mirror of https://github.com/WongKinYiu/yolov7.git
2020 lines
82 KiB
Python
2020 lines
82 KiB
Python
import math
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from copy import copy
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from pathlib import Path
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import numpy as np
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import pandas as pd
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import requests
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torchvision.ops import DeformConv2d
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from PIL import Image
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from torch.cuda import amp
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from utils.datasets import letterbox
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from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh
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from utils.plots import color_list, plot_one_box
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from utils.torch_utils import time_synchronized
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##### basic ####
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def autopad(k, p=None): # kernel, padding
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# Pad to 'same'
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if p is None:
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p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
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return p
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class MP(nn.Module):
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def __init__(self, k=2):
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super(MP, self).__init__()
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self.m = nn.MaxPool2d(kernel_size=k, stride=k)
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def forward(self, x):
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return self.m(x)
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class SP(nn.Module):
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def __init__(self, k=3, s=1):
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super(SP, self).__init__()
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self.m = nn.MaxPool2d(kernel_size=k, stride=s, padding=k // 2)
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def forward(self, x):
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return self.m(x)
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class ReOrg(nn.Module):
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def __init__(self):
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super(ReOrg, self).__init__()
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def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
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return torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)
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class Concat(nn.Module):
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def __init__(self, dimension=1):
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super(Concat, self).__init__()
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self.d = dimension
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def forward(self, x):
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return torch.cat(x, self.d)
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class Chuncat(nn.Module):
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def __init__(self, dimension=1):
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super(Chuncat, self).__init__()
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self.d = dimension
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def forward(self, x):
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x1 = []
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x2 = []
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for xi in x:
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xi1, xi2 = xi.chunk(2, self.d)
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x1.append(xi1)
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x2.append(xi2)
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return torch.cat(x1+x2, self.d)
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class Shortcut(nn.Module):
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def __init__(self, dimension=0):
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super(Shortcut, self).__init__()
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self.d = dimension
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def forward(self, x):
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return x[0]+x[1]
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class Foldcut(nn.Module):
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def __init__(self, dimension=0):
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super(Foldcut, self).__init__()
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self.d = dimension
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def forward(self, x):
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x1, x2 = x.chunk(2, self.d)
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return x1+x2
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class Conv(nn.Module):
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# Standard convolution
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def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
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super(Conv, self).__init__()
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self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
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self.bn = nn.BatchNorm2d(c2)
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self.act = nn.LeakyReLU(0.1, inplace=True) if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
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def forward(self, x):
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return self.act(self.bn(self.conv(x)))
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def fuseforward(self, x):
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return self.act(self.conv(x))
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class RobustConv(nn.Module):
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# Robust convolution (use high kernel size 7-11 for: downsampling and other layers). Train for 300 - 450 epochs.
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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
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super(RobustConv, self).__init__()
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self.conv_dw = Conv(c1, c1, k=k, s=s, p=p, g=c1, act=act)
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self.conv1x1 = nn.Conv2d(c1, c2, 1, 1, 0, groups=1, bias=True)
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self.gamma = nn.Parameter(layer_scale_init_value * torch.ones(c2)) if layer_scale_init_value > 0 else None
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def forward(self, x):
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x = x.to(memory_format=torch.channels_last)
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x = self.conv1x1(self.conv_dw(x))
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if self.gamma is not None:
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x = x.mul(self.gamma.reshape(1, -1, 1, 1))
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return x
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class RobustConv2(nn.Module):
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# Robust convolution 2 (use [32, 5, 2] or [32, 7, 4] or [32, 11, 8] for one of the paths in CSP).
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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
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super(RobustConv2, self).__init__()
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self.conv_strided = Conv(c1, c1, k=k, s=s, p=p, g=c1, act=act)
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self.conv_deconv = nn.ConvTranspose2d(in_channels=c1, out_channels=c2, kernel_size=s, stride=s,
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padding=0, bias=True, dilation=1, groups=1
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)
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self.gamma = nn.Parameter(layer_scale_init_value * torch.ones(c2)) if layer_scale_init_value > 0 else None
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def forward(self, x):
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x = self.conv_deconv(self.conv_strided(x))
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if self.gamma is not None:
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x = x.mul(self.gamma.reshape(1, -1, 1, 1))
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return x
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def DWConv(c1, c2, k=1, s=1, act=True):
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# Depthwise convolution
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return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
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class GhostConv(nn.Module):
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# Ghost Convolution https://github.com/huawei-noah/ghostnet
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def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups
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super(GhostConv, self).__init__()
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c_ = c2 // 2 # hidden channels
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self.cv1 = Conv(c1, c_, k, s, None, g, act)
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self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
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def forward(self, x):
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y = self.cv1(x)
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return torch.cat([y, self.cv2(y)], 1)
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class Stem(nn.Module):
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# Stem
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def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
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super(Stem, self).__init__()
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c_ = int(c2/2) # hidden channels
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self.cv1 = Conv(c1, c_, 3, 2)
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self.cv2 = Conv(c_, c_, 1, 1)
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self.cv3 = Conv(c_, c_, 3, 2)
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self.pool = torch.nn.MaxPool2d(2, stride=2)
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self.cv4 = Conv(2 * c_, c2, 1, 1)
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def forward(self, x):
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x = self.cv1(x)
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return self.cv4(torch.cat((self.cv3(self.cv2(x)), self.pool(x)), dim=1))
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class DownC(nn.Module):
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# Spatial pyramid pooling layer used in YOLOv3-SPP
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def __init__(self, c1, c2, n=1, k=2):
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super(DownC, self).__init__()
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c_ = int(c1) # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = Conv(c_, c2//2, 3, k)
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self.cv3 = Conv(c1, c2//2, 1, 1)
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self.mp = nn.MaxPool2d(kernel_size=k, stride=k)
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def forward(self, x):
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return torch.cat((self.cv2(self.cv1(x)), self.cv3(self.mp(x))), dim=1)
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class SPP(nn.Module):
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# Spatial pyramid pooling layer used in YOLOv3-SPP
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def __init__(self, c1, c2, k=(5, 9, 13)):
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super(SPP, self).__init__()
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c_ = c1 // 2 # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
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self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
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def forward(self, x):
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x = self.cv1(x)
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return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
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class Bottleneck(nn.Module):
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# Darknet bottleneck
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def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
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super(Bottleneck, self).__init__()
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c_ = int(c2 * e) # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = Conv(c_, c2, 3, 1, g=g)
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self.add = shortcut and c1 == c2
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def forward(self, x):
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return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
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class Res(nn.Module):
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# ResNet bottleneck
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def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
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super(Res, self).__init__()
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c_ = int(c2 * e) # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = Conv(c_, c_, 3, 1, g=g)
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self.cv3 = Conv(c_, c2, 1, 1)
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self.add = shortcut and c1 == c2
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def forward(self, x):
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return x + self.cv3(self.cv2(self.cv1(x))) if self.add else self.cv3(self.cv2(self.cv1(x)))
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class ResX(Res):
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# ResNet bottleneck
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def __init__(self, c1, c2, shortcut=True, g=32, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
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super().__init__(c1, c2, shortcu, g, e)
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c_ = int(c2 * e) # hidden channels
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class Ghost(nn.Module):
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# Ghost Bottleneck https://github.com/huawei-noah/ghostnet
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def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride
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super(Ghost, self).__init__()
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c_ = c2 // 2
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self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw
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DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw
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GhostConv(c_, c2, 1, 1, act=False)) # pw-linear
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self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
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Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
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def forward(self, x):
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return self.conv(x) + self.shortcut(x)
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##### end of basic #####
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##### cspnet #####
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class SPPCSPC(nn.Module):
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# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
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def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)):
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super(SPPCSPC, self).__init__()
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c_ = int(2 * c2 * e) # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = Conv(c1, c_, 1, 1)
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self.cv3 = Conv(c_, c_, 3, 1)
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self.cv4 = Conv(c_, c_, 1, 1)
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self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
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self.cv5 = Conv(4 * c_, c_, 1, 1)
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self.cv6 = Conv(c_, c_, 3, 1)
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self.cv7 = Conv(2 * c_, c2, 1, 1)
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def forward(self, x):
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x1 = self.cv4(self.cv3(self.cv1(x)))
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y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1)))
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y2 = self.cv2(x)
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return self.cv7(torch.cat((y1, y2), dim=1))
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class GhostSPPCSPC(SPPCSPC):
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# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
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def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)):
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super().__init__(c1, c2, n, shortcut, g, e, k)
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c_ = int(2 * c2 * e) # hidden channels
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self.cv1 = GhostConv(c1, c_, 1, 1)
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self.cv2 = GhostConv(c1, c_, 1, 1)
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self.cv3 = GhostConv(c_, c_, 3, 1)
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self.cv4 = GhostConv(c_, c_, 1, 1)
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self.cv5 = GhostConv(4 * c_, c_, 1, 1)
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self.cv6 = GhostConv(c_, c_, 3, 1)
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self.cv7 = GhostConv(2 * c_, c2, 1, 1)
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class GhostStem(Stem):
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# Stem
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def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
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super().__init__(c1, c2, k, s, p, g, act)
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c_ = int(c2/2) # hidden channels
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self.cv1 = GhostConv(c1, c_, 3, 2)
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self.cv2 = GhostConv(c_, c_, 1, 1)
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self.cv3 = GhostConv(c_, c_, 3, 2)
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self.cv4 = GhostConv(2 * c_, c2, 1, 1)
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class BottleneckCSPA(nn.Module):
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# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
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super(BottleneckCSPA, self).__init__()
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c_ = int(c2 * e) # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = Conv(c1, c_, 1, 1)
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self.cv3 = Conv(2 * c_, c2, 1, 1)
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self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
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def forward(self, x):
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y1 = self.m(self.cv1(x))
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y2 = self.cv2(x)
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return self.cv3(torch.cat((y1, y2), dim=1))
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class BottleneckCSPB(nn.Module):
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# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
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def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
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super(BottleneckCSPB, self).__init__()
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c_ = int(c2) # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = Conv(c_, c_, 1, 1)
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self.cv3 = Conv(2 * c_, c2, 1, 1)
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self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
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def forward(self, x):
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x1 = self.cv1(x)
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y1 = self.m(x1)
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y2 = self.cv2(x1)
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return self.cv3(torch.cat((y1, y2), dim=1))
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class BottleneckCSPC(nn.Module):
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# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
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super(BottleneckCSPC, self).__init__()
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c_ = int(c2 * e) # hidden channels
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self.cv1 = Conv(c1, c_, 1, 1)
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self.cv2 = Conv(c1, c_, 1, 1)
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self.cv3 = Conv(c_, c_, 1, 1)
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self.cv4 = Conv(2 * c_, c2, 1, 1)
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self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
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def forward(self, x):
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y1 = self.cv3(self.m(self.cv1(x)))
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y2 = self.cv2(x)
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return self.cv4(torch.cat((y1, y2), dim=1))
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class ResCSPA(BottleneckCSPA):
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# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
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super().__init__(c1, c2, n, shortcut, g, e)
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c_ = int(c2 * e) # hidden channels
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self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
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class ResCSPB(BottleneckCSPB):
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# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
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super().__init__(c1, c2, n, shortcut, g, e)
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c_ = int(c2) # hidden channels
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self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
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class ResCSPC(BottleneckCSPC):
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# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
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super().__init__(c1, c2, n, shortcut, g, e)
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c_ = int(c2 * e) # hidden channels
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self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)])
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class ResXCSPA(ResCSPA):
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# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
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def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
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super().__init__(c1, c2, n, shortcut, g, e)
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c_ = int(c2 * e) # hidden channels
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self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
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class ResXCSPB(ResCSPB):
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# CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
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def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
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super().__init__(c1, c2, n, shortcut, g, e)
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c_ = int(c2) # hidden channels
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self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
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|
|
|
|
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=0., 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.LeakyReLU(0.1, inplace=True) 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
|
|
|
|
@torch.no_grad()
|
|
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
|
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H, W = self.input_resolution
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# norm1
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flops += self.dim * H * W
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# W-MSA/SW-MSA
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nW = H * W / self.window_size / self.window_size
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flops += nW * self.attn.flops(self.window_size * self.window_size)
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# mlp
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flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
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# norm2
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flops += self.dim * H * W
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return flops
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|
|
|
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class SwinTransformer2Block(nn.Module):
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def __init__(self, c1, c2, num_heads, num_layers, window_size=7):
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super().__init__()
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self.conv = None
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if c1 != c2:
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self.conv = Conv(c1, c2)
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|
|
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# remove input_resolution
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self.blocks = nn.Sequential(*[SwinTransformerLayer_v2(dim=c2, num_heads=num_heads, window_size=window_size,
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shift_size=0 if (i % 2 == 0) else window_size // 2) for i in range(num_layers)])
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|
|
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def forward(self, x):
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|
if self.conv is not None:
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x = self.conv(x)
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x = self.blocks(x)
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return x
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|
|
|
|
|
class ST2CSPA(nn.Module):
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|
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
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def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
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|
super(ST2CSPA, self).__init__()
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c_ = int(c2 * e) # hidden channels
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|
self.cv1 = Conv(c1, c_, 1, 1)
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|
self.cv2 = Conv(c1, c_, 1, 1)
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|
self.cv3 = Conv(2 * c_, c2, 1, 1)
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|
num_heads = c_ // 32
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|
self.m = SwinTransformer2Block(c_, c_, num_heads, n)
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|
#self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
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|
|
|
def forward(self, x):
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|
y1 = self.m(self.cv1(x))
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|
y2 = self.cv2(x)
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|
return self.cv3(torch.cat((y1, y2), dim=1))
|
|
|
|
|
|
class ST2CSPB(nn.Module):
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|
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
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|
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
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|
super(ST2CSPB, self).__init__()
|
|
c_ = int(c2) # hidden channels
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|
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)
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|
#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 #####
|