XiaXin-Aloys
2 years ago
parent
2a5b5b2a7b
commit
151fc8a14f
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# Copyright (c) ByteDance Inc. All rights reserved.
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from functools import partial
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import torch
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import torch.utils.checkpoint as checkpoint
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from einops import rearrange
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from timm.models.layers import DropPath, trunc_normal_
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from timm.models.registry import register_model
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from torch import nn
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from utils import merge_pre_bn
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NORM_EPS = 1e-5
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class ConvBNReLU(nn.Module):
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def __init__(
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self,
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in_channels,
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out_channels,
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kernel_size,
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stride,
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groups=1):
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super(ConvBNReLU, self).__init__()
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self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride,
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padding=1, groups=groups, bias=False)
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self.norm = nn.BatchNorm2d(out_channels, eps=NORM_EPS)
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self.act = nn.ReLU(inplace=True)
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def forward(self, x):
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x = self.conv(x)
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x = self.norm(x)
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x = self.act(x)
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return x
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def _make_divisible(v, divisor, min_value=None):
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if min_value is None:
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min_value = divisor
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new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
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# Make sure that round down does not go down by more than 10%.
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if new_v < 0.9 * v:
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new_v += divisor
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return new_v
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class PatchEmbed(nn.Module):
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def __init__(self,
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in_channels,
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out_channels,
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stride=1):
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super(PatchEmbed, self).__init__()
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norm_layer = partial(nn.BatchNorm2d, eps=NORM_EPS)
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if stride == 2:
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self.avgpool = nn.AvgPool2d((2, 2), stride=2, ceil_mode=True, count_include_pad=False)
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self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False)
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self.norm = norm_layer(out_channels)
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elif in_channels != out_channels:
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self.avgpool = nn.Identity()
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self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False)
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self.norm = norm_layer(out_channels)
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else:
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self.avgpool = nn.Identity()
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self.conv = nn.Identity()
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self.norm = nn.Identity()
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def forward(self, x):
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return self.norm(self.conv(self.avgpool(x)))
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class MHCA(nn.Module):
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"""
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Multi-Head Convolutional Attention
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"""
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def __init__(self, out_channels, head_dim):
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super(MHCA, self).__init__()
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norm_layer = partial(nn.BatchNorm2d, eps=NORM_EPS)
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self.group_conv3x3 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1,
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padding=1, groups=out_channels // head_dim, bias=False)
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self.norm = norm_layer(out_channels)
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self.act = nn.ReLU(inplace=True)
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self.projection = nn.Conv2d(out_channels, out_channels, kernel_size=1, bias=False)
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def forward(self, x):
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out = self.group_conv3x3(x)
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out = self.norm(out)
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out = self.act(out)
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out = self.projection(out)
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return out
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class Mlp(nn.Module):
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def __init__(self, in_features, out_features=None, mlp_ratio=None, drop=0., bias=True):
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super().__init__()
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out_features = out_features or in_features
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hidden_dim = _make_divisible(in_features * mlp_ratio, 32)
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self.conv1 = nn.Conv2d(in_features, hidden_dim, kernel_size=1, bias=bias)
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self.act = nn.ReLU(inplace=True)
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self.conv2 = nn.Conv2d(hidden_dim, out_features, kernel_size=1, bias=bias)
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self.drop = nn.Dropout(drop)
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def merge_bn(self, pre_norm):
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merge_pre_bn(self.conv1, pre_norm)
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def forward(self, x):
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x = self.conv1(x)
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x = self.act(x)
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x = self.drop(x)
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x = self.conv2(x)
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x = self.drop(x)
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return x
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class NCB(nn.Module):
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"""
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Next Convolution Block
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"""
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def __init__(self, in_channels, out_channels, stride=1, path_dropout=0,
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drop=0, head_dim=32, mlp_ratio=3):
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super(NCB, self).__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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norm_layer = partial(nn.BatchNorm2d, eps=NORM_EPS)
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assert out_channels % head_dim == 0
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self.patch_embed = PatchEmbed(in_channels, out_channels, stride)
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self.mhca = MHCA(out_channels, head_dim)
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self.attention_path_dropout = DropPath(path_dropout)
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self.norm = norm_layer(out_channels)
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self.mlp = Mlp(out_channels, mlp_ratio=mlp_ratio, drop=drop, bias=True)
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self.mlp_path_dropout = DropPath(path_dropout)
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self.is_bn_merged = False
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def merge_bn(self):
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if not self.is_bn_merged:
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self.mlp.merge_bn(self.norm)
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self.is_bn_merged = True
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def forward(self, x):
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x = self.patch_embed(x)
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x = x + self.attention_path_dropout(self.mhca(x))
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if not torch.onnx.is_in_onnx_export() and not self.is_bn_merged:
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out = self.norm(x)
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else:
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out = x
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x = x + self.mlp_path_dropout(self.mlp(out))
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return x
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class E_MHSA(nn.Module):
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"""
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Efficient Multi-Head Self Attention
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"""
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def __init__(self, dim, out_dim=None, head_dim=32, qkv_bias=True, qk_scale=None,
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attn_drop=0, proj_drop=0., sr_ratio=1):
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super().__init__()
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self.dim = dim
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self.out_dim = out_dim if out_dim is not None else dim
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self.num_heads = self.dim // head_dim
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self.scale = qk_scale or head_dim ** -0.5
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self.q = nn.Linear(dim, self.dim, bias=qkv_bias)
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self.k = nn.Linear(dim, self.dim, bias=qkv_bias)
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self.v = nn.Linear(dim, self.dim, bias=qkv_bias)
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self.proj = nn.Linear(self.dim, self.out_dim)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj_drop = nn.Dropout(proj_drop)
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self.sr_ratio = sr_ratio
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self.N_ratio = sr_ratio ** 2
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if sr_ratio > 1:
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self.sr = nn.AvgPool1d(kernel_size=self.N_ratio, stride=self.N_ratio)
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self.norm = nn.BatchNorm1d(dim, eps=NORM_EPS)
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self.is_bn_merged = False
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def merge_bn(self, pre_bn):
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merge_pre_bn(self.q, pre_bn)
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if self.sr_ratio > 1:
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merge_pre_bn(self.k, pre_bn, self.norm)
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merge_pre_bn(self.v, pre_bn, self.norm)
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else:
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merge_pre_bn(self.k, pre_bn)
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merge_pre_bn(self.v, pre_bn)
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self.is_bn_merged = True
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def forward(self, x):
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B, N, C = x.shape
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q = self.q(x)
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q = q.reshape(B, N, self.num_heads, int(C // self.num_heads)).permute(0, 2, 1, 3)
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if self.sr_ratio > 1:
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x_ = x.transpose(1, 2)
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x_ = self.sr(x_)
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if not torch.onnx.is_in_onnx_export() and not self.is_bn_merged:
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x_ = self.norm(x_)
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x_ = x_.transpose(1, 2)
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k = self.k(x_)
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k = k.reshape(B, -1, self.num_heads, int(C // self.num_heads)).permute(0, 2, 3, 1)
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v = self.v(x_)
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v = v.reshape(B, -1, self.num_heads, int(C // self.num_heads)).permute(0, 2, 1, 3)
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else:
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k = self.k(x)
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k = k.reshape(B, -1, self.num_heads, int(C // self.num_heads)).permute(0, 2, 3, 1)
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v = self.v(x)
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v = v.reshape(B, -1, self.num_heads, int(C // self.num_heads)).permute(0, 2, 1, 3)
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attn = (q @ k) * self.scale
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B, N, C)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class NTB(nn.Module):
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"""
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Next Transformer Block
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"""
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def __init__(
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self, in_channels, out_channels, path_dropout, stride=1, sr_ratio=1,
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mlp_ratio=2, head_dim=32, mix_block_ratio=0.75, attn_drop=0, drop=0,
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):
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super(NTB, self).__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.mix_block_ratio = mix_block_ratio
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norm_func = partial(nn.BatchNorm2d, eps=NORM_EPS)
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self.mhsa_out_channels = _make_divisible(int(out_channels * mix_block_ratio), 32)
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self.mhca_out_channels = out_channels - self.mhsa_out_channels
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self.patch_embed = PatchEmbed(in_channels, self.mhsa_out_channels, stride)
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self.norm1 = norm_func(self.mhsa_out_channels)
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self.e_mhsa = E_MHSA(self.mhsa_out_channels, head_dim=head_dim, sr_ratio=sr_ratio,
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attn_drop=attn_drop, proj_drop=drop)
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self.mhsa_path_dropout = DropPath(path_dropout * mix_block_ratio)
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self.projection = PatchEmbed(self.mhsa_out_channels, self.mhca_out_channels, stride=1)
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self.mhca = MHCA(self.mhca_out_channels, head_dim=head_dim)
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self.mhca_path_dropout = DropPath(path_dropout * (1 - mix_block_ratio))
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self.norm2 = norm_func(out_channels)
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self.mlp = Mlp(out_channels, mlp_ratio=mlp_ratio, drop=drop)
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self.mlp_path_dropout = DropPath(path_dropout)
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self.is_bn_merged = False
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def merge_bn(self):
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if not self.is_bn_merged:
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self.e_mhsa.merge_bn(self.norm1)
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self.mlp.merge_bn(self.norm2)
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self.is_bn_merged = True
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def forward(self, x):
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x = self.patch_embed(x)
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B, C, H, W = x.shape
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if not torch.onnx.is_in_onnx_export() and not self.is_bn_merged:
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out = self.norm1(x)
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else:
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out = x
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out = rearrange(out, "b c h w -> b (h w) c") # b n c
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out = self.mhsa_path_dropout(self.e_mhsa(out))
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x = x + rearrange(out, "b (h w) c -> b c h w", h=H)
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out = self.projection(x)
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out = out + self.mhca_path_dropout(self.mhca(out))
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x = torch.cat([x, out], dim=1)
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if not torch.onnx.is_in_onnx_export() and not self.is_bn_merged:
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out = self.norm2(x)
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else:
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out = x
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x = x + self.mlp_path_dropout(self.mlp(out))
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return x
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class NextViT(nn.Module):
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def __init__(self, stem_chs, depths, path_dropout, attn_drop=0, drop=0, num_classes=1000,
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strides=[1, 2, 2, 2], sr_ratios=[8, 4, 2, 1], head_dim=32, mix_block_ratio=0.75,
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use_checkpoint=False):
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super(NextViT, self).__init__()
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self.use_checkpoint = use_checkpoint
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self.stage_out_channels = [[96] * (depths[0]),
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[192] * (depths[1] - 1) + [256],
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[384, 384, 384, 384, 512] * (depths[2] // 5),
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[768] * (depths[3] - 1) + [1024]]
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# Next Hybrid Strategy
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self.stage_block_types = [[NCB] * depths[0],
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[NCB] * (depths[1] - 1) + [NTB],
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[NCB, NCB, NCB, NCB, NTB] * (depths[2] // 5),
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[NCB] * (depths[3] - 1) + [NTB]]
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self.stem = nn.Sequential(
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ConvBNReLU(3, stem_chs[0], kernel_size=3, stride=2),
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ConvBNReLU(stem_chs[0], stem_chs[1], kernel_size=3, stride=1),
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ConvBNReLU(stem_chs[1], stem_chs[2], kernel_size=3, stride=1),
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ConvBNReLU(stem_chs[2], stem_chs[2], kernel_size=3, stride=2),
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)
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input_channel = stem_chs[-1]
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features = []
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idx = 0
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dpr = [x.item() for x in torch.linspace(0, path_dropout, sum(depths))] # stochastic depth decay rule
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for stage_id in range(len(depths)):
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numrepeat = depths[stage_id]
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output_channels = self.stage_out_channels[stage_id]
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block_types = self.stage_block_types[stage_id]
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for block_id in range(numrepeat):
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if strides[stage_id] == 2 and block_id == 0:
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stride = 2
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else:
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stride = 1
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output_channel = output_channels[block_id]
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block_type = block_types[block_id]
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if block_type is NCB:
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layer = NCB(input_channel, output_channel, stride=stride, path_dropout=dpr[idx + block_id],
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drop=drop, head_dim=head_dim)
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features.append(layer)
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elif block_type is NTB:
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layer = NTB(input_channel, output_channel, path_dropout=dpr[idx + block_id], stride=stride,
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sr_ratio=sr_ratios[stage_id], head_dim=head_dim, mix_block_ratio=mix_block_ratio,
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attn_drop=attn_drop, drop=drop)
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features.append(layer)
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input_channel = output_channel
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idx += numrepeat
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self.features = nn.Sequential(*features)
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self.norm = nn.BatchNorm2d(output_channel, eps=NORM_EPS)
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self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
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self.proj_head = nn.Sequential(
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nn.Linear(output_channel, num_classes),
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)
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self.stage_out_idx = [sum(depths[:idx + 1]) - 1 for idx in range(len(depths))]
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print('initialize_weights...')
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self._initialize_weights()
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def merge_bn(self):
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self.eval()
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for idx, module in self.named_modules():
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if isinstance(module, NCB) or isinstance(module, NTB):
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module.merge_bn()
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def _initialize_weights(self):
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for n, m in self.named_modules():
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if isinstance(m, (nn.BatchNorm2d, nn.GroupNorm, nn.LayerNorm, nn.BatchNorm1d)):
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nn.init.constant_(m.weight, 1.0)
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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trunc_normal_(m.weight, std=.02)
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if hasattr(m, 'bias') and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.Conv2d):
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trunc_normal_(m.weight, std=.02)
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if hasattr(m, 'bias') and m.bias is not None:
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nn.init.constant_(m.bias, 0)
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def forward(self, x):
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x = self.stem(x)
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for idx, layer in enumerate(self.features):
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if self.use_checkpoint:
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x = checkpoint.checkpoint(layer, x)
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else:
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x = layer(x)
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x = self.norm(x)
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x = self.avgpool(x)
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x = torch.flatten(x, 1)
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x = self.proj_head(x)
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return x
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@register_model
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def nextvit_small(pretrained=False, pretrained_cfg=None, **kwargs):
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model = NextViT(stem_chs=[64, 32, 64], depths=[3, 4, 10, 3], path_dropout=0.1, **kwargs)
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return model
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@register_model
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def nextvit_base(pretrained=False, pretrained_cfg=None, **kwargs):
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model = NextViT(stem_chs=[64, 32, 64], depths=[3, 4, 20, 3], path_dropout=0.2, **kwargs)
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return model
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@register_model
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def nextvit_large(pretrained=False, pretrained_cfg=None, **kwargs):
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model = NextViT(stem_chs=[64, 32, 64], depths=[3, 4, 30, 3], path_dropout=0.2, **kwargs)
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return model
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