Spaces:
Build error
Build error
import math | |
from copy import copy | |
from pathlib import Path | |
import numpy as np | |
import pandas as pd | |
import requests | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
from torchvision.ops import DeformConv2d | |
from PIL import Image | |
from torch.cuda import amp | |
from utils.datasets import letterbox | |
from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh | |
from utils.plots import color_list, plot_one_box | |
from utils.torch_utils import time_synchronized | |
##### basic #### | |
def autopad(k, p=None): # kernel, padding | |
# Pad to 'same' | |
if p is None: | |
p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad | |
return p | |
class MP(nn.Module): | |
def __init__(self, k=2): | |
super(MP, self).__init__() | |
self.m = nn.MaxPool2d(kernel_size=k, stride=k) | |
def forward(self, x): | |
return self.m(x) | |
class SP(nn.Module): | |
def __init__(self, k=3, s=1): | |
super(SP, self).__init__() | |
self.m = nn.MaxPool2d(kernel_size=k, stride=s, padding=k // 2) | |
def forward(self, x): | |
return self.m(x) | |
class ReOrg(nn.Module): | |
def __init__(self): | |
super(ReOrg, self).__init__() | |
def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) | |
return torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1) | |
class Concat(nn.Module): | |
def __init__(self, dimension=1): | |
super(Concat, self).__init__() | |
self.d = dimension | |
def forward(self, x): | |
return torch.cat(x, self.d) | |
class Chuncat(nn.Module): | |
def __init__(self, dimension=1): | |
super(Chuncat, self).__init__() | |
self.d = dimension | |
def forward(self, x): | |
x1 = [] | |
x2 = [] | |
for xi in x: | |
xi1, xi2 = xi.chunk(2, self.d) | |
x1.append(xi1) | |
x2.append(xi2) | |
return torch.cat(x1+x2, self.d) | |
class Shortcut(nn.Module): | |
def __init__(self, dimension=0): | |
super(Shortcut, self).__init__() | |
self.d = dimension | |
def forward(self, x): | |
return x[0]+x[1] | |
class Foldcut(nn.Module): | |
def __init__(self, dimension=0): | |
super(Foldcut, self).__init__() | |
self.d = dimension | |
def forward(self, x): | |
x1, x2 = x.chunk(2, self.d) | |
return x1+x2 | |
class Conv(nn.Module): | |
# Standard convolution | |
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups | |
super(Conv, self).__init__() | |
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) | |
self.bn = nn.BatchNorm2d(c2) | |
self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) | |
def forward(self, x): | |
return self.act(self.bn(self.conv(x))) | |
def fuseforward(self, x): | |
return self.act(self.conv(x)) | |
class RobustConv(nn.Module): | |
# Robust convolution (use high kernel size 7-11 for: downsampling and other layers). Train for 300 - 450 epochs. | |
def __init__(self, c1, c2, k=7, s=1, p=None, g=1, act=True, layer_scale_init_value=1e-6): # ch_in, ch_out, kernel, stride, padding, groups | |
super(RobustConv, self).__init__() | |
self.conv_dw = Conv(c1, c1, k=k, s=s, p=p, g=c1, act=act) | |
self.conv1x1 = nn.Conv2d(c1, c2, 1, 1, 0, groups=1, bias=True) | |
self.gamma = nn.Parameter(layer_scale_init_value * torch.ones(c2)) if layer_scale_init_value > 0 else None | |
def forward(self, x): | |
x = x.to(memory_format=torch.channels_last) | |
x = self.conv1x1(self.conv_dw(x)) | |
if self.gamma is not None: | |
x = x.mul(self.gamma.reshape(1, -1, 1, 1)) | |
return x | |
class RobustConv2(nn.Module): | |
# Robust convolution 2 (use [32, 5, 2] or [32, 7, 4] or [32, 11, 8] for one of the paths in CSP). | |
def __init__(self, c1, c2, k=7, s=4, p=None, g=1, act=True, layer_scale_init_value=1e-6): # ch_in, ch_out, kernel, stride, padding, groups | |
super(RobustConv2, self).__init__() | |
self.conv_strided = Conv(c1, c1, k=k, s=s, p=p, g=c1, act=act) | |
self.conv_deconv = nn.ConvTranspose2d(in_channels=c1, out_channels=c2, kernel_size=s, stride=s, | |
padding=0, bias=True, dilation=1, groups=1 | |
) | |
self.gamma = nn.Parameter(layer_scale_init_value * torch.ones(c2)) if layer_scale_init_value > 0 else None | |
def forward(self, x): | |
x = self.conv_deconv(self.conv_strided(x)) | |
if self.gamma is not None: | |
x = x.mul(self.gamma.reshape(1, -1, 1, 1)) | |
return x | |
def DWConv(c1, c2, k=1, s=1, act=True): | |
# Depthwise convolution | |
return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act) | |
class GhostConv(nn.Module): | |
# Ghost Convolution https://github.com/huawei-noah/ghostnet | |
def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups | |
super(GhostConv, self).__init__() | |
c_ = c2 // 2 # hidden channels | |
self.cv1 = Conv(c1, c_, k, s, None, g, act) | |
self.cv2 = Conv(c_, c_, 5, 1, None, c_, act) | |
def forward(self, x): | |
y = self.cv1(x) | |
return torch.cat([y, self.cv2(y)], 1) | |
class Stem(nn.Module): | |
# Stem | |
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups | |
super(Stem, self).__init__() | |
c_ = int(c2/2) # hidden channels | |
self.cv1 = Conv(c1, c_, 3, 2) | |
self.cv2 = Conv(c_, c_, 1, 1) | |
self.cv3 = Conv(c_, c_, 3, 2) | |
self.pool = torch.nn.MaxPool2d(2, stride=2) | |
self.cv4 = Conv(2 * c_, c2, 1, 1) | |
def forward(self, x): | |
x = self.cv1(x) | |
return self.cv4(torch.cat((self.cv3(self.cv2(x)), self.pool(x)), dim=1)) | |
class DownC(nn.Module): | |
# Spatial pyramid pooling layer used in YOLOv3-SPP | |
def __init__(self, c1, c2, n=1, k=2): | |
super(DownC, self).__init__() | |
c_ = int(c1) # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c_, c2//2, 3, k) | |
self.cv3 = Conv(c1, c2//2, 1, 1) | |
self.mp = nn.MaxPool2d(kernel_size=k, stride=k) | |
def forward(self, x): | |
return torch.cat((self.cv2(self.cv1(x)), self.cv3(self.mp(x))), dim=1) | |
class SPP(nn.Module): | |
# Spatial pyramid pooling layer used in YOLOv3-SPP | |
def __init__(self, c1, c2, k=(5, 9, 13)): | |
super(SPP, self).__init__() | |
c_ = c1 // 2 # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) | |
self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) | |
def forward(self, x): | |
x = self.cv1(x) | |
return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) | |
class Bottleneck(nn.Module): | |
# Darknet bottleneck | |
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion | |
super(Bottleneck, self).__init__() | |
c_ = int(c2 * e) # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c_, c2, 3, 1, g=g) | |
self.add = shortcut and c1 == c2 | |
def forward(self, x): | |
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) | |
class Res(nn.Module): | |
# ResNet bottleneck | |
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion | |
super(Res, self).__init__() | |
c_ = int(c2 * e) # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c_, c_, 3, 1, g=g) | |
self.cv3 = Conv(c_, c2, 1, 1) | |
self.add = shortcut and c1 == c2 | |
def forward(self, x): | |
return x + self.cv3(self.cv2(self.cv1(x))) if self.add else self.cv3(self.cv2(self.cv1(x))) | |
class ResX(Res): | |
# ResNet bottleneck | |
def __init__(self, c1, c2, shortcut=True, g=32, e=0.5): # ch_in, ch_out, shortcut, groups, expansion | |
super().__init__(c1, c2, shortcut, g, e) | |
c_ = int(c2 * e) # hidden channels | |
class Ghost(nn.Module): | |
# Ghost Bottleneck https://github.com/huawei-noah/ghostnet | |
def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride | |
super(Ghost, self).__init__() | |
c_ = c2 // 2 | |
self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw | |
DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw | |
GhostConv(c_, c2, 1, 1, act=False)) # pw-linear | |
self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), | |
Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity() | |
def forward(self, x): | |
return self.conv(x) + self.shortcut(x) | |
##### end of basic ##### | |
##### cspnet ##### | |
class SPPCSPC(nn.Module): | |
# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)): | |
super(SPPCSPC, self).__init__() | |
c_ = int(2 * c2 * e) # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c1, c_, 1, 1) | |
self.cv3 = Conv(c_, c_, 3, 1) | |
self.cv4 = Conv(c_, c_, 1, 1) | |
self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) | |
self.cv5 = Conv(4 * c_, c_, 1, 1) | |
self.cv6 = Conv(c_, c_, 3, 1) | |
self.cv7 = Conv(2 * c_, c2, 1, 1) | |
def forward(self, x): | |
x1 = self.cv4(self.cv3(self.cv1(x))) | |
y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1))) | |
y2 = self.cv2(x) | |
return self.cv7(torch.cat((y1, y2), dim=1)) | |
class GhostSPPCSPC(SPPCSPC): | |
# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)): | |
super().__init__(c1, c2, n, shortcut, g, e, k) | |
c_ = int(2 * c2 * e) # hidden channels | |
self.cv1 = GhostConv(c1, c_, 1, 1) | |
self.cv2 = GhostConv(c1, c_, 1, 1) | |
self.cv3 = GhostConv(c_, c_, 3, 1) | |
self.cv4 = GhostConv(c_, c_, 1, 1) | |
self.cv5 = GhostConv(4 * c_, c_, 1, 1) | |
self.cv6 = GhostConv(c_, c_, 3, 1) | |
self.cv7 = GhostConv(2 * c_, c2, 1, 1) | |
class GhostStem(Stem): | |
# Stem | |
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups | |
super().__init__(c1, c2, k, s, p, g, act) | |
c_ = int(c2/2) # hidden channels | |
self.cv1 = GhostConv(c1, c_, 3, 2) | |
self.cv2 = GhostConv(c_, c_, 1, 1) | |
self.cv3 = GhostConv(c_, c_, 3, 2) | |
self.cv4 = GhostConv(2 * c_, c2, 1, 1) | |
class BottleneckCSPA(nn.Module): | |
# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super(BottleneckCSPA, self).__init__() | |
c_ = int(c2 * e) # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c1, c_, 1, 1) | |
self.cv3 = Conv(2 * c_, c2, 1, 1) | |
self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) | |
def forward(self, x): | |
y1 = self.m(self.cv1(x)) | |
y2 = self.cv2(x) | |
return self.cv3(torch.cat((y1, y2), dim=1)) | |
class BottleneckCSPB(nn.Module): | |
# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super(BottleneckCSPB, self).__init__() | |
c_ = int(c2) # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c_, c_, 1, 1) | |
self.cv3 = Conv(2 * c_, c2, 1, 1) | |
self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) | |
def forward(self, x): | |
x1 = self.cv1(x) | |
y1 = self.m(x1) | |
y2 = self.cv2(x1) | |
return self.cv3(torch.cat((y1, y2), dim=1)) | |
class BottleneckCSPC(nn.Module): | |
# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super(BottleneckCSPC, self).__init__() | |
c_ = int(c2 * e) # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c1, c_, 1, 1) | |
self.cv3 = Conv(c_, c_, 1, 1) | |
self.cv4 = Conv(2 * c_, c2, 1, 1) | |
self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) | |
def forward(self, x): | |
y1 = self.cv3(self.m(self.cv1(x))) | |
y2 = self.cv2(x) | |
return self.cv4(torch.cat((y1, y2), dim=1)) | |
class ResCSPA(BottleneckCSPA): | |
# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__(c1, c2, n, shortcut, g, e) | |
c_ = int(c2 * e) # hidden channels | |
self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) | |
class ResCSPB(BottleneckCSPB): | |
# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__(c1, c2, n, shortcut, g, e) | |
c_ = int(c2) # hidden channels | |
self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) | |
class ResCSPC(BottleneckCSPC): | |
# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__(c1, c2, n, shortcut, g, e) | |
c_ = int(c2 * e) # hidden channels | |
self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) | |
class ResXCSPA(ResCSPA): | |
# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__(c1, c2, n, shortcut, g, e) | |
c_ = int(c2 * e) # hidden channels | |
self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) | |
class ResXCSPB(ResCSPB): | |
# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__(c1, c2, n, shortcut, g, e) | |
c_ = int(c2) # hidden channels | |
self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) | |
class ResXCSPC(ResCSPC): | |
# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__(c1, c2, n, shortcut, g, e) | |
c_ = int(c2 * e) # hidden channels | |
self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) | |
class GhostCSPA(BottleneckCSPA): | |
# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__(c1, c2, n, shortcut, g, e) | |
c_ = int(c2 * e) # hidden channels | |
self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)]) | |
class GhostCSPB(BottleneckCSPB): | |
# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__(c1, c2, n, shortcut, g, e) | |
c_ = int(c2) # hidden channels | |
self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)]) | |
class GhostCSPC(BottleneckCSPC): | |
# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__(c1, c2, n, shortcut, g, e) | |
c_ = int(c2 * e) # hidden channels | |
self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)]) | |
##### end of cspnet ##### | |
##### yolor ##### | |
class ImplicitA(nn.Module): | |
def __init__(self, channel, mean=0., std=.02): | |
super(ImplicitA, self).__init__() | |
self.channel = channel | |
self.mean = mean | |
self.std = std | |
self.implicit = nn.Parameter(torch.zeros(1, channel, 1, 1)) | |
nn.init.normal_(self.implicit, mean=self.mean, std=self.std) | |
def forward(self, x): | |
return self.implicit + x | |
class ImplicitM(nn.Module): | |
def __init__(self, channel, mean=1., std=.02): | |
super(ImplicitM, self).__init__() | |
self.channel = channel | |
self.mean = mean | |
self.std = std | |
self.implicit = nn.Parameter(torch.ones(1, channel, 1, 1)) | |
nn.init.normal_(self.implicit, mean=self.mean, std=self.std) | |
def forward(self, x): | |
return self.implicit * x | |
##### end of yolor ##### | |
##### repvgg ##### | |
class RepConv(nn.Module): | |
# Represented convolution | |
# https://arxiv.org/abs/2101.03697 | |
def __init__(self, c1, c2, k=3, s=1, p=None, g=1, act=True, deploy=False): | |
super(RepConv, self).__init__() | |
self.deploy = deploy | |
self.groups = g | |
self.in_channels = c1 | |
self.out_channels = c2 | |
assert k == 3 | |
assert autopad(k, p) == 1 | |
padding_11 = autopad(k, p) - k // 2 | |
self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) | |
if deploy: | |
self.rbr_reparam = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=True) | |
else: | |
self.rbr_identity = (nn.BatchNorm2d(num_features=c1) if c2 == c1 and s == 1 else None) | |
self.rbr_dense = nn.Sequential( | |
nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False), | |
nn.BatchNorm2d(num_features=c2), | |
) | |
self.rbr_1x1 = nn.Sequential( | |
nn.Conv2d( c1, c2, 1, s, padding_11, groups=g, bias=False), | |
nn.BatchNorm2d(num_features=c2), | |
) | |
def forward(self, inputs): | |
if hasattr(self, "rbr_reparam"): | |
return self.act(self.rbr_reparam(inputs)) | |
if self.rbr_identity is None: | |
id_out = 0 | |
else: | |
id_out = self.rbr_identity(inputs) | |
return self.act(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out) | |
def get_equivalent_kernel_bias(self): | |
kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense) | |
kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1) | |
kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity) | |
return ( | |
kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, | |
bias3x3 + bias1x1 + biasid, | |
) | |
def _pad_1x1_to_3x3_tensor(self, kernel1x1): | |
if kernel1x1 is None: | |
return 0 | |
else: | |
return nn.functional.pad(kernel1x1, [1, 1, 1, 1]) | |
def _fuse_bn_tensor(self, branch): | |
if branch is None: | |
return 0, 0 | |
if isinstance(branch, nn.Sequential): | |
kernel = branch[0].weight | |
running_mean = branch[1].running_mean | |
running_var = branch[1].running_var | |
gamma = branch[1].weight | |
beta = branch[1].bias | |
eps = branch[1].eps | |
else: | |
assert isinstance(branch, nn.BatchNorm2d) | |
if not hasattr(self, "id_tensor"): | |
input_dim = self.in_channels // self.groups | |
kernel_value = np.zeros( | |
(self.in_channels, input_dim, 3, 3), dtype=np.float32 | |
) | |
for i in range(self.in_channels): | |
kernel_value[i, i % input_dim, 1, 1] = 1 | |
self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device) | |
kernel = self.id_tensor | |
running_mean = branch.running_mean | |
running_var = branch.running_var | |
gamma = branch.weight | |
beta = branch.bias | |
eps = branch.eps | |
std = (running_var + eps).sqrt() | |
t = (gamma / std).reshape(-1, 1, 1, 1) | |
return kernel * t, beta - running_mean * gamma / std | |
def repvgg_convert(self): | |
kernel, bias = self.get_equivalent_kernel_bias() | |
return ( | |
kernel.detach().cpu().numpy(), | |
bias.detach().cpu().numpy(), | |
) | |
def fuse_conv_bn(self, conv, bn): | |
std = (bn.running_var + bn.eps).sqrt() | |
bias = bn.bias - bn.running_mean * bn.weight / std | |
t = (bn.weight / std).reshape(-1, 1, 1, 1) | |
weights = conv.weight * t | |
bn = nn.Identity() | |
conv = nn.Conv2d(in_channels = conv.in_channels, | |
out_channels = conv.out_channels, | |
kernel_size = conv.kernel_size, | |
stride=conv.stride, | |
padding = conv.padding, | |
dilation = conv.dilation, | |
groups = conv.groups, | |
bias = True, | |
padding_mode = conv.padding_mode) | |
conv.weight = torch.nn.Parameter(weights) | |
conv.bias = torch.nn.Parameter(bias) | |
return conv | |
def fuse_repvgg_block(self): | |
if self.deploy: | |
return | |
print(f"RepConv.fuse_repvgg_block") | |
self.rbr_dense = self.fuse_conv_bn(self.rbr_dense[0], self.rbr_dense[1]) | |
self.rbr_1x1 = self.fuse_conv_bn(self.rbr_1x1[0], self.rbr_1x1[1]) | |
rbr_1x1_bias = self.rbr_1x1.bias | |
weight_1x1_expanded = torch.nn.functional.pad(self.rbr_1x1.weight, [1, 1, 1, 1]) | |
# Fuse self.rbr_identity | |
if (isinstance(self.rbr_identity, nn.BatchNorm2d) or isinstance(self.rbr_identity, nn.modules.batchnorm.SyncBatchNorm)): | |
# print(f"fuse: rbr_identity == BatchNorm2d or SyncBatchNorm") | |
identity_conv_1x1 = nn.Conv2d( | |
in_channels=self.in_channels, | |
out_channels=self.out_channels, | |
kernel_size=1, | |
stride=1, | |
padding=0, | |
groups=self.groups, | |
bias=False) | |
identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.to(self.rbr_1x1.weight.data.device) | |
identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.squeeze().squeeze() | |
# print(f" identity_conv_1x1.weight = {identity_conv_1x1.weight.shape}") | |
identity_conv_1x1.weight.data.fill_(0.0) | |
identity_conv_1x1.weight.data.fill_diagonal_(1.0) | |
identity_conv_1x1.weight.data = identity_conv_1x1.weight.data.unsqueeze(2).unsqueeze(3) | |
# print(f" identity_conv_1x1.weight = {identity_conv_1x1.weight.shape}") | |
identity_conv_1x1 = self.fuse_conv_bn(identity_conv_1x1, self.rbr_identity) | |
bias_identity_expanded = identity_conv_1x1.bias | |
weight_identity_expanded = torch.nn.functional.pad(identity_conv_1x1.weight, [1, 1, 1, 1]) | |
else: | |
# print(f"fuse: rbr_identity != BatchNorm2d, rbr_identity = {self.rbr_identity}") | |
bias_identity_expanded = torch.nn.Parameter( torch.zeros_like(rbr_1x1_bias) ) | |
weight_identity_expanded = torch.nn.Parameter( torch.zeros_like(weight_1x1_expanded) ) | |
#print(f"self.rbr_1x1.weight = {self.rbr_1x1.weight.shape}, ") | |
#print(f"weight_1x1_expanded = {weight_1x1_expanded.shape}, ") | |
#print(f"self.rbr_dense.weight = {self.rbr_dense.weight.shape}, ") | |
self.rbr_dense.weight = torch.nn.Parameter(self.rbr_dense.weight + weight_1x1_expanded + weight_identity_expanded) | |
self.rbr_dense.bias = torch.nn.Parameter(self.rbr_dense.bias + rbr_1x1_bias + bias_identity_expanded) | |
self.rbr_reparam = self.rbr_dense | |
self.deploy = True | |
if self.rbr_identity is not None: | |
del self.rbr_identity | |
self.rbr_identity = None | |
if self.rbr_1x1 is not None: | |
del self.rbr_1x1 | |
self.rbr_1x1 = None | |
if self.rbr_dense is not None: | |
del self.rbr_dense | |
self.rbr_dense = None | |
class RepBottleneck(Bottleneck): | |
# Standard bottleneck | |
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion | |
super().__init__(c1, c2, shortcut=True, g=1, e=0.5) | |
c_ = int(c2 * e) # hidden channels | |
self.cv2 = RepConv(c_, c2, 3, 1, g=g) | |
class RepBottleneckCSPA(BottleneckCSPA): | |
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__(c1, c2, n, shortcut, g, e) | |
c_ = int(c2 * e) # hidden channels | |
self.m = nn.Sequential(*[RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) | |
class RepBottleneckCSPB(BottleneckCSPB): | |
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__(c1, c2, n, shortcut, g, e) | |
c_ = int(c2) # hidden channels | |
self.m = nn.Sequential(*[RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) | |
class RepBottleneckCSPC(BottleneckCSPC): | |
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__(c1, c2, n, shortcut, g, e) | |
c_ = int(c2 * e) # hidden channels | |
self.m = nn.Sequential(*[RepBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) | |
class RepRes(Res): | |
# Standard bottleneck | |
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion | |
super().__init__(c1, c2, shortcut, g, e) | |
c_ = int(c2 * e) # hidden channels | |
self.cv2 = RepConv(c_, c_, 3, 1, g=g) | |
class RepResCSPA(ResCSPA): | |
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__(c1, c2, n, shortcut, g, e) | |
c_ = int(c2 * e) # hidden channels | |
self.m = nn.Sequential(*[RepRes(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) | |
class RepResCSPB(ResCSPB): | |
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__(c1, c2, n, shortcut, g, e) | |
c_ = int(c2) # hidden channels | |
self.m = nn.Sequential(*[RepRes(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) | |
class RepResCSPC(ResCSPC): | |
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__(c1, c2, n, shortcut, g, e) | |
c_ = int(c2 * e) # hidden channels | |
self.m = nn.Sequential(*[RepRes(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) | |
class RepResX(ResX): | |
# Standard bottleneck | |
def __init__(self, c1, c2, shortcut=True, g=32, e=0.5): # ch_in, ch_out, shortcut, groups, expansion | |
super().__init__(c1, c2, shortcut, g, e) | |
c_ = int(c2 * e) # hidden channels | |
self.cv2 = RepConv(c_, c_, 3, 1, g=g) | |
class RepResXCSPA(ResXCSPA): | |
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__(c1, c2, n, shortcut, g, e) | |
c_ = int(c2 * e) # hidden channels | |
self.m = nn.Sequential(*[RepResX(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) | |
class RepResXCSPB(ResXCSPB): | |
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=False, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__(c1, c2, n, shortcut, g, e) | |
c_ = int(c2) # hidden channels | |
self.m = nn.Sequential(*[RepResX(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) | |
class RepResXCSPC(ResXCSPC): | |
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__(c1, c2, n, shortcut, g, e) | |
c_ = int(c2 * e) # hidden channels | |
self.m = nn.Sequential(*[RepResX(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) | |
##### end of repvgg ##### | |
##### transformer ##### | |
class TransformerLayer(nn.Module): | |
# Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance) | |
def __init__(self, c, num_heads): | |
super().__init__() | |
self.q = nn.Linear(c, c, bias=False) | |
self.k = nn.Linear(c, c, bias=False) | |
self.v = nn.Linear(c, c, bias=False) | |
self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) | |
self.fc1 = nn.Linear(c, c, bias=False) | |
self.fc2 = nn.Linear(c, c, bias=False) | |
def forward(self, x): | |
x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x | |
x = self.fc2(self.fc1(x)) + x | |
return x | |
class TransformerBlock(nn.Module): | |
# Vision Transformer https://arxiv.org/abs/2010.11929 | |
def __init__(self, c1, c2, num_heads, num_layers): | |
super().__init__() | |
self.conv = None | |
if c1 != c2: | |
self.conv = Conv(c1, c2) | |
self.linear = nn.Linear(c2, c2) # learnable position embedding | |
self.tr = nn.Sequential(*[TransformerLayer(c2, num_heads) for _ in range(num_layers)]) | |
self.c2 = c2 | |
def forward(self, x): | |
if self.conv is not None: | |
x = self.conv(x) | |
b, _, w, h = x.shape | |
p = x.flatten(2) | |
p = p.unsqueeze(0) | |
p = p.transpose(0, 3) | |
p = p.squeeze(3) | |
e = self.linear(p) | |
x = p + e | |
x = self.tr(x) | |
x = x.unsqueeze(3) | |
x = x.transpose(0, 3) | |
x = x.reshape(b, self.c2, w, h) | |
return x | |
##### end of transformer ##### | |
##### yolov5 ##### | |
class Focus(nn.Module): | |
# Focus wh information into c-space | |
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups | |
super(Focus, self).__init__() | |
self.conv = Conv(c1 * 4, c2, k, s, p, g, act) | |
# self.contract = Contract(gain=2) | |
def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) | |
return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)) | |
# return self.conv(self.contract(x)) | |
class SPPF(nn.Module): | |
# Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher | |
def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13)) | |
super().__init__() | |
c_ = c1 // 2 # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c_ * 4, c2, 1, 1) | |
self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) | |
def forward(self, x): | |
x = self.cv1(x) | |
y1 = self.m(x) | |
y2 = self.m(y1) | |
return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1)) | |
class Contract(nn.Module): | |
# Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40) | |
def __init__(self, gain=2): | |
super().__init__() | |
self.gain = gain | |
def forward(self, x): | |
N, C, H, W = x.size() # assert (H / s == 0) and (W / s == 0), 'Indivisible gain' | |
s = self.gain | |
x = x.view(N, C, H // s, s, W // s, s) # x(1,64,40,2,40,2) | |
x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40) | |
return x.view(N, C * s * s, H // s, W // s) # x(1,256,40,40) | |
class Expand(nn.Module): | |
# Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160) | |
def __init__(self, gain=2): | |
super().__init__() | |
self.gain = gain | |
def forward(self, x): | |
N, C, H, W = x.size() # assert C / s ** 2 == 0, 'Indivisible gain' | |
s = self.gain | |
x = x.view(N, s, s, C // s ** 2, H, W) # x(1,2,2,16,80,80) | |
x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2) | |
return x.view(N, C // s ** 2, H * s, W * s) # x(1,16,160,160) | |
class NMS(nn.Module): | |
# Non-Maximum Suppression (NMS) module | |
conf = 0.25 # confidence threshold | |
iou = 0.45 # IoU threshold | |
classes = None # (optional list) filter by class | |
def __init__(self): | |
super(NMS, self).__init__() | |
def forward(self, x): | |
return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) | |
class autoShape(nn.Module): | |
# input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS | |
conf = 0.25 # NMS confidence threshold | |
iou = 0.45 # NMS IoU threshold | |
classes = None # (optional list) filter by class | |
def __init__(self, model): | |
super(autoShape, self).__init__() | |
self.model = model.eval() | |
def autoshape(self): | |
print('autoShape already enabled, skipping... ') # model already converted to model.autoshape() | |
return self | |
def forward(self, imgs, size=640, augment=False, profile=False): | |
# Inference from various sources. For height=640, width=1280, RGB images example inputs are: | |
# filename: imgs = 'data/samples/zidane.jpg' | |
# URI: = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg' | |
# OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) | |
# PIL: = Image.open('image.jpg') # HWC x(640,1280,3) | |
# numpy: = np.zeros((640,1280,3)) # HWC | |
# torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) | |
# multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images | |
t = [time_synchronized()] | |
p = next(self.model.parameters()) # for device and type | |
if isinstance(imgs, torch.Tensor): # torch | |
with amp.autocast(enabled=p.device.type != 'cpu'): | |
return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference | |
# Pre-process | |
n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images | |
shape0, shape1, files = [], [], [] # image and inference shapes, filenames | |
for i, im in enumerate(imgs): | |
f = f'image{i}' # filename | |
if isinstance(im, str): # filename or uri | |
im, f = np.asarray(Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im)), im | |
elif isinstance(im, Image.Image): # PIL Image | |
im, f = np.asarray(im), getattr(im, 'filename', f) or f | |
files.append(Path(f).with_suffix('.jpg').name) | |
if im.shape[0] < 5: # image in CHW | |
im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) | |
im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3) # enforce 3ch input | |
s = im.shape[:2] # HWC | |
shape0.append(s) # image shape | |
g = (size / max(s)) # gain | |
shape1.append([y * g for y in s]) | |
imgs[i] = im # update | |
shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape | |
x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad | |
x = np.stack(x, 0) if n > 1 else x[0][None] # stack | |
x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW | |
x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32 | |
t.append(time_synchronized()) | |
with amp.autocast(enabled=p.device.type != 'cpu'): | |
# Inference | |
y = self.model(x, augment, profile)[0] # forward | |
t.append(time_synchronized()) | |
# Post-process | |
y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS | |
for i in range(n): | |
scale_coords(shape1, y[i][:, :4], shape0[i]) | |
t.append(time_synchronized()) | |
return Detections(imgs, y, files, t, self.names, x.shape) | |
class Detections: | |
# detections class for YOLOv5 inference results | |
def __init__(self, imgs, pred, files, times=None, names=None, shape=None): | |
super(Detections, self).__init__() | |
d = pred[0].device # device | |
gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations | |
self.imgs = imgs # list of images as numpy arrays | |
self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) | |
self.names = names # class names | |
self.files = files # image filenames | |
self.xyxy = pred # xyxy pixels | |
self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels | |
self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized | |
self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized | |
self.n = len(self.pred) # number of images (batch size) | |
self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms) | |
self.s = shape # inference BCHW shape | |
def display(self, pprint=False, show=False, save=False, render=False, save_dir=''): | |
colors = color_list() | |
for i, (img, pred) in enumerate(zip(self.imgs, self.pred)): | |
str = f'image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} ' | |
if pred is not None: | |
for c in pred[:, -1].unique(): | |
n = (pred[:, -1] == c).sum() # detections per class | |
str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string | |
if show or save or render: | |
for *box, conf, cls in pred: # xyxy, confidence, class | |
label = f'{self.names[int(cls)]} {conf:.2f}' | |
plot_one_box(box, img, label=label, color=colors[int(cls) % 10]) | |
img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img # from np | |
if pprint: | |
print(str.rstrip(', ')) | |
if show: | |
img.show(self.files[i]) # show | |
if save: | |
f = self.files[i] | |
img.save(Path(save_dir) / f) # save | |
print(f"{'Saved' * (i == 0)} {f}", end=',' if i < self.n - 1 else f' to {save_dir}\n') | |
if render: | |
self.imgs[i] = np.asarray(img) | |
def print(self): | |
self.display(pprint=True) # print results | |
print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t) | |
def show(self): | |
self.display(show=True) # show results | |
def save(self, save_dir='runs/hub/exp'): | |
save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp') # increment save_dir | |
Path(save_dir).mkdir(parents=True, exist_ok=True) | |
self.display(save=True, save_dir=save_dir) # save results | |
def render(self): | |
self.display(render=True) # render results | |
return self.imgs | |
def pandas(self): | |
# return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0]) | |
new = copy(self) # return copy | |
ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns | |
cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns | |
for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]): | |
a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update | |
setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) | |
return new | |
def tolist(self): | |
# return a list of Detections objects, i.e. 'for result in results.tolist():' | |
x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) for i in range(self.n)] | |
for d in x: | |
for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: | |
setattr(d, k, getattr(d, k)[0]) # pop out of list | |
return x | |
def __len__(self): | |
return self.n | |
class Classify(nn.Module): | |
# Classification head, i.e. x(b,c1,20,20) to x(b,c2) | |
def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups | |
super(Classify, self).__init__() | |
self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1) | |
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1) | |
self.flat = nn.Flatten() | |
def forward(self, x): | |
z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list | |
return self.flat(self.conv(z)) # flatten to x(b,c2) | |
##### end of yolov5 ###### | |
##### orepa ##### | |
def transI_fusebn(kernel, bn): | |
gamma = bn.weight | |
std = (bn.running_var + bn.eps).sqrt() | |
return kernel * ((gamma / std).reshape(-1, 1, 1, 1)), bn.bias - bn.running_mean * gamma / std | |
class ConvBN(nn.Module): | |
def __init__(self, in_channels, out_channels, kernel_size, | |
stride=1, padding=0, dilation=1, groups=1, deploy=False, nonlinear=None): | |
super().__init__() | |
if nonlinear is None: | |
self.nonlinear = nn.Identity() | |
else: | |
self.nonlinear = nonlinear | |
if deploy: | |
self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, | |
stride=stride, padding=padding, dilation=dilation, groups=groups, bias=True) | |
else: | |
self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, | |
stride=stride, padding=padding, dilation=dilation, groups=groups, bias=False) | |
self.bn = nn.BatchNorm2d(num_features=out_channels) | |
def forward(self, x): | |
if hasattr(self, 'bn'): | |
return self.nonlinear(self.bn(self.conv(x))) | |
else: | |
return self.nonlinear(self.conv(x)) | |
def switch_to_deploy(self): | |
kernel, bias = transI_fusebn(self.conv.weight, self.bn) | |
conv = nn.Conv2d(in_channels=self.conv.in_channels, out_channels=self.conv.out_channels, kernel_size=self.conv.kernel_size, | |
stride=self.conv.stride, padding=self.conv.padding, dilation=self.conv.dilation, groups=self.conv.groups, bias=True) | |
conv.weight.data = kernel | |
conv.bias.data = bias | |
for para in self.parameters(): | |
para.detach_() | |
self.__delattr__('conv') | |
self.__delattr__('bn') | |
self.conv = conv | |
class OREPA_3x3_RepConv(nn.Module): | |
def __init__(self, in_channels, out_channels, kernel_size, | |
stride=1, padding=0, dilation=1, groups=1, | |
internal_channels_1x1_3x3=None, | |
deploy=False, nonlinear=None, single_init=False): | |
super(OREPA_3x3_RepConv, self).__init__() | |
self.deploy = deploy | |
if nonlinear is None: | |
self.nonlinear = nn.Identity() | |
else: | |
self.nonlinear = nonlinear | |
self.kernel_size = kernel_size | |
self.in_channels = in_channels | |
self.out_channels = out_channels | |
self.groups = groups | |
assert padding == kernel_size // 2 | |
self.stride = stride | |
self.padding = padding | |
self.dilation = dilation | |
self.branch_counter = 0 | |
self.weight_rbr_origin = nn.Parameter(torch.Tensor(out_channels, int(in_channels/self.groups), kernel_size, kernel_size)) | |
nn.init.kaiming_uniform_(self.weight_rbr_origin, a=math.sqrt(1.0)) | |
self.branch_counter += 1 | |
if groups < out_channels: | |
self.weight_rbr_avg_conv = nn.Parameter(torch.Tensor(out_channels, int(in_channels/self.groups), 1, 1)) | |
self.weight_rbr_pfir_conv = nn.Parameter(torch.Tensor(out_channels, int(in_channels/self.groups), 1, 1)) | |
nn.init.kaiming_uniform_(self.weight_rbr_avg_conv, a=1.0) | |
nn.init.kaiming_uniform_(self.weight_rbr_pfir_conv, a=1.0) | |
self.weight_rbr_avg_conv.data | |
self.weight_rbr_pfir_conv.data | |
self.register_buffer('weight_rbr_avg_avg', torch.ones(kernel_size, kernel_size).mul(1.0/kernel_size/kernel_size)) | |
self.branch_counter += 1 | |
else: | |
raise NotImplementedError | |
self.branch_counter += 1 | |
if internal_channels_1x1_3x3 is None: | |
internal_channels_1x1_3x3 = in_channels if groups < out_channels else 2 * in_channels # For mobilenet, it is better to have 2X internal channels | |
if internal_channels_1x1_3x3 == in_channels: | |
self.weight_rbr_1x1_kxk_idconv1 = nn.Parameter(torch.zeros(in_channels, int(in_channels/self.groups), 1, 1)) | |
id_value = np.zeros((in_channels, int(in_channels/self.groups), 1, 1)) | |
for i in range(in_channels): | |
id_value[i, i % int(in_channels/self.groups), 0, 0] = 1 | |
id_tensor = torch.from_numpy(id_value).type_as(self.weight_rbr_1x1_kxk_idconv1) | |
self.register_buffer('id_tensor', id_tensor) | |
else: | |
self.weight_rbr_1x1_kxk_conv1 = nn.Parameter(torch.Tensor(internal_channels_1x1_3x3, int(in_channels/self.groups), 1, 1)) | |
nn.init.kaiming_uniform_(self.weight_rbr_1x1_kxk_conv1, a=math.sqrt(1.0)) | |
self.weight_rbr_1x1_kxk_conv2 = nn.Parameter(torch.Tensor(out_channels, int(internal_channels_1x1_3x3/self.groups), kernel_size, kernel_size)) | |
nn.init.kaiming_uniform_(self.weight_rbr_1x1_kxk_conv2, a=math.sqrt(1.0)) | |
self.branch_counter += 1 | |
expand_ratio = 8 | |
self.weight_rbr_gconv_dw = nn.Parameter(torch.Tensor(in_channels*expand_ratio, 1, kernel_size, kernel_size)) | |
self.weight_rbr_gconv_pw = nn.Parameter(torch.Tensor(out_channels, in_channels*expand_ratio, 1, 1)) | |
nn.init.kaiming_uniform_(self.weight_rbr_gconv_dw, a=math.sqrt(1.0)) | |
nn.init.kaiming_uniform_(self.weight_rbr_gconv_pw, a=math.sqrt(1.0)) | |
self.branch_counter += 1 | |
if out_channels == in_channels and stride == 1: | |
self.branch_counter += 1 | |
self.vector = nn.Parameter(torch.Tensor(self.branch_counter, self.out_channels)) | |
self.bn = nn.BatchNorm2d(out_channels) | |
self.fre_init() | |
nn.init.constant_(self.vector[0, :], 0.25) #origin | |
nn.init.constant_(self.vector[1, :], 0.25) #avg | |
nn.init.constant_(self.vector[2, :], 0.0) #prior | |
nn.init.constant_(self.vector[3, :], 0.5) #1x1_kxk | |
nn.init.constant_(self.vector[4, :], 0.5) #dws_conv | |
def fre_init(self): | |
prior_tensor = torch.Tensor(self.out_channels, self.kernel_size, self.kernel_size) | |
half_fg = self.out_channels/2 | |
for i in range(self.out_channels): | |
for h in range(3): | |
for w in range(3): | |
if i < half_fg: | |
prior_tensor[i, h, w] = math.cos(math.pi*(h+0.5)*(i+1)/3) | |
else: | |
prior_tensor[i, h, w] = math.cos(math.pi*(w+0.5)*(i+1-half_fg)/3) | |
self.register_buffer('weight_rbr_prior', prior_tensor) | |
def weight_gen(self): | |
weight_rbr_origin = torch.einsum('oihw,o->oihw', self.weight_rbr_origin, self.vector[0, :]) | |
weight_rbr_avg = torch.einsum('oihw,o->oihw', torch.einsum('oihw,hw->oihw', self.weight_rbr_avg_conv, self.weight_rbr_avg_avg), self.vector[1, :]) | |
weight_rbr_pfir = torch.einsum('oihw,o->oihw', torch.einsum('oihw,ohw->oihw', self.weight_rbr_pfir_conv, self.weight_rbr_prior), self.vector[2, :]) | |
weight_rbr_1x1_kxk_conv1 = None | |
if hasattr(self, 'weight_rbr_1x1_kxk_idconv1'): | |
weight_rbr_1x1_kxk_conv1 = (self.weight_rbr_1x1_kxk_idconv1 + self.id_tensor).squeeze() | |
elif hasattr(self, 'weight_rbr_1x1_kxk_conv1'): | |
weight_rbr_1x1_kxk_conv1 = self.weight_rbr_1x1_kxk_conv1.squeeze() | |
else: | |
raise NotImplementedError | |
weight_rbr_1x1_kxk_conv2 = self.weight_rbr_1x1_kxk_conv2 | |
if self.groups > 1: | |
g = self.groups | |
t, ig = weight_rbr_1x1_kxk_conv1.size() | |
o, tg, h, w = weight_rbr_1x1_kxk_conv2.size() | |
weight_rbr_1x1_kxk_conv1 = weight_rbr_1x1_kxk_conv1.view(g, int(t/g), ig) | |
weight_rbr_1x1_kxk_conv2 = weight_rbr_1x1_kxk_conv2.view(g, int(o/g), tg, h, w) | |
weight_rbr_1x1_kxk = torch.einsum('gti,gothw->goihw', weight_rbr_1x1_kxk_conv1, weight_rbr_1x1_kxk_conv2).view(o, ig, h, w) | |
else: | |
weight_rbr_1x1_kxk = torch.einsum('ti,othw->oihw', weight_rbr_1x1_kxk_conv1, weight_rbr_1x1_kxk_conv2) | |
weight_rbr_1x1_kxk = torch.einsum('oihw,o->oihw', weight_rbr_1x1_kxk, self.vector[3, :]) | |
weight_rbr_gconv = self.dwsc2full(self.weight_rbr_gconv_dw, self.weight_rbr_gconv_pw, self.in_channels) | |
weight_rbr_gconv = torch.einsum('oihw,o->oihw', weight_rbr_gconv, self.vector[4, :]) | |
weight = weight_rbr_origin + weight_rbr_avg + weight_rbr_1x1_kxk + weight_rbr_pfir + weight_rbr_gconv | |
return weight | |
def dwsc2full(self, weight_dw, weight_pw, groups): | |
t, ig, h, w = weight_dw.size() | |
o, _, _, _ = weight_pw.size() | |
tg = int(t/groups) | |
i = int(ig*groups) | |
weight_dw = weight_dw.view(groups, tg, ig, h, w) | |
weight_pw = weight_pw.squeeze().view(o, groups, tg) | |
weight_dsc = torch.einsum('gtihw,ogt->ogihw', weight_dw, weight_pw) | |
return weight_dsc.view(o, i, h, w) | |
def forward(self, inputs): | |
weight = self.weight_gen() | |
out = F.conv2d(inputs, weight, bias=None, stride=self.stride, padding=self.padding, dilation=self.dilation, groups=self.groups) | |
return self.nonlinear(self.bn(out)) | |
class RepConv_OREPA(nn.Module): | |
def __init__(self, c1, c2, k=3, s=1, padding=1, dilation=1, groups=1, padding_mode='zeros', deploy=False, use_se=False, nonlinear=nn.SiLU()): | |
super(RepConv_OREPA, self).__init__() | |
self.deploy = deploy | |
self.groups = groups | |
self.in_channels = c1 | |
self.out_channels = c2 | |
self.padding = padding | |
self.dilation = dilation | |
self.groups = groups | |
assert k == 3 | |
assert padding == 1 | |
padding_11 = padding - k // 2 | |
if nonlinear is None: | |
self.nonlinearity = nn.Identity() | |
else: | |
self.nonlinearity = nonlinear | |
if use_se: | |
self.se = SEBlock(self.out_channels, internal_neurons=self.out_channels // 16) | |
else: | |
self.se = nn.Identity() | |
if deploy: | |
self.rbr_reparam = nn.Conv2d(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=k, stride=s, | |
padding=padding, dilation=dilation, groups=groups, bias=True, padding_mode=padding_mode) | |
else: | |
self.rbr_identity = nn.BatchNorm2d(num_features=self.in_channels) if self.out_channels == self.in_channels and s == 1 else None | |
self.rbr_dense = OREPA_3x3_RepConv(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=k, stride=s, padding=padding, groups=groups, dilation=1) | |
self.rbr_1x1 = ConvBN(in_channels=self.in_channels, out_channels=self.out_channels, kernel_size=1, stride=s, padding=padding_11, groups=groups, dilation=1) | |
print('RepVGG Block, identity = ', self.rbr_identity) | |
def forward(self, inputs): | |
if hasattr(self, 'rbr_reparam'): | |
return self.nonlinearity(self.se(self.rbr_reparam(inputs))) | |
if self.rbr_identity is None: | |
id_out = 0 | |
else: | |
id_out = self.rbr_identity(inputs) | |
out1 = self.rbr_dense(inputs) | |
out2 = self.rbr_1x1(inputs) | |
out3 = id_out | |
out = out1 + out2 + out3 | |
return self.nonlinearity(self.se(out)) | |
# Optional. This improves the accuracy and facilitates quantization. | |
# 1. Cancel the original weight decay on rbr_dense.conv.weight and rbr_1x1.conv.weight. | |
# 2. Use like this. | |
# loss = criterion(....) | |
# for every RepVGGBlock blk: | |
# loss += weight_decay_coefficient * 0.5 * blk.get_cust_L2() | |
# optimizer.zero_grad() | |
# loss.backward() | |
# Not used for OREPA | |
def get_custom_L2(self): | |
K3 = self.rbr_dense.weight_gen() | |
K1 = self.rbr_1x1.conv.weight | |
t3 = (self.rbr_dense.bn.weight / ((self.rbr_dense.bn.running_var + self.rbr_dense.bn.eps).sqrt())).reshape(-1, 1, 1, 1).detach() | |
t1 = (self.rbr_1x1.bn.weight / ((self.rbr_1x1.bn.running_var + self.rbr_1x1.bn.eps).sqrt())).reshape(-1, 1, 1, 1).detach() | |
l2_loss_circle = (K3 ** 2).sum() - (K3[:, :, 1:2, 1:2] ** 2).sum() # The L2 loss of the "circle" of weights in 3x3 kernel. Use regular L2 on them. | |
eq_kernel = K3[:, :, 1:2, 1:2] * t3 + K1 * t1 # The equivalent resultant central point of 3x3 kernel. | |
l2_loss_eq_kernel = (eq_kernel ** 2 / (t3 ** 2 + t1 ** 2)).sum() # Normalize for an L2 coefficient comparable to regular L2. | |
return l2_loss_eq_kernel + l2_loss_circle | |
def get_equivalent_kernel_bias(self): | |
kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense) | |
kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1) | |
kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity) | |
return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid | |
def _pad_1x1_to_3x3_tensor(self, kernel1x1): | |
if kernel1x1 is None: | |
return 0 | |
else: | |
return torch.nn.functional.pad(kernel1x1, [1,1,1,1]) | |
def _fuse_bn_tensor(self, branch): | |
if branch is None: | |
return 0, 0 | |
if not isinstance(branch, nn.BatchNorm2d): | |
if isinstance(branch, OREPA_3x3_RepConv): | |
kernel = branch.weight_gen() | |
elif isinstance(branch, ConvBN): | |
kernel = branch.conv.weight | |
else: | |
raise NotImplementedError | |
running_mean = branch.bn.running_mean | |
running_var = branch.bn.running_var | |
gamma = branch.bn.weight | |
beta = branch.bn.bias | |
eps = branch.bn.eps | |
else: | |
if not hasattr(self, 'id_tensor'): | |
input_dim = self.in_channels // self.groups | |
kernel_value = np.zeros((self.in_channels, input_dim, 3, 3), dtype=np.float32) | |
for i in range(self.in_channels): | |
kernel_value[i, i % input_dim, 1, 1] = 1 | |
self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device) | |
kernel = self.id_tensor | |
running_mean = branch.running_mean | |
running_var = branch.running_var | |
gamma = branch.weight | |
beta = branch.bias | |
eps = branch.eps | |
std = (running_var + eps).sqrt() | |
t = (gamma / std).reshape(-1, 1, 1, 1) | |
return kernel * t, beta - running_mean * gamma / std | |
def switch_to_deploy(self): | |
if hasattr(self, 'rbr_reparam'): | |
return | |
print(f"RepConv_OREPA.switch_to_deploy") | |
kernel, bias = self.get_equivalent_kernel_bias() | |
self.rbr_reparam = nn.Conv2d(in_channels=self.rbr_dense.in_channels, out_channels=self.rbr_dense.out_channels, | |
kernel_size=self.rbr_dense.kernel_size, stride=self.rbr_dense.stride, | |
padding=self.rbr_dense.padding, dilation=self.rbr_dense.dilation, groups=self.rbr_dense.groups, bias=True) | |
self.rbr_reparam.weight.data = kernel | |
self.rbr_reparam.bias.data = bias | |
for para in self.parameters(): | |
para.detach_() | |
self.__delattr__('rbr_dense') | |
self.__delattr__('rbr_1x1') | |
if hasattr(self, 'rbr_identity'): | |
self.__delattr__('rbr_identity') | |
##### end of orepa ##### | |
##### swin transformer ##### | |
class WindowAttention(nn.Module): | |
def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): | |
super().__init__() | |
self.dim = dim | |
self.window_size = window_size # Wh, Ww | |
self.num_heads = num_heads | |
head_dim = dim // num_heads | |
self.scale = qk_scale or head_dim ** -0.5 | |
# define a parameter table of relative position bias | |
self.relative_position_bias_table = nn.Parameter( | |
torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH | |
# get pair-wise relative position index for each token inside the window | |
coords_h = torch.arange(self.window_size[0]) | |
coords_w = torch.arange(self.window_size[1]) | |
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww | |
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww | |
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww | |
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 | |
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 | |
relative_coords[:, :, 1] += self.window_size[1] - 1 | |
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 | |
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww | |
self.register_buffer("relative_position_index", relative_position_index) | |
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) | |
self.attn_drop = nn.Dropout(attn_drop) | |
self.proj = nn.Linear(dim, dim) | |
self.proj_drop = nn.Dropout(proj_drop) | |
nn.init.normal_(self.relative_position_bias_table, std=.02) | |
self.softmax = nn.Softmax(dim=-1) | |
def forward(self, x, mask=None): | |
B_, N, C = x.shape | |
qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) | |
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) | |
q = q * self.scale | |
attn = (q @ k.transpose(-2, -1)) | |
relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( | |
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH | |
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww | |
attn = attn + relative_position_bias.unsqueeze(0) | |
if mask is not None: | |
nW = mask.shape[0] | |
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) | |
attn = attn.view(-1, self.num_heads, N, N) | |
attn = self.softmax(attn) | |
else: | |
attn = self.softmax(attn) | |
attn = self.attn_drop(attn) | |
# print(attn.dtype, v.dtype) | |
try: | |
x = (attn @ v).transpose(1, 2).reshape(B_, N, C) | |
except: | |
#print(attn.dtype, v.dtype) | |
x = (attn.half() @ v).transpose(1, 2).reshape(B_, N, C) | |
x = self.proj(x) | |
x = self.proj_drop(x) | |
return x | |
class Mlp(nn.Module): | |
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0.): | |
super().__init__() | |
out_features = out_features or in_features | |
hidden_features = hidden_features or in_features | |
self.fc1 = nn.Linear(in_features, hidden_features) | |
self.act = act_layer() | |
self.fc2 = nn.Linear(hidden_features, out_features) | |
self.drop = nn.Dropout(drop) | |
def forward(self, x): | |
x = self.fc1(x) | |
x = self.act(x) | |
x = self.drop(x) | |
x = self.fc2(x) | |
x = self.drop(x) | |
return x | |
def window_partition(x, window_size): | |
B, H, W, C = x.shape | |
assert H % window_size == 0, 'feature map h and w can not divide by window size' | |
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) | |
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) | |
return windows | |
def window_reverse(windows, window_size, H, W): | |
B = int(windows.shape[0] / (H * W / window_size / window_size)) | |
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) | |
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) | |
return x | |
class SwinTransformerLayer(nn.Module): | |
def __init__(self, dim, num_heads, window_size=8, shift_size=0, | |
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0., | |
act_layer=nn.SiLU, norm_layer=nn.LayerNorm): | |
super().__init__() | |
self.dim = dim | |
self.num_heads = num_heads | |
self.window_size = window_size | |
self.shift_size = shift_size | |
self.mlp_ratio = mlp_ratio | |
# if min(self.input_resolution) <= self.window_size: | |
# # if window size is larger than input resolution, we don't partition windows | |
# self.shift_size = 0 | |
# self.window_size = min(self.input_resolution) | |
assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" | |
self.norm1 = norm_layer(dim) | |
self.attn = WindowAttention( | |
dim, window_size=(self.window_size, self.window_size), num_heads=num_heads, | |
qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) | |
self.drop_path = DropPath(drop_path) if drop_path > 0. else 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) | |
def create_mask(self, H, W): | |
# calculate attention mask for SW-MSA | |
img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 | |
h_slices = (slice(0, -self.window_size), | |
slice(-self.window_size, -self.shift_size), | |
slice(-self.shift_size, None)) | |
w_slices = (slice(0, -self.window_size), | |
slice(-self.window_size, -self.shift_size), | |
slice(-self.shift_size, None)) | |
cnt = 0 | |
for h in h_slices: | |
for w in w_slices: | |
img_mask[:, h, w, :] = cnt | |
cnt += 1 | |
mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 | |
mask_windows = mask_windows.view(-1, self.window_size * self.window_size) | |
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) | |
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) | |
return attn_mask | |
def forward(self, x): | |
# reshape x[b c h w] to x[b l c] | |
_, _, H_, W_ = x.shape | |
Padding = False | |
if min(H_, W_) < self.window_size or H_ % self.window_size!=0 or W_ % self.window_size!=0: | |
Padding = True | |
# print(f'img_size {min(H_, W_)} is less than (or not divided by) window_size {self.window_size}, Padding.') | |
pad_r = (self.window_size - W_ % self.window_size) % self.window_size | |
pad_b = (self.window_size - H_ % self.window_size) % self.window_size | |
x = F.pad(x, (0, pad_r, 0, pad_b)) | |
# print('2', x.shape) | |
B, C, H, W = x.shape | |
L = H * W | |
x = x.permute(0, 2, 3, 1).contiguous().view(B, L, C) # b, L, c | |
# create mask from init to forward | |
if self.shift_size > 0: | |
attn_mask = self.create_mask(H, W).to(x.device) | |
else: | |
attn_mask = None | |
shortcut = x | |
x = self.norm1(x) | |
x = x.view(B, H, W, C) | |
# cyclic shift | |
if self.shift_size > 0: | |
shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) | |
else: | |
shifted_x = x | |
# partition windows | |
x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C | |
x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C | |
# W-MSA/SW-MSA | |
attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C | |
# merge windows | |
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) | |
shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C | |
# reverse cyclic shift | |
if self.shift_size > 0: | |
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) | |
else: | |
x = shifted_x | |
x = x.view(B, H * W, C) | |
# FFN | |
x = shortcut + self.drop_path(x) | |
x = x + self.drop_path(self.mlp(self.norm2(x))) | |
x = x.permute(0, 2, 1).contiguous().view(-1, C, H, W) # b c h w | |
if Padding: | |
x = x[:, :, :H_, :W_] # reverse padding | |
return x | |
class SwinTransformerBlock(nn.Module): | |
def __init__(self, c1, c2, num_heads, num_layers, window_size=8): | |
super().__init__() | |
self.conv = None | |
if c1 != c2: | |
self.conv = Conv(c1, c2) | |
# remove input_resolution | |
self.blocks = nn.Sequential(*[SwinTransformerLayer(dim=c2, num_heads=num_heads, window_size=window_size, | |
shift_size=0 if (i % 2 == 0) else window_size // 2) for i in range(num_layers)]) | |
def forward(self, x): | |
if self.conv is not None: | |
x = self.conv(x) | |
x = self.blocks(x) | |
return x | |
class STCSPA(nn.Module): | |
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super(STCSPA, self).__init__() | |
c_ = int(c2 * e) # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c1, c_, 1, 1) | |
self.cv3 = Conv(2 * c_, c2, 1, 1) | |
num_heads = c_ // 32 | |
self.m = SwinTransformerBlock(c_, c_, num_heads, n) | |
#self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) | |
def forward(self, x): | |
y1 = self.m(self.cv1(x)) | |
y2 = self.cv2(x) | |
return self.cv3(torch.cat((y1, y2), dim=1)) | |
class STCSPB(nn.Module): | |
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super(STCSPB, self).__init__() | |
c_ = int(c2) # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c_, c_, 1, 1) | |
self.cv3 = Conv(2 * c_, c2, 1, 1) | |
num_heads = c_ // 32 | |
self.m = SwinTransformerBlock(c_, c_, num_heads, n) | |
#self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) | |
def forward(self, x): | |
x1 = self.cv1(x) | |
y1 = self.m(x1) | |
y2 = self.cv2(x1) | |
return self.cv3(torch.cat((y1, y2), dim=1)) | |
class STCSPC(nn.Module): | |
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super(STCSPC, self).__init__() | |
c_ = int(c2 * e) # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c1, c_, 1, 1) | |
self.cv3 = Conv(c_, c_, 1, 1) | |
self.cv4 = Conv(2 * c_, c2, 1, 1) | |
num_heads = c_ // 32 | |
self.m = SwinTransformerBlock(c_, c_, num_heads, n) | |
#self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) | |
def forward(self, x): | |
y1 = self.cv3(self.m(self.cv1(x))) | |
y2 = self.cv2(x) | |
return self.cv4(torch.cat((y1, y2), dim=1)) | |
##### end of swin transformer ##### | |
##### swin transformer v2 ##### | |
class WindowAttention_v2(nn.Module): | |
def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0., | |
pretrained_window_size=[0, 0]): | |
super().__init__() | |
self.dim = dim | |
self.window_size = window_size # Wh, Ww | |
self.pretrained_window_size = pretrained_window_size | |
self.num_heads = num_heads | |
self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True) | |
# mlp to generate continuous relative position bias | |
self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True), | |
nn.ReLU(inplace=True), | |
nn.Linear(512, num_heads, bias=False)) | |
# get relative_coords_table | |
relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32) | |
relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32) | |
relative_coords_table = torch.stack( | |
torch.meshgrid([relative_coords_h, | |
relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2 | |
if pretrained_window_size[0] > 0: | |
relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1) | |
relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1) | |
else: | |
relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1) | |
relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1) | |
relative_coords_table *= 8 # normalize to -8, 8 | |
relative_coords_table = torch.sign(relative_coords_table) * torch.log2( | |
torch.abs(relative_coords_table) + 1.0) / np.log2(8) | |
self.register_buffer("relative_coords_table", relative_coords_table) | |
# get pair-wise relative position index for each token inside the window | |
coords_h = torch.arange(self.window_size[0]) | |
coords_w = torch.arange(self.window_size[1]) | |
coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww | |
coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww | |
relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww | |
relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2 | |
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 | |
relative_coords[:, :, 1] += self.window_size[1] - 1 | |
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 | |
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww | |
self.register_buffer("relative_position_index", relative_position_index) | |
self.qkv = nn.Linear(dim, dim * 3, bias=False) | |
if qkv_bias: | |
self.q_bias = nn.Parameter(torch.zeros(dim)) | |
self.v_bias = nn.Parameter(torch.zeros(dim)) | |
else: | |
self.q_bias = None | |
self.v_bias = None | |
self.attn_drop = nn.Dropout(attn_drop) | |
self.proj = nn.Linear(dim, dim) | |
self.proj_drop = nn.Dropout(proj_drop) | |
self.softmax = nn.Softmax(dim=-1) | |
def forward(self, x, mask=None): | |
B_, N, C = x.shape | |
qkv_bias = None | |
if self.q_bias is not None: | |
qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) | |
qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) | |
qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) | |
q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) | |
# cosine attention | |
attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)) | |
logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01))).exp() | |
attn = attn * logit_scale | |
relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads) | |
relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view( | |
self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH | |
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww | |
relative_position_bias = 16 * torch.sigmoid(relative_position_bias) | |
attn = attn + relative_position_bias.unsqueeze(0) | |
if mask is not None: | |
nW = mask.shape[0] | |
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) | |
attn = attn.view(-1, self.num_heads, N, N) | |
attn = self.softmax(attn) | |
else: | |
attn = self.softmax(attn) | |
attn = self.attn_drop(attn) | |
try: | |
x = (attn @ v).transpose(1, 2).reshape(B_, N, C) | |
except: | |
x = (attn.half() @ v).transpose(1, 2).reshape(B_, N, C) | |
x = self.proj(x) | |
x = self.proj_drop(x) | |
return x | |
def extra_repr(self) -> str: | |
return f'dim={self.dim}, window_size={self.window_size}, ' \ | |
f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}' | |
def flops(self, N): | |
# calculate flops for 1 window with token length of N | |
flops = 0 | |
# qkv = self.qkv(x) | |
flops += N * self.dim * 3 * self.dim | |
# attn = (q @ k.transpose(-2, -1)) | |
flops += self.num_heads * N * (self.dim // self.num_heads) * N | |
# x = (attn @ v) | |
flops += self.num_heads * N * N * (self.dim // self.num_heads) | |
# x = self.proj(x) | |
flops += N * self.dim * self.dim | |
return flops | |
class Mlp_v2(nn.Module): | |
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.SiLU, drop=0.): | |
super().__init__() | |
out_features = out_features or in_features | |
hidden_features = hidden_features or in_features | |
self.fc1 = nn.Linear(in_features, hidden_features) | |
self.act = act_layer() | |
self.fc2 = nn.Linear(hidden_features, out_features) | |
self.drop = nn.Dropout(drop) | |
def forward(self, x): | |
x = self.fc1(x) | |
x = self.act(x) | |
x = self.drop(x) | |
x = self.fc2(x) | |
x = self.drop(x) | |
return x | |
def window_partition_v2(x, window_size): | |
B, H, W, C = x.shape | |
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) | |
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) | |
return windows | |
def window_reverse_v2(windows, window_size, H, W): | |
B = int(windows.shape[0] / (H * W / window_size / window_size)) | |
x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1) | |
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) | |
return x | |
class SwinTransformerLayer_v2(nn.Module): | |
def __init__(self, dim, num_heads, window_size=7, shift_size=0, | |
mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0., | |
act_layer=nn.SiLU, norm_layer=nn.LayerNorm, pretrained_window_size=0): | |
super().__init__() | |
self.dim = dim | |
#self.input_resolution = input_resolution | |
self.num_heads = num_heads | |
self.window_size = window_size | |
self.shift_size = shift_size | |
self.mlp_ratio = mlp_ratio | |
#if min(self.input_resolution) <= self.window_size: | |
# # if window size is larger than input resolution, we don't partition windows | |
# self.shift_size = 0 | |
# self.window_size = min(self.input_resolution) | |
assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size" | |
self.norm1 = norm_layer(dim) | |
self.attn = WindowAttention_v2( | |
dim, window_size=(self.window_size, self.window_size), num_heads=num_heads, | |
qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, | |
pretrained_window_size=(pretrained_window_size, pretrained_window_size)) | |
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() | |
self.norm2 = norm_layer(dim) | |
mlp_hidden_dim = int(dim * mlp_ratio) | |
self.mlp = Mlp_v2(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) | |
def create_mask(self, H, W): | |
# calculate attention mask for SW-MSA | |
img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 | |
h_slices = (slice(0, -self.window_size), | |
slice(-self.window_size, -self.shift_size), | |
slice(-self.shift_size, None)) | |
w_slices = (slice(0, -self.window_size), | |
slice(-self.window_size, -self.shift_size), | |
slice(-self.shift_size, None)) | |
cnt = 0 | |
for h in h_slices: | |
for w in w_slices: | |
img_mask[:, h, w, :] = cnt | |
cnt += 1 | |
mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1 | |
mask_windows = mask_windows.view(-1, self.window_size * self.window_size) | |
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) | |
attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0)) | |
return attn_mask | |
def forward(self, x): | |
# reshape x[b c h w] to x[b l c] | |
_, _, H_, W_ = x.shape | |
Padding = False | |
if min(H_, W_) < self.window_size or H_ % self.window_size!=0 or W_ % self.window_size!=0: | |
Padding = True | |
# print(f'img_size {min(H_, W_)} is less than (or not divided by) window_size {self.window_size}, Padding.') | |
pad_r = (self.window_size - W_ % self.window_size) % self.window_size | |
pad_b = (self.window_size - H_ % self.window_size) % self.window_size | |
x = F.pad(x, (0, pad_r, 0, pad_b)) | |
# print('2', x.shape) | |
B, C, H, W = x.shape | |
L = H * W | |
x = x.permute(0, 2, 3, 1).contiguous().view(B, L, C) # b, L, c | |
# create mask from init to forward | |
if self.shift_size > 0: | |
attn_mask = self.create_mask(H, W).to(x.device) | |
else: | |
attn_mask = None | |
shortcut = x | |
x = x.view(B, H, W, C) | |
# cyclic shift | |
if self.shift_size > 0: | |
shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) | |
else: | |
shifted_x = x | |
# partition windows | |
x_windows = window_partition_v2(shifted_x, self.window_size) # nW*B, window_size, window_size, C | |
x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C | |
# W-MSA/SW-MSA | |
attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C | |
# merge windows | |
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) | |
shifted_x = window_reverse_v2(attn_windows, self.window_size, H, W) # B H' W' C | |
# reverse cyclic shift | |
if self.shift_size > 0: | |
x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) | |
else: | |
x = shifted_x | |
x = x.view(B, H * W, C) | |
x = shortcut + self.drop_path(self.norm1(x)) | |
# FFN | |
x = x + self.drop_path(self.norm2(self.mlp(x))) | |
x = x.permute(0, 2, 1).contiguous().view(-1, C, H, W) # b c h w | |
if Padding: | |
x = x[:, :, :H_, :W_] # reverse padding | |
return x | |
def extra_repr(self) -> str: | |
return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \ | |
f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" | |
def flops(self): | |
flops = 0 | |
H, W = self.input_resolution | |
# norm1 | |
flops += self.dim * H * W | |
# W-MSA/SW-MSA | |
nW = H * W / self.window_size / self.window_size | |
flops += nW * self.attn.flops(self.window_size * self.window_size) | |
# mlp | |
flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio | |
# norm2 | |
flops += self.dim * H * W | |
return flops | |
class SwinTransformer2Block(nn.Module): | |
def __init__(self, c1, c2, num_heads, num_layers, window_size=7): | |
super().__init__() | |
self.conv = None | |
if c1 != c2: | |
self.conv = Conv(c1, c2) | |
# remove input_resolution | |
self.blocks = nn.Sequential(*[SwinTransformerLayer_v2(dim=c2, num_heads=num_heads, window_size=window_size, | |
shift_size=0 if (i % 2 == 0) else window_size // 2) for i in range(num_layers)]) | |
def forward(self, x): | |
if self.conv is not None: | |
x = self.conv(x) | |
x = self.blocks(x) | |
return x | |
class ST2CSPA(nn.Module): | |
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super(ST2CSPA, self).__init__() | |
c_ = int(c2 * e) # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c1, c_, 1, 1) | |
self.cv3 = Conv(2 * c_, c2, 1, 1) | |
num_heads = c_ // 32 | |
self.m = SwinTransformer2Block(c_, c_, num_heads, n) | |
#self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) | |
def forward(self, x): | |
y1 = self.m(self.cv1(x)) | |
y2 = self.cv2(x) | |
return self.cv3(torch.cat((y1, y2), dim=1)) | |
class ST2CSPB(nn.Module): | |
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super(ST2CSPB, self).__init__() | |
c_ = int(c2) # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c_, c_, 1, 1) | |
self.cv3 = Conv(2 * c_, c2, 1, 1) | |
num_heads = c_ // 32 | |
self.m = SwinTransformer2Block(c_, c_, num_heads, n) | |
#self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) | |
def forward(self, x): | |
x1 = self.cv1(x) | |
y1 = self.m(x1) | |
y2 = self.cv2(x1) | |
return self.cv3(torch.cat((y1, y2), dim=1)) | |
class ST2CSPC(nn.Module): | |
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super(ST2CSPC, self).__init__() | |
c_ = int(c2 * e) # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c1, c_, 1, 1) | |
self.cv3 = Conv(c_, c_, 1, 1) | |
self.cv4 = Conv(2 * c_, c2, 1, 1) | |
num_heads = c_ // 32 | |
self.m = SwinTransformer2Block(c_, c_, num_heads, n) | |
#self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) | |
def forward(self, x): | |
y1 = self.cv3(self.m(self.cv1(x))) | |
y2 = self.cv2(x) | |
return self.cv4(torch.cat((y1, y2), dim=1)) | |
##### end of swin transformer v2 ##### | |