commit

yolov6/layers/common.py

@@ -0,0 +1,501 @@
+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+
+import warnings
+from pathlib import Path
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from yolov6.layers.dbb_transforms import *
+
+
+class SiLU(nn.Module):
+    '''Activation of SiLU'''
+    @staticmethod
+    def forward(x):
+        return x * torch.sigmoid(x)
+
+
+class Conv(nn.Module):
+    '''Normal Conv with SiLU activation'''
+    def __init__(self, in_channels, out_channels, kernel_size, stride, groups=1, bias=False):
+        super().__init__()
+        padding = kernel_size // 2
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            groups=groups,
+            bias=bias,
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.act = nn.SiLU()
+
+    def forward(self, x):
+        return self.act(self.bn(self.conv(x)))
+
+    def forward_fuse(self, x):
+        return self.act(self.conv(x))
+
+
+class SimConv(nn.Module):
+    '''Normal Conv with ReLU activation'''
+    def __init__(self, in_channels, out_channels, kernel_size, stride, groups=1, bias=False):
+        super().__init__()
+        padding = kernel_size // 2
+        self.conv = nn.Conv2d(
+            in_channels,
+            out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            groups=groups,
+            bias=bias,
+        )
+        self.bn = nn.BatchNorm2d(out_channels)
+        self.act = nn.ReLU()
+
+    def forward(self, x):
+        return self.act(self.bn(self.conv(x)))
+
+    def forward_fuse(self, x):
+        return self.act(self.conv(x))
+
+
+class SimSPPF(nn.Module):
+    '''Simplified SPPF with ReLU activation'''
+    def __init__(self, in_channels, out_channels, kernel_size=5):
+        super().__init__()
+        c_ = in_channels // 2  # hidden channels
+        self.cv1 = SimConv(in_channels, c_, 1, 1)
+        self.cv2 = SimConv(c_ * 4, out_channels, 1, 1)
+        self.m = nn.MaxPool2d(kernel_size=kernel_size, stride=1, padding=kernel_size // 2)
+
+    def forward(self, x):
+        x = self.cv1(x)
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore')
+            y1 = self.m(x)
+            y2 = self.m(y1)
+            return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))
+
+
+class Transpose(nn.Module):
+    '''Normal Transpose, default for upsampling'''
+    def __init__(self, in_channels, out_channels, kernel_size=2, stride=2):
+        super().__init__()
+        self.upsample_transpose = torch.nn.ConvTranspose2d(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            bias=True
+        )
+
+    def forward(self, x):
+        return self.upsample_transpose(x)
+
+
+class Concat(nn.Module):
+    def __init__(self, dimension=1):
+        super().__init__()
+        self.d = dimension
+
+    def forward(self, x):
+        return torch.cat(x, self.d)
+
+
+def conv_bn(in_channels, out_channels, kernel_size, stride, padding, groups=1):
+    '''Basic cell for rep-style block, including conv and bn'''
+    result = nn.Sequential()
+    result.add_module('conv', nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
+                                                  kernel_size=kernel_size, stride=stride, padding=padding, groups=groups, bias=False))
+    result.add_module('bn', nn.BatchNorm2d(num_features=out_channels))
+    return result
+
+
+class RepBlock(nn.Module):
+    '''
+        RepBlock is a stage block with rep-style basic block
+    '''
+    def __init__(self, in_channels, out_channels, n=1):
+        super().__init__()
+        self.conv1 = RepVGGBlock(in_channels, out_channels)
+        self.block = nn.Sequential(*(RepVGGBlock(out_channels, out_channels) for _ in range(n - 1))) if n > 1 else None
+
+    def forward(self, x):
+        x = self.conv1(x)
+        if self.block is not None:
+            x = self.block(x)
+        return x
+
+
+class RepVGGBlock(nn.Module):
+    '''RepVGGBlock is a basic rep-style block, including training and deploy status
+    This code is based on https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py
+    '''
+    def __init__(self, in_channels, out_channels, kernel_size=3,
+                 stride=1, padding=1, dilation=1, groups=1, padding_mode='zeros', deploy=False, use_se=False):
+        super(RepVGGBlock, self).__init__()
+        """ Initialization of the class.
+        Args:
+            in_channels (int): Number of channels in the input image
+            out_channels (int): Number of channels produced by the convolution
+            kernel_size (int or tuple): Size of the convolving kernel
+            stride (int or tuple, optional): Stride of the convolution. Default: 1
+            padding (int or tuple, optional): Zero-padding added to both sides of
+                the input. Default: 1
+            dilation (int or tuple, optional): Spacing between kernel elements. Default: 1
+            groups (int, optional): Number of blocked connections from input
+                channels to output channels. Default: 1
+            padding_mode (string, optional): Default: 'zeros'
+            deploy: Whether to be deploy status or training status. Default: False
+            use_se: Whether to use se. Default: False
+        """
+        self.deploy = deploy
+        self.groups = groups
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+
+        assert kernel_size == 3
+        assert padding == 1
+
+        padding_11 = padding - kernel_size // 2
+
+        self.nonlinearity = nn.ReLU()
+
+        if use_se:
+            raise NotImplementedError("se block not supported yet")
+        else:
+            self.se = nn.Identity()
+
+        if deploy:
+            self.rbr_reparam = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride,
+                                         padding=padding, dilation=dilation, groups=groups, bias=True, padding_mode=padding_mode)
+
+        else:
+            self.rbr_identity = nn.BatchNorm2d(num_features=in_channels) if out_channels == in_channels and stride == 1 else None
+            self.rbr_dense = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, groups=groups)
+            self.rbr_1x1 = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=stride, padding=padding_11, groups=groups)
+
+    def forward(self, inputs):
+        '''Forward process'''
+        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)
+
+        return self.nonlinearity(self.se(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 torch.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.conv.weight
+            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:
+            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 switch_to_deploy(self):
+        if hasattr(self, 'rbr_reparam'):
+            return
+        kernel, bias = self.get_equivalent_kernel_bias()
+        self.rbr_reparam = nn.Conv2d(in_channels=self.rbr_dense.conv.in_channels, out_channels=self.rbr_dense.conv.out_channels,
+                                     kernel_size=self.rbr_dense.conv.kernel_size, stride=self.rbr_dense.conv.stride,
+                                     padding=self.rbr_dense.conv.padding, dilation=self.rbr_dense.conv.dilation, groups=self.rbr_dense.conv.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')
+        if hasattr(self, 'id_tensor'):
+            self.__delattr__('id_tensor')
+        self.deploy = True
+
+
+def conv_bn_v2(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1,
+               padding_mode='zeros'):
+    conv_layer = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size,
+                           stride=stride, padding=padding, dilation=dilation, groups=groups,
+                           bias=False, padding_mode=padding_mode)
+    bn_layer = nn.BatchNorm2d(num_features=out_channels, affine=True)
+    se = nn.Sequential()
+    se.add_module('conv', conv_layer)
+    se.add_module('bn', bn_layer)
+    return se
+
+
+class IdentityBasedConv1x1(nn.Conv2d):
+
+    def __init__(self, channels, groups=1):
+        super(IdentityBasedConv1x1, self).__init__(in_channels=channels, out_channels=channels, kernel_size=1, stride=1, padding=0, groups=groups, bias=False)
+
+        assert channels % groups == 0
+        input_dim = channels // groups
+        id_value = np.zeros((channels, input_dim, 1, 1))
+        for i in range(channels):
+            id_value[i, i % input_dim, 0, 0] = 1
+        self.id_tensor = torch.from_numpy(id_value).type_as(self.weight)
+        nn.init.zeros_(self.weight)
+
+    def forward(self, input):
+        kernel = self.weight + self.id_tensor.to(self.weight.device)
+        result = F.conv2d(input, kernel, None, stride=1, padding=0, dilation=self.dilation, groups=self.groups)
+        return result
+
+    def get_actual_kernel(self):
+        return self.weight + self.id_tensor.to(self.weight.device)
+
+
+class BNAndPadLayer(nn.Module):
+    def __init__(self,
+                 pad_pixels,
+                 num_features,
+                 eps=1e-5,
+                 momentum=0.1,
+                 affine=True,
+                 track_running_stats=True):
+        super(BNAndPadLayer, self).__init__()
+        self.bn = nn.BatchNorm2d(num_features, eps, momentum, affine, track_running_stats)
+        self.pad_pixels = pad_pixels
+
+    def forward(self, input):
+        output = self.bn(input)
+        if self.pad_pixels > 0:
+            if self.bn.affine:
+                pad_values = self.bn.bias.detach() - self.bn.running_mean * self.bn.weight.detach() / torch.sqrt(self.bn.running_var + self.bn.eps)
+            else:
+                pad_values = - self.bn.running_mean / torch.sqrt(self.bn.running_var + self.bn.eps)
+            output = F.pad(output, [self.pad_pixels] * 4)
+            pad_values = pad_values.view(1, -1, 1, 1)
+            output[:, :, 0:self.pad_pixels, :] = pad_values
+            output[:, :, -self.pad_pixels:, :] = pad_values
+            output[:, :, :, 0:self.pad_pixels] = pad_values
+            output[:, :, :, -self.pad_pixels:] = pad_values
+        return output
+
+    @property
+    def bn_weight(self):
+        return self.bn.weight
+
+    @property
+    def bn_bias(self):
+        return self.bn.bias
+
+    @property
+    def running_mean(self):
+        return self.bn.running_mean
+
+    @property
+    def running_var(self):
+        return self.bn.running_var
+
+    @property
+    def eps(self):
+        return self.bn.eps
+
+
+class DBBBlock(nn.Module):
+    '''
+        RepBlock is a stage block with rep-style basic block
+    '''
+    def __init__(self, in_channels, out_channels, n=1):
+        super().__init__()
+        self.conv1 = DiverseBranchBlock(in_channels, out_channels)
+        self.block = nn.Sequential(*(DiverseBranchBlock(out_channels, out_channels) for _ in range(n - 1))) if n > 1 else None
+
+    def forward(self, x):
+        x = self.conv1(x)
+        if self.block is not None:
+            x = self.block(x)
+        return x
+
+
+class DiverseBranchBlock(nn.Module):
+
+    def __init__(self, in_channels, out_channels, kernel_size=3,
+                 stride=1, padding=1, dilation=1, groups=1,
+                 internal_channels_1x1_3x3=None,
+                 deploy=False, nonlinear=nn.ReLU(), single_init=False):
+        super(DiverseBranchBlock, self).__init__()
+        self.deploy = deploy
+
+        if nonlinear is None:
+            self.nonlinear = nn.Identity()
+        else:
+            self.nonlinear = nonlinear
+
+        self.kernel_size = kernel_size
+        self.out_channels = out_channels
+        self.groups = groups
+        assert padding == kernel_size // 2
+
+        if deploy:
+            self.dbb_reparam = 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.dbb_origin = conv_bn_v2(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups)
+
+            self.dbb_avg = nn.Sequential()
+            if groups < out_channels:
+                self.dbb_avg.add_module('conv',
+                                        nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1,
+                                                  stride=1, padding=0, groups=groups, bias=False))
+                self.dbb_avg.add_module('bn', BNAndPadLayer(pad_pixels=padding, num_features=out_channels))
+                self.dbb_avg.add_module('avg', nn.AvgPool2d(kernel_size=kernel_size, stride=stride, padding=0))
+                self.dbb_1x1 = conv_bn_v2(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=stride,
+                                       padding=0, groups=groups)
+            else:
+                self.dbb_avg.add_module('avg', nn.AvgPool2d(kernel_size=kernel_size, stride=stride, padding=padding))
+
+            self.dbb_avg.add_module('avgbn', nn.BatchNorm2d(out_channels))
+
+            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
+
+            self.dbb_1x1_kxk = nn.Sequential()
+            if internal_channels_1x1_3x3 == in_channels:
+                self.dbb_1x1_kxk.add_module('idconv1', IdentityBasedConv1x1(channels=in_channels, groups=groups))
+            else:
+                self.dbb_1x1_kxk.add_module('conv1', nn.Conv2d(in_channels=in_channels, out_channels=internal_channels_1x1_3x3,
+                                                            kernel_size=1, stride=1, padding=0, groups=groups, bias=False))
+            self.dbb_1x1_kxk.add_module('bn1', BNAndPadLayer(pad_pixels=padding, num_features=internal_channels_1x1_3x3, affine=True))
+            self.dbb_1x1_kxk.add_module('conv2', nn.Conv2d(in_channels=internal_channels_1x1_3x3, out_channels=out_channels,
+                                                            kernel_size=kernel_size, stride=stride, padding=0, groups=groups, bias=False))
+            self.dbb_1x1_kxk.add_module('bn2', nn.BatchNorm2d(out_channels))
+
+        #   The experiments reported in the paper used the default initialization of bn.weight (all as 1). But changing the initialization may be useful in some cases.
+        if single_init:
+            #   Initialize the bn.weight of dbb_origin as 1 and others as 0. This is not the default setting.
+            self.single_init()
+
+    def get_equivalent_kernel_bias(self):
+        k_origin, b_origin = transI_fusebn(self.dbb_origin.conv.weight, self.dbb_origin.bn)
+
+        if hasattr(self, 'dbb_1x1'):
+            k_1x1, b_1x1 = transI_fusebn(self.dbb_1x1.conv.weight, self.dbb_1x1.bn)
+            k_1x1 = transVI_multiscale(k_1x1, self.kernel_size)
+        else:
+            k_1x1, b_1x1 = 0, 0
+
+        if hasattr(self.dbb_1x1_kxk, 'idconv1'):
+            k_1x1_kxk_first = self.dbb_1x1_kxk.idconv1.get_actual_kernel()
+        else:
+            k_1x1_kxk_first = self.dbb_1x1_kxk.conv1.weight
+        k_1x1_kxk_first, b_1x1_kxk_first = transI_fusebn(k_1x1_kxk_first, self.dbb_1x1_kxk.bn1)
+        k_1x1_kxk_second, b_1x1_kxk_second = transI_fusebn(self.dbb_1x1_kxk.conv2.weight, self.dbb_1x1_kxk.bn2)
+        k_1x1_kxk_merged, b_1x1_kxk_merged = transIII_1x1_kxk(k_1x1_kxk_first, b_1x1_kxk_first, k_1x1_kxk_second, b_1x1_kxk_second, groups=self.groups)
+
+        k_avg = transV_avg(self.out_channels, self.kernel_size, self.groups)
+        k_1x1_avg_second, b_1x1_avg_second = transI_fusebn(k_avg.to(self.dbb_avg.avgbn.weight.device), self.dbb_avg.avgbn)
+        if hasattr(self.dbb_avg, 'conv'):
+            k_1x1_avg_first, b_1x1_avg_first = transI_fusebn(self.dbb_avg.conv.weight, self.dbb_avg.bn)
+            k_1x1_avg_merged, b_1x1_avg_merged = transIII_1x1_kxk(k_1x1_avg_first, b_1x1_avg_first, k_1x1_avg_second, b_1x1_avg_second, groups=self.groups)
+        else:
+            k_1x1_avg_merged, b_1x1_avg_merged = k_1x1_avg_second, b_1x1_avg_second
+
+        return transII_addbranch((k_origin, k_1x1, k_1x1_kxk_merged, k_1x1_avg_merged), (b_origin, b_1x1, b_1x1_kxk_merged, b_1x1_avg_merged))
+
+    def switch_to_deploy(self):
+        if hasattr(self, 'dbb_reparam'):
+            return
+        kernel, bias = self.get_equivalent_kernel_bias()
+        self.dbb_reparam = nn.Conv2d(in_channels=self.dbb_origin.conv.in_channels, out_channels=self.dbb_origin.conv.out_channels,
+                                     kernel_size=self.dbb_origin.conv.kernel_size, stride=self.dbb_origin.conv.stride,
+                                     padding=self.dbb_origin.conv.padding, dilation=self.dbb_origin.conv.dilation, groups=self.dbb_origin.conv.groups, bias=True)
+        self.dbb_reparam.weight.data = kernel
+        self.dbb_reparam.bias.data = bias
+        for para in self.parameters():
+            para.detach_()
+        self.__delattr__('dbb_origin')
+        self.__delattr__('dbb_avg')
+        if hasattr(self, 'dbb_1x1'):
+            self.__delattr__('dbb_1x1')
+        self.__delattr__('dbb_1x1_kxk')
+
+    def forward(self, inputs):
+
+        if hasattr(self, 'dbb_reparam'):
+            return self.nonlinear(self.dbb_reparam(inputs))
+
+        out = self.dbb_origin(inputs)
+        if hasattr(self, 'dbb_1x1'):
+            out += self.dbb_1x1(inputs)
+        out += self.dbb_avg(inputs)
+        out += self.dbb_1x1_kxk(inputs)
+        return self.nonlinear(out)
+
+    def init_gamma(self, gamma_value):
+        if hasattr(self, "dbb_origin"):
+            torch.nn.init.constant_(self.dbb_origin.bn.weight, gamma_value)
+        if hasattr(self, "dbb_1x1"):
+            torch.nn.init.constant_(self.dbb_1x1.bn.weight, gamma_value)
+        if hasattr(self, "dbb_avg"):
+            torch.nn.init.constant_(self.dbb_avg.avgbn.weight, gamma_value)
+        if hasattr(self, "dbb_1x1_kxk"):
+            torch.nn.init.constant_(self.dbb_1x1_kxk.bn2.weight, gamma_value)
+
+    def single_init(self):
+        self.init_gamma(0.0)
+        if hasattr(self, "dbb_origin"):
+            torch.nn.init.constant_(self.dbb_origin.bn.weight, 1.0)
+
+
+class DetectBackend(nn.Module):
+    def __init__(self, weights='yolov6s.pt', device=None, dnn=True):
+
+        super().__init__()
+        assert isinstance(weights, str) and Path(weights).suffix == '.pt', f'{Path(weights).suffix} format is not supported.'
+        from yolov6.utils.checkpoint import load_checkpoint
+        model = load_checkpoint(weights, map_location=device)
+        stride = int(model.stride.max())
+        self.__dict__.update(locals())  # assign all variables to self
+
+    def forward(self, im, val=False):
+        y = self.model(im)
+        if isinstance(y, np.ndarray):
+            y = torch.tensor(y, device=self.device)
+        return y