504 lines
17 KiB
Python
504 lines
17 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
|
|
# Part of the implementation is borrowed and modified from Next-ViT,
|
|
# publicly available at https://github.com/bytedance/Next-ViT
|
|
import collections.abc
|
|
import itertools
|
|
import math
|
|
import os
|
|
import warnings
|
|
from functools import partial
|
|
from typing import Dict, Sequence
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
from einops import rearrange
|
|
from mmcls.models.backbones.base_backbone import BaseBackbone
|
|
from mmcls.models.builder import BACKBONES
|
|
from mmcv.cnn.bricks import DropPath, build_activation_layer, build_norm_layer
|
|
from mmcv.runner import BaseModule
|
|
from torch.nn.modules.batchnorm import _BatchNorm
|
|
|
|
from ..utils import trunc_normal_
|
|
|
|
NORM_EPS = 1e-5
|
|
|
|
|
|
class ConvBNReLU(nn.Module):
|
|
|
|
def __init__(self,
|
|
in_channels,
|
|
out_channels,
|
|
kernel_size,
|
|
stride,
|
|
groups=1):
|
|
super(ConvBNReLU, self).__init__()
|
|
self.conv = nn.Conv2d(
|
|
in_channels,
|
|
out_channels,
|
|
kernel_size=kernel_size,
|
|
stride=stride,
|
|
padding=1,
|
|
groups=groups,
|
|
bias=False)
|
|
self.norm = nn.BatchNorm2d(out_channels, eps=NORM_EPS)
|
|
self.act = nn.ReLU(inplace=True)
|
|
|
|
def forward(self, x):
|
|
x = self.conv(x)
|
|
x = self.norm(x)
|
|
x = self.act(x)
|
|
return x
|
|
|
|
|
|
def _make_divisible(v, divisor, min_value=None):
|
|
if min_value is None:
|
|
min_value = divisor
|
|
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
|
|
# Make sure that round down does not go down by more than 10%.
|
|
if new_v < 0.9 * v:
|
|
new_v += divisor
|
|
return new_v
|
|
|
|
|
|
class PatchEmbed(nn.Module):
|
|
|
|
def __init__(self, in_channels, out_channels, stride=1):
|
|
super(PatchEmbed, self).__init__()
|
|
norm_layer = partial(nn.BatchNorm2d, eps=NORM_EPS)
|
|
if stride == 2:
|
|
self.avgpool = nn.AvgPool2d((2, 2),
|
|
stride=2,
|
|
ceil_mode=True,
|
|
count_include_pad=False)
|
|
self.conv = nn.Conv2d(
|
|
in_channels, out_channels, kernel_size=1, stride=1, bias=False)
|
|
self.norm = norm_layer(out_channels)
|
|
elif in_channels != out_channels:
|
|
self.avgpool = nn.Identity()
|
|
self.conv = nn.Conv2d(
|
|
in_channels, out_channels, kernel_size=1, stride=1, bias=False)
|
|
self.norm = norm_layer(out_channels)
|
|
else:
|
|
self.avgpool = nn.Identity()
|
|
self.conv = nn.Identity()
|
|
self.norm = nn.Identity()
|
|
|
|
def forward(self, x):
|
|
return self.norm(self.conv(self.avgpool(x)))
|
|
|
|
|
|
class MHCA(nn.Module):
|
|
"""
|
|
Multi-Head Convolutional Attention
|
|
"""
|
|
|
|
def __init__(self, out_channels, head_dim):
|
|
super(MHCA, self).__init__()
|
|
norm_layer = partial(nn.BatchNorm2d, eps=NORM_EPS)
|
|
self.group_conv3x3 = nn.Conv2d(
|
|
out_channels,
|
|
out_channels,
|
|
kernel_size=3,
|
|
stride=1,
|
|
padding=1,
|
|
groups=out_channels // head_dim,
|
|
bias=False)
|
|
self.norm = norm_layer(out_channels)
|
|
self.act = nn.ReLU(inplace=True)
|
|
self.projection = nn.Conv2d(
|
|
out_channels, out_channels, kernel_size=1, bias=False)
|
|
|
|
def forward(self, x):
|
|
out = self.group_conv3x3(x)
|
|
out = self.norm(out)
|
|
out = self.act(out)
|
|
out = self.projection(out)
|
|
return out
|
|
|
|
|
|
class Mlp(nn.Module):
|
|
|
|
def __init__(self,
|
|
in_features,
|
|
out_features=None,
|
|
mlp_ratio=None,
|
|
drop=0.,
|
|
bias=True):
|
|
super().__init__()
|
|
out_features = out_features or in_features
|
|
hidden_dim = _make_divisible(in_features * mlp_ratio, 32)
|
|
self.conv1 = nn.Conv2d(
|
|
in_features, hidden_dim, kernel_size=1, bias=bias)
|
|
self.act = nn.ReLU(inplace=True)
|
|
self.conv2 = nn.Conv2d(
|
|
hidden_dim, out_features, kernel_size=1, bias=bias)
|
|
self.drop = nn.Dropout(drop)
|
|
|
|
def forward(self, x):
|
|
x = self.conv1(x)
|
|
x = self.act(x)
|
|
x = self.drop(x)
|
|
x = self.conv2(x)
|
|
x = self.drop(x)
|
|
return x
|
|
|
|
|
|
class NCB(nn.Module):
|
|
"""
|
|
Next Convolution Block
|
|
"""
|
|
|
|
def __init__(self,
|
|
in_channels,
|
|
out_channels,
|
|
stride=1,
|
|
path_dropout=0,
|
|
drop=0,
|
|
head_dim=32,
|
|
mlp_ratio=3):
|
|
super(NCB, self).__init__()
|
|
self.in_channels = in_channels
|
|
self.out_channels = out_channels
|
|
norm_layer = partial(nn.BatchNorm2d, eps=NORM_EPS)
|
|
assert out_channels % head_dim == 0
|
|
|
|
self.patch_embed = PatchEmbed(in_channels, out_channels, stride)
|
|
self.mhca = MHCA(out_channels, head_dim)
|
|
self.attention_path_dropout = DropPath(path_dropout)
|
|
|
|
self.norm = norm_layer(out_channels)
|
|
self.mlp = Mlp(out_channels, mlp_ratio=mlp_ratio, drop=drop, bias=True)
|
|
self.mlp_path_dropout = DropPath(path_dropout)
|
|
self.is_bn_merged = False
|
|
|
|
def forward(self, x):
|
|
x = self.patch_embed(x)
|
|
x = x + self.attention_path_dropout(self.mhca(x))
|
|
if not torch.onnx.is_in_onnx_export() and not self.is_bn_merged:
|
|
out = self.norm(x)
|
|
else:
|
|
out = x
|
|
x = x + self.mlp_path_dropout(self.mlp(out))
|
|
return x
|
|
|
|
|
|
class E_MHSA(nn.Module):
|
|
"""
|
|
Efficient Multi-Head Self Attention
|
|
"""
|
|
|
|
def __init__(self,
|
|
dim,
|
|
out_dim=None,
|
|
head_dim=32,
|
|
qkv_bias=True,
|
|
qk_scale=None,
|
|
attn_drop=0,
|
|
proj_drop=0.,
|
|
sr_ratio=1):
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.out_dim = out_dim if out_dim is not None else dim
|
|
self.num_heads = self.dim // head_dim
|
|
self.scale = qk_scale or head_dim**-0.5
|
|
self.q = nn.Linear(dim, self.dim, bias=qkv_bias)
|
|
self.k = nn.Linear(dim, self.dim, bias=qkv_bias)
|
|
self.v = nn.Linear(dim, self.dim, bias=qkv_bias)
|
|
self.proj = nn.Linear(self.dim, self.out_dim)
|
|
self.attn_drop = nn.Dropout(attn_drop)
|
|
self.proj_drop = nn.Dropout(proj_drop)
|
|
|
|
self.sr_ratio = sr_ratio
|
|
self.N_ratio = sr_ratio**2
|
|
if sr_ratio > 1:
|
|
self.sr = nn.AvgPool1d(
|
|
kernel_size=self.N_ratio, stride=self.N_ratio)
|
|
self.norm = nn.BatchNorm1d(dim, eps=NORM_EPS)
|
|
self.is_bn_merge = False
|
|
|
|
def forward(self, x):
|
|
B, N, C = x.shape
|
|
q = self.q(x)
|
|
q = q.reshape(B, N, self.num_heads,
|
|
int(C // self.num_heads)).permute(0, 2, 1, 3)
|
|
|
|
if self.sr_ratio > 1:
|
|
x_ = x.transpose(1, 2)
|
|
x_ = self.sr(x_)
|
|
if not torch.onnx.is_in_onnx_export() and not self.is_bn_merge:
|
|
x_ = self.norm(x_)
|
|
x_ = x_.transpose(1, 2)
|
|
k = self.k(x_)
|
|
k = k.reshape(B, -1, self.num_heads,
|
|
int(C // self.num_heads)).permute(0, 2, 3, 1)
|
|
v = self.v(x_)
|
|
v = v.reshape(B, -1, self.num_heads,
|
|
int(C // self.num_heads)).permute(0, 2, 1, 3)
|
|
else:
|
|
k = self.k(x)
|
|
k = k.reshape(B, -1, self.num_heads,
|
|
int(C // self.num_heads)).permute(0, 2, 3, 1)
|
|
v = self.v(x)
|
|
v = v.reshape(B, -1, self.num_heads,
|
|
int(C // self.num_heads)).permute(0, 2, 1, 3)
|
|
attn = (q @ k) * self.scale
|
|
|
|
attn = attn.softmax(dim=-1)
|
|
attn = self.attn_drop(attn)
|
|
|
|
x = (attn @ v).transpose(1, 2).reshape(B, N, C)
|
|
x = self.proj(x)
|
|
x = self.proj_drop(x)
|
|
return x
|
|
|
|
|
|
class NTB(nn.Module):
|
|
"""
|
|
Next Transformer Block
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
in_channels,
|
|
out_channels,
|
|
path_dropout,
|
|
stride=1,
|
|
sr_ratio=1,
|
|
mlp_ratio=2,
|
|
head_dim=32,
|
|
mix_block_ratio=0.75,
|
|
attn_drop=0,
|
|
drop=0,
|
|
):
|
|
super(NTB, self).__init__()
|
|
self.in_channels = in_channels
|
|
self.out_channels = out_channels
|
|
self.mix_block_ratio = mix_block_ratio
|
|
norm_func = partial(nn.BatchNorm2d, eps=NORM_EPS)
|
|
|
|
self.mhsa_out_channels = _make_divisible(
|
|
int(out_channels * mix_block_ratio), 32)
|
|
self.mhca_out_channels = out_channels - self.mhsa_out_channels
|
|
|
|
self.patch_embed = PatchEmbed(in_channels, self.mhsa_out_channels,
|
|
stride)
|
|
self.norm1 = norm_func(self.mhsa_out_channels)
|
|
self.e_mhsa = E_MHSA(
|
|
self.mhsa_out_channels,
|
|
head_dim=head_dim,
|
|
sr_ratio=sr_ratio,
|
|
attn_drop=attn_drop,
|
|
proj_drop=drop)
|
|
self.mhsa_path_dropout = DropPath(path_dropout * mix_block_ratio)
|
|
|
|
self.projection = PatchEmbed(
|
|
self.mhsa_out_channels, self.mhca_out_channels, stride=1)
|
|
self.mhca = MHCA(self.mhca_out_channels, head_dim=head_dim)
|
|
self.mhca_path_dropout = DropPath(path_dropout * (1 - mix_block_ratio))
|
|
|
|
self.norm2 = norm_func(out_channels)
|
|
self.mlp = Mlp(out_channels, mlp_ratio=mlp_ratio, drop=drop)
|
|
self.mlp_path_dropout = DropPath(path_dropout)
|
|
|
|
self.is_bn_merged = False
|
|
|
|
def forward(self, x):
|
|
x = self.patch_embed(x)
|
|
B, C, H, W = x.shape
|
|
if not torch.onnx.is_in_onnx_export() and not self.is_bn_merged:
|
|
out = self.norm1(x)
|
|
else:
|
|
out = x
|
|
out = rearrange(out, 'b c h w -> b (h w) c') # b n c
|
|
out = self.mhsa_path_dropout(self.e_mhsa(out))
|
|
x = x + rearrange(out, 'b (h w) c -> b c h w', h=H)
|
|
|
|
out = self.projection(x)
|
|
out = out + self.mhca_path_dropout(self.mhca(out))
|
|
x = torch.cat([x, out], dim=1)
|
|
|
|
if not torch.onnx.is_in_onnx_export() and not self.is_bn_merged:
|
|
out = self.norm2(x)
|
|
else:
|
|
out = x
|
|
x = x + self.mlp_path_dropout(self.mlp(out))
|
|
return x
|
|
|
|
|
|
@BACKBONES.register_module()
|
|
class NextViT(BaseBackbone):
|
|
stem_chs = {
|
|
'x_small': [64, 32, 64],
|
|
'small': [64, 32, 64],
|
|
'base': [64, 32, 64],
|
|
'large': [64, 32, 64],
|
|
}
|
|
depths = {
|
|
'x_small': [1, 1, 5, 1],
|
|
'small': [3, 4, 10, 3],
|
|
'base': [3, 4, 20, 3],
|
|
'large': [3, 4, 30, 3],
|
|
}
|
|
|
|
def __init__(self,
|
|
arch='small',
|
|
path_dropout=0.2,
|
|
attn_drop=0,
|
|
drop=0,
|
|
strides=[1, 2, 2, 2],
|
|
sr_ratios=[8, 4, 2, 1],
|
|
head_dim=32,
|
|
mix_block_ratio=0.75,
|
|
resume='',
|
|
with_extra_norm=True,
|
|
norm_eval=False,
|
|
norm_cfg=None,
|
|
out_indices=-1,
|
|
frozen_stages=-1,
|
|
init_cfg=None):
|
|
super().__init__(init_cfg=init_cfg)
|
|
|
|
stem_chs = self.stem_chs[arch]
|
|
depths = self.depths[arch]
|
|
|
|
self.frozen_stages = frozen_stages
|
|
self.with_extra_norm = with_extra_norm
|
|
self.norm_eval = norm_eval
|
|
self.stage1_out_channels = [96] * (depths[0])
|
|
self.stage2_out_channels = [192] * (depths[1] - 1) + [256]
|
|
self.stage3_out_channels = [384, 384, 384, 384, 512] * (depths[2] // 5)
|
|
self.stage4_out_channels = [768] * (depths[3] - 1) + [1024]
|
|
self.stage_out_channels = [
|
|
self.stage1_out_channels, self.stage2_out_channels,
|
|
self.stage3_out_channels, self.stage4_out_channels
|
|
]
|
|
|
|
# Next Hybrid Strategy
|
|
self.stage1_block_types = [NCB] * depths[0]
|
|
self.stage2_block_types = [NCB] * (depths[1] - 1) + [NTB]
|
|
self.stage3_block_types = [NCB, NCB, NCB, NCB, NTB] * (depths[2] // 5)
|
|
self.stage4_block_types = [NCB] * (depths[3] - 1) + [NTB]
|
|
self.stage_block_types = [
|
|
self.stage1_block_types, self.stage2_block_types,
|
|
self.stage3_block_types, self.stage4_block_types
|
|
]
|
|
|
|
self.stem = nn.Sequential(
|
|
ConvBNReLU(3, stem_chs[0], kernel_size=3, stride=2),
|
|
ConvBNReLU(stem_chs[0], stem_chs[1], kernel_size=3, stride=1),
|
|
ConvBNReLU(stem_chs[1], stem_chs[2], kernel_size=3, stride=1),
|
|
ConvBNReLU(stem_chs[2], stem_chs[2], kernel_size=3, stride=2),
|
|
)
|
|
input_channel = stem_chs[-1]
|
|
features = []
|
|
idx = 0
|
|
dpr = [x.item() for x in torch.linspace(0, path_dropout, sum(depths))
|
|
] # stochastic depth decay rule
|
|
for stage_id in range(len(depths)):
|
|
numrepeat = depths[stage_id]
|
|
output_channels = self.stage_out_channels[stage_id]
|
|
block_types = self.stage_block_types[stage_id]
|
|
for block_id in range(numrepeat):
|
|
if strides[stage_id] == 2 and block_id == 0:
|
|
stride = 2
|
|
else:
|
|
stride = 1
|
|
output_channel = output_channels[block_id]
|
|
block_type = block_types[block_id]
|
|
if block_type is NCB:
|
|
layer = NCB(
|
|
input_channel,
|
|
output_channel,
|
|
stride=stride,
|
|
path_dropout=dpr[idx + block_id],
|
|
drop=drop,
|
|
head_dim=head_dim)
|
|
features.append(layer)
|
|
elif block_type is NTB:
|
|
layer = NTB(
|
|
input_channel,
|
|
output_channel,
|
|
path_dropout=dpr[idx + block_id],
|
|
stride=stride,
|
|
sr_ratio=sr_ratios[stage_id],
|
|
head_dim=head_dim,
|
|
mix_block_ratio=mix_block_ratio,
|
|
attn_drop=attn_drop,
|
|
drop=drop)
|
|
features.append(layer)
|
|
input_channel = output_channel
|
|
idx += numrepeat
|
|
self.features = nn.Sequential(*features)
|
|
self.norm = nn.BatchNorm2d(output_channel, eps=NORM_EPS)
|
|
|
|
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] = sum(depths) + index
|
|
assert out_indices[i] >= 0, f'Invalid out_indices {index}'
|
|
self.stage_out_idx = out_indices
|
|
|
|
if norm_cfg is not None:
|
|
self = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self)
|
|
|
|
def init_weights(self):
|
|
super(NextViT, self).init_weights()
|
|
if (isinstance(self.init_cfg, dict)
|
|
and self.init_cfg['type'] == 'Pretrained'):
|
|
# Suppress default init if use pretrained model.
|
|
return
|
|
|
|
self._initialize_weights()
|
|
|
|
def _initialize_weights(self):
|
|
for n, m in self.named_modules():
|
|
if isinstance(m, (nn.BatchNorm2d,
|
|
nn.BatchNorm1d)): # nn.GroupNorm, nn.LayerNorm,
|
|
nn.init.constant_(m.weight, 1.0)
|
|
nn.init.constant_(m.bias, 0)
|
|
elif isinstance(m, nn.Linear):
|
|
trunc_normal_(m.weight, std=.02)
|
|
if hasattr(m, 'bias') and m.bias is not None:
|
|
nn.init.constant_(m.bias, 0)
|
|
elif isinstance(m, nn.Conv2d):
|
|
trunc_normal_(m.weight, std=.02)
|
|
if hasattr(m, 'bias') and m.bias is not None:
|
|
nn.init.constant_(m.bias, 0)
|
|
|
|
def forward(self, x):
|
|
outputs = list()
|
|
x = self.stem(x)
|
|
stage_id = 0
|
|
for idx, layer in enumerate(self.features):
|
|
x = layer(x)
|
|
if idx == self.stage_out_idx[stage_id]:
|
|
if self.with_extra_norm:
|
|
x = self.norm(x)
|
|
outputs.append(x)
|
|
stage_id += 1
|
|
return tuple(outputs)
|
|
|
|
def _freeze_stages(self):
|
|
if self.frozen_stages > 0:
|
|
self.stem.eval()
|
|
for param in self.stem.parameters():
|
|
param.requires_grad = False
|
|
for idx, layer in enumerate(self.features):
|
|
if idx <= self.stage_out_idx[self.frozen_stages - 1]:
|
|
layer.eval()
|
|
for param in layer.parameters():
|
|
param.requires_grad = False
|
|
|
|
def train(self, mode=True):
|
|
super(NextViT, self).train(mode)
|
|
self._freeze_stages()
|
|
if mode and self.norm_eval:
|
|
for m in self.modules():
|
|
# trick: eval have effect on BatchNorm only
|
|
if isinstance(m, _BatchNorm):
|
|
m.eval()
|