MaskFeatViT¶
- class mmpretrain.models.selfsup.MaskFeatViT(arch='b', img_size=224, patch_size=16, out_indices=-1, drop_rate=0, drop_path_rate=0, norm_cfg={'eps': 1e-06, 'type': 'LN'}, final_norm=True, out_type='raw', interpolate_mode='bicubic', patch_cfg={}, layer_cfgs={}, init_cfg=None)[source]¶
Vision Transformer for MaskFeat pre-training.
A PyTorch implement of: Masked Feature Prediction for Self-Supervised Visual Pre-Training.
- Parameters:
arch (str | dict) – Vision Transformer architecture Default: ‘b’
out_indices (Sequence | int) – Output from which stages. Defaults to -1, means the last stage.
drop_rate (float) – Probability of an element to be zeroed. Defaults to 0.
drop_path_rate (float) – stochastic depth rate. Defaults to 0.
norm_cfg (dict) – Config dict for normalization layer. Defaults to
dict(type='LN')
.final_norm (bool) – Whether to add a additional layer to normalize final feature map. Defaults to True.
out_type (str) –
The type of output features. Please choose from
"cls_token"
: The class token tensor with shape (B, C)."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)."raw"
: The raw feature tensor includes patch tokens and class tokens with shape (B, L, C).
It only works without input mask. Defaults to
"avg_featmap"
.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.
- forward(x, mask)[source]¶
Generate features for masked images.
The function supports two kind of forward behaviors. If the
mask
is notNone
, the forward function will be executed as masked image modeling pre-training; if themask
isNone
, the forward function will callsuper().forward()
, which extract features from images without mask.- Parameters:
x (torch.Tensor) – Input images.
mask (torch.Tensor, optional) – Input masks.
- Returns:
Features with cls_tokens.
- Return type: