195 lines
7.5 KiB
Python
195 lines
7.5 KiB
Python
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# Copyright (c) OpenMMLab. All rights reserved.
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from typing import Dict, List, Optional, Sequence, Tuple, Union
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import torch
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import torch.nn.functional as F
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from mmpretrain.models.selfsup.mae import MAE, MAEViT
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from mmpretrain.registry import MODELS
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from mmpretrain.structures import DataSample
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@MODELS.register_module()
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class MFFViT(MAEViT):
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"""Vision Transformer for MFF Pretraining.
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This class inherits all these functionalities from ``MAEViT``, and
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add multi-level feature fusion to it. For more details, you can
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refer to `Improving Pixel-based MIM by Reducing Wasted Modeling
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Capability`.
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Args:
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arch (str | dict): Vision Transformer architecture
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Default: 'b'
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img_size (int | tuple): Input image size
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patch_size (int | tuple): The patch size
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out_indices (Sequence | int): Output from which stages.
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Defaults to -1, means the last stage.
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drop_rate (float): Probability of an element to be zeroed.
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Defaults to 0.
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drop_path_rate (float): stochastic depth rate. Defaults to 0.
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norm_cfg (dict): Config dict for normalization layer.
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Defaults to ``dict(type='LN')``.
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final_norm (bool): Whether to add a additional layer to normalize
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final feature map. Defaults to True.
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out_type (str): The type of output features. Please choose from
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- ``"cls_token"``: The class token tensor with shape (B, C).
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- ``"featmap"``: The feature map tensor from the patch tokens
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with shape (B, C, H, W).
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- ``"avg_featmap"``: The global averaged feature map tensor
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with shape (B, C).
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- ``"raw"``: The raw feature tensor includes patch tokens and
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class tokens with shape (B, L, C).
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It only works without input mask. Defaults to ``"avg_featmap"``.
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interpolate_mode (str): Select the interpolate mode for position
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embeding vector resize. Defaults to "bicubic".
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patch_cfg (dict): Configs of patch embeding. Defaults to an empty dict.
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layer_cfgs (Sequence | dict): Configs of each transformer layer in
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encoder. Defaults to an empty dict.
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mask_ratio (bool): The ratio of total number of patches to be masked.
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Defaults to 0.75.
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init_cfg (Union[List[dict], dict], optional): Initialization config
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dict. Defaults to None.
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"""
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def __init__(self,
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arch: Union[str, dict] = 'b',
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img_size: int = 224,
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patch_size: int = 16,
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out_indices: Union[Sequence, int] = -1,
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drop_rate: float = 0,
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drop_path_rate: float = 0,
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norm_cfg: dict = dict(type='LN', eps=1e-6),
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final_norm: bool = True,
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out_type: str = 'raw',
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interpolate_mode: str = 'bicubic',
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patch_cfg: dict = dict(),
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layer_cfgs: dict = dict(),
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mask_ratio: float = 0.75,
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init_cfg: Optional[Union[List[dict], dict]] = None) -> None:
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super().__init__(
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arch=arch,
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img_size=img_size,
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patch_size=patch_size,
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out_indices=out_indices,
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drop_rate=drop_rate,
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drop_path_rate=drop_path_rate,
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norm_cfg=norm_cfg,
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final_norm=final_norm,
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out_type=out_type,
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interpolate_mode=interpolate_mode,
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patch_cfg=patch_cfg,
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layer_cfgs=layer_cfgs,
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mask_ratio=mask_ratio,
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init_cfg=init_cfg)
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proj_layers = [
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torch.nn.Linear(self.embed_dims, self.embed_dims)
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for _ in range(len(self.out_indices) - 1)
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]
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self.proj_layers = torch.nn.ModuleList(proj_layers)
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self.proj_weights = torch.nn.Parameter(
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torch.ones(len(self.out_indices)).view(-1, 1, 1, 1))
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if len(self.out_indices) == 1:
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self.proj_weights.requires_grad = False
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def forward(
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self,
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x: torch.Tensor,
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mask: Optional[bool] = True
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""Generate features for masked images.
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The function supports two kind of forward behaviors. If the ``mask`` is
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``True``, the function will generate mask to masking some patches
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randomly and get the hidden features for visible patches, which means
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the function will be executed as masked imagemodeling pre-training;
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if the ``mask`` is ``None`` or ``False``, the forward function will
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call ``super().forward()``, which extract features from images without
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mask.
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Args:
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x (torch.Tensor): Input images, which is of shape B x C x H x W.
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mask (bool, optional): To indicate whether the forward function
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generating ``mask`` or not.
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Returns:
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Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: Hidden features,
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mask and the ids to restore original image.
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- ``x`` (torch.Tensor): hidden features, which is of shape
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B x (L * mask_ratio) x C.
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- ``mask`` (torch.Tensor): mask used to mask image.
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- ``ids_restore`` (torch.Tensor): ids to restore original image.
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"""
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if mask is None or False:
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return super().forward(x)
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else:
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B = x.shape[0]
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x = self.patch_embed(x)[0]
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# add pos embed w/o cls token
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x = x + self.pos_embed[:, 1:, :]
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# masking: length -> length * mask_ratio
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x, mask, ids_restore = self.random_masking(x, self.mask_ratio)
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# append cls token
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cls_token = self.cls_token + self.pos_embed[:, :1, :]
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cls_tokens = cls_token.expand(B, -1, -1)
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x = torch.cat((cls_tokens, x), dim=1)
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res = []
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for i, layer in enumerate(self.layers):
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x = layer(x)
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if i in self.out_indices:
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if i != self.out_indices[-1]:
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proj_x = self.proj_layers[self.out_indices.index(i)](x)
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else:
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proj_x = x
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res.append(proj_x)
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res = torch.stack(res)
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proj_weights = F.softmax(self.proj_weights, dim=0)
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res = res * proj_weights
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res = res.sum(dim=0)
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# Use final norm
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x = self.norm1(res)
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return (x, mask, ids_restore, proj_weights.view(-1))
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@MODELS.register_module()
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class MFF(MAE):
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"""MFF.
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Implementation of `Improving Pixel-based MIM by Reducing Wasted Modeling
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Capability`.
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"""
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def loss(self, inputs: torch.Tensor, data_samples: List[DataSample],
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**kwargs) -> Dict[str, torch.Tensor]:
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"""The forward function in training.
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Args:
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inputs (torch.Tensor): The input images.
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data_samples (List[DataSample]): All elements required
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during the forward function.
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Returns:
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Dict[str, torch.Tensor]: A dictionary of loss components.
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"""
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# ids_restore: the same as that in original repo, which is used
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# to recover the original order of tokens in decoder.
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latent, mask, ids_restore, weights = self.backbone(inputs)
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pred = self.neck(latent, ids_restore)
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loss = self.head.loss(pred, inputs, mask)
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weight_params = {
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f'weight_{i}': weights[i]
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for i in range(weights.size(0))
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}
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losses = dict(loss=loss)
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losses.update(weight_params)
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return losses
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