# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Common modules
"""

import ast
import contextlib
import json
import math
import platform
import warnings
import zipfile
from collections import OrderedDict, namedtuple
from copy import copy
from pathlib import Path
from urllib.parse import urlparse

import cv2
import numpy as np
import pandas as pd
import requests
import torch
import torch.nn as nn
from PIL import Image
from torch.cuda import amp

from thirdTool.yolo5x_qr.utils import TryExcept
from thirdTool.yolo5x_qr.utils.dataloaders import exif_transpose, letterbox
from thirdTool.yolo5x_qr.utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr,
                           increment_path, is_jupyter, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy,
                           xyxy2xywh, yaml_load)
from thirdTool.yolo5x_qr.utils.plots import Annotator, colors, save_one_box
from thirdTool.yolo5x_qr.utils.torch_utils import copy_attr, smart_inference_mode

from torch.nn import functional as F
from .Functions import Encoding, Mean, DropPath, Mlp, GroupNorm, ConvBlock


def autopad(k, p=None, d=1):  # kernel, padding, dilation
    # Pad to 'same' shape outputs
    if d > 1:
        k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k]  # actual kernel-size
    if p is None:
        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
    return p

def export_formats():
    # YOLOv5 export formats
    x = [
        ['PyTorch', '-', '.pt', True, True],
        ['TorchScript', 'torchscript', '.torchscript', True, True],
        ['ONNX', 'onnx', '.onnx', True, True],
        ['OpenVINO', 'openvino', '_openvino_model', True, False],
        ['TensorRT', 'engine', '.engine', False, True],
        ['CoreML', 'coreml', '.mlmodel', True, False],
        ['TensorFlow SavedModel', 'saved_model', '_saved_model', True, True],
        ['TensorFlow GraphDef', 'pb', '.pb', True, True],
        ['TensorFlow Lite', 'tflite', '.tflite', True, False],
        ['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False],
        ['TensorFlow.js', 'tfjs', '_web_model', False, False],
        ['PaddlePaddle', 'paddle', '_paddle_model', True, True],]
    return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])

class Conv(nn.Module):
    # Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)
    default_act = nn.SiLU()  # default activation

    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
        super().__init__()
        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)
        self.bn = nn.BatchNorm2d(c2)
        self.act = self.default_act 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 forward_fuse(self, x):
        return self.act(self.conv(x))


class DWConv(Conv):
    # Depth-wise convolution
    def __init__(self, c1, c2, k=1, s=1, d=1, act=True):  # ch_in, ch_out, kernel, stride, dilation, activation
        super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act)


class DWConvTranspose2d(nn.ConvTranspose2d):
    # Depth-wise transpose convolution
    def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0):  # ch_in, ch_out, kernel, stride, padding, padding_out
        super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2))


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).permute(2, 0, 1)
        return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h)


class Bottleneck(nn.Module):
    # Standard bottleneck
    def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion
        super().__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 BottleneckCSP(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().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
        self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
        self.cv4 = Conv(2 * c_, c2, 1, 1)
        self.bn = nn.BatchNorm2d(2 * c_)  # applied to cat(cv2, cv3)
        self.act = nn.SiLU()
        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(self.act(self.bn(torch.cat((y1, y2), 1))))


class CrossConv(nn.Module):
    # Cross Convolution Downsample
    def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
        # ch_in, ch_out, kernel, stride, groups, expansion, shortcut
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, (1, k), (1, s))
        self.cv2 = Conv(c_, c2, (k, 1), (s, 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 C3(nn.Module):
    # CSP Bottleneck with 3 convolutions
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
        super().__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)  # optional act=FReLU(c2)
        self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))

    def forward(self, x):
        return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))


class C3x(C3):
    # C3 module with cross-convolutions
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
        super().__init__(c1, c2, n, shortcut, g, e)
        c_ = int(c2 * e)
        self.m = nn.Sequential(*(CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)))


class C3TR(C3):
    # C3 module with TransformerBlock()
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
        super().__init__(c1, c2, n, shortcut, g, e)
        c_ = int(c2 * e)
        self.m = TransformerBlock(c_, c_, 4, n)


class C3SPP(C3):
    # C3 module with SPP()
    def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5):
        super().__init__(c1, c2, n, shortcut, g, e)
        c_ = int(c2 * e)
        self.m = SPP(c_, c_, k)


class C3Ghost(C3):
    # C3 module with GhostBottleneck()
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
        super().__init__(c1, c2, n, shortcut, g, e)
        c_ = int(c2 * e)  # hidden channels
        self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n)))


class SPP(nn.Module):
    # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729
    def __init__(self, c1, c2, k=(5, 9, 13)):
        super().__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)
        with warnings.catch_warnings():
            warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warning
            return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))


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)
        with warnings.catch_warnings():
            warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warning
            y1 = self.m(x)
            y2 = self.m(y1)
            return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))


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().__init__()
        self.conv = Conv(c1 * 4, c2, k, s, p, g, act=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 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().__init__()
        c_ = c2 // 2  # hidden channels
        self.cv1 = Conv(c1, c_, k, s, None, g, act=act)
        self.cv2 = Conv(c_, c_, 5, 1, None, c_, act=act)

    def forward(self, x):
        y = self.cv1(x)
        return torch.cat((y, self.cv2(y)), 1)


class GhostBottleneck(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().__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)


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):
        b, c, h, w = x.size()  # assert (h / s == 0) and (W / s == 0), 'Indivisible gain'
        s = self.gain
        x = x.view(b, 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(b, 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):
        b, c, h, w = x.size()  # assert C / s ** 2 == 0, 'Indivisible gain'
        s = self.gain
        x = x.view(b, 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(b, c // s ** 2, h * s, w * s)  # x(1,16,160,160)


class Concat(nn.Module):
    # Concatenate a list of tensors along dimension
    def __init__(self, dimension=1):
        super().__init__()
        self.d = dimension

    def forward(self, x):
        return torch.cat(x, self.d)

#by AI&CV C2F
class v8_C2fBottleneck(nn.Module):
    # Standard bottleneck
    def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):  # ch_in, ch_out, shortcut, groups, kernels, expand
        super().__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, k[0], 1)
        self.cv2 = Conv(c_, c2, k[1], 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 C2f(nn.Module):
    # CSP Bottleneck with 2 convolutions
    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansion
        super().__init__()
        self.c = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, 2 * self.c, 1, 1)
        self.cv2 = Conv((2 + n) * self.c, c2, 1)  # optional act=FReLU(c2)
        self.m = nn.ModuleList(
            v8_C2fBottleneck(self.c, self.c, shortcut, g, k=((3, 3), (3, 3)), e=1.0) for _ in range(n))

    def forward(self, x):
        y = list(self.cv1(x).split((self.c, self.c), 1))
        y.extend(m(y[-1]) for m in self.m)
        return self.cv2(torch.cat(y, 1))


# by AI&CV EVCBlock  ####### start #########################

class SiLU(nn.Module):
    """export-friendly version of nn.SiLU()"""

    @staticmethod
    def forward(x):
        return x * torch.sigmoid(x)


def get_activation(name="silu", inplace=True):
    if name == "silu":
        module = nn.SiLU(inplace=inplace)
    elif name == "relu":
        module = nn.ReLU(inplace=inplace)
    elif name == "lrelu":
        module = nn.LeakyReLU(0.1, inplace=inplace)
    else:
        raise AttributeError("Unsupported act type: {}".format(name))
    return module

class BaseConv(nn.Module):
    """A Conv2d -> Batchnorm -> silu/leaky relu block""" # CBL

    def __init__(
        self, in_channels, out_channels, ksize, stride, groups=1, bias=False, act="silu"
    ):
        super().__init__()
        # same padding
        pad = (ksize - 1) // 2
        self.conv = nn.Conv2d(
            in_channels,
            out_channels,
            kernel_size=ksize,
            stride=stride,
            padding=pad,
            groups=groups,
            bias=bias,
        )
        self.bn = nn.BatchNorm2d(out_channels)
        self.act = get_activation(act, inplace=True)

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

    def fuseforward(self, x):
        return self.act(self.conv(x))

class DWConv(nn.Module):
    """Depthwise Conv + Conv"""
    def __init__(self, in_channels, out_channels, ksize, stride=1, act="silu"):
        super().__init__()
        self.dconv = BaseConv(
            in_channels,
            in_channels,
            ksize=ksize,
            stride=stride,
            groups=in_channels,
            act=act,
        )
        self.pconv = BaseConv(
            in_channels, out_channels, ksize=1, stride=1, groups=1, act=act
        )

    def forward(self, x):
        x = self.dconv(x)
        return self.pconv(x)

class LVCBlock(nn.Module):
    def __init__(self, c1, c2, num_codes, channel_ratio=0.25, base_channel=64):
        super(LVCBlock, self).__init__()
        self.c2 = c2
        self.num_codes = num_codes
        num_codes = 64

        self.conv_1 = ConvBlock(c1, c1, res_conv=True, stride=1)

        self.LVC = nn.Sequential(
            nn.Conv2d(c1, c1, 1, bias=False),
            nn.BatchNorm2d(c1),
            nn.ReLU(inplace=True),
            Encoding(c1, num_codes=num_codes),
            nn.BatchNorm1d(num_codes),
            nn.ReLU(inplace=True),
            Mean(dim=1))
        self.fc = nn.Sequential(nn.Linear(c1, c1), nn.Sigmoid())

    def forward(self, x):
        x = self.conv_1(x, return_x_2=False)
        en = self.LVC(x)
        gam = self.fc(en)
        b, in_channels, _, _ = x.size()
        y = gam.view(b, in_channels, 1, 1)
        x = F.relu_(x + x * y)
        return x


# LightMLPBlock
class LightMLPBlock(nn.Module):
    def __init__(self, c1, c2, ksize=1, stride=1, act="silu",
    mlp_ratio=4., drop=0., act_layer=nn.GELU,
    use_layer_scale=True, layer_scale_init_value=1e-5, drop_path=0., norm_layer=GroupNorm):  # act_layer=nn.GELU,
        super().__init__()
        self.dw = DWConv(c1, c2, ksize=1, stride=1, act="silu")
        self.linear = nn.Linear(c2, c2)  # learnable position embedding
        self.c2 = c2

        self.norm1 = norm_layer(c1)
        self.norm2 = norm_layer(c1)

        mlp_hidden_dim = int(c1 * mlp_ratio)
        self.mlp = Mlp(in_features=c1, hidden_features=mlp_hidden_dim, act_layer=nn.GELU,
                       drop=drop)

        self.drop_path = DropPath(drop_path) if drop_path > 0. \
            else nn.Identity()

        self.use_layer_scale = use_layer_scale
        if use_layer_scale:
            self.layer_scale_1 = nn.Parameter(
                layer_scale_init_value * torch.ones((c2)), requires_grad=True)
            self.layer_scale_2 = nn.Parameter(
                layer_scale_init_value * torch.ones((c2)), requires_grad=True)

    def forward(self, x):
        if self.use_layer_scale:
            x = x + self.drop_path(self.layer_scale_1.unsqueeze(-1).unsqueeze(-1) * self.dw(self.norm1(x)))
            x = x + self.drop_path(self.layer_scale_2.unsqueeze(-1).unsqueeze(-1) * self.mlp(self.norm2(x)))
        else:
            x = x + self.drop_path(self.dw(self.norm1(x)))
            x = x + self.drop_path(self.mlp(self.norm2(x)))
        return x


# EVCBlock
class EVCBlock(nn.Module):
    def __init__(self, c1, c2, channel_ratio=4, base_channel=16):
        super().__init__()
        expansion = 2
        ch = c2 * expansion
        # Stem stage: get the feature maps by conv block (copied form resnet.py) 进入conformer框架之前的处理
        self.conv1 = nn.Conv2d(c1, c1, kernel_size=7, stride=1, padding=3, bias=False)  # 1 / 2 [112, 112]
        self.bn1 = nn.BatchNorm2d(c1)
        self.act1 = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)  # 1 / 4 [56, 56]

        # LVC
        self.lvc = LVCBlock(c1, c2, num_codes=64)  # c1值暂时未定
        # LightMLPBlock
        self.l_MLP = LightMLPBlock(c1, c2, ksize=1, stride=1, act="silu", act_layer=nn.GELU, mlp_ratio=4., drop=0.,
                                     use_layer_scale=True, layer_scale_init_value=1e-5, drop_path=0., norm_layer=GroupNorm)
        self.cnv1 = nn.Conv2d(ch, c2, kernel_size=1, stride=1, padding=0)

    def forward(self, x):
        x1 = self.maxpool(self.act1(self.bn1(self.conv1(x))))
        # LVCBlock
        x_lvc = self.lvc(x1)
        # LightMLPBlock
        x_lmlp = self.l_MLP(x1)
        # concat
        x = torch.cat((x_lvc, x_lmlp), dim=1)
        x = self.cnv1(x)
        return x
# by AI&CV EVCBlock  ####### end #########################

# ------------------------------------ODConv start -------------------------------------
class ODConv2d_3rd(nn.Conv2d):

    def __init__(self, in_channels, out_channels, kernel_size,
                 stride=1, padding=0, dilation=1, groups=1, bias=True,
                 K=4, r=1 / 16, save_parameters=False,
                 padding_mode='zeros', device=None, dtype=None) -> None:
        factory_kwargs = {'device': device, 'dtype': dtype}
        self.K = K
        self.r = r
        self.save_parameters = save_parameters

        super().__init__(in_channels, out_channels, kernel_size, stride,
                         padding, dilation, groups, bias, padding_mode)

        del self.weight
        self.weight = nn.Parameter(torch.empty((
            K,
            out_channels,
            in_channels // groups,
            *self.kernel_size,
        ), **factory_kwargs))

        if bias:
            del self.bias
            self.bias = nn.Parameter(torch.empty(K, out_channels, **factory_kwargs))

        hidden_dim = max(int(in_channels * r), 16)  # 设置下限为16
        self.gap = nn.AdaptiveAvgPool2d(1)
        self.reduction = nn.Linear(in_channels, hidden_dim)
        self.fc = nn.Conv2d(in_channels, hidden_dim, 1, bias=False)
        self.bn = nn.BatchNorm2d(hidden_dim)
        self.act = nn.ReLU(inplace=True)
        # self.act = nn.SiLU(inplace=True)

        self.fc_f = nn.Linear(hidden_dim, out_channels)
        if not save_parameters or self.kernel_size[0] * self.kernel_size[1] > 1:
            self.fc_s = nn.Linear(hidden_dim, self.kernel_size[0] * self.kernel_size[1])
        if not save_parameters or in_channels // groups > 1:
            self.fc_c = nn.Linear(hidden_dim, in_channels // groups)
        if not save_parameters or K > 1:
            self.fc_w = nn.Linear(hidden_dim, K)

        self.reset_parameters()

    def reset_parameters(self) -> None:
        fan_out = self.kernel_size[0] * self.kernel_size[1] * self.out_channels // self.groups
        for i in range(self.K):
            self.weight.data[i].normal_(0, math.sqrt(2.0 / fan_out))
        if self.bias is not None:
            self.bias.data.zero_()

    def extra_repr(self):
        return super().extra_repr() + f', K={self.K}, r={self.r:.4}'

    def get_weight_bias(self, context):
        B, C, H, W = context.shape

        if C != self.in_channels:
            raise ValueError(
                f"Expected context{[B, C, H, W]} to have {self.in_channels} channels, but got {C} channels instead")

        # x = self.gap(context).squeeze(-1).squeeze(-1)  # B, c_in
        # x = self.reduction(x)  # B, hidden_dim
        x = self.gap(context)
        x = self.fc(x)
        if x.size(0) > 1:
            x = self.bn(x)
        x = x.squeeze(-1).squeeze(-1)
        x = self.act(x)

        attn_f = self.fc_f(x).sigmoid()  # B, c_out
        attn = attn_f.view(B, 1, -1, 1, 1, 1)  # B, 1, c_out, 1, 1, 1
        if hasattr(self, 'fc_s'):
            attn_s = self.fc_s(x).sigmoid()  # B, k * k
            attn = attn * attn_s.view(B, 1, 1, 1, *self.kernel_size)  # B, 1, c_out, 1, k, k
        if hasattr(self, 'fc_c'):
            attn_c = self.fc_c(x).sigmoid()  # B, c_in // groups
            attn = attn * attn_c.view(B, 1, 1, -1, 1, 1)  # B, 1, c_out, c_in // groups, k, k
        if hasattr(self, 'fc_w'):
            attn_w = self.fc_w(x).softmax(-1)  # B, n
            attn = attn * attn_w.view(B, -1, 1, 1, 1, 1)  # B, n, c_out, c_in // groups, k, k

        weight = (attn * self.weight).sum(1)  # B, c_out, c_in // groups, k, k
        weight = weight.view(-1, self.in_channels // self.groups, *self.kernel_size)  # B * c_out, c_in // groups, k, k

        bias = None
        if self.bias is not None:
            if hasattr(self, 'fc_w'):
                bias = attn_w @ self.bias
            else:
                bias = self.bias.tile(B, 1)
            bias = bias.view(-1)  # B * c_out

        return weight, bias

    def forward(self, input, context=None):
        B, C, H, W = input.shape

        if C != self.in_channels:
            raise ValueError(
                f"Expected input{[B, C, H, W]} to have {self.in_channels} channels, but got {C} channels instead")

        weight, bias = self.get_weight_bias(context or input)

        output = nn.functional.conv2d(
            input.view(1, B * C, H, W), weight, bias,
            self.stride, self.padding, self.dilation, B * self.groups)  # 1, B * c_out, h_out, w_out
        output = output.view(B, self.out_channels, *output.shape[2:])

        return output

    def debug(self, input, context=None):
        B, C, H, W = input.shape

        if C != self.in_channels:
            raise ValueError(
                f"Expected input{[B, C, H, W]} to have {self.in_channels} channels, but got {C} channels instead")

        output_size = [
            ((H, W)[i] + 2 * self.padding[i] - self.dilation[i] * (self.kernel_size[i] - 1) - 1) // self.stride[i] + 1
            for i in range(2)
        ]

        weight, bias = self.get_weight_bias(context or input)

        weight = weight.view(B, self.groups, self.out_channels // self.groups,
                             -1)  # B, groups, c_out // groups, c_in // groups * k * k

        unfold = nn.functional.unfold(
            input, self.kernel_size, self.dilation, self.padding, self.stride)  # B, c_in * k * k, H_out * W_out
        unfold = unfold.view(B, self.groups, -1,
                             output_size[0] * output_size[1])  # B, groups, c_in // groups * k * k, H_out * W_out

        output = weight @ unfold  # B, groups, c_out // groups, H_out * W_out
        output = output.view(B, self.out_channels, *output_size)  # B, c_out, H_out * W_out

        if bias is not None:
            output = output + bias.view(B, self.out_channels, 1, 1)

        return output


class ODConv_3rd(nn.Module):
    # Standard convolution
    def __init__(self, c1, c2, k=1, s=1, kerNums=1, g=1, p=None,
                 act=True):  # ch_in, ch_out, kernel, stride, padding, groups
        super().__init__()
        self.conv = ODConv2d_3rd(c1, c2, k, s, autopad(k, p), groups=g, K=kerNums)
        self.bn = nn.BatchNorm2d(c2)
        self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())

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

    def forward_fuse(self, x):
        return self.act(self.conv(x))


# ------------------------------------ODConv end -------------------------------------
# ------------------------------------Convnext start -------------------------------------
# ConvNextBlock
class ConvNextBlock(nn.Module):

    def __init__(self, inputdim, dim, drop_path=0., layer_scale_init_value=1e-6,
                 kersize=7):  # demo: [64, 64, 1]  1 denotes the number of repeats
        super().__init__()
        # 匹配yolov5配置文件加入outdim输出通道
        # self.flag = True if dim == outdim else False

        self.dwconv = nn.Conv2d(dim, dim, kernel_size=kersize, padding=kersize // 2, groups=dim)  # depthwise conv
        self.norm = LayerNorm_s(dim, eps=1e-6)
        self.pwconv1 = nn.Linear(dim, 4 * dim)
        self.act = nn.GELU()
        self.pwconv2 = nn.Linear(4 * dim, dim)
        self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)),
                                  requires_grad=True) if layer_scale_init_value > 0 else None
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()

    def forward(self, x):
        # if self.flag == False:
        #     raise ValueError(
        #         f"Expected input out to have {dim} channels, but got {outdim} channels instead")

        input = x
        x = self.dwconv(x)
        x = x.permute(0, 2, 3, 1)  # (N, C, H, W) -> (N, H, W, C)
        x = self.norm(x)
        x = self.pwconv1(x)
        x = self.act(x)
        x = self.pwconv2(x)
        if self.gamma is not None:
            x = self.gamma * x
        x = x.permute(0, 3, 1, 2)  # (N, H, W, C) -> (N, C, H, W)

        x = input + self.drop_path(x)
        return x


class LayerNorm_s(nn.Module):

    def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(normalized_shape))
        self.bias = nn.Parameter(torch.zeros(normalized_shape))
        self.eps = eps
        self.data_format = data_format
        if self.data_format not in ["channels_last", "channels_first"]:
            raise NotImplementedError
        self.normalized_shape = (normalized_shape,)

    def forward(self, x):
        if self.data_format == "channels_last":
            return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
        elif self.data_format == "channels_first":
            u = x.mean(1, keepdim=True)
            s = (x - u).pow(2).mean(1, keepdim=True)
            x = (x - u) / torch.sqrt(s + self.eps)
            x = self.weight[:, None, None] * x + self.bias[:, None, None]
            return x


class DropPath(nn.Module):
    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
    """

    def __init__(self, drop_prob=None):
        super(DropPath, self).__init__()
        self.drop_prob = drop_prob

    def forward(self, x):
        return drop_path_f(x, self.drop_prob, self.training)


def drop_path_f(x, drop_prob: float = 0., training: bool = False):
    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
    'survival rate' as the argument.
    """
    if drop_prob == 0. or not training:
        return x
    keep_prob = 1 - drop_prob
    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
    random_tensor.floor_()  # binarize
    output = x.div(keep_prob) * random_tensor
    return output


# ------------------------------------Convnext end -------------------------------------

class DetectMultiBackend(nn.Module):
    # YOLOv5 MultiBackend class for python inference on various backends
    def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=True):
        # Usage:
        #   PyTorch:              weights = *.pt
        #   TorchScript:                    *.torchscript
        #   ONNX Runtime:                   *.onnx
        #   ONNX OpenCV DNN:                *.onnx --dnn
        #   OpenVINO:                       *_openvino_model
        #   CoreML:                         *.mlmodel
        #   TensorRT:                       *.engine
        #   TensorFlow SavedModel:          *_saved_model
        #   TensorFlow GraphDef:            *.pb
        #   TensorFlow Lite:                *.tflite
        #   TensorFlow Edge TPU:            *_edgetpu.tflite
        #   PaddlePaddle:                   *_paddle_model
        from .experimental import attempt_download, attempt_load  # scoped to avoid circular import

        super().__init__()
        w = str(weights[0] if isinstance(weights, list) else weights)
        pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w)
        fp16 &= pt or jit or onnx or engine  # FP16
        nhwc = coreml or saved_model or pb or tflite or edgetpu  # BHWC formats (vs torch BCWH)
        stride = 32  # default stride
        cuda = torch.cuda.is_available() and device.type != 'cpu'  # use CUDA
        if not (pt or triton):
            w = attempt_download(w)  # download if not local

        if pt:  # PyTorch
            model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse)
            stride = max(int(model.stride.max()), 32)  # model stride
            names = model.module.names if hasattr(model, 'module') else model.names  # get class names
            model.half() if fp16 else model.float()
            self.model = model  # explicitly assign for to(), cpu(), cuda(), half()
        elif jit:  # TorchScript
            LOGGER.info(f'Loading {w} for TorchScript inference...')
            extra_files = {'config.txt': ''}  # model metadata
            model = torch.jit.load(w, _extra_files=extra_files, map_location=device)
            model.half() if fp16 else model.float()
            if extra_files['config.txt']:  # load metadata dict
                d = json.loads(extra_files['config.txt'],
                               object_hook=lambda d: {int(k) if k.isdigit() else k: v
                                                      for k, v in d.items()})
                stride, names = int(d['stride']), d['names']
        elif dnn:  # ONNX OpenCV DNN
            LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...')
            check_requirements('opencv-python>=4.5.4')
            net = cv2.dnn.readNetFromONNX(w)
        elif onnx:  # ONNX Runtime
            LOGGER.info(f'Loading {w} for ONNX Runtime inference...')
            check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))
            import onnxruntime
            providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
            session = onnxruntime.InferenceSession(w, providers=providers)
            output_names = [x.name for x in session.get_outputs()]
            meta = session.get_modelmeta().custom_metadata_map  # metadata
            if 'stride' in meta:
                stride, names = int(meta['stride']), eval(meta['names'])
        elif xml:  # OpenVINO
            LOGGER.info(f'Loading {w} for OpenVINO inference...')
            check_requirements('openvino')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
            from openvino.runtime import Core, Layout, get_batch
            ie = Core()
            if not Path(w).is_file():  # if not *.xml
                w = next(Path(w).glob('*.xml'))  # get *.xml file from *_openvino_model dir
            network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin'))
            if network.get_parameters()[0].get_layout().empty:
                network.get_parameters()[0].set_layout(Layout('NCHW'))
            batch_dim = get_batch(network)
            if batch_dim.is_static:
                batch_size = batch_dim.get_length()
            executable_network = ie.compile_model(network, device_name='CPU')  # device_name="MYRIAD" for Intel NCS2
            stride, names = self._load_metadata(Path(w).with_suffix('.yaml'))  # load metadata
        elif engine:  # TensorRT
            LOGGER.info(f'Loading {w} for TensorRT inference...')
            import tensorrt as trt  # https://developer.nvidia.com/nvidia-tensorrt-download
            check_version(trt.__version__, '7.0.0', hard=True)  # require tensorrt>=7.0.0
            if device.type == 'cpu':
                device = torch.device('cuda:0')
            Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))
            logger = trt.Logger(trt.Logger.INFO)
            with open(w, 'rb') as f, trt.Runtime(logger) as runtime:
                model = runtime.deserialize_cuda_engine(f.read())
            context = model.create_execution_context()
            bindings = OrderedDict()
            output_names = []
            fp16 = False  # default updated below
            dynamic = False
            for i in range(model.num_bindings):
                name = model.get_binding_name(i)
                dtype = trt.nptype(model.get_binding_dtype(i))
                if model.binding_is_input(i):
                    if -1 in tuple(model.get_binding_shape(i)):  # dynamic
                        dynamic = True
                        context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2]))
                    if dtype == np.float16:
                        fp16 = True
                else:  # output
                    output_names.append(name)
                shape = tuple(context.get_binding_shape(i))
                im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)
                bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr()))
            binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
            batch_size = bindings['images'].shape[0]  # if dynamic, this is instead max batch size
        elif coreml:  # CoreML
            LOGGER.info(f'Loading {w} for CoreML inference...')
            import coremltools as ct
            model = ct.models.MLModel(w)
        elif saved_model:  # TF SavedModel
            LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...')
            import tensorflow as tf
            keras = False  # assume TF1 saved_model
            model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w)
        elif pb:  # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
            LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...')
            import tensorflow as tf

            def wrap_frozen_graph(gd, inputs, outputs):
                x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=''), [])  # wrapped
                ge = x.graph.as_graph_element
                return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))

            def gd_outputs(gd):
                name_list, input_list = [], []
                for node in gd.node:  # tensorflow.core.framework.node_def_pb2.NodeDef
                    name_list.append(node.name)
                    input_list.extend(node.input)
                return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp'))

            gd = tf.Graph().as_graph_def()  # TF GraphDef
            with open(w, 'rb') as f:
                gd.ParseFromString(f.read())
            frozen_func = wrap_frozen_graph(gd, inputs='x:0', outputs=gd_outputs(gd))
        elif tflite or edgetpu:  # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
            try:  # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu
                from tflite_runtime.interpreter import Interpreter, load_delegate
            except ImportError:
                import tensorflow as tf
                Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate,
            if edgetpu:  # TF Edge TPU https://coral.ai/software/#edgetpu-runtime
                LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...')
                delegate = {
                    'Linux': 'libedgetpu.so.1',
                    'Darwin': 'libedgetpu.1.dylib',
                    'Windows': 'edgetpu.dll'}[platform.system()]
                interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)])
            else:  # TFLite
                LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')
                interpreter = Interpreter(model_path=w)  # load TFLite model
            interpreter.allocate_tensors()  # allocate
            input_details = interpreter.get_input_details()  # inputs
            output_details = interpreter.get_output_details()  # outputs
            # load metadata
            with contextlib.suppress(zipfile.BadZipFile):
                with zipfile.ZipFile(w, 'r') as model:
                    meta_file = model.namelist()[0]
                    meta = ast.literal_eval(model.read(meta_file).decode('utf-8'))
                    stride, names = int(meta['stride']), meta['names']
        elif tfjs:  # TF.js
            raise NotImplementedError('ERROR: YOLOv5 TF.js inference is not supported')
        elif paddle:  # PaddlePaddle
            LOGGER.info(f'Loading {w} for PaddlePaddle inference...')
            check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle')
            import paddle.inference as pdi
            if not Path(w).is_file():  # if not *.pdmodel
                w = next(Path(w).rglob('*.pdmodel'))  # get *.pdmodel file from *_paddle_model dir
            weights = Path(w).with_suffix('.pdiparams')
            config = pdi.Config(str(w), str(weights))
            if cuda:
                config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0)
            predictor = pdi.create_predictor(config)
            input_handle = predictor.get_input_handle(predictor.get_input_names()[0])
            output_names = predictor.get_output_names()
        elif triton:  # NVIDIA Triton Inference Server
            LOGGER.info(f'Using {w} as Triton Inference Server...')
            check_requirements('tritonclient[all]')
            from thirdTool.yolo5x_qr.utils.triton import TritonRemoteModel
            model = TritonRemoteModel(url=w)
            nhwc = model.runtime.startswith('tensorflow')
        else:
            raise NotImplementedError(f'ERROR: {w} is not a supported format')

        # class names
        if 'names' not in locals():
            names = yaml_load(data)['names'] if data else {i: f'class{i}' for i in range(999)}
        if names[0] == 'n01440764' and len(names) == 1000:  # ImageNet
            names = yaml_load(ROOT / 'data/ImageNet.yaml')['names']  # human-readable names

        self.__dict__.update(locals())  # assign all variables to self

    def forward(self, im, augment=False, visualize=False):
        # YOLOv5 MultiBackend inference
        b, ch, h, w = im.shape  # batch, channel, height, width
        if self.fp16 and im.dtype != torch.float16:
            im = im.half()  # to FP16
        if self.nhwc:
            im = im.permute(0, 2, 3, 1)  # torch BCHW to numpy BHWC shape(1,320,192,3)

        if self.pt:  # PyTorch
            y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)
        elif self.jit:  # TorchScript
            y = self.model(im)
        elif self.dnn:  # ONNX OpenCV DNN
            im = im.cpu().numpy()  # torch to numpy
            self.net.setInput(im)
            y = self.net.forward()
        elif self.onnx:  # ONNX Runtime
            im = im.cpu().numpy()  # torch to numpy
            y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
        elif self.xml:  # OpenVINO
            im = im.cpu().numpy()  # FP32
            y = list(self.executable_network([im]).values())
        elif self.engine:  # TensorRT
            if self.dynamic and im.shape != self.bindings['images'].shape:
                i = self.model.get_binding_index('images')
                self.context.set_binding_shape(i, im.shape)  # reshape if dynamic
                self.bindings['images'] = self.bindings['images']._replace(shape=im.shape)
                for name in self.output_names:
                    i = self.model.get_binding_index(name)
                    self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i)))
            s = self.bindings['images'].shape
            assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}"
            self.binding_addrs['images'] = int(im.data_ptr())
            self.context.execute_v2(list(self.binding_addrs.values()))
            y = [self.bindings[x].data for x in sorted(self.output_names)]
        elif self.coreml:  # CoreML
            im = im.cpu().numpy()
            im = Image.fromarray((im[0] * 255).astype('uint8'))
            # im = im.resize((192, 320), Image.ANTIALIAS)
            y = self.model.predict({'image': im})  # coordinates are xywh normalized
            if 'confidence' in y:
                box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]])  # xyxy pixels
                conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)
                y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
            else:
                y = list(reversed(y.values()))  # reversed for segmentation models (pred, proto)
        elif self.paddle:  # PaddlePaddle
            im = im.cpu().numpy().astype(np.float32)
            self.input_handle.copy_from_cpu(im)
            self.predictor.run()
            y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names]
        elif self.triton:  # NVIDIA Triton Inference Server
            y = self.model(im)
        else:  # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
            im = im.cpu().numpy()
            if self.saved_model:  # SavedModel
                y = self.model(im, training=False) if self.keras else self.model(im)
            elif self.pb:  # GraphDef
                y = self.frozen_func(x=self.tf.constant(im))
            else:  # Lite or Edge TPU
                input = self.input_details[0]
                int8 = input['dtype'] == np.uint8  # is TFLite quantized uint8 model
                if int8:
                    scale, zero_point = input['quantization']
                    im = (im / scale + zero_point).astype(np.uint8)  # de-scale
                self.interpreter.set_tensor(input['index'], im)
                self.interpreter.invoke()
                y = []
                for output in self.output_details:
                    x = self.interpreter.get_tensor(output['index'])
                    if int8:
                        scale, zero_point = output['quantization']
                        x = (x.astype(np.float32) - zero_point) * scale  # re-scale
                    y.append(x)
            y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y]
            y[0][..., :4] *= [w, h, w, h]  # xywh normalized to pixels

        if isinstance(y, (list, tuple)):
            return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y]
        else:
            return self.from_numpy(y)

    def from_numpy(self, x):
        return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x

    def warmup(self, imgsz=(1, 3, 640, 640)):
        # Warmup model by running inference once
        warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton
        if any(warmup_types) and (self.device.type != 'cpu' or self.triton):
            im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device)  # input
            for _ in range(2 if self.jit else 1):  #
                self.forward(im)  # warmup

    @staticmethod
    def _model_type(p='path/to/model.pt'):
        # Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx
        # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle]
        # from export import export_formats
        from thirdTool.yolo5x_qr.utils.downloads import is_url
        sf = list(export_formats().Suffix)  # export suffixes
        if not is_url(p, check=False):
            check_suffix(p, sf)  # checks
        url = urlparse(p)  # if url may be Triton inference server
        types = [s in Path(p).name for s in sf]
        types[8] &= not types[9]  # tflite &= not edgetpu
        triton = not any(types) and all([any(s in url.scheme for s in ['http', 'grpc']), url.netloc])
        return types + [triton]

    @staticmethod
    def _load_metadata(f=Path('path/to/meta.yaml')):
        # Load metadata from meta.yaml if it exists
        if f.exists():
            d = yaml_load(f)
            return d['stride'], d['names']  # assign stride, names
        return None, None


class AutoShape(nn.Module):
    # YOLOv5 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
    agnostic = False  # NMS class-agnostic
    multi_label = False  # NMS multiple labels per box
    classes = None  # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
    max_det = 1000  # maximum number of detections per image
    amp = False  # Automatic Mixed Precision (AMP) inference

    def __init__(self, model, verbose=True):
        super().__init__()
        if verbose:
            LOGGER.info('Adding AutoShape... ')
        copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=())  # copy attributes
        self.dmb = isinstance(model, DetectMultiBackend)  # DetectMultiBackend() instance
        self.pt = not self.dmb or model.pt  # PyTorch model
        self.model = model.eval()
        if self.pt:
            m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()
            m.inplace = False  # Detect.inplace=False for safe multithread inference
            m.export = True  # do not output loss values

    def _apply(self, fn):
        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
        self = super()._apply(fn)
        if self.pt:
            m = self.model.model.model[-1] if self.dmb else self.model.model[-1]  # Detect()
            m.stride = fn(m.stride)
            m.grid = list(map(fn, m.grid))
            if isinstance(m.anchor_grid, list):
                m.anchor_grid = list(map(fn, m.anchor_grid))
        return self

    @smart_inference_mode()
    def forward(self, ims, size=640, augment=False, profile=False):
        # Inference from various sources. For size(height=640, width=1280), RGB images example inputs are:
        #   file:        ims = 'data/images/zidane.jpg'  # str or PosixPath
        #   URI:             = 'https://ultralytics.com/images/zidane.jpg'
        #   OpenCV:          = cv2.imread('image.jpg')[:,:,::-1]  # HWC BGR to RGB x(640,1280,3)
        #   PIL:             = Image.open('image.jpg') or ImageGrab.grab()  # 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

        dt = (Profile(), Profile(), Profile())
        with dt[0]:
            if isinstance(size, int):  # expand
                size = (size, size)
            p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device)  # param
            autocast = self.amp and (p.device.type != 'cpu')  # Automatic Mixed Precision (AMP) inference
            if isinstance(ims, torch.Tensor):  # torch
                with amp.autocast(autocast):
                    return self.model(ims.to(p.device).type_as(p), augment=augment)  # inference

            # Pre-process
            n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims])  # number, list of images
            shape0, shape1, files = [], [], []  # image and inference shapes, filenames
            for i, im in enumerate(ims):
                f = f'image{i}'  # filename
                if isinstance(im, (str, Path)):  # filename or uri
                    im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im
                    im = np.asarray(exif_transpose(im))
                elif isinstance(im, Image.Image):  # PIL Image
                    im, f = np.asarray(exif_transpose(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 cv2.cvtColor(im, cv2.COLOR_GRAY2BGR)  # enforce 3ch input
                s = im.shape[:2]  # HWC
                shape0.append(s)  # image shape
                g = max(size) / max(s)  # gain
                shape1.append([int(y * g) for y in s])
                ims[i] = im if im.data.contiguous else np.ascontiguousarray(im)  # update
            shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)]  # inf shape
            x = [letterbox(im, shape1, auto=False)[0] for im in ims]  # pad
            x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2)))  # stack and BHWC to BCHW
            x = torch.from_numpy(x).to(p.device).type_as(p) / 255  # uint8 to fp16/32

        with amp.autocast(autocast):
            # Inference
            with dt[1]:
                y = self.model(x, augment=augment)  # forward

            # Post-process
            with dt[2]:
                y = non_max_suppression(y if self.dmb else y[0],
                                        self.conf,
                                        self.iou,
                                        self.classes,
                                        self.agnostic,
                                        self.multi_label,
                                        max_det=self.max_det)  # NMS
                for i in range(n):
                    scale_boxes(shape1, y[i][:, :4], shape0[i])

            return Detections(ims, y, files, dt, self.names, x.shape)


class Detections:
    # YOLOv5 detections class for inference results
    def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None):
        super().__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 ims]  # normalizations
        self.ims = ims  # 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.times = times  # profiling times
        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(x.t / self.n * 1E3 for x in times)  # timestamps (ms)
        self.s = tuple(shape)  # inference BCHW shape

    def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')):
        s, crops = '', []
        for i, (im, pred) in enumerate(zip(self.ims, self.pred)):
            s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} '  # string
            if pred.shape[0]:
                for c in pred[:, -1].unique():
                    n = (pred[:, -1] == c).sum()  # detections per class
                    s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, "  # add to string
                s = s.rstrip(', ')
                if show or save or render or crop:
                    annotator = Annotator(im, example=str(self.names))
                    for *box, conf, cls in reversed(pred):  # xyxy, confidence, class
                        label = f'{self.names[int(cls)]} {conf:.2f}'
                        if crop:
                            file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None
                            crops.append({
                                'box': box,
                                'conf': conf,
                                'cls': cls,
                                'label': label,
                                'im': save_one_box(box, im, file=file, save=save)})
                        else:  # all others
                            annotator.box_label(box, label if labels else '', color=colors(cls))
                    im = annotator.im
            else:
                s += '(no detections)'

            im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im  # from np
            if show:
                if is_jupyter():
                    from IPython.display import display
                    display(im)
                else:
                    im.show(self.files[i])
            if save:
                f = self.files[i]
                im.save(save_dir / f)  # save
                if i == self.n - 1:
                    LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}")
            if render:
                self.ims[i] = np.asarray(im)
        if pprint:
            s = s.lstrip('\n')
            return f'{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t
        if crop:
            if save:
                LOGGER.info(f'Saved results to {save_dir}\n')
            return crops

    @TryExcept('Showing images is not supported in this environment')
    def show(self, labels=True):
        self._run(show=True, labels=labels)  # show results

    def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False):
        save_dir = increment_path(save_dir, exist_ok, mkdir=True)  # increment save_dir
        self._run(save=True, labels=labels, save_dir=save_dir)  # save results

    def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False):
        save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None
        return self._run(crop=True, save=save, save_dir=save_dir)  # crop results

    def render(self, labels=True):
        self._run(render=True, labels=labels)  # render results
        return self.ims

    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():'
        r = range(self.n)  # iterable
        x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
        # for d in x:
        #    for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
        #        setattr(d, k, getattr(d, k)[0])  # pop out of list
        return x

    def print(self):
        LOGGER.info(self.__str__())

    def __len__(self):  # override len(results)
        return self.n

    def __str__(self):  # override print(results)
        return self._run(pprint=True)  # print results

    def __repr__(self):
        return f'YOLOv5 {self.__class__} instance\n' + self.__str__()


class Proto(nn.Module):
    # YOLOv5 mask Proto module for segmentation models
    def __init__(self, c1, c_=256, c2=32):  # ch_in, number of protos, number of masks
        super().__init__()
        self.cv1 = Conv(c1, c_, k=3)
        self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
        self.cv2 = Conv(c_, c_, k=3)
        self.cv3 = Conv(c_, c2)

    def forward(self, x):
        return self.cv3(self.cv2(self.upsample(self.cv1(x))))


class Classify(nn.Module):
    # YOLOv5 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,
                 dropout_p=0.0):  # ch_in, ch_out, kernel, stride, padding, groups, dropout probability
        super().__init__()
        c_ = 1280  # efficientnet_b0 size
        self.conv = Conv(c1, c_, k, s, autopad(k, p), g)
        self.pool = nn.AdaptiveAvgPool2d(1)  # to x(b,c_,1,1)
        self.drop = nn.Dropout(p=dropout_p, inplace=True)
        self.linear = nn.Linear(c_, c2)  # to x(b,c2)

    def forward(self, x):
        if isinstance(x, list):
            x = torch.cat(x, 1)
        return self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))


from torch.autograd import Function

# by AI&CV SOCA  ####### start #########################
# SOCA moudle 单幅图像超分辨率
class Covpool(Function):
    @staticmethod
    def forward(ctx, input):
        x = input
        batchSize = x.data.shape[0]
        dim = x.data.shape[1]
        h = x.data.shape[2]
        w = x.data.shape[3]
        M = h * w
        x = x.reshape(batchSize, dim, M)
        I_hat = (-1. / M / M) * torch.ones(M, M, device=x.device) + (1. / M) * torch.eye(M, M, device=x.device)
        I_hat = I_hat.view(1, M, M).repeat(batchSize, 1, 1).type(x.dtype)
        y = x.bmm(I_hat).bmm(x.transpose(1, 2))
        ctx.save_for_backward(input, I_hat)
        return y

    @staticmethod
    def backward(ctx, grad_output):
        input, I_hat = ctx.saved_tensors
        x = input
        batchSize = x.data.shape[0]
        dim = x.data.shape[1]
        h = x.data.shape[2]
        w = x.data.shape[3]
        M = h * w
        x = x.reshape(batchSize, dim, M)
        grad_input = grad_output + grad_output.transpose(1, 2)
        grad_input = grad_input.bmm(x).bmm(I_hat)
        grad_input = grad_input.reshape(batchSize, dim, h, w)
        return grad_input


class Sqrtm(Function):
    @staticmethod
    def forward(ctx, input, iterN):
        x = input
        batchSize = x.data.shape[0]
        dim = x.data.shape[1]
        dtype = x.dtype
        I3 = 3.0 * torch.eye(dim, dim, device=x.device).view(1, dim, dim).repeat(batchSize, 1, 1).type(dtype)
        normA = (1.0 / 3.0) * x.mul(I3).sum(dim=1).sum(dim=1)
        A = x.div(normA.view(batchSize, 1, 1).expand_as(x))
        Y = torch.zeros(batchSize, iterN, dim, dim, requires_grad=False, device=x.device)
        Z = torch.eye(dim, dim, device=x.device).view(1, dim, dim).repeat(batchSize, iterN, 1, 1)
        if iterN < 2:
            ZY = 0.5 * (I3 - A)
            Y[:, 0, :, :] = A.bmm(ZY)
        else:
            ZY = 0.5 * (I3 - A)
            Y[:, 0, :, :] = A.bmm(ZY)
            Z[:, 0, :, :] = ZY
            for i in range(1, iterN - 1):
                ZY = 0.5 * (I3 - Z[:, i - 1, :, :].bmm(Y[:, i - 1, :, :]))
                Y[:, i, :, :] = Y[:, i - 1, :, :].bmm(ZY)
                Z[:, i, :, :] = ZY.bmm(Z[:, i - 1, :, :])
            ZY = 0.5 * Y[:, iterN - 2, :, :].bmm(I3 - Z[:, iterN - 2, :, :].bmm(Y[:, iterN - 2, :, :]))
        y = ZY * torch.sqrt(normA).view(batchSize, 1, 1).expand_as(x)
        ctx.save_for_backward(input, A, ZY, normA, Y, Z)
        ctx.iterN = iterN
        return y

    @staticmethod
    def backward(ctx, grad_output):
        input, A, ZY, normA, Y, Z = ctx.saved_tensors
        iterN = ctx.iterN
        x = input
        batchSize = x.data.shape[0]
        dim = x.data.shape[1]
        dtype = x.dtype
        der_postCom = grad_output * torch.sqrt(normA).view(batchSize, 1, 1).expand_as(x)
        der_postComAux = (grad_output * ZY).sum(dim=1).sum(dim=1).div(2 * torch.sqrt(normA))
        I3 = 3.0 * torch.eye(dim, dim, device=x.device).view(1, dim, dim).repeat(batchSize, 1, 1).type(dtype)
        if iterN < 2:
            der_NSiter = 0.5 * (der_postCom.bmm(I3 - A) - A.bmm(der_sacleTrace))
        else:
            dldY = 0.5 * (der_postCom.bmm(I3 - Y[:, iterN - 2, :, :].bmm(Z[:, iterN - 2, :, :])) -
                          Z[:, iterN - 2, :, :].bmm(Y[:, iterN - 2, :, :]).bmm(der_postCom))
            dldZ = -0.5 * Y[:, iterN - 2, :, :].bmm(der_postCom).bmm(Y[:, iterN - 2, :, :])
            for i in range(iterN - 3, -1, -1):
                YZ = I3 - Y[:, i, :, :].bmm(Z[:, i, :, :])
                ZY = Z[:, i, :, :].bmm(Y[:, i, :, :])
                dldY_ = 0.5 * (dldY.bmm(YZ) -
                               Z[:, i, :, :].bmm(dldZ).bmm(Z[:, i, :, :]) -
                               ZY.bmm(dldY))
                dldZ_ = 0.5 * (YZ.bmm(dldZ) -
                               Y[:, i, :, :].bmm(dldY).bmm(Y[:, i, :, :]) -
                               dldZ.bmm(ZY))
                dldY = dldY_
                dldZ = dldZ_
            der_NSiter = 0.5 * (dldY.bmm(I3 - A) - dldZ - A.bmm(dldY))
        grad_input = der_NSiter.div(normA.view(batchSize, 1, 1).expand_as(x))
        grad_aux = der_NSiter.mul(x).sum(dim=1).sum(dim=1)
        for i in range(batchSize):
            grad_input[i, :, :] += (der_postComAux[i] \
                                    - grad_aux[i] / (normA[i] * normA[i])) \
                                   * torch.ones(dim, device=x.device).diag()
        return grad_input, None


def CovpoolLayer(var):
    return Covpool.apply(var)


def SqrtmLayer(var, iterN):
    return Sqrtm.apply(var, iterN)


class SOCA(nn.Module):
    # second-order Channel attention
    def __init__(self, channel, reduction=8):
        super(SOCA, self).__init__()
        self.max_pool = nn.MaxPool2d(kernel_size=2)

        self.conv_du = nn.Sequential(
            nn.Conv2d(channel, channel // reduction, 1, padding=0, bias=True),
            nn.ReLU(inplace=True),
            nn.Conv2d(channel // reduction, channel, 1, padding=0, bias=True),
            nn.Sigmoid()
        )

    def forward(self, x):
        batch_size, C, h, w = x.shape  # x: NxCxHxW
        N = int(h * w)
        min_h = min(h, w)
        h1 = 1000
        w1 = 1000
        if h < h1 and w < w1:
            x_sub = x
        elif h < h1 and w > w1:
            W = (w - w1) // 2
            x_sub = x[:, :, :, W:(W + w1)]
        elif w < w1 and h > h1:
            H = (h - h1) // 2
            x_sub = x[:, :, H:H + h1, :]
        else:
            H = (h - h1) // 2
            W = (w - w1) // 2
            x_sub = x[:, :, H:(H + h1), W:(W + w1)]
        cov_mat = CovpoolLayer(x_sub)  # Global Covariance pooling layer
        cov_mat_sqrt = SqrtmLayer(cov_mat,
                                  5)  # Matrix square root layer( including pre-norm,Newton-Schulz iter. and post-com. with 5 iteration)
        cov_mat_sum = torch.mean(cov_mat_sqrt, 1)
        cov_mat_sum = cov_mat_sum.view(batch_size, C, 1, 1)
        y_cov = self.conv_du(cov_mat_sum)
        return y_cov * x


# by AI&CV SOCA  ####### end #########################