607 lines
22 KiB
Python
607 lines
22 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import itertools
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from typing import Optional, Sequence
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import torch
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import torch.nn as nn
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from mmcv.cnn.bricks import (ConvModule, DropPath, build_activation_layer,
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build_norm_layer)
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from mmengine.model import BaseModule, ModuleList, Sequential
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from mmpretrain.registry import MODELS
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from ..utils import LayerScale
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from .base_backbone import BaseBackbone
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from .poolformer import Pooling
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class AttentionWithBias(BaseModule):
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"""Multi-head Attention Module with attention_bias.
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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. Defaults to 8.
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key_dim (int): The dimension of q, k. Defaults to 32.
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attn_ratio (float): The dimension of v equals to
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``key_dim * attn_ratio``. Defaults to 4.
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resolution (int): The height and width of attention_bias.
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Defaults to 7.
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init_cfg (dict, optional): The Config for initialization.
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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=8,
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key_dim=32,
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attn_ratio=4.,
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resolution=7,
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init_cfg=None):
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super().__init__(init_cfg=init_cfg)
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self.num_heads = num_heads
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self.scale = key_dim**-0.5
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self.attn_ratio = attn_ratio
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self.key_dim = key_dim
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self.nh_kd = key_dim * num_heads
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self.d = int(attn_ratio * key_dim)
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self.dh = int(attn_ratio * key_dim) * num_heads
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h = self.dh + self.nh_kd * 2
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self.qkv = nn.Linear(embed_dims, h)
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self.proj = nn.Linear(self.dh, embed_dims)
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points = list(itertools.product(range(resolution), range(resolution)))
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N = len(points)
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attention_offsets = {}
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idxs = []
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for p1 in points:
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for p2 in points:
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offset = (abs(p1[0] - p2[0]), abs(p1[1] - p2[1]))
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if offset not in attention_offsets:
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attention_offsets[offset] = len(attention_offsets)
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idxs.append(attention_offsets[offset])
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self.attention_biases = nn.Parameter(
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torch.zeros(num_heads, len(attention_offsets)))
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self.register_buffer('attention_bias_idxs',
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torch.LongTensor(idxs).view(N, N))
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@torch.no_grad()
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def train(self, mode=True):
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"""change the mode of model."""
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super().train(mode)
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if mode and hasattr(self, 'ab'):
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del self.ab
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else:
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self.ab = self.attention_biases[:, self.attention_bias_idxs]
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def forward(self, x):
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"""forward function.
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Args:
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x (tensor): input features with shape of (B, N, C)
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"""
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B, N, _ = x.shape
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qkv = self.qkv(x)
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qkv = qkv.reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
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q, k, v = qkv.split([self.key_dim, self.key_dim, self.d], dim=-1)
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attn = ((q @ k.transpose(-2, -1)) * self.scale +
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(self.attention_biases[:, self.attention_bias_idxs]
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if self.training else self.ab))
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attn = attn.softmax(dim=-1)
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x = (attn @ v).transpose(1, 2).reshape(B, N, self.dh)
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x = self.proj(x)
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return x
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class Flat(nn.Module):
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"""Flat the input from (B, C, H, W) to (B, H*W, C)."""
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def __init__(self, ):
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super().__init__()
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def forward(self, x: torch.Tensor):
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x = x.flatten(2).transpose(1, 2)
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return x
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class LinearMlp(BaseModule):
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"""Mlp implemented with linear.
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The shape of input and output tensor are (B, N, C).
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Args:
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in_features (int): Dimension of input features.
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hidden_features (int): Dimension of hidden features.
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out_features (int): Dimension of output features.
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norm_cfg (dict): Config dict for normalization layer.
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Defaults to ``dict(type='BN')``.
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act_cfg (dict): The config dict for activation between pointwise
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convolution. Defaults to ``dict(type='GELU')``.
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drop (float): Dropout rate. Defaults to 0.0.
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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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in_features: int,
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hidden_features: Optional[int] = None,
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out_features: Optional[int] = None,
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act_cfg=dict(type='GELU'),
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drop=0.,
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init_cfg=None):
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super().__init__(init_cfg=init_cfg)
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.fc1 = nn.Linear(in_features, hidden_features)
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self.act = build_activation_layer(act_cfg)
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self.drop1 = nn.Dropout(drop)
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self.fc2 = nn.Linear(hidden_features, out_features)
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self.drop2 = nn.Dropout(drop)
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def forward(self, x):
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"""
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Args:
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x (torch.Tensor): input tensor with shape (B, N, C).
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Returns:
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torch.Tensor: output tensor with shape (B, N, C).
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"""
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x = self.drop1(self.act(self.fc1(x)))
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x = self.drop2(self.fc2(x))
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return x
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class ConvMlp(BaseModule):
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"""Mlp implemented with 1*1 convolutions.
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Args:
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in_features (int): Dimension of input features.
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hidden_features (int): Dimension of hidden features.
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out_features (int): Dimension of output features.
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norm_cfg (dict): Config dict for normalization layer.
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Defaults to ``dict(type='BN')``.
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act_cfg (dict): The config dict for activation between pointwise
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convolution. Defaults to ``dict(type='GELU')``.
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drop (float): Dropout rate. Defaults to 0.0.
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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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in_features,
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hidden_features=None,
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out_features=None,
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norm_cfg=dict(type='BN'),
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act_cfg=dict(type='GELU'),
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drop=0.,
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init_cfg=None):
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super().__init__(init_cfg=init_cfg)
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.fc1 = nn.Conv2d(in_features, hidden_features, 1)
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self.act = build_activation_layer(act_cfg)
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self.fc2 = nn.Conv2d(hidden_features, out_features, 1)
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self.norm1 = build_norm_layer(norm_cfg, hidden_features)[1]
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self.norm2 = build_norm_layer(norm_cfg, out_features)[1]
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self.drop = nn.Dropout(drop)
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def forward(self, x):
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"""
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Args:
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x (torch.Tensor): input tensor with shape (B, C, H, W).
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Returns:
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torch.Tensor: output tensor with shape (B, C, H, W).
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"""
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x = self.act(self.norm1(self.fc1(x)))
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x = self.drop(x)
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x = self.norm2(self.fc2(x))
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x = self.drop(x)
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return x
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class Meta3D(BaseModule):
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"""Meta Former block using 3 dimensions inputs, ``torch.Tensor`` with shape
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(B, N, C)."""
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def __init__(self,
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dim,
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mlp_ratio=4.,
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norm_cfg=dict(type='LN'),
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act_cfg=dict(type='GELU'),
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drop=0.,
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drop_path=0.,
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use_layer_scale=True,
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init_cfg=None):
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super().__init__(init_cfg=init_cfg)
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self.norm1 = build_norm_layer(norm_cfg, dim)[1]
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self.token_mixer = AttentionWithBias(dim)
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self.norm2 = build_norm_layer(norm_cfg, dim)[1]
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = LinearMlp(
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in_features=dim,
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hidden_features=mlp_hidden_dim,
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act_cfg=act_cfg,
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drop=drop)
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self.drop_path = DropPath(drop_path) if drop_path > 0. \
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else nn.Identity()
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if use_layer_scale:
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self.ls1 = LayerScale(dim)
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self.ls2 = LayerScale(dim)
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else:
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self.ls1, self.ls2 = nn.Identity(), nn.Identity()
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def forward(self, x):
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x = x + self.drop_path(self.ls1(self.token_mixer(self.norm1(x))))
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x = x + self.drop_path(self.ls2(self.mlp(self.norm2(x))))
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return x
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class Meta4D(BaseModule):
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"""Meta Former block using 4 dimensions inputs, ``torch.Tensor`` with shape
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(B, C, H, W)."""
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def __init__(self,
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dim,
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pool_size=3,
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mlp_ratio=4.,
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act_cfg=dict(type='GELU'),
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drop=0.,
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drop_path=0.,
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use_layer_scale=True,
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init_cfg=None):
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super().__init__(init_cfg=init_cfg)
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self.token_mixer = Pooling(pool_size=pool_size)
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.mlp = ConvMlp(
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in_features=dim,
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hidden_features=mlp_hidden_dim,
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act_cfg=act_cfg,
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drop=drop)
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self.drop_path = DropPath(drop_path) if drop_path > 0. \
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else nn.Identity()
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if use_layer_scale:
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self.ls1 = LayerScale(dim, data_format='channels_first')
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self.ls2 = LayerScale(dim, data_format='channels_first')
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else:
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self.ls1, self.ls2 = nn.Identity(), nn.Identity()
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def forward(self, x):
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x = x + self.drop_path(self.ls1(self.token_mixer(x)))
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x = x + self.drop_path(self.ls2(self.mlp(x)))
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return x
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def basic_blocks(in_channels,
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out_channels,
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index,
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layers,
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pool_size=3,
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mlp_ratio=4.,
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act_cfg=dict(type='GELU'),
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drop_rate=.0,
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drop_path_rate=0.,
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use_layer_scale=True,
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vit_num=1,
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has_downsamper=False):
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"""generate EfficientFormer blocks for a stage."""
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blocks = []
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if has_downsamper:
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blocks.append(
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ConvModule(
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in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=3,
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stride=2,
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padding=1,
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bias=True,
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norm_cfg=dict(type='BN'),
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act_cfg=None))
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if index == 3 and vit_num == layers[index]:
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blocks.append(Flat())
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for block_idx in range(layers[index]):
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block_dpr = drop_path_rate * (block_idx + sum(layers[:index])) / (
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sum(layers) - 1)
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if index == 3 and layers[index] - block_idx <= vit_num:
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blocks.append(
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Meta3D(
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out_channels,
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mlp_ratio=mlp_ratio,
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act_cfg=act_cfg,
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drop=drop_rate,
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drop_path=block_dpr,
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use_layer_scale=use_layer_scale,
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))
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else:
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blocks.append(
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Meta4D(
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out_channels,
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pool_size=pool_size,
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act_cfg=act_cfg,
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drop=drop_rate,
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drop_path=block_dpr,
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use_layer_scale=use_layer_scale))
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if index == 3 and layers[index] - block_idx - 1 == vit_num:
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blocks.append(Flat())
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blocks = nn.Sequential(*blocks)
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return blocks
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@MODELS.register_module()
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class EfficientFormer(BaseBackbone):
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"""EfficientFormer.
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A PyTorch implementation of EfficientFormer introduced by:
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`EfficientFormer: Vision Transformers at MobileNet Speed <https://arxiv.org/abs/2206.01191>`_
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Modified from the `official repo
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<https://github.com/snap-research/EfficientFormer>`.
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Args:
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arch (str | dict): The model's architecture. If string, it should be
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one of architecture in ``EfficientFormer.arch_settings``. And if dict,
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it should include the following 4 keys:
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- layers (list[int]): Number of blocks at each stage.
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- embed_dims (list[int]): The number of channels at each stage.
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- downsamples (list[int]): Has downsample or not in the four stages.
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- vit_num (int): The num of vit blocks in the last stage.
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Defaults to 'l1'.
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in_channels (int): The num of input channels. Defaults to 3.
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pool_size (int): The pooling size of ``Meta4D`` blocks. Defaults to 3.
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mlp_ratios (int): The dimension ratio of multi-head attention mechanism
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in ``Meta4D`` blocks. Defaults to 3.
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reshape_last_feat (bool): Whether to reshape the feature map from
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(B, N, C) to (B, C, H, W) in the last stage, when the ``vit-num``
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in ``arch`` is not 0. Defaults to False. Usually set to True
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in downstream tasks.
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out_indices (Sequence[int]): Output from which stages.
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Defaults to -1.
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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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act_cfg (dict): The config dict for activation between pointwise
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convolution. Defaults to ``dict(type='GELU')``.
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drop_rate (float): Dropout rate. Defaults to 0.
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drop_path_rate (float): Stochastic depth rate. Defaults to 0.
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use_layer_scale (bool): Whether to use use_layer_scale in MetaFormer
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block. Defaults to True.
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init_cfg (dict, optional): Initialization config dict.
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Defaults to None.
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Example:
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>>> from mmpretrain.models import EfficientFormer
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>>> import torch
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>>> inputs = torch.rand((1, 3, 224, 224))
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>>> # build EfficientFormer backbone for classification task
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>>> model = EfficientFormer(arch="l1")
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>>> model.eval()
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>>> level_outputs = model(inputs)
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>>> for level_out in level_outputs:
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... print(tuple(level_out.shape))
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(1, 448, 49)
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>>> # build EfficientFormer backbone for downstream task
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>>> model = EfficientFormer(
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>>> arch="l3",
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>>> out_indices=(0, 1, 2, 3),
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>>> reshape_last_feat=True)
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>>> model.eval()
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>>> level_outputs = model(inputs)
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>>> for level_out in level_outputs:
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... print(tuple(level_out.shape))
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(1, 64, 56, 56)
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(1, 128, 28, 28)
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(1, 320, 14, 14)
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(1, 512, 7, 7)
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""" # noqa: E501
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# --layers: [x,x,x,x], numbers of layers for the four stages
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# --embed_dims: [x,x,x,x], embedding dims for the four stages
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# --downsamples: [x,x,x,x], has downsample or not in the four stages
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# --vit_num：(int), the num of vit blocks in the last stage
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arch_settings = {
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'l1': {
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'layers': [3, 2, 6, 4],
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'embed_dims': [48, 96, 224, 448],
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'downsamples': [False, True, True, True],
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'vit_num': 1,
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},
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'l3': {
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'layers': [4, 4, 12, 6],
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'embed_dims': [64, 128, 320, 512],
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'downsamples': [False, True, True, True],
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'vit_num': 4,
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},
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'l7': {
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'layers': [6, 6, 18, 8],
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'embed_dims': [96, 192, 384, 768],
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'downsamples': [False, True, True, True],
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'vit_num': 8,
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},
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}
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def __init__(self,
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arch='l1',
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in_channels=3,
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pool_size=3,
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mlp_ratios=4,
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reshape_last_feat=False,
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out_indices=-1,
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frozen_stages=-1,
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act_cfg=dict(type='GELU'),
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drop_rate=0.,
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drop_path_rate=0.,
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use_layer_scale=True,
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init_cfg=None):
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super().__init__(init_cfg=init_cfg)
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self.num_extra_tokens = 0 # no cls_token, no dist_token
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if isinstance(arch, str):
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assert arch in self.arch_settings, \
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f'Unavailable arch, please choose from ' \
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f'({set(self.arch_settings)}) or pass a dict.'
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arch = self.arch_settings[arch]
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elif isinstance(arch, dict):
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default_keys = set(self.arch_settings['l1'].keys())
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assert set(arch.keys()) == default_keys, \
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f'The arch dict must have {default_keys}, ' \
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f'but got {list(arch.keys())}.'
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self.layers = arch['layers']
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self.embed_dims = arch['embed_dims']
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self.downsamples = arch['downsamples']
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assert isinstance(self.layers, list) and isinstance(
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self.embed_dims, list) and isinstance(self.downsamples, list)
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assert len(self.layers) == len(self.embed_dims) == len(
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self.downsamples)
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self.vit_num = arch['vit_num']
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self.reshape_last_feat = reshape_last_feat
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assert self.vit_num >= 0, "'vit_num' must be an integer " \
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'greater than or equal to 0.'
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assert self.vit_num <= self.layers[-1], (
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"'vit_num' must be an integer smaller than layer number")
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self._make_stem(in_channels, self.embed_dims[0])
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# set the main block in network
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network = []
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for i in range(len(self.layers)):
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if i != 0:
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in_channels = self.embed_dims[i - 1]
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else:
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in_channels = self.embed_dims[i]
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out_channels = self.embed_dims[i]
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stage = basic_blocks(
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in_channels,
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out_channels,
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i,
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self.layers,
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pool_size=pool_size,
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mlp_ratio=mlp_ratios,
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act_cfg=act_cfg,
|
||
drop_rate=drop_rate,
|
||
drop_path_rate=drop_path_rate,
|
||
vit_num=self.vit_num,
|
||
use_layer_scale=use_layer_scale,
|
||
has_downsamper=self.downsamples[i])
|
||
network.append(stage)
|
||
|
||
self.network = ModuleList(network)
|
||
|
||
if isinstance(out_indices, int):
|
||
out_indices = [out_indices]
|
||
assert isinstance(out_indices, Sequence), \
|
||
f'"out_indices" must by a sequence or int, ' \
|
||
f'get {type(out_indices)} instead.'
|
||
for i, index in enumerate(out_indices):
|
||
if index < 0:
|
||
out_indices[i] = 4 + index
|
||
assert out_indices[i] >= 0, f'Invalid out_indices {index}'
|
||
|
||
self.out_indices = out_indices
|
||
for i_layer in self.out_indices:
|
||
if not self.reshape_last_feat and \
|
||
i_layer == 3 and self.vit_num > 0:
|
||
layer = build_norm_layer(
|
||
dict(type='LN'), self.embed_dims[i_layer])[1]
|
||
else:
|
||
# use GN with 1 group as channel-first LN2D
|
||
layer = build_norm_layer(
|
||
dict(type='GN', num_groups=1), self.embed_dims[i_layer])[1]
|
||
|
||
layer_name = f'norm{i_layer}'
|
||
self.add_module(layer_name, layer)
|
||
|
||
self.frozen_stages = frozen_stages
|
||
self._freeze_stages()
|
||
|
||
def _make_stem(self, in_channels: int, stem_channels: int):
|
||
"""make 2-ConvBNReLu stem layer."""
|
||
self.patch_embed = Sequential(
|
||
ConvModule(
|
||
in_channels,
|
||
stem_channels // 2,
|
||
kernel_size=3,
|
||
stride=2,
|
||
padding=1,
|
||
bias=True,
|
||
conv_cfg=None,
|
||
norm_cfg=dict(type='BN'),
|
||
inplace=True),
|
||
ConvModule(
|
||
stem_channels // 2,
|
||
stem_channels,
|
||
kernel_size=3,
|
||
stride=2,
|
||
padding=1,
|
||
bias=True,
|
||
conv_cfg=None,
|
||
norm_cfg=dict(type='BN'),
|
||
inplace=True))
|
||
|
||
def forward_tokens(self, x):
|
||
outs = []
|
||
for idx, block in enumerate(self.network):
|
||
if idx == len(self.network) - 1:
|
||
N, _, H, W = x.shape
|
||
if self.downsamples[idx]:
|
||
H, W = H // 2, W // 2
|
||
x = block(x)
|
||
if idx in self.out_indices:
|
||
norm_layer = getattr(self, f'norm{idx}')
|
||
|
||
if idx == len(self.network) - 1 and x.dim() == 3:
|
||
# when ``vit-num`` > 0 and in the last stage,
|
||
# if `self.reshape_last_feat`` is True, reshape the
|
||
# features to `BCHW` format before the final normalization.
|
||
# if `self.reshape_last_feat`` is False, do
|
||
# normalization directly and permute the features to `BCN`.
|
||
if self.reshape_last_feat:
|
||
x = x.permute((0, 2, 1)).reshape(N, -1, H, W)
|
||
x_out = norm_layer(x)
|
||
else:
|
||
x_out = norm_layer(x).permute((0, 2, 1))
|
||
else:
|
||
x_out = norm_layer(x)
|
||
|
||
outs.append(x_out.contiguous())
|
||
return tuple(outs)
|
||
|
||
def forward(self, x):
|
||
# input embedding
|
||
x = self.patch_embed(x)
|
||
# through stages
|
||
x = self.forward_tokens(x)
|
||
return x
|
||
|
||
def _freeze_stages(self):
|
||
if self.frozen_stages >= 0:
|
||
self.patch_embed.eval()
|
||
for param in self.patch_embed.parameters():
|
||
param.requires_grad = False
|
||
|
||
for i in range(self.frozen_stages):
|
||
# Include both block and downsample layer.
|
||
module = self.network[i]
|
||
module.eval()
|
||
for param in module.parameters():
|
||
param.requires_grad = False
|
||
if i in self.out_indices:
|
||
norm_layer = getattr(self, f'norm{i}')
|
||
norm_layer.eval()
|
||
for param in norm_layer.parameters():
|
||
param.requires_grad = False
|
||
|
||
def train(self, mode=True):
|
||
super(EfficientFormer, self).train(mode)
|
||
self._freeze_stages()
|