mobileone.py

import torch.nn as nn
import numpy as np
import torch
import copy
# from torchvision.models import MobileNetV2

def conv_bn(in_channels, out_channels, kernel_size, stride, padding, groups=1):
    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 DepthWiseConv(nn.Module):
    def __init__(self, inc, kernel_size, stride=1):
        super().__init__()
        padding = 1
        if kernel_size == 1:
            padding = 0
        # self.conv = nn.Sequential(
        #     nn.Conv2d(inc, inc, kernel_size, stride, padding, groups=inc, bias=False,),
        #     nn.BatchNorm2d(inc),
        # )
        self.conv = conv_bn(inc, inc,kernel_size, stride, padding, inc)

    def forward(self, x):
        return self.conv(x)
    

class PointWiseConv(nn.Module):
    def __init__(self, inc, outc):
        super().__init__()
        # self.conv = nn.Sequential(
        #     nn.Conv2d(inc, outc, 1, 1, 0, bias=False),
        #     nn.BatchNorm2d(outc),
        # )
        self.conv = conv_bn(inc, outc, 1, 1, 0)
    def forward(self, x):
        return self.conv(x)



class MobileOneBlock(nn.Module):

    def __init__(self, in_channels, out_channels, k,
                 stride=1, dilation=1, padding_mode='zeros', deploy=False, use_se=False):
        super(MobileOneBlock, self).__init__()
        self.deploy = deploy
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.deploy = deploy
        kernel_size = 3
        padding = 1
        assert kernel_size == 3
        assert padding == 1
        self.k = k
        padding_11 = padding - kernel_size // 2

        self.nonlinearity = nn.ReLU()

        if use_se:
            # self.se = SEBlock(out_channels, internal_neurons=out_channels // 16)
            ...
        else:
            self.se = nn.Identity()

        if deploy:
            self.dw_reparam = nn.Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=kernel_size, stride=stride,
                                      padding=padding, dilation=dilation, groups=in_channels, bias=True, padding_mode=padding_mode)
            self.pw_reparam = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, bias=True)

        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)
            # print('RepVGG Block, identity = ', self.rbr_identity)
            self.dw_bn_layer = nn.BatchNorm2d(in_channels) if out_channels == in_channels and stride == 1 else None
            for k_idx in range(k):
                setattr(self, f'dw_3x3_{k_idx}', 
                    DepthWiseConv(in_channels, 3, stride=stride)
                )
            self.dw_1x1 = DepthWiseConv(in_channels, 1, stride=stride)

            self.pw_bn_layer = nn.BatchNorm2d(in_channels) if out_channels == in_channels and stride == 1 else None
            for k_idx in range(k):
                setattr(self, f'pw_1x1_{k_idx}', 
                    PointWiseConv(in_channels, out_channels)
                )


    def forward(self, inputs):
        if self.deploy:
            x = self.dw_reparam(inputs)
            x = self.nonlinearity(x)
            x = self.pw_reparam(x)
            x = self.nonlinearity(x)
            return x

        if self.dw_bn_layer is None:
            id_out = 0
        else:
            id_out = self.dw_bn_layer(inputs)
        
        x_conv_3x3 = []
        for k_idx in range(self.k):
            x = getattr(self, f'dw_3x3_{k_idx}')(inputs)
            # print(x.shape)
            x_conv_3x3.append(x)
        x_conv_1x1 = self.dw_1x1(inputs)
        # print(x_conv_1x1.shape, x_conv_3x3[0].shape)
        # print(x_conv_1x1.shape)
        # print(id_out)
        x = id_out + x_conv_1x1 + sum(x_conv_3x3)
        x = self.nonlinearity(self.se(x))

         # 1x1 conv
        if self.pw_bn_layer is None:
            id_out = 0
        else:
            id_out = self.pw_bn_layer(x)
        x_conv_1x1 = []
        for k_idx in range(self.k):
            x_conv_1x1.append(getattr(self, f'pw_1x1_{k_idx}')(x))
        x = id_out + sum(x_conv_1x1)
        x = self.nonlinearity(x)
        return x


    #   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()
    def get_custom_L2(self):
        # K3 = self.rbr_dense.conv.weight
        # 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
        ...



#   This func derives the equivalent kernel and bias in a DIFFERENTIABLE way.
#   You can get the equivalent kernel and bias at any time and do whatever you want,
    #   for example, apply some penalties or constraints during training, just like you do to the other models.
#   May be useful for quantization or pruning.
    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

        dw_kernel_3x3 = []
        dw_bias_3x3 = []
        for k_idx in range(self.k):
            k3, b3 = self._fuse_bn_tensor(getattr(self, f"dw_3x3_{k_idx}").conv)
            # print(k3.shape, b3.shape)
            dw_kernel_3x3.append(k3)
            dw_bias_3x3.append(b3)
        dw_kernel_1x1, dw_bias_1x1 = self._fuse_bn_tensor(self.dw_1x1.conv)
        dw_kernel_id, dw_bias_id = self._fuse_bn_tensor(self.dw_bn_layer, self.in_channels)
        dw_kernel = sum(dw_kernel_3x3) + self._pad_1x1_to_3x3_tensor(dw_kernel_1x1) + dw_kernel_id
        dw_bias = sum(dw_bias_3x3) + dw_bias_1x1 + dw_bias_id
        # pw
        pw_kernel = []
        pw_bias = []
        for k_idx in range(self.k):
            k1, b1 = self._fuse_bn_tensor(getattr(self, f"pw_1x1_{k_idx}").conv)
            # print(k1.shape)
            pw_kernel.append(k1)
            pw_bias.append(b1)
        pw_kernel_id, pw_bias_id = self._fuse_bn_tensor(self.pw_bn_layer, 1)

        pw_kernel_1x1 = sum(pw_kernel) + pw_kernel_id
        pw_bias_1x1 = sum(pw_bias) + pw_bias_id
        return dw_kernel, dw_bias, pw_kernel_1x1, pw_bias_1x1


        

        

    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, groups=None):
        if branch is None:
            return 0, 0
        if isinstance(branch, nn.Sequential):
            kernel = branch.conv.weight
            bias = branch.conv.bias
            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 // groups # self.groups
            if groups == 1:
                ks = 1
            else:
                ks = 3
            kernel_value = np.zeros((self.in_channels, input_dim, ks, ks), dtype=np.float32)
            for i in range(self.in_channels):
                if ks == 1:
                    kernel_value[i, i % input_dim, 0, 0] = 1
                else:
                    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):
        dw_kernel, dw_bias, pw_kernel, pw_bias = self.get_equivalent_kernel_bias()

        self.dw_reparam = nn.Conv2d(
            in_channels=self.pw_1x1_0.conv.conv.in_channels, 
            out_channels=self.pw_1x1_0.conv.conv.in_channels,                              
            kernel_size=self.dw_3x3_0.conv.conv.kernel_size, 
            stride=self.dw_3x3_0.conv.conv.stride,
            padding=self.dw_3x3_0.conv.conv.padding, 
            groups=self.dw_3x3_0.conv.conv.in_channels, 
            bias=True, 
            )
        self.pw_reparam = nn.Conv2d(
            in_channels=self.pw_1x1_0.conv.conv.in_channels,
            out_channels=self.pw_1x1_0.conv.conv.out_channels, 
            kernel_size=1, 
            stride=1, 
            bias=True
            )

        self.dw_reparam.weight.data = dw_kernel
        self.dw_reparam.bias.data = dw_bias
        self.pw_reparam.weight.data = pw_kernel
        self.pw_reparam.bias.data = pw_bias

        for para in self.parameters():
            para.detach_()
        self.__delattr__('dw_1x1')
        for k_idx in range(self.k):
            self.__delattr__(f'dw_3x3_{k_idx}')
            self.__delattr__(f'pw_1x1_{k_idx}')
        if hasattr(self, 'dw_bn_layer'):
            self.__delattr__('dw_bn_layer')
        if hasattr(self, 'pw_bn_layer'):
            self.__delattr__('pw_bn_layer')
        if hasattr(self, 'id_tensor'):
            self.__delattr__('id_tensor')
        self.deploy = True
        
     



class MobileOneNet(nn.Module):
    def __init__(self, blocks, ks, channels, strides, width_muls, num_classes, deploy=False):
        super().__init__()

        self.stage_num = len(blocks)
        # self.stage0 = MobileOneBlock(3, int(channels[0] * width_muls[0]), ks[0], stride=strides[0], deploy=deploy)
        self.stage0 = nn.Sequential(
            nn.Conv2d(3, int(channels[0] * width_muls[0]), 3, 2, 1, bias=False),
            nn.BatchNorm2d(int(channels[0] * width_muls[0])),
            nn.ReLU(),
        )
        in_channels = int(channels[0] * width_muls[0])
        for idx, block_num in enumerate(blocks[1:]):
            idx += 1
            module = []
            out_channels = int(channels[idx] * width_muls[idx])
            for b_idx in range(block_num):
                stride = strides[idx] if b_idx == 0 else 1
                block = MobileOneBlock(in_channels, out_channels, ks[idx], stride, deploy=deploy)
                in_channels = out_channels
                module.append(block)
            setattr(self, f"stage{idx}", nn.Sequential(*module))

        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc1 = nn.Sequential(
            nn.Linear(out_channels, num_classes,),
        )


    def forward(self, x):
        # for s_idx in range(self.stage_num):
        #     x = getattr(self, f'stage{s_idx}')(x)
        x0 = self.stage0(x)
        # print(x0[0,:,0,0])
        # return x0
        x1 = self.stage1(x0)
        x2 = self.stage2(x1)
        x3 = self.stage3(x2)
        x4 = self.stage4(x3)
        x5 = self.stage5(x4)
        assert x5.shape[-1] == 7
        x = self.avg_pool(x5)
        x = torch.flatten(x, start_dim=1) # b, c
        x = self.fc1(x)
        return x
    

def make_mobileone_s0(deploy=False):
    blocks = [
        1,2,8,5,5,1
    ]
    strides = [
        2,2,2,2,1,2
    ]
    ks = [
        4,4,4,4,4,4
    ] if deploy is False else \
        [
            1,1,1,1,1,1
        ]
    width_muls = [
        0.75, 0.75, 1, 1, 1, 2
    ] # 261 M flops
    channels = [
        64, 64, 128, 256, 256, 512, 512
    ]
    num_classes = 1000

    model = MobileOneNet(blocks, ks, channels, strides, width_muls, num_classes, deploy)
    return model

def repvgg_model_convert(model:torch.nn.Module, do_copy=True, input=None, output=None):
    if do_copy:
        model = copy.deepcopy(model)
    for module in model.modules():
        if hasattr(module, 'switch_to_deploy'):
            module.switch_to_deploy()
    print('swith done. Checking....')
    deploy_model = make_mobileone_s0(deploy=True)
    deploy_model.eval()
    deploy_model.load_state_dict(model.state_dict())
    if input is not None:
        o = deploy_model(x)
        # print(o)
        # print(output)
        print((output - o).sum())
    # if save_path is not None:
    #     torch.save(model.state_dict(), save_path)
    return deploy_model