ViTSAM¶
- class mmpretrain.models.backbones.ViTSAM(arch='base', img_size=224, patch_size=16, in_channels=3, out_channels=256, out_indices=-1, out_type='raw', drop_rate=0.0, drop_path_rate=0.0, qkv_bias=True, use_abs_pos=True, use_rel_pos=True, window_size=14, norm_cfg={'eps': 1e-06, 'type': 'LN'}, frozen_stages=-1, interpolate_mode='bicubic', patch_cfg={}, layer_cfgs={}, init_cfg=None)[源代码]¶
Vision Transformer as image encoder used in SAM.
A PyTorch implement of backbone: Segment Anything
- 参数:
Vision Transformer architecture. If use string, choose from ‘base’, ‘large’, ‘huge’. If use dict, it should have below keys:
embed_dims (int): The dimensions of embedding.
num_layers (int): The number of transformer encoder layers.
num_heads (int): The number of heads in attention modules.
feedforward_channels (int): The hidden dimensions in feedforward modules.
global_attn_indexes (int): The index of layers with global attention.
Defaults to ‘base’.
img_size (int | tuple) – The expected input image shape. Because we support dynamic input shape, just set the argument to the most common input image shape. Defaults to 224.
patch_size (int | tuple) – The patch size in patch embedding. Defaults to 16.
in_channels (int) – The num of input channels. Defaults to 3.
out_channels (int) – The num of output channels, if equal to 0, the channel reduction layer is disabled. Defaults to 256.
out_indices (Sequence | int) – Output from which stages. Defaults to -1, means the last stage.
out_type (str) –
The type of output features. Please choose from
"raw"or"featmap": The feature map tensor from the patch tokens with shape (B, C, H, W)."avg_featmap": The global averaged feature map tensor with shape (B, C).
Defaults to
"raw".drop_rate (float) – Probability of an element to be zeroed. Defaults to 0.
drop_path_rate (float) – stochastic depth rate. Defaults to 0.
qkv_bias (bool) – Whether to add bias for qkv in attention modules. Defaults to True.
use_abs_pos (bool) – Whether to use absolute position embedding. Defaults to True.
use_rel_pos (bool) – Whether to use relative position embedding. Defaults to True.
window_size (int) – Window size for window attention. Defaults to 14.
norm_cfg (dict) – Config dict for normalization layer. Defaults to
dict(type='LN').frozen_stages (int) – Stages to be frozen (stop grad and set eval mode). -1 means not freezing any parameters. Defaults to -1.
interpolate_mode (str) – Select the interpolate mode for position embeding vector resize. Defaults to “bicubic”.
patch_cfg (dict) – Configs of patch embeding. Defaults to an empty dict.
layer_cfgs (Sequence | dict) – Configs of each transformer layer in encoder. Defaults to an empty dict.
init_cfg (dict, optional) – Initialization config dict. Defaults to None.