416 lines
16 KiB
Python
416 lines
16 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import math
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import warnings
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import torch
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import torch.nn as nn
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from mmcv.cnn import (Conv2d, build_activation_layer, build_norm_layer,
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constant_init, normal_init, trunc_normal_init)
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from mmcv.cnn.bricks.drop import build_dropout
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from mmcv.cnn.bricks.transformer import MultiheadAttention
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from mmcv.runner import BaseModule, ModuleList, Sequential, _load_checkpoint
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from ...utils import get_root_logger
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from ..builder import BACKBONES
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from ..utils import PatchEmbed, nchw_to_nlc, nlc_to_nchw
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class MixFFN(BaseModule):
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"""An implementation of MixFFN of Segformer.
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The differences between MixFFN & FFN:
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1. Use 1X1 Conv to replace Linear layer.
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2. Introduce 3X3 Conv to encode positional information.
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Args:
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embed_dims (int): The feature dimension. Same as
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`MultiheadAttention`. Defaults: 256.
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feedforward_channels (int): The hidden dimension of FFNs.
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Defaults: 1024.
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act_cfg (dict, optional): The activation config for FFNs.
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Default: dict(type='ReLU')
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ffn_drop (float, optional): Probability of an element to be
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zeroed in FFN. Default 0.0.
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dropout_layer (obj:`ConfigDict`): The dropout_layer used
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when adding the shortcut.
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init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
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Default: None.
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"""
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def __init__(self,
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embed_dims,
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feedforward_channels,
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act_cfg=dict(type='GELU'),
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ffn_drop=0.,
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dropout_layer=None,
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init_cfg=None):
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super(MixFFN, self).__init__(init_cfg)
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self.embed_dims = embed_dims
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self.feedforward_channels = feedforward_channels
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self.act_cfg = act_cfg
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self.activate = build_activation_layer(act_cfg)
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in_channels = embed_dims
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fc1 = Conv2d(
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in_channels=in_channels,
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out_channels=feedforward_channels,
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kernel_size=1,
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stride=1,
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bias=True)
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# 3x3 depth wise conv to provide positional encode information
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pe_conv = Conv2d(
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in_channels=feedforward_channels,
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out_channels=feedforward_channels,
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kernel_size=3,
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stride=1,
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padding=(3 - 1) // 2,
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bias=True,
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groups=feedforward_channels)
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fc2 = Conv2d(
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in_channels=feedforward_channels,
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out_channels=in_channels,
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kernel_size=1,
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stride=1,
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bias=True)
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drop = nn.Dropout(ffn_drop)
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layers = [fc1, pe_conv, self.activate, drop, fc2, drop]
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self.layers = Sequential(*layers)
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self.dropout_layer = build_dropout(
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dropout_layer) if dropout_layer else torch.nn.Identity()
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def forward(self, x, hw_shape, identity=None):
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out = nlc_to_nchw(x, hw_shape)
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out = self.layers(out)
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out = nchw_to_nlc(out)
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if identity is None:
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identity = x
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return identity + self.dropout_layer(out)
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class EfficientMultiheadAttention(MultiheadAttention):
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"""An implementation of Efficient Multi-head Attention of Segformer.
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This module is modified from MultiheadAttention which is a module from
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mmcv.cnn.bricks.transformer.
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Args:
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embed_dims (int): The embedding dimension.
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num_heads (int): Parallel attention heads.
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attn_drop (float): A Dropout layer on attn_output_weights.
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Default: 0.0.
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proj_drop (float): A Dropout layer after `nn.MultiheadAttention`.
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Default: 0.0.
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dropout_layer (obj:`ConfigDict`): The dropout_layer used
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when adding the shortcut. Default: None.
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init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
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Default: None.
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batch_first (bool): Key, Query and Value are shape of
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(batch, n, embed_dim)
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or (n, batch, embed_dim). Default: False.
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qkv_bias (bool): enable bias for qkv if True. Default True.
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norm_cfg (dict): Config dict for normalization layer.
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Default: dict(type='LN').
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sr_ratio (int): The ratio of spatial reduction of Efficient Multi-head
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Attention of Segformer. Default: 1.
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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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attn_drop=0.,
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proj_drop=0.,
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dropout_layer=None,
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init_cfg=None,
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batch_first=True,
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qkv_bias=False,
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norm_cfg=dict(type='LN'),
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sr_ratio=1):
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super().__init__(
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embed_dims,
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num_heads,
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attn_drop,
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proj_drop,
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dropout_layer=dropout_layer,
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init_cfg=init_cfg,
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batch_first=batch_first,
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bias=qkv_bias)
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self.sr_ratio = sr_ratio
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if sr_ratio > 1:
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self.sr = Conv2d(
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in_channels=embed_dims,
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out_channels=embed_dims,
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kernel_size=sr_ratio,
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stride=sr_ratio)
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# The ret[0] of build_norm_layer is norm name.
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self.norm = build_norm_layer(norm_cfg, embed_dims)[1]
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def forward(self, x, hw_shape, identity=None):
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x_q = x
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if self.sr_ratio > 1:
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x_kv = nlc_to_nchw(x, hw_shape)
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x_kv = self.sr(x_kv)
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x_kv = nchw_to_nlc(x_kv)
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x_kv = self.norm(x_kv)
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else:
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x_kv = x
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if identity is None:
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identity = x_q
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# `need_weights=True` will let nn.MultiHeadAttention
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# `return attn_output, attn_output_weights.sum(dim=1) / num_heads`
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# The `attn_output_weights.sum(dim=1)` may cause cuda error. So, we set
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# `need_weights=False` to ignore `attn_output_weights.sum(dim=1)`.
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# This issue - `https://github.com/pytorch/pytorch/issues/37583` report
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# the error that large scale tensor sum operation may cause cuda error.
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out = self.attn(query=x_q, key=x_kv, value=x_kv, need_weights=False)[0]
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return identity + self.dropout_layer(self.proj_drop(out))
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class TransformerEncoderLayer(BaseModule):
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"""Implements one encoder layer in Segformer.
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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. Default 0.0.
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attn_drop_rate (float): The drop out rate for attention layer.
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Default 0.0.
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drop_path_rate (float): stochastic depth rate. Default 0.0.
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qkv_bias (bool): enable bias for qkv if True.
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Default: True.
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act_cfg (dict): The activation config for FFNs.
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Defalut: dict(type='GELU').
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norm_cfg (dict): Config dict for normalization layer.
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Default: dict(type='LN').
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batch_first (bool): Key, Query and Value are shape of
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(batch, n, embed_dim)
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or (n, batch, embed_dim). Default: False.
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init_cfg (dict, optional): Initialization config dict.
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Default:None.
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sr_ratio (int): The ratio of spatial reduction of Efficient Multi-head
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Attention of Segformer. Default: 1.
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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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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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batch_first=True,
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sr_ratio=1):
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super(TransformerEncoderLayer, self).__init__()
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# The ret[0] of build_norm_layer is norm name.
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self.norm1 = build_norm_layer(norm_cfg, embed_dims)[1]
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self.attn = EfficientMultiheadAttention(
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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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batch_first=batch_first,
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qkv_bias=qkv_bias,
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norm_cfg=norm_cfg,
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sr_ratio=sr_ratio)
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# The ret[0] of build_norm_layer is norm name.
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self.norm2 = build_norm_layer(norm_cfg, embed_dims)[1]
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self.ffn = MixFFN(
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embed_dims=embed_dims,
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feedforward_channels=feedforward_channels,
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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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def forward(self, x, hw_shape):
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x = self.attn(self.norm1(x), hw_shape, identity=x)
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x = self.ffn(self.norm2(x), hw_shape, identity=x)
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return x
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@BACKBONES.register_module()
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class MixVisionTransformer(BaseModule):
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"""The backbone of Segformer.
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This backbone is the implementation of `SegFormer: Simple and
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Efficient Design for Semantic Segmentation with
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Transformers <https://arxiv.org/abs/2105.15203>`_.
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Args:
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in_channels (int): Number of input channels. Default: 3.
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embed_dims (int): Embedding dimension. Default: 768.
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num_stags (int): The num of stages. Default: 4.
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num_layers (Sequence[int]): The layer number of each transformer encode
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layer. Default: [3, 4, 6, 3].
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num_heads (Sequence[int]): The attention heads of each transformer
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encode layer. Default: [1, 2, 4, 8].
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patch_sizes (Sequence[int]): The patch_size of each overlapped patch
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embedding. Default: [7, 3, 3, 3].
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strides (Sequence[int]): The stride of each overlapped patch embedding.
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Default: [4, 2, 2, 2].
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sr_ratios (Sequence[int]): The spatial reduction rate of each
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transformer encode layer. Default: [8, 4, 2, 1].
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out_indices (Sequence[int] | int): Output from which stages.
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Default: (0, 1, 2, 3).
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mlp_ratio (int): ratio of mlp hidden dim to embedding dim.
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Default: 4.
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qkv_bias (bool): Enable bias for qkv if True. Default: True.
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drop_rate (float): Probability of an element to be zeroed.
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Default 0.0
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attn_drop_rate (float): The drop out rate for attention layer.
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Default 0.0
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drop_path_rate (float): stochastic depth rate. Default 0.0
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norm_cfg (dict): Config dict for normalization layer.
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Default: dict(type='LN')
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act_cfg (dict): The activation config for FFNs.
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Defalut: dict(type='GELU').
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pretrain_style (str): Choose to use official or mmcls pretrain weights.
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Default: official.
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pretrained (str, optional): model pretrained path. Default: None.
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init_cfg (dict or list[dict], optional): Initialization config dict.
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Default: None.
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"""
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def __init__(self,
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in_channels=3,
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embed_dims=64,
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num_stages=4,
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num_layers=[3, 4, 6, 3],
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num_heads=[1, 2, 4, 8],
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patch_sizes=[7, 3, 3, 3],
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strides=[4, 2, 2, 2],
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sr_ratios=[8, 4, 2, 1],
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out_indices=(0, 1, 2, 3),
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mlp_ratio=4,
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qkv_bias=True,
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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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act_cfg=dict(type='GELU'),
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norm_cfg=dict(type='LN', eps=1e-6),
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pretrain_style='official',
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pretrained=None,
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init_cfg=None):
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super().__init__()
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assert pretrain_style in [
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'official', 'mmcls'
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], 'we only support official weights or mmcls weights.'
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if isinstance(pretrained, str) or pretrained is None:
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warnings.warn('DeprecationWarning: pretrained is a deprecated, '
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'please use "init_cfg" instead')
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else:
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raise TypeError('pretrained must be a str or None')
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self.embed_dims = embed_dims
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self.num_stages = num_stages
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self.num_layers = num_layers
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self.num_heads = num_heads
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self.patch_sizes = patch_sizes
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self.strides = strides
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self.sr_ratios = sr_ratios
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assert num_stages == len(num_layers) == len(num_heads) \
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== len(patch_sizes) == len(strides) == len(sr_ratios)
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self.out_indices = out_indices
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assert max(out_indices) < self.num_stages
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self.pretrain_style = pretrain_style
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self.pretrained = pretrained
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self.init_cfg = init_cfg
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# transformer encoder
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dpr = [
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x.item()
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for x in torch.linspace(0, drop_path_rate, sum(num_layers))
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] # stochastic num_layer decay rule
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cur = 0
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self.layers = ModuleList()
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for i, num_layer in enumerate(num_layers):
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embed_dims_i = embed_dims * num_heads[i]
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patch_embed = PatchEmbed(
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in_channels=in_channels,
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embed_dims=embed_dims_i,
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kernel_size=patch_sizes[i],
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stride=strides[i],
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padding=patch_sizes[i] // 2,
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pad_to_patch_size=False,
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norm_cfg=norm_cfg)
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layer = ModuleList([
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TransformerEncoderLayer(
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embed_dims=embed_dims_i,
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num_heads=num_heads[i],
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feedforward_channels=mlp_ratio * embed_dims_i,
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drop_rate=drop_rate,
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attn_drop_rate=attn_drop_rate,
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drop_path_rate=dpr[cur + idx],
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qkv_bias=qkv_bias,
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act_cfg=act_cfg,
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norm_cfg=norm_cfg,
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sr_ratio=sr_ratios[i]) for idx in range(num_layer)
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])
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in_channels = embed_dims_i
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# The ret[0] of build_norm_layer is norm name.
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norm = build_norm_layer(norm_cfg, embed_dims_i)[1]
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self.layers.append(ModuleList([patch_embed, layer, norm]))
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cur += num_layer
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def init_weights(self):
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if self.pretrained is None:
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for m in self.modules():
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if isinstance(m, nn.Linear):
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trunc_normal_init(m.weight, std=.02)
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if m.bias is not None:
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constant_init(m.bias, 0)
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elif isinstance(m, nn.LayerNorm):
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constant_init(m.bias, 0)
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constant_init(m.weight, 1.0)
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elif isinstance(m, nn.Conv2d):
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fan_out = m.kernel_size[0] * m.kernel_size[
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1] * m.out_channels
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fan_out //= m.groups
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normal_init(m.weight, 0, math.sqrt(2.0 / fan_out))
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if m.bias is not None:
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constant_init(m.bias, 0)
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elif isinstance(self.pretrained, str):
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logger = get_root_logger()
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checkpoint = _load_checkpoint(
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self.pretrained, logger=logger, map_location='cpu')
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if 'state_dict' in checkpoint:
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state_dict = checkpoint['state_dict']
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else:
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state_dict = checkpoint
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self.load_state_dict(state_dict, False)
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def forward(self, x):
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outs = []
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for i, layer in enumerate(self.layers):
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x, H, W = layer[0](x), layer[0].DH, layer[0].DW
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hw_shape = (H, W)
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for block in layer[1]:
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x = block(x, hw_shape)
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x = layer[2](x)
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x = nlc_to_nchw(x, hw_shape)
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if i in self.out_indices:
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outs.append(x)
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return outs
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