531 lines
20 KiB
Python
531 lines
20 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from typing import Sequence
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import numpy as np
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import torch
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import torch.nn as nn
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from mmcv.cnn.bricks.transformer import FFN, PatchEmbed
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from mmengine.model import BaseModule, ModuleList
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from mmengine.model.weight_init import trunc_normal_
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from mmpretrain.registry import MODELS
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from ..utils import (MultiheadAttention, SwiGLUFFNFused, build_norm_layer,
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resize_pos_embed, to_2tuple)
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from .base_backbone import BaseBackbone
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class TransformerEncoderLayer(BaseModule):
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"""Implements one encoder layer in Vision Transformer.
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Args:
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embed_dims (int): The feature dimension
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num_heads (int): Parallel attention heads
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feedforward_channels (int): The hidden dimension for FFNs
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layer_scale_init_value (float or torch.Tensor): Init value of layer
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scale. Defaults to 0.
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drop_rate (float): Probability of an element to be zeroed
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after the feed forward layer. Defaults to 0.
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attn_drop_rate (float): The drop out rate for attention output weights.
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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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num_fcs (int): The number of fully-connected layers for FFNs.
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Defaults to 2.
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qkv_bias (bool): enable bias for qkv if True. Defaults to True.
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ffn_type (str): Select the type of ffn layers. Defaults to 'origin'.
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act_cfg (dict): The activation config for FFNs.
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Defaults to ``dict(type='GELU')``.
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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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init_cfg (dict, optional): Initialization config dict.
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Defaults to None.
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"""
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def __init__(self,
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embed_dims,
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num_heads,
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feedforward_channels,
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layer_scale_init_value=0.,
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drop_rate=0.,
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attn_drop_rate=0.,
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drop_path_rate=0.,
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num_fcs=2,
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qkv_bias=True,
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ffn_type='origin',
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act_cfg=dict(type='GELU'),
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norm_cfg=dict(type='LN'),
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init_cfg=None):
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super(TransformerEncoderLayer, self).__init__(init_cfg=init_cfg)
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self.embed_dims = embed_dims
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self.ln1 = build_norm_layer(norm_cfg, self.embed_dims)
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self.attn = MultiheadAttention(
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embed_dims=embed_dims,
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num_heads=num_heads,
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attn_drop=attn_drop_rate,
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proj_drop=drop_rate,
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dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
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qkv_bias=qkv_bias,
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layer_scale_init_value=layer_scale_init_value)
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self.ln2 = build_norm_layer(norm_cfg, self.embed_dims)
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if ffn_type == 'origin':
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self.ffn = FFN(
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embed_dims=embed_dims,
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feedforward_channels=feedforward_channels,
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num_fcs=num_fcs,
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ffn_drop=drop_rate,
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dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
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act_cfg=act_cfg,
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layer_scale_init_value=layer_scale_init_value)
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elif ffn_type == 'swiglu_fused':
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self.ffn = SwiGLUFFNFused(
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embed_dims=embed_dims,
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feedforward_channels=feedforward_channels,
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layer_scale_init_value=layer_scale_init_value)
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else:
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raise NotImplementedError
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@property
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def norm1(self):
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return self.ln1
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@property
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def norm2(self):
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return self.ln2
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def init_weights(self):
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super(TransformerEncoderLayer, self).init_weights()
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for m in self.ffn.modules():
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if isinstance(m, nn.Linear):
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nn.init.xavier_uniform_(m.weight)
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nn.init.normal_(m.bias, std=1e-6)
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def forward(self, x):
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x = x + self.attn(self.ln1(x))
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x = self.ffn(self.ln2(x), identity=x)
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return x
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@MODELS.register_module()
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class VisionTransformer(BaseBackbone):
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"""Vision Transformer.
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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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Args:
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arch (str | dict): Vision Transformer architecture. If use string,
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choose from 'small', 'base', 'large', 'deit-tiny', 'deit-small'
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and 'deit-base'. If use dict, it should have below keys:
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- **embed_dims** (int): The dimensions of embedding.
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- **num_layers** (int): The number of transformer encoder layers.
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- **num_heads** (int): The number of heads in attention modules.
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- **feedforward_channels** (int): The hidden dimensions in
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feedforward modules.
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Defaults to 'base'.
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img_size (int | tuple): The expected input image shape. Because we
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support dynamic input shape, just set the argument to the most
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common input image shape. Defaults to 224.
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patch_size (int | tuple): The patch size in patch embedding.
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Defaults to 16.
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in_channels (int): The num of input channels. Defaults to 3.
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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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qkv_bias (bool): Whether to add bias for qkv in attention modules.
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Defaults to True.
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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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Defaults to ``"cls_token"``.
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with_cls_token (bool): Whether concatenating class token into image
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tokens as transformer input. Defaults to True.
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frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
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-1 means not freezing any parameters. Defaults to -1.
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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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layer_scale_init_value (float or torch.Tensor): Init value of layer
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scale. Defaults to 0.
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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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init_cfg (dict, optional): Initialization config dict.
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Defaults to None.
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"""
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arch_zoo = {
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**dict.fromkeys(
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['s', 'small'], {
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'embed_dims': 768,
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'num_layers': 8,
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'num_heads': 8,
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'feedforward_channels': 768 * 3,
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}),
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**dict.fromkeys(
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['b', 'base'], {
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'embed_dims': 768,
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'num_layers': 12,
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'num_heads': 12,
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'feedforward_channels': 3072
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}),
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**dict.fromkeys(
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['l', 'large'], {
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'embed_dims': 1024,
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'num_layers': 24,
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'num_heads': 16,
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'feedforward_channels': 4096
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}),
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**dict.fromkeys(
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['h', 'huge'],
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{
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# The same as the implementation in MAE
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# <https://arxiv.org/abs/2111.06377>
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'embed_dims': 1280,
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'num_layers': 32,
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'num_heads': 16,
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'feedforward_channels': 5120
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}),
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**dict.fromkeys(
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['eva-g', 'eva-giant'],
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{
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# The implementation in EVA
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# <https://arxiv.org/abs/2211.07636>
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'embed_dims': 1408,
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'num_layers': 40,
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'num_heads': 16,
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'feedforward_channels': 6144
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}),
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**dict.fromkeys(
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['deit-t', 'deit-tiny'], {
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'embed_dims': 192,
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'num_layers': 12,
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'num_heads': 3,
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'feedforward_channels': 192 * 4
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}),
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**dict.fromkeys(
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['deit-s', 'deit-small', 'dinov2-s', 'dinov2-small'], {
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'embed_dims': 384,
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'num_layers': 12,
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'num_heads': 6,
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'feedforward_channels': 384 * 4
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}),
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**dict.fromkeys(
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['deit-b', 'deit-base'], {
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'embed_dims': 768,
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'num_layers': 12,
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'num_heads': 12,
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'feedforward_channels': 768 * 4
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}),
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**dict.fromkeys(
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['dinov2-g', 'dinov2-giant'], {
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'embed_dims': 1536,
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'num_layers': 40,
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'num_heads': 24,
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'feedforward_channels': 6144
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}),
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}
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num_extra_tokens = 1 # class token
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OUT_TYPES = {'raw', 'cls_token', 'featmap', 'avg_featmap'}
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def __init__(self,
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arch='base',
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img_size=224,
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patch_size=16,
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in_channels=3,
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out_indices=-1,
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drop_rate=0.,
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drop_path_rate=0.,
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qkv_bias=True,
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norm_cfg=dict(type='LN', eps=1e-6),
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final_norm=True,
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out_type='cls_token',
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with_cls_token=True,
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frozen_stages=-1,
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interpolate_mode='bicubic',
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layer_scale_init_value=0.,
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patch_cfg=dict(),
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layer_cfgs=dict(),
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pre_norm=False,
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init_cfg=None):
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super(VisionTransformer, self).__init__(init_cfg)
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if isinstance(arch, str):
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arch = arch.lower()
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assert arch in set(self.arch_zoo), \
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f'Arch {arch} is not in default archs {set(self.arch_zoo)}'
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self.arch_settings = self.arch_zoo[arch]
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else:
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essential_keys = {
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'embed_dims', 'num_layers', 'num_heads', 'feedforward_channels'
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}
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assert isinstance(arch, dict) and essential_keys <= set(arch), \
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f'Custom arch needs a dict with keys {essential_keys}'
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self.arch_settings = arch
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self.embed_dims = self.arch_settings['embed_dims']
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self.num_layers = self.arch_settings['num_layers']
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self.img_size = to_2tuple(img_size)
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# Set patch embedding
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_patch_cfg = dict(
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in_channels=in_channels,
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input_size=img_size,
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embed_dims=self.embed_dims,
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conv_type='Conv2d',
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kernel_size=patch_size,
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stride=patch_size,
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bias=not pre_norm, # disable bias if pre_norm is used(e.g., CLIP)
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)
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_patch_cfg.update(patch_cfg)
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self.patch_embed = PatchEmbed(**_patch_cfg)
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self.patch_resolution = self.patch_embed.init_out_size
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num_patches = self.patch_resolution[0] * self.patch_resolution[1]
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# Set out type
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if out_type not in self.OUT_TYPES:
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raise ValueError(f'Unsupported `out_type` {out_type}, please '
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f'choose from {self.OUT_TYPES}')
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self.out_type = out_type
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# Set cls token
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self.with_cls_token = with_cls_token
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if with_cls_token:
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self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dims))
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elif out_type != 'cls_token':
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self.cls_token = None
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self.num_extra_tokens = 0
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else:
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raise ValueError(
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'with_cls_token must be True when `out_type="cls_token"`.')
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# Set position embedding
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self.interpolate_mode = interpolate_mode
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self.pos_embed = nn.Parameter(
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torch.zeros(1, num_patches + self.num_extra_tokens,
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self.embed_dims))
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self._register_load_state_dict_pre_hook(self._prepare_pos_embed)
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self.drop_after_pos = nn.Dropout(p=drop_rate)
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if isinstance(out_indices, int):
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out_indices = [out_indices]
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assert isinstance(out_indices, Sequence), \
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f'"out_indices" must by a sequence or int, ' \
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f'get {type(out_indices)} instead.'
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for i, index in enumerate(out_indices):
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if index < 0:
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out_indices[i] = self.num_layers + index
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assert 0 <= out_indices[i] <= self.num_layers, \
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f'Invalid out_indices {index}'
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self.out_indices = out_indices
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# stochastic depth decay rule
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dpr = np.linspace(0, drop_path_rate, self.num_layers)
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self.layers = ModuleList()
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if isinstance(layer_cfgs, dict):
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layer_cfgs = [layer_cfgs] * self.num_layers
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for i in range(self.num_layers):
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_layer_cfg = dict(
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embed_dims=self.embed_dims,
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num_heads=self.arch_settings['num_heads'],
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feedforward_channels=self.
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arch_settings['feedforward_channels'],
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layer_scale_init_value=layer_scale_init_value,
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drop_rate=drop_rate,
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drop_path_rate=dpr[i],
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qkv_bias=qkv_bias,
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norm_cfg=norm_cfg)
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_layer_cfg.update(layer_cfgs[i])
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self.layers.append(TransformerEncoderLayer(**_layer_cfg))
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self.frozen_stages = frozen_stages
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if pre_norm:
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self.pre_norm = build_norm_layer(norm_cfg, self.embed_dims)
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else:
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self.pre_norm = nn.Identity()
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self.final_norm = final_norm
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if final_norm:
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self.ln1 = build_norm_layer(norm_cfg, self.embed_dims)
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if self.out_type == 'avg_featmap':
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self.ln2 = build_norm_layer(norm_cfg, self.embed_dims)
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# freeze stages only when self.frozen_stages > 0
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if self.frozen_stages > 0:
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self._freeze_stages()
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@property
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def norm1(self):
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return self.ln1
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@property
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def norm2(self):
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return self.ln2
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def init_weights(self):
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super(VisionTransformer, self).init_weights()
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if not (isinstance(self.init_cfg, dict)
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and self.init_cfg['type'] == 'Pretrained'):
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if self.pos_embed is not None:
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trunc_normal_(self.pos_embed, std=0.02)
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def _prepare_pos_embed(self, state_dict, prefix, *args, **kwargs):
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name = prefix + 'pos_embed'
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if name not in state_dict.keys():
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return
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ckpt_pos_embed_shape = state_dict[name].shape
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if (not self.with_cls_token
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and ckpt_pos_embed_shape[1] == self.pos_embed.shape[1] + 1):
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# Remove cls token from state dict if it's not used.
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state_dict[name] = state_dict[name][:, 1:]
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ckpt_pos_embed_shape = state_dict[name].shape
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if self.pos_embed.shape != ckpt_pos_embed_shape:
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from mmengine.logging import MMLogger
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logger = MMLogger.get_current_instance()
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logger.info(
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f'Resize the pos_embed shape from {ckpt_pos_embed_shape} '
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f'to {self.pos_embed.shape}.')
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ckpt_pos_embed_shape = to_2tuple(
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int(np.sqrt(ckpt_pos_embed_shape[1] - self.num_extra_tokens)))
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pos_embed_shape = self.patch_embed.init_out_size
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state_dict[name] = resize_pos_embed(state_dict[name],
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ckpt_pos_embed_shape,
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pos_embed_shape,
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self.interpolate_mode,
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self.num_extra_tokens)
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@staticmethod
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def resize_pos_embed(*args, **kwargs):
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"""Interface for backward-compatibility."""
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return resize_pos_embed(*args, **kwargs)
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def _freeze_stages(self):
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# freeze position embedding
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if self.pos_embed is not None:
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self.pos_embed.requires_grad = False
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# set dropout to eval model
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self.drop_after_pos.eval()
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# freeze patch embedding
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self.patch_embed.eval()
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for param in self.patch_embed.parameters():
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param.requires_grad = False
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# freeze pre-norm
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for param in self.pre_norm.parameters():
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param.requires_grad = False
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# freeze cls_token
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self.cls_token.requires_grad = False
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# freeze layers
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for i in range(1, self.frozen_stages + 1):
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m = self.layers[i - 1]
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m.eval()
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for param in m.parameters():
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param.requires_grad = False
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# freeze the last layer norm
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if self.frozen_stages == len(self.layers) and self.final_norm:
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self.ln1.eval()
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for param in self.ln1.parameters():
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param.requires_grad = False
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def forward(self, x):
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B = x.shape[0]
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x, patch_resolution = self.patch_embed(x)
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if self.cls_token is not None:
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# stole cls_tokens impl from Phil Wang, thanks
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cls_token = self.cls_token.expand(B, -1, -1)
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x = torch.cat((cls_token, x), dim=1)
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x = x + resize_pos_embed(
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self.pos_embed,
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self.patch_resolution,
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patch_resolution,
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mode=self.interpolate_mode,
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num_extra_tokens=self.num_extra_tokens)
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x = self.drop_after_pos(x)
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x = self.pre_norm(x)
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outs = []
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for i, layer in enumerate(self.layers):
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x = layer(x)
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if i == len(self.layers) - 1 and self.final_norm:
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x = self.ln1(x)
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if i in self.out_indices:
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outs.append(self._format_output(x, patch_resolution))
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return tuple(outs)
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def _format_output(self, x, hw):
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if self.out_type == 'raw':
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return x
|
|
if self.out_type == 'cls_token':
|
|
return x[:, 0]
|
|
|
|
patch_token = x[:, self.num_extra_tokens:]
|
|
if self.out_type == 'featmap':
|
|
B = x.size(0)
|
|
# (B, N, C) -> (B, H, W, C) -> (B, C, H, W)
|
|
return patch_token.reshape(B, *hw, -1).permute(0, 3, 1, 2)
|
|
if self.out_type == 'avg_featmap':
|
|
return self.ln2(patch_token.mean(dim=1))
|
|
|
|
def get_layer_depth(self, param_name: str, prefix: str = ''):
|
|
"""Get the layer-wise depth of a parameter.
|
|
|
|
Args:
|
|
param_name (str): The name of the parameter.
|
|
prefix (str): The prefix for the parameter.
|
|
Defaults to an empty string.
|
|
|
|
Returns:
|
|
Tuple[int, int]: The layer-wise depth and the num of layers.
|
|
|
|
Note:
|
|
The first depth is the stem module (``layer_depth=0``), and the
|
|
last depth is the subsequent module (``layer_depth=num_layers-1``)
|
|
"""
|
|
num_layers = self.num_layers + 2
|
|
|
|
if not param_name.startswith(prefix):
|
|
# For subsequent module like head
|
|
return num_layers - 1, num_layers
|
|
|
|
param_name = param_name[len(prefix):]
|
|
|
|
if param_name in ('cls_token', 'pos_embed'):
|
|
layer_depth = 0
|
|
elif param_name.startswith('patch_embed'):
|
|
layer_depth = 0
|
|
elif param_name.startswith('layers'):
|
|
layer_id = int(param_name.split('.')[1])
|
|
layer_depth = layer_id + 1
|
|
else:
|
|
layer_depth = num_layers - 1
|
|
|
|
return layer_depth, num_layers
|