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# 2022.06.08-Changed for implementation of TokenFusion
# Huawei Technologies Co., Ltd. <[email protected]>
# ---------------------------------------------------------------
# Copyright (c) 2021, NVIDIA Corporation. All rights reserved.
#
# This work is licensed under the NVIDIA Source Code License
# ---------------------------------------------------------------
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from .modules import ModuleParallel, LayerNormParallel, num_parallel
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = ModuleParallel(nn.Linear(in_features, hidden_features))
self.dwconv = DWConv(hidden_features)
self.act = ModuleParallel(act_layer())
self.fc2 = ModuleParallel(nn.Linear(hidden_features, out_features))
self.drop = ModuleParallel(nn.Dropout(drop))
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x, H, W):
x = self.fc1(x)
x = [self.dwconv(x[0], H, W)]
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class Attention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., sr_ratio=1):
super().__init__()
assert dim % num_heads == 0, f"dim {dim} should be divided by num_heads {num_heads}."
self.dim = dim
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.q = ModuleParallel(nn.Linear(dim, dim, bias=qkv_bias))
self.kv = ModuleParallel(nn.Linear(dim, dim * 2, bias=qkv_bias))
self.attn_drop = ModuleParallel(nn.Dropout(attn_drop))
self.proj = ModuleParallel(nn.Linear(dim, dim))
self.proj_drop = ModuleParallel(nn.Dropout(proj_drop))
self.sr_ratio = sr_ratio
if sr_ratio > 1:
self.sr = ModuleParallel(nn.Conv2d(dim, dim, kernel_size=sr_ratio, stride=sr_ratio))
self.norm = LayerNormParallel(dim)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x, H, W):
B, N, C = x[0].shape
q = self.q(x)
q = [q_.reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3) for q_ in q]
if self.sr_ratio > 1:
x = [x_.permute(0, 2, 1).reshape(B, C, H, W) for x_ in x]
x = self.sr(x)
x = [x_.reshape(B, C, -1).permute(0, 2, 1) for x_ in x]
x = self.norm(x)
kv = self.kv(x)
kv = [kv_.reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) for kv_ in kv]
else:
kv = self.kv(x)
kv = [kv_.reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) for kv_ in kv]
# k, v = [kv[0][0], kv[1][0]], [kv[0][1], kv[1][1]]
k, v = [kv[0][0]], [kv[0][1]]
attn = [(q_ @ k_.transpose(-2, -1)) * self.scale for (q_, k_) in zip(q, k)]
attn = [attn_.softmax(dim=-1) for attn_ in attn]
attn = self.attn_drop(attn)
x = [(attn_ @ v_).transpose(1, 2).reshape(B, N, C) for (attn_, v_) in zip(attn, v)]
x = self.proj(x)
x = self.proj_drop(x)
# x = [x_ * mask_.unsqueeze(2) for (x_, mask_) in zip(x, mask)]
return x
# class PredictorLG(nn.Module):
# """ Image to Patch Embedding from DydamicVit
# """
# def __init__(self, embed_dim=384):
# super().__init__()
# self.score_nets = nn.ModuleList([nn.Sequential(
# nn.LayerNorm(embed_dim),
# nn.Linear(embed_dim, embed_dim),
# nn.GELU(),
# nn.Linear(embed_dim, embed_dim // 2),
# nn.GELU(),
# nn.Linear(embed_dim // 2, embed_dim // 4),
# nn.GELU(),
# nn.Linear(embed_dim // 4, 2),
# nn.LogSoftmax(dim=-1)
# ) for _ in range(num_parallel)])
# def forward(self, x):
# x = [self.score_nets[i](x[i]) for i in range(num_parallel)]
# return x
class Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=LayerNormParallel, sr_ratio=1):
super().__init__()
self.norm1 = norm_layer(dim)
# self.score = PredictorLG(dim)
self.attn = Attention(
dim,
num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
attn_drop=attn_drop, proj_drop=drop, sr_ratio=sr_ratio)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = ModuleParallel(DropPath(drop_path)) if drop_path > 0. else ModuleParallel(nn.Identity())
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
# self.exchange = TokenExchange()
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x, H, W):
B = x[0].shape[0]
# norm1 = self.norm1(x)
# score = self.score(norm1)
# mask = [F.gumbel_softmax(score_.reshape(B, -1, 2), hard=True)[:, :, 0] for score_ in score]
# if mask is not None:
# norm = [norm_ * mask_.unsqueeze(2) for (norm_, mask_) in zip(norm, mask)]
f = self.drop_path(self.attn(self.norm1(x), H, W))
x = [x_ + f_ for (x_, f_) in zip (x, f)]
f = self.drop_path(self.mlp(self.norm2(x), H, W))
x = [x_ + f_ for (x_, f_) in zip (x, f)]
# if mask is not None:
# x = self.exchange(x, mask, mask_threshold=0.02)
return x
class OverlapPatchEmbed(nn.Module):
""" Image to Patch Embedding
"""
def __init__(self, img_size=224, patch_size=7, stride=4, in_chans=3, embed_dim=768):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
self.img_size = img_size
self.patch_size = patch_size
self.H, self.W = img_size[0] // patch_size[0], img_size[1] // patch_size[1]
self.num_patches = self.H * self.W
self.proj = ModuleParallel(nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=stride,
padding=(patch_size[0] // 2, patch_size[1] // 2)))
self.norm = LayerNormParallel(embed_dim)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
def forward(self, x):
x = self.proj(x)
_, _, H, W = x[0].shape
x = [x_.flatten(2).transpose(1, 2) for x_ in x]
x = self.norm(x)
return x, H, W
class MixVisionTransformer(nn.Module):
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dims=[64, 128, 256, 512],
num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qkv_bias=False, qk_scale=None, drop_rate=0.,
attn_drop_rate=0., drop_path_rate=0., norm_layer=LayerNormParallel,
depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1]):
super().__init__()
self.num_classes = num_classes
self.depths = depths
self.embed_dims = embed_dims
# patch_embed
self.patch_embed1 = OverlapPatchEmbed(img_size=img_size, patch_size=7, stride=4, in_chans=in_chans,
embed_dim=embed_dims[0])
self.patch_embed2 = OverlapPatchEmbed(img_size=img_size // 4, patch_size=3, stride=2, in_chans=embed_dims[0],
embed_dim=embed_dims[1])
self.patch_embed3 = OverlapPatchEmbed(img_size=img_size // 8, patch_size=3, stride=2, in_chans=embed_dims[1],
embed_dim=embed_dims[2])
self.patch_embed4 = OverlapPatchEmbed(img_size=img_size // 16, patch_size=3, stride=2, in_chans=embed_dims[2],
embed_dim=embed_dims[3])
# predictor_list = [PredictorLG(embed_dims[i]) for i in range(len(depths))]
# self.score_predictor = nn.ModuleList(predictor_list)
# transformer encoder
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
cur = 0
self.block1 = nn.ModuleList([Block(
dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=mlp_ratios[0], qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
sr_ratio=sr_ratios[0])
for i in range(depths[0])])
self.norm1 = norm_layer(embed_dims[0])
cur += depths[0]
self.block2 = nn.ModuleList([Block(
dim=embed_dims[1], num_heads=num_heads[1], mlp_ratio=mlp_ratios[1], qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
sr_ratio=sr_ratios[1])
for i in range(depths[1])])
self.norm2 = norm_layer(embed_dims[1])
cur += depths[1]
self.block3 = nn.ModuleList([Block(
dim=embed_dims[2], num_heads=num_heads[2], mlp_ratio=mlp_ratios[2], qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
sr_ratio=sr_ratios[2])
for i in range(depths[2])])
self.norm3 = norm_layer(embed_dims[2])
cur += depths[2]
self.block4 = nn.ModuleList([Block(
dim=embed_dims[3], num_heads=num_heads[3], mlp_ratio=mlp_ratios[3], qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[cur + i], norm_layer=norm_layer,
sr_ratio=sr_ratios[3])
for i in range(depths[3])])
self.norm4 = norm_layer(embed_dims[3])
# classification head
# self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity()
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
elif isinstance(m, nn.Conv2d):
fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
fan_out //= m.groups
m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
if m.bias is not None:
m.bias.data.zero_()
'''
def init_weights(self, pretrained=None):
if isinstance(pretrained, str):
logger = get_root_logger()
load_checkpoint(self, pretrained, map_location='cpu', strict=False, logger=logger)
'''
def reset_drop_path(self, drop_path_rate):
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(self.depths))]
cur = 0
for i in range(self.depths[0]):
self.block1[i].drop_path.drop_prob = dpr[cur + i]
cur += self.depths[0]
for i in range(self.depths[1]):
self.block2[i].drop_path.drop_prob = dpr[cur + i]
cur += self.depths[1]
for i in range(self.depths[2]):
self.block3[i].drop_path.drop_prob = dpr[cur + i]
cur += self.depths[2]
for i in range(self.depths[3]):
self.block4[i].drop_path.drop_prob = dpr[cur + i]
def freeze_patch_emb(self):
self.patch_embed1.requires_grad = False
@torch.jit.ignore
def no_weight_decay(self):
return {'pos_embed1', 'pos_embed2', 'pos_embed3', 'pos_embed4', 'cls_token'} # has pos_embed may be better
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=''):
self.num_classes = num_classes
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
B = x[0].shape[0]
outs0 = []
# masks = []
# stage 1
x, H, W = self.patch_embed1(x)
for i, blk in enumerate(self.block1):
# score = self.score_predictor[0](x)
# mask = [F.softmax(score_.reshape(B, -1, 2), dim=2)[:, :, 0] for score_ in score] # mask_: [B, N]
# masks.append(mask)
x = blk(x, H, W)
x = self.norm1(x)
x = [x_.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() for x_ in x]
outs0.append(x[0])
# stage 2
x, H, W = self.patch_embed2(x)
for i, blk in enumerate(self.block2):
# score = self.score_predictor[1](x)
# mask = [F.softmax(score_.reshape(B, -1, 2), dim=2)[:, :, 0] for score_ in score] # mask_: [B, N]
# masks.append(mask)
x = blk(x, H, W)
x = self.norm2(x)
x = [x_.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() for x_ in x]
outs0.append(x[0])
# stage 3
x, H, W = self.patch_embed3(x)
for i, blk in enumerate(self.block3):
# score = self.score_predictor[2](x)
# mask = [F.softmax(score_.reshape(B, -1, 2), dim=2)[:, :, 0] for score_ in score] # mask_: [B, N]
# masks.append(mask)
x = blk(x, H, W)
x = self.norm3(x)
x = [x_.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() for x_ in x]
outs0.append(x[0])
# stage 4
x, H, W = self.patch_embed4(x)
for i, blk in enumerate(self.block4):
# score = self.score_predictor[3](x)
# mask = [F.softmax(score_.reshape(B, -1, 2), dim=2)[:, :, 0] for score_ in score] # mask_: [B, N]
# masks.append(mask)
x = blk(x, H, W)
x = self.norm4(x)
x = [x_.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous() for x_ in x]
outs0.append(x[0])
return [outs0]
def forward(self, x):
x = self.forward_features(x)
return x
class DWConv(nn.Module):
def __init__(self, dim=768):
super(DWConv, self).__init__()
self.dwconv = nn.Conv2d(dim, dim, 3, 1, 1, bias=True, groups=dim)
def forward(self, x, H, W):
B, N, C = x.shape
x = x.transpose(1, 2).contiguous().view(B, C, H, W)
x = self.dwconv(x)
x = x.flatten(2).transpose(1, 2).contiguous()
return x
class mit_b0(MixVisionTransformer):
def __init__(self, **kwargs):
super(mit_b0, self).__init__(patch_size=4, embed_dims=[32, 64, 160, 256], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
qkv_bias=True, norm_layer=LayerNormParallel, depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
drop_rate=0.0, drop_path_rate=0.1)
class mit_b1(MixVisionTransformer):
def __init__(self, **kwargs):
super(mit_b1, self).__init__(patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
qkv_bias=True, norm_layer=LayerNormParallel, depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1],
drop_rate=0.0, drop_path_rate=0.1)
class mit_b2(MixVisionTransformer):
def __init__(self, **kwargs):
super(mit_b2, self).__init__(patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
qkv_bias=True, norm_layer=LayerNormParallel, depths=[3, 4, 6, 3], sr_ratios=[8, 4, 2, 1],
drop_rate=0.0, drop_path_rate=0.1)
class mit_b3(MixVisionTransformer):
def __init__(self, **kwargs):
super(mit_b3, self).__init__(patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
qkv_bias=True, norm_layer=LayerNormParallel, depths=[3, 4, 18, 3], sr_ratios=[8, 4, 2, 1],
drop_rate=0.0, drop_path_rate=0.1)
class mit_b4(MixVisionTransformer):
def __init__(self, **kwargs):
super(mit_b4, self).__init__(patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
qkv_bias=True, norm_layer=LayerNormParallel, depths=[3, 8, 27, 3], sr_ratios=[8, 4, 2, 1],
drop_rate=0.0, drop_path_rate=0.1)
class mit_b5(MixVisionTransformer):
def __init__(self, **kwargs):
super(mit_b5, self).__init__(patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4],
qkv_bias=True, norm_layer=LayerNormParallel, depths=[3, 6, 40, 3], sr_ratios=[8, 4, 2, 1],
drop_rate=0.0, drop_path_rate=0.1)
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