import math from copy import copy from pathlib import Path import numpy as np import pandas as pd import requests import torch import torch.nn as nn import torch.nn.functional as F from torchvision.ops import DeformConv2d from PIL import Image from torch.cuda import amp from utils.datasets import letterbox from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh from utils.plots import color_list, plot_one_box from utils.torch_utils import time_synchronized ##### basic #### def autopad(k, p=None): # kernel, padding # Pad to 'same' if p is None: p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad return p class MP(nn.Module): def __init__(self, k=2): super(MP, self).__init__() self.m = nn.MaxPool2d(kernel_size=k, stride=k) def forward(self, x): return self.m(x) class SP(nn.Module): def __init__(self, k=3, s=1): super(SP, self).__init__() self.m = nn.MaxPool2d(kernel_size=k, stride=s, padding=k // 2) def forward(self, x): return self.m(x) class ReOrg(nn.Module): def __init__(self): super(ReOrg, self).__init__() def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) return torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1) class Concat(nn.Module): def __init__(self, dimension=1): super(Concat, self).__init__() self.d = dimension def forward(self, x): return torch.cat(x, self.d) class Chuncat(nn.Module): def __init__(self, dimension=1): super(Chuncat, self).__init__() self.d = dimension def forward(self, x): x1 = [] x2 = [] for xi in x: xi1, xi2 = xi.chunk(2, self.d) x1.append(xi1) x2.append(xi2) return torch.cat(x1+x2, self.d) class Shortcut(nn.Module): def __init__(self, dimension=0): super(Shortcut, self).__init__() self.d = dimension def forward(self, x): return x[0]+x[1] class Foldcut(nn.Module): def __init__(self, dimension=0): super(Foldcut, self).__init__() self.d = dimension def forward(self, x): x1, x2 = x.chunk(2, self.d) return x1+x2 class Conv(nn.Module): # Standard convolution def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups super(Conv, self).__init__() self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) self.bn = nn.BatchNorm2d(c2) self.act = nn.LeakyReLU(0.1, inplace=True) if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) def forward(self, x): return self.act(self.bn(self.conv(x))) def fuseforward(self, x): return self.act(self.conv(x)) class RobustConv(nn.Module): # Robust convolution (use high kernel size 7-11 for: downsampling and other layers). Train for 300 - 450 epochs. def __init__(self, c1, c2, k=7, s=1, p=None, g=1, act=True, layer_scale_init_value=1e-6): # ch_in, ch_out, kernel, stride, padding, groups super(RobustConv, self).__init__() self.conv_dw = Conv(c1, c1, k=k, s=s, p=p, g=c1, act=act) self.conv1x1 = nn.Conv2d(c1, c2, 1, 1, 0, groups=1, bias=True) self.gamma = nn.Parameter(layer_scale_init_value * torch.ones(c2)) if layer_scale_init_value > 0 else None def forward(self, x): x = x.to(memory_format=torch.channels_last) x = self.conv1x1(self.conv_dw(x)) if self.gamma is not None: x = x.mul(self.gamma.reshape(1, -1, 1, 1)) return x class RobustConv2(nn.Module): # Robust convolution 2 (use [32, 5, 2] or [32, 7, 4] or [32, 11, 8] for one of the paths in CSP). def __init__(self, c1, c2, k=7, s=4, p=None, g=1, act=True, layer_scale_init_value=1e-6): # ch_in, ch_out, kernel, stride, padding, groups super(RobustConv2, self).__init__() self.conv_strided = Conv(c1, c1, k=k, s=s, p=p, g=c1, act=act) self.conv_deconv = nn.ConvTranspose2d(in_channels=c1, out_channels=c2, kernel_size=s, stride=s, padding=0, bias=True, dilation=1, groups=1 ) self.gamma = nn.Parameter(layer_scale_init_value * torch.ones(c2)) if layer_scale_init_value > 0 else None def forward(self, x): x = self.conv_deconv(self.conv_strided(x)) if self.gamma is not None: x = x.mul(self.gamma.reshape(1, -1, 1, 1)) return x def DWConv(c1, c2, k=1, s=1, act=True): # Depthwise convolution return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act) class GhostConv(nn.Module): # Ghost Convolution https://github.com/huawei-noah/ghostnet def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups super(GhostConv, self).__init__() c_ = c2 // 2 # hidden channels self.cv1 = Conv(c1, c_, k, s, None, g, act) self.cv2 = Conv(c_, c_, 5, 1, None, c_, act) def forward(self, x): y = self.cv1(x) return torch.cat([y, self.cv2(y)], 1) class Stem(nn.Module): # Stem def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups super(Stem, self).__init__() c_ = int(c2/2) # hidden channels self.cv1 = Conv(c1, c_, 3, 2) self.cv2 = Conv(c_, c_, 1, 1) self.cv3 = Conv(c_, c_, 3, 2) self.pool = torch.nn.MaxPool2d(2, stride=2) self.cv4 = Conv(2 * c_, c2, 1, 1) def forward(self, x): x = self.cv1(x) return self.cv4(torch.cat((self.cv3(self.cv2(x)), self.pool(x)), dim=1)) class DownC(nn.Module): # Spatial pyramid pooling layer used in YOLOv3-SPP def __init__(self, c1, c2, n=1, k=2): super(DownC, self).__init__() c_ = int(c1) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_, c2//2, 3, k) self.cv3 = Conv(c1, c2//2, 1, 1) self.mp = nn.MaxPool2d(kernel_size=k, stride=k) def forward(self, x): return torch.cat((self.cv2(self.cv1(x)), self.cv3(self.mp(x))), dim=1) class SPP(nn.Module): # Spatial pyramid pooling layer used in YOLOv3-SPP def __init__(self, c1, c2, k=(5, 9, 13)): super(SPP, self).__init__() c_ = c1 // 2 # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) def forward(self, x): x = self.cv1(x) return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) class Bottleneck(nn.Module): # Darknet bottleneck def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion super(Bottleneck, self).__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_, c2, 3, 1, g=g) self.add = shortcut and c1 == c2 def forward(self, x): return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) class Res(nn.Module): # ResNet bottleneck def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion super(Res, self).__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_, c_, 3, 1, g=g) self.cv3 = Conv(c_, c2, 1, 1) self.add = shortcut and c1 == c2 def forward(self, x): return x + self.cv3(self.cv2(self.cv1(x))) if self.add else self.cv3(self.cv2(self.cv1(x))) class ResX(Res): # ResNet bottleneck def __init__(self, c1, c2, shortcut=True, g=32, e=0.5): # ch_in, ch_out, shortcut, groups, expansion super().__init__(c1, c2, shortcu, g, e) c_ = int(c2 * e) # hidden channels class Ghost(nn.Module): # Ghost Bottleneck https://github.com/huawei-noah/ghostnet def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride super(Ghost, self).__init__() c_ = c2 // 2 self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw GhostConv(c_, c2, 1, 1, act=False)) # pw-linear self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity() def forward(self, x): return self.conv(x) + self.shortcut(x) ##### end of basic ##### ##### cspnet ##### class SPPCSPC(nn.Module): # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)): super(SPPCSPC, self).__init__() c_ = int(2 * c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c1, c_, 1, 1) self.cv3 = Conv(c_, c_, 3, 1) self.cv4 = Conv(c_, c_, 1, 1) self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) self.cv5 = Conv(4 * c_, c_, 1, 1) self.cv6 = Conv(c_, c_, 3, 1) self.cv7 = Conv(2 * c_, c2, 1, 1) def forward(self, x): x1 = self.cv4(self.cv3(self.cv1(x))) y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1))) y2 = self.cv2(x) return self.cv7(torch.cat((y1, y2), dim=1)) class GhostSPPCSPC(SPPCSPC): # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)): super().__init__(c1, c2, n, shortcut, g, e, k) c_ = int(2 * c2 * e) # hidden channels self.cv1 = GhostConv(c1, c_, 1, 1) self.cv2 = GhostConv(c1, c_, 1, 1) self.cv3 = GhostConv(c_, c_, 3, 1) self.cv4 = GhostConv(c_, c_, 1, 1) self.cv5 = GhostConv(4 * c_, c_, 1, 1) self.cv6 = GhostConv(c_, c_, 3, 1) self.cv7 = GhostConv(2 * c_, c2, 1, 1) class GhostStem(Stem): # Stem def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups super().__init__(c1, c2, k, s, p, g, act) c_ = int(c2/2) # hidden channels self.cv1 = GhostConv(c1, c_, 3, 2) self.cv2 = GhostConv(c_, c_, 1, 1) self.cv3 = GhostConv(c_, c_, 3, 2) self.cv4 = GhostConv(2 * c_, c2, 1, 1) class BottleneckCSPA(nn.Module): # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super(BottleneckCSPA, self).__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c1, c_, 1, 1) self.cv3 = Conv(2 * c_, c2, 1, 1) self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) def forward(self, x): y1 = self.m(self.cv1(x)) y2 = self.cv2(x) return self.cv3(torch.cat((y1, y2), dim=1)) class BottleneckCSPB(nn.Module): # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super(BottleneckCSPB, self).__init__() c_ = int(c2) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_, c_, 1, 1) self.cv3 = Conv(2 * c_, c2, 1, 1) self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) def forward(self, x): x1 = self.cv1(x) y1 = self.m(x1) y2 = self.cv2(x1) return self.cv3(torch.cat((y1, y2), dim=1)) class BottleneckCSPC(nn.Module): # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super(BottleneckCSPC, self).__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c1, c_, 1, 1) self.cv3 = Conv(c_, c_, 1, 1) self.cv4 = Conv(2 * c_, c2, 1, 1) self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) def forward(self, x): y1 = self.cv3(self.m(self.cv1(x))) y2 = self.cv2(x) return self.cv4(torch.cat((y1, y2), dim=1)) class ResCSPA(BottleneckCSPA): # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super().__init__(c1, c2, n, shortcut, g, e) c_ = int(c2 * e) # hidden channels self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) class ResCSPB(BottleneckCSPB): # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super().__init__(c1, c2, n, shortcut, g, e) c_ = int(c2) # hidden channels self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) class ResCSPC(BottleneckCSPC): # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super().__init__(c1, c2, n, shortcut, g, e) c_ = int(c2 * e) # hidden channels self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=0.5) for _ in range(n)]) class ResXCSPA(ResCSPA): # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super().__init__(c1, c2, n, shortcut, g, e) c_ = int(c2 * e) # hidden channels self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) class ResXCSPB(ResCSPB): # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super().__init__(c1, c2, n, shortcut, g, e) c_ = int(c2) # hidden channels self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) class ResXCSPC(ResCSPC): # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=True, g=32, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super().__init__(c1, c2, n, shortcut, g, e) c_ = int(c2 * e) # hidden channels self.m = nn.Sequential(*[Res(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) class GhostCSPA(BottleneckCSPA): # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super().__init__(c1, c2, n, shortcut, g, e) c_ = int(c2 * e) # hidden channels self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)]) class GhostCSPB(BottleneckCSPB): # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super().__init__(c1, c2, n, shortcut, g, e) c_ = int(c2) # hidden channels self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)]) class GhostCSPC(BottleneckCSPC): # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super().__init__(c1, c2, n, shortcut, g, e) c_ = int(c2 * e) # hidden channels self.m = nn.Sequential(*[Ghost(c_, c_) for _ in range(n)]) ##### end of cspnet ##### ##### yolor ##### class ImplicitA(nn.Module): def __init__(self, channel, mean=0., std=.02): super(ImplicitA, self).__init__() self.channel = channel self.mean = mean self.std = std self.implicit = nn.Parameter(torch.zeros(1, channel, 1, 1)) nn.init.normal_(self.implicit, mean=self.mean, std=self.std) def forward(self, x): return self.implicit + x class ImplicitM(nn.Module): def __init__(self, channel, mean=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 H, W = self.input_resolution # norm1 flops += self.dim * H * W # W-MSA/SW-MSA nW = H * W / self.window_size / self.window_size flops += nW * self.attn.flops(self.window_size * self.window_size) # mlp flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio # norm2 flops += self.dim * H * W return flops class SwinTransformer2Block(nn.Module): def __init__(self, c1, c2, num_heads, num_layers, window_size=7): super().__init__() self.conv = None if c1 != c2: self.conv = Conv(c1, c2) # remove input_resolution self.blocks = nn.Sequential(*[SwinTransformerLayer_v2(dim=c2, num_heads=num_heads, window_size=window_size, shift_size=0 if (i % 2 == 0) else window_size // 2) for i in range(num_layers)]) def forward(self, x): if self.conv is not None: x = self.conv(x) x = self.blocks(x) return x class ST2CSPA(nn.Module): # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super(ST2CSPA, self).__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c1, c_, 1, 1) self.cv3 = Conv(2 * c_, c2, 1, 1) num_heads = c_ // 32 self.m = SwinTransformer2Block(c_, c_, num_heads, n) #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) def forward(self, x): y1 = self.m(self.cv1(x)) y2 = self.cv2(x) return self.cv3(torch.cat((y1, y2), dim=1)) class ST2CSPB(nn.Module): # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super(ST2CSPB, self).__init__() c_ = int(c2) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_, c_, 1, 1) self.cv3 = Conv(2 * c_, c2, 1, 1) num_heads = c_ // 32 self.m = SwinTransformer2Block(c_, c_, num_heads, n) #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) def forward(self, x): x1 = self.cv1(x) y1 = self.m(x1) y2 = self.cv2(x1) return self.cv3(torch.cat((y1, y2), dim=1)) class ST2CSPC(nn.Module): # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super(ST2CSPC, self).__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c1, c_, 1, 1) self.cv3 = Conv(c_, c_, 1, 1) self.cv4 = Conv(2 * c_, c2, 1, 1) num_heads = c_ // 32 self.m = SwinTransformer2Block(c_, c_, num_heads, n) #self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) def forward(self, x): y1 = self.cv3(self.m(self.cv1(x))) y2 = self.cv2(x) return self.cv4(torch.cat((y1, y2), dim=1)) ##### end of swin transformer v2 #####