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import timm
import torch.nn as nn
from pathlib import Path
from .utils import activations, forward_default, get_activation
# thygate: just dropped the file in place here together with the single function import merge_pre_bn from Next_ViT repo which is no longer required :
#file = open( Path.joinpath(Path.cwd(), "/extensions/stable-diffusion-webui-depthmap-script/midas/externals/Next_ViT/classification/nextvit.py"), "r")
#source_code = file.read().replace(" utils", " externals.Next_ViT.classification.utils")
#exec(source_code)
#start of file : Next_ViT/classification/nextvit.py :
# Copyright (c) ByteDance Inc. All rights reserved.
from functools import partial
import torch
import torch.utils.checkpoint as checkpoint
from einops import rearrange
from timm.models.layers import DropPath, trunc_normal_
from timm.models.registry import register_model
from torch import nn
# function from Next_ViT/classification/utils.py : merge_pre_bn
# copied here to get rid of Next_ViT repo dependancy
def merge_pre_bn(module, pre_bn_1, pre_bn_2=None):
""" Merge pre BN to reduce inference runtime.
"""
weight = module.weight.data
if module.bias is None:
zeros = torch.zeros(module.out_channels, device=weight.device).type(weight.type())
module.bias = nn.Parameter(zeros)
bias = module.bias.data
if pre_bn_2 is None:
assert pre_bn_1.track_running_stats is True, "Unsupport bn_module.track_running_stats is False"
assert pre_bn_1.affine is True, "Unsupport bn_module.affine is False"
scale_invstd = pre_bn_1.running_var.add(pre_bn_1.eps).pow(-0.5)
extra_weight = scale_invstd * pre_bn_1.weight
extra_bias = pre_bn_1.bias - pre_bn_1.weight * pre_bn_1.running_mean * scale_invstd
else:
assert pre_bn_1.track_running_stats is True, "Unsupport bn_module.track_running_stats is False"
assert pre_bn_1.affine is True, "Unsupport bn_module.affine is False"
assert pre_bn_2.track_running_stats is True, "Unsupport bn_module.track_running_stats is False"
assert pre_bn_2.affine is True, "Unsupport bn_module.affine is False"
scale_invstd_1 = pre_bn_1.running_var.add(pre_bn_1.eps).pow(-0.5)
scale_invstd_2 = pre_bn_2.running_var.add(pre_bn_2.eps).pow(-0.5)
extra_weight = scale_invstd_1 * pre_bn_1.weight * scale_invstd_2 * pre_bn_2.weight
extra_bias = scale_invstd_2 * pre_bn_2.weight *(pre_bn_1.bias - pre_bn_1.weight * pre_bn_1.running_mean * scale_invstd_1 - pre_bn_2.running_mean) + pre_bn_2.bias
if isinstance(module, nn.Linear):
extra_bias = weight @ extra_bias
weight.mul_(extra_weight.view(1, weight.size(1)).expand_as(weight))
elif isinstance(module, nn.Conv2d):
assert weight.shape[2] == 1 and weight.shape[3] == 1
weight = weight.reshape(weight.shape[0], weight.shape[1])
extra_bias = weight @ extra_bias
weight.mul_(extra_weight.view(1, weight.size(1)).expand_as(weight))
weight = weight.reshape(weight.shape[0], weight.shape[1], 1, 1)
bias.add_(extra_bias)
module.weight.data = weight
module.bias.data = bias
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 merge_bn(self, pre_norm):
merge_pre_bn(self.conv1, pre_norm)
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 merge_bn(self):
if not self.is_bn_merged:
self.mlp.merge_bn(self.norm)
self.is_bn_merged = True
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_merged = False
def merge_bn(self, pre_bn):
merge_pre_bn(self.q, pre_bn)
if self.sr_ratio > 1:
merge_pre_bn(self.k, pre_bn, self.norm)
merge_pre_bn(self.v, pre_bn, self.norm)
else:
merge_pre_bn(self.k, pre_bn)
merge_pre_bn(self.v, pre_bn)
self.is_bn_merged = True
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_merged:
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 merge_bn(self):
if not self.is_bn_merged:
self.e_mhsa.merge_bn(self.norm1)
self.mlp.merge_bn(self.norm2)
self.is_bn_merged = True
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
class NextViT(nn.Module):
def __init__(self, stem_chs, depths, path_dropout, attn_drop=0, drop=0, num_classes=1000,
strides=[1, 2, 2, 2], sr_ratios=[8, 4, 2, 1], head_dim=32, mix_block_ratio=0.75,
use_checkpoint=False):
super(NextViT, self).__init__()
self.use_checkpoint = use_checkpoint
self.stage_out_channels = [[96] * (depths[0]),
[192] * (depths[1] - 1) + [256],
[384, 384, 384, 384, 512] * (depths[2] // 5),
[768] * (depths[3] - 1) + [1024]]
# Next Hybrid Strategy
self.stage_block_types = [[NCB] * depths[0],
[NCB] * (depths[1] - 1) + [NTB],
[NCB, NCB, NCB, NCB, NTB] * (depths[2] // 5),
[NCB] * (depths[3] - 1) + [NTB]]
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)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.proj_head = nn.Sequential(
nn.Linear(output_channel, num_classes),
)
self.stage_out_idx = [sum(depths[:idx + 1]) - 1 for idx in range(len(depths))]
print('initialize_weights...')
self._initialize_weights()
def merge_bn(self):
self.eval()
for idx, module in self.named_modules():
if isinstance(module, NCB) or isinstance(module, NTB):
module.merge_bn()
def _initialize_weights(self):
for n, m in self.named_modules():
if isinstance(m, (nn.BatchNorm2d, nn.GroupNorm, nn.LayerNorm, nn.BatchNorm1d)):
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):
x = self.stem(x)
for idx, layer in enumerate(self.features):
if self.use_checkpoint:
x = checkpoint.checkpoint(layer, x)
else:
x = layer(x)
x = self.norm(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.proj_head(x)
return x
@register_model
def nextvit_small(pretrained=False, pretrained_cfg=None, **kwargs):
model = NextViT(stem_chs=[64, 32, 64], depths=[3, 4, 10, 3], path_dropout=0.1, **kwargs)
return model
@register_model
def nextvit_base(pretrained=False, pretrained_cfg=None, **kwargs):
model = NextViT(stem_chs=[64, 32, 64], depths=[3, 4, 20, 3], path_dropout=0.2, **kwargs)
return model
@register_model
def nextvit_large(pretrained=False, pretrained_cfg=None, **kwargs):
model = NextViT(stem_chs=[64, 32, 64], depths=[3, 4, 30, 3], path_dropout=0.2, **kwargs)
return model
# end of Next_ViT/classification/nextvit.py
def forward_next_vit(pretrained, x):
return forward_default(pretrained, x, "forward")
def _make_next_vit_backbone(
model,
hooks=[2, 6, 36, 39],
):
pretrained = nn.Module()
pretrained.model = model
pretrained.model.features[hooks[0]].register_forward_hook(get_activation("1"))
pretrained.model.features[hooks[1]].register_forward_hook(get_activation("2"))
pretrained.model.features[hooks[2]].register_forward_hook(get_activation("3"))
pretrained.model.features[hooks[3]].register_forward_hook(get_activation("4"))
pretrained.activations = activations
return pretrained
def _make_pretrained_next_vit_large_6m(hooks=None):
model = timm.create_model("nextvit_large")
hooks = [2, 6, 36, 39] if hooks == None else hooks
return _make_next_vit_backbone(
model,
hooks=hooks,
)
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