mindformers.models.mae.ViTMAEConfig

class mindformers.models.mae.ViTMAEConfig(mask_ratio: float = 0.75, image_size: int = 224, patch_size: int = 16, num_channels: int = 3, initializer_range: float = 0.02, hidden_size: int = 768, num_hidden_layers: int = 12, num_attention_heads: int = 12, intermediate_size: int = 3072, qkv_bias: bool = True, hidden_act: str = 'gelu', post_layernorm_residual: bool = False, layer_norm_eps: float = 1e-06, attention_probs_dropout_prob: float = 0.0, hidden_dropout_prob: float = 0.0, drop_path_rate: float = 0.0, decoder_hidden_size: int = 512, decoder_num_hidden_layers: int = 8, decoder_num_attention_heads: int = 16, decoder_intermediate_size: int = 2048, norm_pix_loss: bool = True, checkpoint_name_or_path: str = '', layernorm_compute_type: <module 'mindspore.common.dtype' from '/home/docs/checkouts/readthedocs.org/user_builds/mindformerstest/envs/stable/lib/python3.7/site-packages/mindspore/common/dtype.py'> = mindspore.float32, softmax_compute_type: <module 'mindspore.common.dtype' from '/home/docs/checkouts/readthedocs.org/user_builds/mindformerstest/envs/stable/lib/python3.7/site-packages/mindspore/common/dtype.py'> = mindspore.float32, param_init_type: <module 'mindspore.common.dtype' from '/home/docs/checkouts/readthedocs.org/user_builds/mindformerstest/envs/stable/lib/python3.7/site-packages/mindspore/common/dtype.py'> = mindspore.float32, parallel_config: mindformers.modules.transformer.transformer.TransformerOpParallelConfig = <mindformers.modules.transformer.transformer.TransformerOpParallelConfig object>, moe_config: mindformers.modules.transformer.moe.MoEConfig = <mindformers.modules.transformer.moe.MoEConfig object>, **kwargs)

Config for Mae model

实际案例

>>> # init a config with a model name
>>> config_a = ViTMAEConfig.from_pretrained('mae_vit_base_p16')
>>> # init a config with a config path
>>> import os
>>> from mindformers.mindformer_book import MindFormerBook
>>> config_path = os.path.join(MindFormerBook.get_project_path(),
>>>                        'configs', 'mae', 'run_mae_vit_base_p16_224_800ep.yaml')
>>> config_b = ViTMAEConfig.from_pretrained(config_path)
>>> # init a config with args
>>> config_c = ViTMAEConfig(
>>>     patch_size=16,
>>>     in_chans=3,
>>>     ...
>>>     )