417 lines
16 KiB
Python
417 lines
16 KiB
Python
# 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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from mmpretrain.models import HiViT, VisionTransformer
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from mmpretrain.registry import MODELS
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from mmpretrain.structures import DataSample
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from ..utils import build_2d_sincos_position_embedding
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from .base import BaseSelfSupervisor
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@MODELS.register_module()
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class MAEViT(VisionTransformer):
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"""Vision Transformer for MAE pre-training.
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A PyTorch implement of: `An Image is Worth 16x16 Words: Transformers
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for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>`_.
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This module implements the patch masking in MAE and initialize the
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position embedding with sine-cosine position embedding.
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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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with_cls_token=True,
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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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init_cfg=init_cfg)
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# position embedding is not learnable during pretraining
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self.pos_embed.requires_grad = False
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self.mask_ratio = mask_ratio
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self.num_patches = self.patch_resolution[0] * self.patch_resolution[1]
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def init_weights(self) -> None:
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"""Initialize position embedding, patch embedding and cls token."""
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super().init_weights()
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pos_embed = build_2d_sincos_position_embedding(
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int(self.num_patches**.5),
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self.pos_embed.shape[-1],
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cls_token=True)
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self.pos_embed.data.copy_(pos_embed.float())
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w = self.patch_embed.projection.weight.data
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torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
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torch.nn.init.normal_(self.cls_token, std=.02)
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def random_masking(
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self,
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x: torch.Tensor,
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mask_ratio: float = 0.75
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""Generate the mask for MAE Pre-training.
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Args:
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x (torch.Tensor): Image with data augmentation applied, which is
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of shape B x L x C.
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mask_ratio (float): The mask ratio of total patches.
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Defaults to 0.75.
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Returns:
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Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: masked image, mask
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and the ids to restore original image.
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- ``x_masked`` (torch.Tensor): masked image.
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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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N, L, D = x.shape # batch, length, dim
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len_keep = int(L * (1 - mask_ratio))
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noise = torch.rand(N, L, device=x.device) # noise in [0, 1]
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# sort noise for each sample
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ids_shuffle = torch.argsort(
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noise, dim=1) # ascend: small is keep, large is remove
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ids_restore = torch.argsort(ids_shuffle, dim=1)
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# keep the first subset
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ids_keep = ids_shuffle[:, :len_keep]
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x_masked = torch.gather(
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x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
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# generate the binary mask: 0 is keep, 1 is remove
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mask = torch.ones([N, L], device=x.device)
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mask[:, :len_keep] = 0
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# unshuffle to get the binary mask
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mask = torch.gather(mask, dim=1, index=ids_restore)
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return x_masked, mask, ids_restore
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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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for _, layer in enumerate(self.layers):
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x = layer(x)
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# Use final norm
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x = self.norm1(x)
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return (x, mask, ids_restore)
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@MODELS.register_module()
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class MAE(BaseSelfSupervisor):
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"""MAE.
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Implementation of `Masked Autoencoders Are Scalable Vision Learners
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<https://arxiv.org/abs/2111.06377>`_.
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"""
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def extract_feat(self, inputs: torch.Tensor):
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return self.backbone(inputs, mask=None)
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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 = 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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losses = dict(loss=loss)
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return losses
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@MODELS.register_module()
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class MAEHiViT(HiViT):
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"""HiViT for MAE pre-training.
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A PyTorch implement of: `HiViT: A Simple and More Efficient Design
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of Hierarchical Vision Transformer <https://arxiv.org/abs/2205.14949>`_.
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This module implements the patch masking in MAE and initialize the
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position embedding with sine-cosine position embedding.
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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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Defaults to 4, to downsample 4x at the first stage
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inner_patches (int): The inner patches within a token
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Defaults to 4
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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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ape (bool): the absolute position embedding
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rpe (bool): the relative position embedding
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Defaults to False
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layer_scale_init_value (float): the layer scale init value
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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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inner_patches: int = 4,
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out_indices: Union[list, int] = [23],
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drop_rate: float = 0.0,
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drop_path_rate: float = 0.0,
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norm_cfg: dict = dict(type='LN', eps=1e-6),
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ape: bool = True,
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rpe: bool = False,
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layer_scale_init_value: float = 0.0,
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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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inner_patches=inner_patches,
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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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ape=ape,
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rpe=rpe,
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layer_scale_init_value=layer_scale_init_value,
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init_cfg=init_cfg)
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self.pos_embed.requires_grad = False
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self.mask_ratio = mask_ratio
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self.num_patches = self.patch_embed.num_patches
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def init_weights(self) -> None:
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"""Initialize position embedding, patch embedding."""
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super().apply(self._init_weights)
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pos_embed = build_2d_sincos_position_embedding(
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int(self.num_patches**.5),
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self.pos_embed.shape[-1],
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cls_token=False)
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self.pos_embed.data.copy_(pos_embed.float())
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w = self.patch_embed.proj.weight.data
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torch.nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
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def masking_id(
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self, batch_size,
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mask_ratio) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""Generate the mask for MAE Pre-training.
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Args:
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batch_size: The batch size of input data
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mask_ratio: The mask ratio of total patches.
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Defaults to 0.75.
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Returns:
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Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: the ids
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for the tokens retained, the ids to restore original image,
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and the mask
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"""
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N, L = batch_size, self.pos_embed.size(1)
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len_keep = int(L * (1 - mask_ratio))
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noise = torch.rand(
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N, L, device=self.pos_embed.device) # noise in [0, 1]
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# sort noise for each sample
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ids_shuffle = torch.argsort(
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noise, dim=1) # ascend: small is keep, large is remove
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ids_restore = torch.argsort(ids_shuffle, dim=1)
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# keep the first subset
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ids_keep = ids_shuffle[:, :len_keep]
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# generate the binary mask: 0 is keep, 1 is remove
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mask = torch.ones([N, L], device=self.pos_embed.device)
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mask[:, :ids_keep.size(1)] = 0
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# unshuffle to get the binary mask
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mask = torch.gather(mask, dim=1, index=ids_restore)
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return ids_keep, ids_restore, mask
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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, C, H, W = x.shape
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ids_keep, ids_restore, mask = self.masking_id(B, self.mask_ratio)
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x = self.patch_embed(x)
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x = torch.gather(
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x,
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dim=1,
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index=ids_keep[:, :, None, None,
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None].expand(-1, -1, *x.shape[2:]))
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for blk in self.blocks[:-self.num_main_blocks]:
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x = blk(x)
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x = x[..., 0, 0, :]
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if self.ape:
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pos_embed = self.interpolate_pos_encoding(x, H, W)
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pos_embed = torch.gather(
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pos_embed.expand(B, -1, -1),
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dim=1,
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index=ids_keep[:, :, None].expand(-1, -1,
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pos_embed.shape[2]),
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)
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x = x + pos_embed
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x = self.pos_drop(x)
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for blk in self.blocks[-self.num_main_blocks:]:
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x = blk(x)
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return (x, mask, ids_restore)
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