train) [25][3600/3757] lr: 9.5496e-06 eta: 2:57:07 time: 0.5546 data_time: 0.0174 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0375 loss: 1.0375 2022/07/31 03:57:16 - mmengine - INFO - Epoch(train) [25][3700/3757] lr: 9.5496e-06 eta: 2:56:11 time: 0.5554 data_time: 0.0151 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0417 loss: 1.0417 2022/07/31 03:57:47 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 03:57:47 - mmengine - INFO - Epoch(train) [25][3757/3757] lr: 9.5496e-06 eta: 2:55:49 time: 0.5462 data_time: 0.0156 memory: 33632 top1_acc: 0.5714 top5_acc: 0.7143 loss_cls: 1.0562 loss: 1.0562 2022/07/31 03:58:31 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 03:58:45 - mmengine - INFO - Epoch(train) [26][100/3757] lr: 6.6991e-06 eta: 2:54:41 time: 0.5550 data_time: 0.0150 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9820 loss: 0.9820 2022/07/31 03:59:41 - mmengine - INFO - Epoch(train) [26][200/3757] lr: 6.6991e-06 eta: 2:53:45 time: 0.5571 data_time: 0.0151 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8961 loss: 0.8961 2022/07/31 04:00:36 - mmengine - INFO - Epoch(train) [26][300/3757] lr: 6.6991e-06 eta: 2:52:49 time: 0.5549 data_time: 0.0144 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.8826 loss: 0.8826 2022/07/31 04:01:32 - mmengine - INFO - Epoch(train) [26][400/3757] lr: 6.6991e-06 eta: 2:51:53 time: 0.5552 data_time: 0.0153 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9721 loss: 0.9721 2022/07/31 04:02:28 - mmengine - INFO - Epoch(train) [26][500/3757] lr: 6.6991e-06 eta: 2:50:57 time: 0.5537 data_time: 0.0153 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8508 loss: 0.8508 2022/07/31 04:03:24 - mmengine - INFO - Epoch(train) [26][600/3757] lr: 6.6991e-06 eta: 2:50:00 time: 0.5570 data_time: 0.0147 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8958 loss: 0.8958 2022/07/31 04:04:19 - mmengine - INFO - Epoch(train) [26][700/3757] lr: 6.6991e-06 eta: 2:49:04 time: 0.5529 data_time: 0.0150 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9452 loss: 0.9452 2022/07/31 04:05:15 - mmengine - INFO - Epoch(train) [26][800/3757] lr: 6.6991e-06 eta: 2:48:08 time: 0.5646 data_time: 0.0168 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 0.8735 loss: 0.8735 2022/07/31 04:06:11 - mmengine - INFO - Epoch(train) [26][900/3757] lr: 6.6991e-06 eta: 2:47:12 time: 0.5551 data_time: 0.0150 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1911 loss: 1.1911 2022/07/31 04:07:07 - mmengine - INFO - Epoch(train) [26][1000/3757] lr: 6.6991e-06 eta: 2:46:16 time: 0.5512 data_time: 0.0143 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0707 loss: 1.0707 2022/07/31 04:07:49 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 04:08:03 - mmengine - INFO - Epoch(train) [26][1100/3757] lr: 6.6991e-06 eta: 2:45:20 time: 0.5537 data_time: 0.0154 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 0.9299 loss: 0.9299 2022/07/31 04:08:58 - mmengine - INFO - Epoch(train) [26][1200/3757] lr: 6.6991e-06 eta: 2:44:23 time: 0.5594 data_time: 0.0156 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0961 loss: 1.0961 2022/07/31 04:09:54 - mmengine - INFO - Epoch(train) [26][1300/3757] lr: 6.6991e-06 eta: 2:43:27 time: 0.5557 data_time: 0.0142 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1735 loss: 1.1735 2022/07/31 04:10:49 - mmengine - INFO - Epoch(train) [26][1400/3757] lr: 6.6991e-06 eta: 2:42:31 time: 0.5518 data_time: 0.0145 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0423 loss: 1.0423 2022/07/31 04:11:45 - mmengine - INFO - Epoch(train) [26][1500/3757] lr: 6.6991e-06 eta: 2:41:35 time: 0.5563 data_time: 0.0157 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.9462 loss: 0.9462 2022/07/31 04:12:41 - mmengine - INFO - Epoch(train) [26][1600/3757] lr: 6.6991e-06 eta: 2:40:39 time: 0.5526 data_time: 0.0150 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0678 loss: 1.0678 2022/07/31 04:13:37 - mmengine - INFO - Epoch(train) [26][1700/3757] lr: 6.6991e-06 eta: 2:39:43 time: 0.5576 data_time: 0.0157 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0886 loss: 1.0886 2022/07/31 04:14:32 - mmengine - INFO - Epoch(train) [26][1800/3757] lr: 6.6991e-06 eta: 2:38:46 time: 0.5548 data_time: 0.0148 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0129 loss: 1.0129 2022/07/31 04:15:28 - mmengine - INFO - Epoch(train) [26][1900/3757] lr: 6.6991e-06 eta: 2:37:50 time: 0.5545 data_time: 0.0151 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0906 loss: 1.0906 2022/07/31 04:16:23 - mmengine - INFO - Epoch(train) [26][2000/3757] lr: 6.6991e-06 eta: 2:36:54 time: 0.5532 data_time: 0.0155 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0708 loss: 1.0708 2022/07/31 04:17:05 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 04:17:19 - mmengine - INFO - Epoch(train) [26][2100/3757] lr: 6.6991e-06 eta: 2:35:58 time: 0.5538 data_time: 0.0152 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2272 loss: 1.2272 2022/07/31 04:18:15 - mmengine - INFO - Epoch(train) [26][2200/3757] lr: 6.6991e-06 eta: 2:35:02 time: 0.5552 data_time: 0.0162 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0205 loss: 1.0205 2022/07/31 04:19:10 - mmengine - INFO - Epoch(train) [26][2300/3757] lr: 6.6991e-06 eta: 2:34:05 time: 0.5563 data_time: 0.0150 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1341 loss: 1.1341 2022/07/31 04:20:06 - mmengine - INFO - Epoch(train) [26][2400/3757] lr: 6.6991e-06 eta: 2:33:09 time: 0.5614 data_time: 0.0150 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1892 loss: 1.1892 2022/07/31 04:21:02 - mmengine - INFO - Epoch(train) [26][2500/3757] lr: 6.6991e-06 eta: 2:32:13 time: 0.5513 data_time: 0.0140 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0256 loss: 1.0256 2022/07/31 04:21:57 - mmengine - INFO - Epoch(train) [26][2600/3757] lr: 6.6991e-06 eta: 2:31:17 time: 0.5524 data_time: 0.0142 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0009 loss: 1.0009 2022/07/31 04:22:53 - mmengine - INFO - Epoch(train) [26][2700/3757] lr: 6.6991e-06 eta: 2:30:21 time: 0.5544 data_time: 0.0151 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9640 loss: 0.9640 2022/07/31 04:23:48 - mmengine - INFO - Epoch(train) [26][2800/3757] lr: 6.6991e-06 eta: 2:29:25 time: 0.5583 data_time: 0.0151 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9525 loss: 0.9525 2022/07/31 04:24:44 - mmengine - INFO - Epoch(train) [26][2900/3757] lr: 6.6991e-06 eta: 2:28:28 time: 0.5523 data_time: 0.0145 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 0.9812 loss: 0.9812 2022/07/31 04:25:39 - mmengine - INFO - Epoch(train) [26][3000/3757] lr: 6.6991e-06 eta: 2:27:32 time: 0.5597 data_time: 0.0143 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8431 loss: 0.8431 2022/07/31 04:26:21 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 04:26:35 - mmengine - INFO - Epoch(train) [26][3100/3757] lr: 6.6991e-06 eta: 2:26:36 time: 0.5534 data_time: 0.0146 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9534 loss: 0.9534 2022/07/31 04:27:31 - mmengine - INFO - Epoch(train) [26][3200/3757] lr: 6.6991e-06 eta: 2:25:40 time: 0.5521 data_time: 0.0143 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1716 loss: 1.1716 2022/07/31 04:28:26 - mmengine - INFO - Epoch(train) [26][3300/3757] lr: 6.6991e-06 eta: 2:24:44 time: 0.5523 data_time: 0.0157 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9743 loss: 0.9743 2022/07/31 04:29:22 - mmengine - INFO - Epoch(train) [26][3400/3757] lr: 6.6991e-06 eta: 2:23:48 time: 0.5521 data_time: 0.0138 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.2352 loss: 1.2352 2022/07/31 04:30:17 - mmengine - INFO - Epoch(train) [26][3500/3757] lr: 6.6991e-06 eta: 2:22:51 time: 0.5510 data_time: 0.0152 memory: 33632 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 0.9876 loss: 0.9876 2022/07/31 04:31:13 - mmengine - INFO - Epoch(train) [26][3600/3757] lr: 6.6991e-06 eta: 2:21:55 time: 0.5553 data_time: 0.0135 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.7223 loss: 0.7223 2022/07/31 04:32:09 - mmengine - INFO - Epoch(train) [26][3700/3757] lr: 6.6991e-06 eta: 2:20:59 time: 0.5598 data_time: 0.0144 memory: 33632 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 1.1031 loss: 1.1031 2022/07/31 04:32:40 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 04:32:40 - mmengine - INFO - Epoch(train) [26][3757/3757] lr: 6.6991e-06 eta: 2:20:37 time: 0.5433 data_time: 0.0142 memory: 33632 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 0.9062 loss: 0.9062 2022/07/31 04:33:38 - mmengine - INFO - Epoch(train) [27][100/3757] lr: 4.3229e-06 eta: 2:19:30 time: 0.5527 data_time: 0.0149 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 0.8071 loss: 0.8071 2022/07/31 04:34:33 - mmengine - INFO - Epoch(train) [27][200/3757] lr: 4.3229e-06 eta: 2:18:34 time: 0.5544 data_time: 0.0150 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8163 loss: 0.8163 2022/07/31 04:35:29 - mmengine - INFO - Epoch(train) [27][300/3757] lr: 4.3229e-06 eta: 2:17:38 time: 0.5519 data_time: 0.0147 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.3439 loss: 1.3439 2022/07/31 04:35:39 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 04:36:25 - mmengine - INFO - Epoch(train) [27][400/3757] lr: 4.3229e-06 eta: 2:16:41 time: 0.5548 data_time: 0.0152 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0946 loss: 1.0946 2022/07/31 04:37:20 - mmengine - INFO - Epoch(train) [27][500/3757] lr: 4.3229e-06 eta: 2:15:45 time: 0.5588 data_time: 0.0148 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8884 loss: 0.8884 2022/07/31 04:38:16 - mmengine - INFO - Epoch(train) [27][600/3757] lr: 4.3229e-06 eta: 2:14:49 time: 0.5551 data_time: 0.0147 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.8920 loss: 0.8920 2022/07/31 04:39:12 - mmengine - INFO - Epoch(train) [27][700/3757] lr: 4.3229e-06 eta: 2:13:53 time: 0.5503 data_time: 0.0143 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9152 loss: 0.9152 2022/07/31 04:40:07 - mmengine - INFO - Epoch(train) [27][800/3757] lr: 4.3229e-06 eta: 2:12:57 time: 0.5595 data_time: 0.0152 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0629 loss: 1.0629 2022/07/31 04:41:03 - mmengine - INFO - Epoch(train) [27][900/3757] lr: 4.3229e-06 eta: 2:12:01 time: 0.5552 data_time: 0.0147 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9906 loss: 0.9906 2022/07/31 04:41:59 - mmengine - INFO - Epoch(train) [27][1000/3757] lr: 4.3229e-06 eta: 2:11:05 time: 0.5513 data_time: 0.0143 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0753 loss: 1.0753 2022/07/31 04:42:54 - mmengine - INFO - Epoch(train) [27][1100/3757] lr: 4.3229e-06 eta: 2:10:08 time: 0.5566 data_time: 0.0151 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0910 loss: 1.0910 2022/07/31 04:43:50 - mmengine - INFO - Epoch(train) [27][1200/3757] lr: 4.3229e-06 eta: 2:09:12 time: 0.5577 data_time: 0.0153 memory: 33632 top1_acc: 0.3750 top5_acc: 1.0000 loss_cls: 1.0465 loss: 1.0465 2022/07/31 04:44:46 - mmengine - INFO - Epoch(train) [27][1300/3757] lr: 4.3229e-06 eta: 2:08:16 time: 0.5539 data_time: 0.0155 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0614 loss: 1.0614 2022/07/31 04:44:56 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 04:45:41 - mmengine - INFO - Epoch(train) [27][1400/3757] lr: 4.3229e-06 eta: 2:07:20 time: 0.5524 data_time: 0.0142 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.1360 loss: 1.1360 2022/07/31 04:46:37 - mmengine - INFO - Epoch(train) [27][1500/3757] lr: 4.3229e-06 eta: 2:06:24 time: 0.5597 data_time: 0.0151 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1763 loss: 1.1763 2022/07/31 04:47:33 - mmengine - INFO - Epoch(train) [27][1600/3757] lr: 4.3229e-06 eta: 2:05:28 time: 0.5535 data_time: 0.0151 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0333 loss: 1.0333 2022/07/31 04:48:28 - mmengine - INFO - Epoch(train) [27][1700/3757] lr: 4.3229e-06 eta: 2:04:32 time: 0.5602 data_time: 0.0158 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.3370 loss: 1.3370 2022/07/31 04:49:24 - mmengine - INFO - Epoch(train) [27][1800/3757] lr: 4.3229e-06 eta: 2:03:36 time: 0.5564 data_time: 0.0152 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.3238 loss: 1.3238 2022/07/31 04:50:20 - mmengine - INFO - Epoch(train) [27][1900/3757] lr: 4.3229e-06 eta: 2:02:39 time: 0.5535 data_time: 0.0156 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.2887 loss: 1.2887 2022/07/31 04:51:15 - mmengine - INFO - Epoch(train) [27][2000/3757] lr: 4.3229e-06 eta: 2:01:43 time: 0.5586 data_time: 0.0141 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8334 loss: 0.8334 2022/07/31 04:52:11 - mmengine - INFO - Epoch(train) [27][2100/3757] lr: 4.3229e-06 eta: 2:00:47 time: 0.5536 data_time: 0.0143 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 1.0142 loss: 1.0142 2022/07/31 04:53:07 - mmengine - INFO - Epoch(train) [27][2200/3757] lr: 4.3229e-06 eta: 1:59:51 time: 0.5565 data_time: 0.0143 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9017 loss: 0.9017 2022/07/31 04:54:02 - mmengine - INFO - Epoch(train) [27][2300/3757] lr: 4.3229e-06 eta: 1:58:55 time: 0.5542 data_time: 0.0158 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8677 loss: 0.8677 2022/07/31 04:54:12 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 04:54:58 - mmengine - INFO - Epoch(train) [27][2400/3757] lr: 4.3229e-06 eta: 1:57:59 time: 0.5524 data_time: 0.0143 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0071 loss: 1.0071 2022/07/31 04:55:53 - mmengine - INFO - Epoch(train) [27][2500/3757] lr: 4.3229e-06 eta: 1:57:03 time: 0.5525 data_time: 0.0148 memory: 33632 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.0147 loss: 1.0147 2022/07/31 04:56:49 - mmengine - INFO - Epoch(train) [27][2600/3757] lr: 4.3229e-06 eta: 1:56:07 time: 0.5558 data_time: 0.0154 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.1469 loss: 1.1469 2022/07/31 04:57:45 - mmengine - INFO - Epoch(train) [27][2700/3757] lr: 4.3229e-06 eta: 1:55:11 time: 0.5573 data_time: 0.0147 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1088 loss: 1.1088 2022/07/31 04:58:40 - mmengine - INFO - Epoch(train) [27][2800/3757] lr: 4.3229e-06 eta: 1:54:14 time: 0.5587 data_time: 0.0152 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0806 loss: 1.0806 2022/07/31 04:59:36 - mmengine - INFO - Epoch(train) [27][2900/3757] lr: 4.3229e-06 eta: 1:53:18 time: 0.5526 data_time: 0.0158 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1077 loss: 1.1077 2022/07/31 05:00:32 - mmengine - INFO - Epoch(train) [27][3000/3757] lr: 4.3229e-06 eta: 1:52:22 time: 0.5520 data_time: 0.0154 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0918 loss: 1.0918 2022/07/31 05:01:27 - mmengine - INFO - Epoch(train) [27][3100/3757] lr: 4.3229e-06 eta: 1:51:26 time: 0.5527 data_time: 0.0156 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.7619 loss: 0.7619 2022/07/31 05:02:23 - mmengine - INFO - Epoch(train) [27][3200/3757] lr: 4.3229e-06 eta: 1:50:30 time: 0.5620 data_time: 0.0148 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9029 loss: 0.9029 2022/07/31 05:03:18 - mmengine - INFO - Epoch(train) [27][3300/3757] lr: 4.3229e-06 eta: 1:49:34 time: 0.5513 data_time: 0.0141 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0124 loss: 1.0124 2022/07/31 05:03:29 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 05:04:14 - mmengine - INFO - Epoch(train) [27][3400/3757] lr: 4.3229e-06 eta: 1:48:38 time: 0.5533 data_time: 0.0153 memory: 33632 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 0.9261 loss: 0.9261 2022/07/31 05:05:10 - mmengine - INFO - Epoch(train) [27][3500/3757] lr: 4.3229e-06 eta: 1:47:42 time: 0.5578 data_time: 0.0160 memory: 33632 top1_acc: 0.3750 top5_acc: 0.6250 loss_cls: 1.1917 loss: 1.1917 2022/07/31 05:06:05 - mmengine - INFO - Epoch(train) [27][3600/3757] lr: 4.3229e-06 eta: 1:46:46 time: 0.5560 data_time: 0.0148 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9157 loss: 0.9157 2022/07/31 05:07:01 - mmengine - INFO - Epoch(train) [27][3700/3757] lr: 4.3229e-06 eta: 1:45:49 time: 0.5535 data_time: 0.0149 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1364 loss: 1.1364 2022/07/31 05:07:32 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 05:07:33 - mmengine - INFO - Epoch(train) [27][3757/3757] lr: 4.3229e-06 eta: 1:45:27 time: 0.5423 data_time: 0.0139 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0378 loss: 1.0378 2022/07/31 05:07:33 - mmengine - INFO - Saving checkpoint at 27 epochs 2022/07/31 05:08:01 - mmengine - INFO - Epoch(val) [27][100/310] eta: 0:00:47 time: 0.2268 data_time: 0.0204 memory: 6325 2022/07/31 05:08:23 - mmengine - INFO - Epoch(val) [27][200/310] eta: 0:00:24 time: 0.2230 data_time: 0.0170 memory: 6325 2022/07/31 05:08:44 - mmengine - INFO - Epoch(val) [27][300/310] eta: 0:00:01 time: 0.1953 data_time: 0.0086 memory: 6325 2022/07/31 05:08:47 - mmengine - INFO - Epoch(val) [27][310/310] acc/top1: 0.7521 acc/top5: 0.9171 acc/mean1: 0.7520 2022/07/31 05:08:47 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb/best_acc/top1_epoch_25.pth is removed 2022/07/31 05:08:50 - mmengine - INFO - The best checkpoint with 0.7521 acc/top1 at 28 epoch is saved to best_acc/top1_epoch_28.pth. 2022/07/31 05:09:46 - mmengine - INFO - Epoch(train) [28][100/3757] lr: 2.4473e-06 eta: 1:44:20 time: 0.5521 data_time: 0.0151 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0989 loss: 1.0989 2022/07/31 05:10:41 - mmengine - INFO - Epoch(train) [28][200/3757] lr: 2.4473e-06 eta: 1:43:24 time: 0.5547 data_time: 0.0147 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1949 loss: 1.1949 2022/07/31 05:11:37 - mmengine - INFO - Epoch(train) [28][300/3757] lr: 2.4473e-06 eta: 1:42:28 time: 0.5519 data_time: 0.0137 memory: 33632 top1_acc: 0.2500 top5_acc: 0.8750 loss_cls: 1.1409 loss: 1.1409 2022/07/31 05:12:33 - mmengine - INFO - Epoch(train) [28][400/3757] lr: 2.4473e-06 eta: 1:41:32 time: 0.5574 data_time: 0.0148 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8786 loss: 0.8786 2022/07/31 05:13:28 - mmengine - INFO - Epoch(train) [28][500/3757] lr: 2.4473e-06 eta: 1:40:36 time: 0.5548 data_time: 0.0148 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 0.9444 loss: 0.9444 2022/07/31 05:14:02 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 05:14:24 - mmengine - INFO - Epoch(train) [28][600/3757] lr: 2.4473e-06 eta: 1:39:40 time: 0.5613 data_time: 0.0155 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1124 loss: 1.1124 2022/07/31 05:15:20 - mmengine - INFO - Epoch(train) [28][700/3757] lr: 2.4473e-06 eta: 1:38:44 time: 0.5524 data_time: 0.0156 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0208 loss: 1.0208 2022/07/31 05:16:16 - mmengine - INFO - Epoch(train) [28][800/3757] lr: 2.4473e-06 eta: 1:37:48 time: 0.5512 data_time: 0.0155 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9181 loss: 0.9181 2022/07/31 05:17:12 - mmengine - INFO - Epoch(train) [28][900/3757] lr: 2.4473e-06 eta: 1:36:52 time: 0.5601 data_time: 0.0152 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0357 loss: 1.0357 2022/07/31 05:18:07 - mmengine - INFO - Epoch(train) [28][1000/3757] lr: 2.4473e-06 eta: 1:35:56 time: 0.5581 data_time: 0.0140 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0713 loss: 1.0713 2022/07/31 05:19:03 - mmengine - INFO - Epoch(train) [28][1100/3757] lr: 2.4473e-06 eta: 1:35:00 time: 0.5743 data_time: 0.0151 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1411 loss: 1.1411 2022/07/31 05:19:59 - mmengine - INFO - Epoch(train) [28][1200/3757] lr: 2.4473e-06 eta: 1:34:03 time: 0.5538 data_time: 0.0142 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.2410 loss: 1.2410 2022/07/31 05:20:55 - mmengine - INFO - Epoch(train) [28][1300/3757] lr: 2.4473e-06 eta: 1:33:07 time: 0.5508 data_time: 0.0142 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0465 loss: 1.0465 2022/07/31 05:21:51 - mmengine - INFO - Epoch(train) [28][1400/3757] lr: 2.4473e-06 eta: 1:32:11 time: 0.5769 data_time: 0.0158 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1427 loss: 1.1427 2022/07/31 05:22:47 - mmengine - INFO - Epoch(train) [28][1500/3757] lr: 2.4473e-06 eta: 1:31:15 time: 0.5528 data_time: 0.0153 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0765 loss: 1.0765 2022/07/31 05:23:20 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 05:23:42 - mmengine - INFO - Epoch(train) [28][1600/3757] lr: 2.4473e-06 eta: 1:30:19 time: 0.5630 data_time: 0.0159 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0747 loss: 1.0747 2022/07/31 05:24:38 - mmengine - INFO - Epoch(train) [28][1700/3757] lr: 2.4473e-06 eta: 1:29:23 time: 0.5557 data_time: 0.0146 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1858 loss: 1.1858 2022/07/31 05:25:33 - mmengine - INFO - Epoch(train) [28][1800/3757] lr: 2.4473e-06 eta: 1:28:27 time: 0.5552 data_time: 0.0151 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0947 loss: 1.0947 2022/07/31 05:26:30 - mmengine - INFO - Epoch(train) [28][1900/3757] lr: 2.4473e-06 eta: 1:27:31 time: 0.5651 data_time: 0.0154 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9492 loss: 0.9492 2022/07/31 05:27:25 - mmengine - INFO - Epoch(train) [28][2000/3757] lr: 2.4473e-06 eta: 1:26:35 time: 0.5593 data_time: 0.0143 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2054 loss: 1.2054 2022/07/31 05:28:21 - mmengine - INFO - Epoch(train) [28][2100/3757] lr: 2.4473e-06 eta: 1:25:39 time: 0.5531 data_time: 0.0151 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.3856 loss: 1.3856 2022/07/31 05:29:16 - mmengine - INFO - Epoch(train) [28][2200/3757] lr: 2.4473e-06 eta: 1:24:43 time: 0.5550 data_time: 0.0144 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.8076 loss: 0.8076 2022/07/31 05:30:12 - mmengine - INFO - Epoch(train) [28][2300/3757] lr: 2.4473e-06 eta: 1:23:47 time: 0.5563 data_time: 0.0158 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.0096 loss: 1.0096 2022/07/31 05:31:08 - mmengine - INFO - Epoch(train) [28][2400/3757] lr: 2.4473e-06 eta: 1:22:51 time: 0.5581 data_time: 0.0150 memory: 33632 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.1400 loss: 1.1400 2022/07/31 05:32:04 - mmengine - INFO - Epoch(train) [28][2500/3757] lr: 2.4473e-06 eta: 1:21:55 time: 0.5544 data_time: 0.0162 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9496 loss: 0.9496 2022/07/31 05:32:37 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 05:32:59 - mmengine - INFO - Epoch(train) [28][2600/3757] lr: 2.4473e-06 eta: 1:20:58 time: 0.5602 data_time: 0.0144 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1083 loss: 1.1083 2022/07/31 05:33:55 - mmengine - INFO - Epoch(train) [28][2700/3757] lr: 2.4473e-06 eta: 1:20:02 time: 0.5571 data_time: 0.0147 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1703 loss: 1.1703 2022/07/31 05:34:50 - mmengine - INFO - Epoch(train) [28][2800/3757] lr: 2.4473e-06 eta: 1:19:06 time: 0.5586 data_time: 0.0146 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 1.0171 loss: 1.0171 2022/07/31 05:35:46 - mmengine - INFO - Epoch(train) [28][2900/3757] lr: 2.4473e-06 eta: 1:18:10 time: 0.5558 data_time: 0.0146 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.2644 loss: 1.2644 2022/07/31 05:36:41 - mmengine - INFO - Epoch(train) [28][3000/3757] lr: 2.4473e-06 eta: 1:17:14 time: 0.5515 data_time: 0.0139 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0786 loss: 1.0786 2022/07/31 05:37:37 - mmengine - INFO - Epoch(train) [28][3100/3757] lr: 2.4473e-06 eta: 1:16:18 time: 0.5517 data_time: 0.0145 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0800 loss: 1.0800 2022/07/31 05:38:32 - mmengine - INFO - Epoch(train) [28][3200/3757] lr: 2.4473e-06 eta: 1:15:22 time: 0.5570 data_time: 0.0161 memory: 33632 top1_acc: 0.5000 top5_acc: 0.8750 loss_cls: 1.0352 loss: 1.0352 2022/07/31 05:39:28 - mmengine - INFO - Epoch(train) [28][3300/3757] lr: 2.4473e-06 eta: 1:14:26 time: 0.5582 data_time: 0.0148 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0191 loss: 1.0191 2022/07/31 05:40:24 - mmengine - INFO - Epoch(train) [28][3400/3757] lr: 2.4473e-06 eta: 1:13:30 time: 0.5609 data_time: 0.0172 memory: 33632 top1_acc: 0.6250 top5_acc: 0.6250 loss_cls: 1.2248 loss: 1.2248 2022/07/31 05:41:19 - mmengine - INFO - Epoch(train) [28][3500/3757] lr: 2.4473e-06 eta: 1:12:34 time: 0.5550 data_time: 0.0146 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.3052 loss: 1.3052 2022/07/31 05:41:53 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 05:42:15 - mmengine - INFO - Epoch(train) [28][3600/3757] lr: 2.4473e-06 eta: 1:11:38 time: 0.5573 data_time: 0.0144 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8304 loss: 0.8304 2022/07/31 05:43:11 - mmengine - INFO - Epoch(train) [28][3700/3757] lr: 2.4473e-06 eta: 1:10:42 time: 0.5542 data_time: 0.0159 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.2666 loss: 1.2666 2022/07/31 05:43:42 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 05:43:42 - mmengine - INFO - Epoch(train) [28][3757/3757] lr: 2.4473e-06 eta: 1:10:19 time: 0.5443 data_time: 0.0150 memory: 33632 top1_acc: 0.8571 top5_acc: 1.0000 loss_cls: 1.1245 loss: 1.1245 2022/07/31 05:44:39 - mmengine - INFO - Epoch(train) [29][100/3757] lr: 1.0927e-06 eta: 1:09:13 time: 0.5539 data_time: 0.0144 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9230 loss: 0.9230 2022/07/31 05:45:35 - mmengine - INFO - Epoch(train) [29][200/3757] lr: 1.0927e-06 eta: 1:08:17 time: 0.5529 data_time: 0.0147 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8474 loss: 0.8474 2022/07/31 05:46:30 - mmengine - INFO - Epoch(train) [29][300/3757] lr: 1.0927e-06 eta: 1:07:21 time: 0.5520 data_time: 0.0148 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1896 loss: 1.1896 2022/07/31 05:47:26 - mmengine - INFO - Epoch(train) [29][400/3757] lr: 1.0927e-06 eta: 1:06:25 time: 0.5577 data_time: 0.0145 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.1731 loss: 1.1731 2022/07/31 05:48:22 - mmengine - INFO - Epoch(train) [29][500/3757] lr: 1.0927e-06 eta: 1:05:29 time: 0.5549 data_time: 0.0154 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.8492 loss: 0.8492 2022/07/31 05:49:17 - mmengine - INFO - Epoch(train) [29][600/3757] lr: 1.0927e-06 eta: 1:04:33 time: 0.5621 data_time: 0.0153 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1143 loss: 1.1143 2022/07/31 05:50:13 - mmengine - INFO - Epoch(train) [29][700/3757] lr: 1.0927e-06 eta: 1:03:37 time: 0.5534 data_time: 0.0153 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1679 loss: 1.1679 2022/07/31 05:51:09 - mmengine - INFO - Epoch(train) [29][800/3757] lr: 1.0927e-06 eta: 1:02:41 time: 0.5513 data_time: 0.0142 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.1559 loss: 1.1559 2022/07/31 05:51:11 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 05:52:04 - mmengine - INFO - Epoch(train) [29][900/3757] lr: 1.0927e-06 eta: 1:01:45 time: 0.5550 data_time: 0.0157 memory: 33632 top1_acc: 0.5000 top5_acc: 1.0000 loss_cls: 0.9285 loss: 0.9285 2022/07/31 05:53:00 - mmengine - INFO - Epoch(train) [29][1000/3757] lr: 1.0927e-06 eta: 1:00:49 time: 0.5567 data_time: 0.0149 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.8630 loss: 0.8630 2022/07/31 05:53:56 - mmengine - INFO - Epoch(train) [29][1100/3757] lr: 1.0927e-06 eta: 0:59:53 time: 0.5635 data_time: 0.0158 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9926 loss: 0.9926 2022/07/31 05:54:51 - mmengine - INFO - Epoch(train) [29][1200/3757] lr: 1.0927e-06 eta: 0:58:57 time: 0.5525 data_time: 0.0150 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1742 loss: 1.1742 2022/07/31 05:55:47 - mmengine - INFO - Epoch(train) [29][1300/3757] lr: 1.0927e-06 eta: 0:58:01 time: 0.5521 data_time: 0.0156 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0572 loss: 1.0572 2022/07/31 05:56:43 - mmengine - INFO - Epoch(train) [29][1400/3757] lr: 1.0927e-06 eta: 0:57:05 time: 0.5520 data_time: 0.0146 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9592 loss: 0.9592 2022/07/31 05:57:38 - mmengine - INFO - Epoch(train) [29][1500/3757] lr: 1.0927e-06 eta: 0:56:08 time: 0.5529 data_time: 0.0149 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.2706 loss: 1.2706 2022/07/31 05:58:34 - mmengine - INFO - Epoch(train) [29][1600/3757] lr: 1.0927e-06 eta: 0:55:12 time: 0.5538 data_time: 0.0150 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0089 loss: 1.0089 2022/07/31 05:59:30 - mmengine - INFO - Epoch(train) [29][1700/3757] lr: 1.0927e-06 eta: 0:54:16 time: 0.5548 data_time: 0.0151 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2712 loss: 1.2712 2022/07/31 06:00:27 - mmengine - INFO - Epoch(train) [29][1800/3757] lr: 1.0927e-06 eta: 0:53:20 time: 0.5518 data_time: 0.0138 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8929 loss: 0.8929 2022/07/31 06:00:29 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 06:01:22 - mmengine - INFO - Epoch(train) [29][1900/3757] lr: 1.0927e-06 eta: 0:52:24 time: 0.5514 data_time: 0.0148 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.1362 loss: 1.1362 2022/07/31 06:02:18 - mmengine - INFO - Epoch(train) [29][2000/3757] lr: 1.0927e-06 eta: 0:51:28 time: 0.5554 data_time: 0.0167 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1438 loss: 1.1438 2022/07/31 06:03:13 - mmengine - INFO - Epoch(train) [29][2100/3757] lr: 1.0927e-06 eta: 0:50:32 time: 0.5527 data_time: 0.0155 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.1876 loss: 1.1876 2022/07/31 06:04:09 - mmengine - INFO - Epoch(train) [29][2200/3757] lr: 1.0927e-06 eta: 0:49:36 time: 0.5541 data_time: 0.0156 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.9242 loss: 0.9242 2022/07/31 06:05:04 - mmengine - INFO - Epoch(train) [29][2300/3757] lr: 1.0927e-06 eta: 0:48:40 time: 0.5594 data_time: 0.0159 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9198 loss: 0.9198 2022/07/31 06:06:00 - mmengine - INFO - Epoch(train) [29][2400/3757] lr: 1.0927e-06 eta: 0:47:44 time: 0.5571 data_time: 0.0151 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8085 loss: 0.8085 2022/07/31 06:06:56 - mmengine - INFO - Epoch(train) [29][2500/3757] lr: 1.0927e-06 eta: 0:46:48 time: 0.5596 data_time: 0.0155 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.8220 loss: 0.8220 2022/07/31 06:07:52 - mmengine - INFO - Epoch(train) [29][2600/3757] lr: 1.0927e-06 eta: 0:45:52 time: 0.5521 data_time: 0.0140 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0324 loss: 1.0324 2022/07/31 06:08:47 - mmengine - INFO - Epoch(train) [29][2700/3757] lr: 1.0927e-06 eta: 0:44:56 time: 0.5530 data_time: 0.0151 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0009 loss: 1.0009 2022/07/31 06:09:43 - mmengine - INFO - Epoch(train) [29][2800/3757] lr: 1.0927e-06 eta: 0:44:00 time: 0.5573 data_time: 0.0159 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.7611 loss: 0.7611 2022/07/31 06:09:45 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 06:10:38 - mmengine - INFO - Epoch(train) [29][2900/3757] lr: 1.0927e-06 eta: 0:43:04 time: 0.5548 data_time: 0.0155 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0552 loss: 1.0552 2022/07/31 06:11:34 - mmengine - INFO - Epoch(train) [29][3000/3757] lr: 1.0927e-06 eta: 0:42:08 time: 0.5526 data_time: 0.0150 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9036 loss: 0.9036 2022/07/31 06:12:30 - mmengine - INFO - Epoch(train) [29][3100/3757] lr: 1.0927e-06 eta: 0:41:12 time: 0.5541 data_time: 0.0155 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0055 loss: 1.0055 2022/07/31 06:13:25 - mmengine - INFO - Epoch(train) [29][3200/3757] lr: 1.0927e-06 eta: 0:40:16 time: 0.5509 data_time: 0.0138 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.2349 loss: 1.2349 2022/07/31 06:14:21 - mmengine - INFO - Epoch(train) [29][3300/3757] lr: 1.0927e-06 eta: 0:39:20 time: 0.5604 data_time: 0.0147 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 0.8902 loss: 0.8902 2022/07/31 06:15:17 - mmengine - INFO - Epoch(train) [29][3400/3757] lr: 1.0927e-06 eta: 0:38:24 time: 0.5544 data_time: 0.0156 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.0080 loss: 1.0080 2022/07/31 06:16:12 - mmengine - INFO - Epoch(train) [29][3500/3757] lr: 1.0927e-06 eta: 0:37:28 time: 0.5544 data_time: 0.0153 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 1.0276 loss: 1.0276 2022/07/31 06:17:08 - mmengine - INFO - Epoch(train) [29][3600/3757] lr: 1.0927e-06 eta: 0:36:32 time: 0.5566 data_time: 0.0167 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.9049 loss: 0.9049 2022/07/31 06:18:03 - mmengine - INFO - Epoch(train) [29][3700/3757] lr: 1.0927e-06 eta: 0:35:36 time: 0.5525 data_time: 0.0153 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.0169 loss: 1.0169 2022/07/31 06:18:35 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 06:18:35 - mmengine - INFO - Epoch(train) [29][3757/3757] lr: 1.0927e-06 eta: 0:35:13 time: 0.5455 data_time: 0.0153 memory: 33632 top1_acc: 0.7143 top5_acc: 0.8571 loss_cls: 1.0755 loss: 1.0755 2022/07/31 06:19:03 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 06:19:33 - mmengine - INFO - Epoch(train) [30][100/3757] lr: 2.7392e-07 eta: 0:34:08 time: 0.5544 data_time: 0.0148 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0074 loss: 1.0074 2022/07/31 06:20:28 - mmengine - INFO - Epoch(train) [30][200/3757] lr: 2.7392e-07 eta: 0:33:12 time: 0.5588 data_time: 0.0152 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9217 loss: 0.9217 2022/07/31 06:21:24 - mmengine - INFO - Epoch(train) [30][300/3757] lr: 2.7392e-07 eta: 0:32:16 time: 0.5514 data_time: 0.0145 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.1727 loss: 1.1727 2022/07/31 06:22:19 - mmengine - INFO - Epoch(train) [30][400/3757] lr: 2.7392e-07 eta: 0:31:20 time: 0.5556 data_time: 0.0150 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.9701 loss: 0.9701 2022/07/31 06:23:15 - mmengine - INFO - Epoch(train) [30][500/3757] lr: 2.7392e-07 eta: 0:30:23 time: 0.5531 data_time: 0.0153 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0987 loss: 1.0987 2022/07/31 06:24:11 - mmengine - INFO - Epoch(train) [30][600/3757] lr: 2.7392e-07 eta: 0:29:27 time: 0.5536 data_time: 0.0139 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.8182 loss: 0.8182 2022/07/31 06:25:06 - mmengine - INFO - Epoch(train) [30][700/3757] lr: 2.7392e-07 eta: 0:28:31 time: 0.5526 data_time: 0.0147 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0464 loss: 1.0464 2022/07/31 06:26:02 - mmengine - INFO - Epoch(train) [30][800/3757] lr: 2.7392e-07 eta: 0:27:35 time: 0.5589 data_time: 0.0154 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 1.2376 loss: 1.2376 2022/07/31 06:26:58 - mmengine - INFO - Epoch(train) [30][900/3757] lr: 2.7392e-07 eta: 0:26:39 time: 0.5613 data_time: 0.0154 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.7706 loss: 0.7706 2022/07/31 06:27:53 - mmengine - INFO - Epoch(train) [30][1000/3757] lr: 2.7392e-07 eta: 0:25:43 time: 0.5536 data_time: 0.0144 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.0320 loss: 1.0320 2022/07/31 06:28:20 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 06:28:49 - mmengine - INFO - Epoch(train) [30][1100/3757] lr: 2.7392e-07 eta: 0:24:47 time: 0.5528 data_time: 0.0143 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.9271 loss: 0.9271 2022/07/31 06:29:45 - mmengine - INFO - Epoch(train) [30][1200/3757] lr: 2.7392e-07 eta: 0:23:51 time: 0.5560 data_time: 0.0160 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8828 loss: 0.8828 2022/07/31 06:30:40 - mmengine - INFO - Epoch(train) [30][1300/3757] lr: 2.7392e-07 eta: 0:22:55 time: 0.5591 data_time: 0.0162 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 0.9376 loss: 0.9376 2022/07/31 06:31:36 - mmengine - INFO - Epoch(train) [30][1400/3757] lr: 2.7392e-07 eta: 0:21:59 time: 0.5514 data_time: 0.0142 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0193 loss: 1.0193 2022/07/31 06:32:32 - mmengine - INFO - Epoch(train) [30][1500/3757] lr: 2.7392e-07 eta: 0:21:03 time: 0.5579 data_time: 0.0153 memory: 33632 top1_acc: 0.8750 top5_acc: 1.0000 loss_cls: 1.0673 loss: 1.0673 2022/07/31 06:33:27 - mmengine - INFO - Epoch(train) [30][1600/3757] lr: 2.7392e-07 eta: 0:20:07 time: 0.5572 data_time: 0.0152 memory: 33632 top1_acc: 0.5000 top5_acc: 0.7500 loss_cls: 0.9504 loss: 0.9504 2022/07/31 06:34:23 - mmengine - INFO - Epoch(train) [30][1700/3757] lr: 2.7392e-07 eta: 0:19:11 time: 0.5598 data_time: 0.0152 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 0.8890 loss: 0.8890 2022/07/31 06:35:18 - mmengine - INFO - Epoch(train) [30][1800/3757] lr: 2.7392e-07 eta: 0:18:15 time: 0.5583 data_time: 0.0157 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 0.9120 loss: 0.9120 2022/07/31 06:36:14 - mmengine - INFO - Epoch(train) [30][1900/3757] lr: 2.7392e-07 eta: 0:17:19 time: 0.5587 data_time: 0.0157 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1088 loss: 1.1088 2022/07/31 06:37:10 - mmengine - INFO - Epoch(train) [30][2000/3757] lr: 2.7392e-07 eta: 0:16:23 time: 0.5582 data_time: 0.0152 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1046 loss: 1.1046 2022/07/31 06:37:36 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 06:38:06 - mmengine - INFO - Epoch(train) [30][2100/3757] lr: 2.7392e-07 eta: 0:15:27 time: 0.5537 data_time: 0.0146 memory: 33632 top1_acc: 0.6250 top5_acc: 1.0000 loss_cls: 0.9410 loss: 0.9410 2022/07/31 06:39:01 - mmengine - INFO - Epoch(train) [30][2200/3757] lr: 2.7392e-07 eta: 0:14:31 time: 0.5518 data_time: 0.0138 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.8889 loss: 0.8889 2022/07/31 06:39:57 - mmengine - INFO - Epoch(train) [30][2300/3757] lr: 2.7392e-07 eta: 0:13:35 time: 0.5578 data_time: 0.0152 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 0.8439 loss: 0.8439 2022/07/31 06:40:52 - mmengine - INFO - Epoch(train) [30][2400/3757] lr: 2.7392e-07 eta: 0:12:39 time: 0.5510 data_time: 0.0133 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0259 loss: 1.0259 2022/07/31 06:41:48 - mmengine - INFO - Epoch(train) [30][2500/3757] lr: 2.7392e-07 eta: 0:11:43 time: 0.5534 data_time: 0.0146 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 1.0502 loss: 1.0502 2022/07/31 06:42:43 - mmengine - INFO - Epoch(train) [30][2600/3757] lr: 2.7392e-07 eta: 0:10:47 time: 0.5541 data_time: 0.0155 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.1521 loss: 1.1521 2022/07/31 06:43:39 - mmengine - INFO - Epoch(train) [30][2700/3757] lr: 2.7392e-07 eta: 0:09:51 time: 0.5587 data_time: 0.0164 memory: 33632 top1_acc: 0.6250 top5_acc: 0.7500 loss_cls: 0.9763 loss: 0.9763 2022/07/31 06:44:35 - mmengine - INFO - Epoch(train) [30][2800/3757] lr: 2.7392e-07 eta: 0:08:55 time: 0.5532 data_time: 0.0149 memory: 33632 top1_acc: 0.7500 top5_acc: 1.0000 loss_cls: 0.7517 loss: 0.7517 2022/07/31 06:45:31 - mmengine - INFO - Epoch(train) [30][2900/3757] lr: 2.7392e-07 eta: 0:07:59 time: 0.5551 data_time: 0.0167 memory: 33632 top1_acc: 0.7500 top5_acc: 0.7500 loss_cls: 1.1491 loss: 1.1491 2022/07/31 06:46:26 - mmengine - INFO - Epoch(train) [30][3000/3757] lr: 2.7392e-07 eta: 0:07:03 time: 0.5517 data_time: 0.0154 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9451 loss: 0.9451 2022/07/31 06:46:52 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 06:47:22 - mmengine - INFO - Epoch(train) [30][3100/3757] lr: 2.7392e-07 eta: 0:06:07 time: 0.5526 data_time: 0.0148 memory: 33632 top1_acc: 0.5000 top5_acc: 0.6250 loss_cls: 1.1877 loss: 1.1877 2022/07/31 06:48:18 - mmengine - INFO - Epoch(train) [30][3200/3757] lr: 2.7392e-07 eta: 0:05:11 time: 0.5560 data_time: 0.0157 memory: 33632 top1_acc: 0.7500 top5_acc: 0.8750 loss_cls: 0.9706 loss: 0.9706 2022/07/31 06:49:13 - mmengine - INFO - Epoch(train) [30][3300/3757] lr: 2.7392e-07 eta: 0:04:15 time: 0.5668 data_time: 0.0160 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 1.0401 loss: 1.0401 2022/07/31 06:50:09 - mmengine - INFO - Epoch(train) [30][3400/3757] lr: 2.7392e-07 eta: 0:03:19 time: 0.5517 data_time: 0.0147 memory: 33632 top1_acc: 0.8750 top5_acc: 0.8750 loss_cls: 0.9463 loss: 0.9463 2022/07/31 06:51:04 - mmengine - INFO - Epoch(train) [30][3500/3757] lr: 2.7392e-07 eta: 0:02:23 time: 0.5529 data_time: 0.0151 memory: 33632 top1_acc: 1.0000 top5_acc: 1.0000 loss_cls: 1.1197 loss: 1.1197 2022/07/31 06:52:00 - mmengine - INFO - Epoch(train) [30][3600/3757] lr: 2.7392e-07 eta: 0:01:27 time: 0.5575 data_time: 0.0156 memory: 33632 top1_acc: 0.6250 top5_acc: 0.8750 loss_cls: 1.2415 loss: 1.2415 2022/07/31 06:52:55 - mmengine - INFO - Epoch(train) [30][3700/3757] lr: 2.7392e-07 eta: 0:00:31 time: 0.5524 data_time: 0.0147 memory: 33632 top1_acc: 0.2500 top5_acc: 0.7500 loss_cls: 0.9200 loss: 0.9200 2022/07/31 06:53:27 - mmengine - INFO - Exp name: swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220730_130320 2022/07/31 06:53:27 - mmengine - INFO - Epoch(train) [30][3757/3757] lr: 2.7392e-07 eta: 0:00:09 time: 0.5617 data_time: 0.0159 memory: 33632 top1_acc: 0.8571 top5_acc: 0.8571 loss_cls: 0.8993 loss: 0.8993 2022/07/31 06:53:27 - mmengine - INFO - Saving checkpoint at 30 epochs 2022/07/31 06:53:56 - mmengine - INFO - Epoch(val) [30][100/310] eta: 0:00:45 time: 0.2183 data_time: 0.0171 memory: 6325 2022/07/31 06:54:18 - mmengine - INFO - Epoch(val) [30][200/310] eta: 0:00:24 time: 0.2206 data_time: 0.0186 memory: 6325 2022/07/31 06:54:40 - mmengine - INFO - Epoch(val) [30][300/310] eta: 0:00:01 time: 0.1968 data_time: 0.0086 memory: 6325 2022/07/31 06:54:43 - mmengine - INFO - Epoch(val) [30][310/310] acc/top1: 0.7533 acc/top5: 0.9171 acc/mean1: 0.7532 2022/07/31 06:54:43 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/mmaction2/work_dirs/swin_small_imagenet_1k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb/best_acc/top1_epoch_28.pth is removed 2022/07/31 06:54:46 - mmengine - INFO - The best checkpoint with 0.7533 acc/top1 at 31 epoch is saved to best_acc/top1_epoch_31.pth.