384 lines
14 KiB
Python
384 lines
14 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 import build_norm_layer
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from mmcv.cnn.bricks.transformer import FFN, PatchEmbed
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from mmcv.cnn.utils.weight_init import trunc_normal_
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from mmcv.runner.base_module import BaseModule, ModuleList
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from mmcls.utils import get_root_logger
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from ..builder import BACKBONES
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from ..utils import MultiheadAttention, 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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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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act_cfg (dict): The activation config for FFNs.
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Defaluts 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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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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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.norm1_name, norm1 = build_norm_layer(
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norm_cfg, self.embed_dims, postfix=1)
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self.add_module(self.norm1_name, norm1)
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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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self.norm2_name, norm2 = build_norm_layer(
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norm_cfg, self.embed_dims, postfix=2)
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self.add_module(self.norm2_name, norm2)
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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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@property
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def norm1(self):
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return getattr(self, self.norm1_name)
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@property
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def norm2(self):
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return getattr(self, self.norm2_name)
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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.norm1(x))
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x = self.ffn(self.norm2(x), identity=x)
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return x
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@BACKBONES.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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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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output_cls_token (bool): Whether output the cls_token. If set True,
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``with_cls_token`` must be True. Defaults to True.
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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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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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['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'], {
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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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}
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# Some structures have multiple extra tokens, like DeiT.
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num_extra_tokens = 1 # cls_token
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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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with_cls_token=True,
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output_cls_token=True,
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interpolate_mode='bicubic',
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patch_cfg=dict(),
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layer_cfgs=dict(),
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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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)
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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 cls token
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if output_cls_token:
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assert with_cls_token is True, f'with_cls_token must be True if' \
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f'set output_cls_token to True, but got {with_cls_token}'
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self.with_cls_token = with_cls_token
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self.output_cls_token = output_cls_token
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self.cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dims))
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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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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.final_norm = final_norm
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if final_norm:
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self.norm1_name, norm1 = build_norm_layer(
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norm_cfg, self.embed_dims, postfix=1)
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self.add_module(self.norm1_name, norm1)
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@property
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def norm1(self):
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return getattr(self, self.norm1_name)
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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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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 self.pos_embed.shape != ckpt_pos_embed_shape:
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from mmcv.utils import print_log
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logger = get_root_logger()
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print_log(
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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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logger=logger)
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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 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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# stole cls_tokens impl from Phil Wang, thanks
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cls_tokens = self.cls_token.expand(B, -1, -1)
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x = torch.cat((cls_tokens, 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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if not self.with_cls_token:
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# Remove class token for transformer encoder input
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x = x[:, 1:]
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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.norm1(x)
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if i in self.out_indices:
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B, _, C = x.shape
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if self.with_cls_token:
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patch_token = x[:, 1:].reshape(B, *patch_resolution, C)
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patch_token = patch_token.permute(0, 3, 1, 2)
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cls_token = x[:, 0]
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else:
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patch_token = x.reshape(B, *patch_resolution, C)
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patch_token = patch_token.permute(0, 3, 1, 2)
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cls_token = None
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if self.output_cls_token:
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out = [patch_token, cls_token]
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else:
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out = patch_token
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outs.append(out)
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return tuple(outs)
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