"""
original code from rwightman:
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
"""
from functools import partial
from collections import OrderedDict

import torch
import torch.nn as nn
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.hub
from functools import partial
# import mat
# from vision_transformer.ir50 import Backbone


import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.hub
from functools import partial
import math

from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.registry import register_model
from timm.models.vision_transformer import _cfg, Mlp, Block
# from .ir50 import Backbone


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


def drop_path(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


class BasicBlock(nn.Module):
    __constants__ = ['downsample']

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        norm_layer = nn.BatchNorm2d
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


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(x, self.drop_prob, self.training)


class PatchEmbed(nn.Module):
    """
    2D Image to Patch Embedding
    """

    def __init__(self, img_size=14, patch_size=16, in_c=256, embed_dim=768, norm_layer=None):
        super().__init__()
        img_size = (img_size, img_size)
        patch_size = (patch_size, patch_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
        self.num_patches = self.grid_size[0] * self.grid_size[1]

        self.proj = nn.Conv2d(256, 768, kernel_size=1)
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

    def forward(self, x):
        B, C, H, W = x.shape
        # assert H == self.img_size[0] and W == self.img_size[1], \
        #     f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
        # print(x.shape)

        # flatten: [B, C, H, W] -> [B, C, HW]
        # transpose: [B, C, HW] -> [B, HW, C]
        x = self.proj(x).flatten(2).transpose(1, 2)
        x = self.norm(x)
        return x


class Attention(nn.Module):
    def __init__(self,
                 dim, in_chans,  # 输入token的dim
                 num_heads=8,
                 qkv_bias=False,
                 qk_scale=None,
                 attn_drop_ratio=0.,
                 proj_drop_ratio=0.):
        super(Attention, self).__init__()
        self.num_heads = 8
        self.img_chanel = in_chans + 1
        head_dim = dim // num_heads
        self.scale = head_dim ** -0.5
        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.attn_drop = nn.Dropout(attn_drop_ratio)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop_ratio)

    def forward(self, x):
        x_img = x[:, :self.img_chanel, :]
        # [batch_size, num_patches + 1, total_embed_dim]
        B, N, C = x_img.shape
        # print(C)
        qkv = self.qkv(x_img).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]
        # k, v = kv.unbind(0)  # make torchscript happy (cannot use tensor as tuple)
        # q = x_img.reshape(B, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
        attn = (q @ k.transpose(-2, -1)) * self.scale
        attn = attn.softmax(dim=-1)
        attn = self.attn_drop(attn)

        x_img = (attn @ v).transpose(1, 2).reshape(B, N, C)
        x_img = self.proj(x_img)
        x_img = self.proj_drop(x_img)
        #
        #
        # # qkv(): -> [batch_size, num_patches + 1, 3 * total_embed_dim]
        # # reshape: -> [batch_size, num_patches + 1, 3, num_heads, embed_dim_per_head]
        # # permute: -> [3, batch_size, num_heads, num_patches + 1, embed_dim_per_head]
        # qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        # # [batch_size, num_heads, num_patches + 1, embed_dim_per_head]
        # q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
        #
        # # transpose: -> [batch_size, num_heads, embed_dim_per_head, num_patches + 1]
        # # @: multiply -> [batch_size, num_heads, num_patches + 1, num_patches + 1]
        # attn = (q @ k.transpose(-2, -1)) * self.scale
        # attn = attn.softmax(dim=-1)
        # attn = self.attn_drop(attn)
        #
        # # @: multiply -> [batch_size, num_heads, num_patches + 1, embed_dim_per_head]
        # # transpose: -> [batch_size, num_patches + 1, num_heads, embed_dim_per_head]
        # # reshape: -> [batch_size, num_patches + 1, total_embed_dim]
        # x = (attn @ v).transpose(1, 2).reshape(B, N, C)
        # x = self.proj(x)
        # x = self.proj_drop(x)
        return x_img


class AttentionBlock(nn.Module):
    __constants__ = ['downsample']

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(AttentionBlock, self).__init__()
        norm_layer = nn.BatchNorm2d
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride
        # self.cbam = CBAM(planes, 16)
        self.inplanes = inplanes
        self.eca_block = eca_block()

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        inplanes = self.inplanes
        out = self.eca_block(out)
        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class Mlp(nn.Module):
    """
    MLP as used in Vision Transformer, MLP-Mixer and related networks
    """

    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class Block(nn.Module):
    def __init__(self,
                 dim, in_chans,
                 num_heads,
                 mlp_ratio=4.,
                 qkv_bias=False,
                 qk_scale=None,
                 drop_ratio=0.,
                 attn_drop_ratio=0.,
                 drop_path_ratio=0.,
                 act_layer=nn.GELU,
                 norm_layer=nn.LayerNorm):
        super(Block, self).__init__()
        self.norm1 = norm_layer(dim)
        self.img_chanel = in_chans + 1

        self.conv = nn.Conv1d(self.img_chanel, self.img_chanel, 1)
        self.attn = Attention(dim, in_chans=in_chans, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale,
                              attn_drop_ratio=attn_drop_ratio, proj_drop_ratio=drop_ratio)
        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
        self.drop_path = DropPath(drop_path_ratio) if drop_path_ratio > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop_ratio)

    def forward(self, x):
        # x = x + self.drop_path(self.attn(self.norm1(x)))
        # x = x + self.drop_path(self.mlp(self.norm2(x)))

        x_img = x
        # [:, :self.img_chanel, :]
        # x_lm = x[:, self.img_chanel:, :]
        x_img = x_img + self.drop_path(self.attn(self.norm1(x)))
        x = x_img + self.drop_path(self.mlp(self.norm2(x_img)))
        #
        # x_lm = x_lm + self.drop_path(self.attn_lm(self.norm3(x)))
        # x_lm = x_lm + self.drop_path(self.mlp2(self.norm4(x_lm)))
        # x = torch.cat((x_img, x_lm), dim=1)
        # x = self.conv(x)

        return x


class ClassificationHead(nn.Module):
    def __init__(self, input_dim: int, target_dim: int):
        super().__init__()
        self.linear = torch.nn.Linear(input_dim, target_dim)

    def forward(self, x):
        x = x.view(x.size(0), -1)
        y_hat = self.linear(x)
        return y_hat


def load_pretrained_weights(model, checkpoint):
    import collections
    if 'state_dict' in checkpoint:
        state_dict = checkpoint['state_dict']
    else:
        state_dict = checkpoint
    model_dict = model.state_dict()
    new_state_dict = collections.OrderedDict()
    matched_layers, discarded_layers = [], []
    for k, v in state_dict.items():
        # If the pretrained state_dict was saved as nn.DataParallel,
        # keys would contain "module.", which should be ignored.
        if k.startswith('module.'):
            k = k[7:]
        if k in model_dict and model_dict[k].size() == v.size():
            new_state_dict[k] = v
            matched_layers.append(k)
        else:
            discarded_layers.append(k)
    # new_state_dict.requires_grad = False
    model_dict.update(new_state_dict)

    model.load_state_dict(model_dict)
    print('load_weight', len(matched_layers))
    return model

class eca_block(nn.Module):
    def __init__(self, channel=128, b=1, gamma=2):
        super(eca_block, self).__init__()
        kernel_size = int(abs((math.log(channel, 2) + b) / gamma))
        kernel_size = kernel_size if kernel_size % 2 else kernel_size + 1

        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=(kernel_size - 1) // 2, bias=False)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        y = self.avg_pool(x)
        y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
        y = self.sigmoid(y)
        return x * y.expand_as(x)
#
#
# class IR20(nn.Module):
#     def __init__(self, img_size_=112, num_classes=7, layers=[2, 2, 2, 2]):
#         super().__init__()
#         norm_layer = nn.BatchNorm2d
#         self.img_size = img_size_
#         self._norm_layer = norm_layer
#         self.num_classes = num_classes
#         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
#         self.bn1 = norm_layer(64)
#         self.relu = nn.ReLU(inplace=True)
#         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
#         # self.face_landback = MobileFaceNet([112, 112],136)
#         # face_landback_checkpoint = torch.load('./models/pretrain/mobilefacenet_model_best.pth.tar', map_location=lambda storage, loc: storage)
#         # self.face_landback.load_state_dict(face_landback_checkpoint['state_dict'])
#         self.layer1 = self._make_layer(BasicBlock, 64, 64, layers[0])
#         self.layer2 = self._make_layer(BasicBlock, 64, 128, layers[1], stride=2)
#         self.layer3 = self._make_layer(AttentionBlock, 128, 256, layers[2], stride=2)
#         self.layer4 = self._make_layer(AttentionBlock, 256, 256, layers[3], stride=1)
#         self.ir_back = Backbone(50, 51, 52, 0.0, 'ir')
#         self.ir_layer = nn.Linear(1024, 512)
#         # ir_checkpoint = torch.load(r'F:\0815crossvit\vision_transformer\models\pretrain\Pretrained_on_MSCeleb.pth.tar',
#         #                          map_location=lambda storage, loc: storage)
#         # ir_checkpoint = ir_checkpoint['state_dict']
#         # self.face_landback.load_state_dict(face_landback_checkpoint['state_dict'])
#         # checkpoint = torch.load('./checkpoint/Pretrained_on_MSCeleb.pth.tar')
#         # pre_trained_dict = checkpoint['state_dict']
#         # IR20.load_state_dict(ir_checkpoint, strict=False)
#         # self.IR = load_pretrained_weights(IR, ir_checkpoint)
#
#     def _make_layer(self, block, inplanes, planes, blocks, stride=1):
#         norm_layer = self._norm_layer
#         downsample = None
#         if stride != 1 or inplanes != planes:
#             downsample = nn.Sequential(conv1x1(inplanes, planes, stride), norm_layer(planes))
#         layers = []
#         layers.append(block(inplanes, planes, stride, downsample))
#         inplanes = planes
#         for _ in range(1, blocks):
#             layers.append(block(inplanes, planes))
#         return nn.Sequential(*layers)
#
#     def forward(self, x):
#         x_ir = self.ir_back(x)
#         # x_ir = self.ir_layer(x_ir)
#         # print(x_ir.shape)
#         # x = F.interpolate(x, size=112)
#         # x = self.conv1(x)
#         # x = self.bn1(x)
#         # x = self.relu(x)
#         # x = self.maxpool(x)
#         #
#         # x = self.layer1(x)
#         # x = self.layer2(x)
#         # x = self.layer3(x)
#         # x = self.layer4(x)
#         # print(x.shape)
#         # print(x)
#         out = x_ir
#
#         return out
#
#
# class IR(nn.Module):
#     def __init__(self, img_size_=112, num_classes=7):
#         super().__init__()
#         depth = 8
#         # if type == "small":
#         #     depth = 4
#         # if type == "base":
#         #     depth = 6
#         # if type == "large":
#         #     depth = 8
#
#         self.img_size = img_size_
#         self.num_classes = num_classes
#         self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
#         # self.bn1 = norm_layer(64)
#         self.relu = nn.ReLU(inplace=True)
#         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
#         # self.face_landback = MobileFaceNet([112, 112],136)
#         # face_landback_checkpoint = torch.load('./models/pretrain/mobilefacenet_model_best.pth.tar', map_location=lambda storage, loc: storage)
#         # self.face_landback.load_state_dict(face_landback_checkpoint['state_dict'])
#
#         # for param in self.face_landback.parameters():
#         #     param.requires_grad = False
#
#         ###########################################################################333
#
#         self.ir_back = IR20()
#
#         # ir_checkpoint = torch.load(r'F:\0815crossvit\vision_transformer\models\pretrain\ir50.pth',
#         #                            map_location=lambda storage, loc: storage)
#         # # ir_checkpoint = ir_checkpoint["model"]
#         # self.ir_back = load_pretrained_weights(self.ir_back, ir_checkpoint)
#         # load_state_dict(checkpoint_model, strict=False)
#         # self.ir_layer = nn.Linear(1024,512)
#
#         #############################################################3
#         #
#         # self.pyramid_fuse = HyVisionTransformer(in_chans=49, q_chanel = 49, embed_dim=512,
#         #                                      depth=depth, num_heads=8, mlp_ratio=2.,
#         #                                      drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1)
#
#         # self.se_block = SE_block(input_dim=512)
#         self.head = ClassificationHead(input_dim=768, target_dim=self.num_classes)
#
#     def forward(self, x):
#         B_ = x.shape[0]
#         # x_face = F.interpolate(x, size=112)
#         # _, x_face = self.face_landback(x_face)
#         # x_face = x_face.view(B_, -1, 49).transpose(1,2)
#         ###############  landmark x_face ([B, 49, 512])
#         x_ir = self.ir_back(x)
#         # print(x_ir.shape)
#         # x_ir = self.ir_layer(x_ir)
#         # print(x_ir.shape)
#         ###############  image x_ir ([B, 49, 512])
#
#         # y_hat = self.pyramid_fuse(x_ir, x_face)
#         # y_hat = self.se_block(y_hat)
#         # y_feat = y_hat
#
#         # out = self.head(x_ir)
#
#         out = x_ir
#         return out


class eca_block(nn.Module):
    def __init__(self, channel=196, b=1, gamma=2):
        super(eca_block, self).__init__()
        kernel_size = int(abs((math.log(channel, 2) + b) / gamma))
        kernel_size = kernel_size if kernel_size % 2 else kernel_size + 1

        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=(kernel_size - 1) // 2, bias=False)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        y = self.avg_pool(x)
        y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
        y = self.sigmoid(y)
        return x * y.expand_as(x)

class SE_block(nn.Module):
    def __init__(self, input_dim: int):
        super().__init__()
        self.linear1 = torch.nn.Linear(input_dim, input_dim)
        self.relu = nn.ReLU()
        self.linear2 = torch.nn.Linear(input_dim, input_dim)
        self.sigmod = nn.Sigmoid()

    def forward(self, x):
        x1 = self.linear1(x)
        x1 = self.relu(x1)
        x1 = self.linear2(x1)
        x1 = self.sigmod(x1)
        x = x * x1
        return x


class VisionTransformer(nn.Module):
    def __init__(self, img_size=14, patch_size=14, in_c=147, num_classes=7,
                 embed_dim=768, depth=6, num_heads=8, mlp_ratio=4.0, qkv_bias=True,
                 qk_scale=None, representation_size=None, distilled=False, drop_ratio=0.,
                 attn_drop_ratio=0., drop_path_ratio=0., embed_layer=PatchEmbed, norm_layer=None,
                 act_layer=None):
        """
        Args:
            img_size (int, tuple): input image size
            patch_size (int, tuple): patch size
            in_c (int): number of input channels
            num_classes (int): number of classes for classification head
            embed_dim (int): embedding dimension
            depth (int): depth of transformer
            num_heads (int): number of attention heads
            mlp_ratio (int): ratio of mlp hidden dim to embedding dim
            qkv_bias (bool): enable bias for qkv if True
            qk_scale (float): override default qk scale of head_dim ** -0.5 if set
            representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
            distilled (bool): model includes a distillation token and head as in DeiT models
            drop_ratio (float): dropout rate
            attn_drop_ratio (float): attention dropout rate
            drop_path_ratio (float): stochastic depth rate
            embed_layer (nn.Module): patch embedding layer
            norm_layer: (nn.Module): normalization layer
        """
        super(VisionTransformer, self).__init__()
        self.num_classes = num_classes
        self.num_features = self.embed_dim = embed_dim  # num_features for consistency with other models
        self.num_tokens = 2 if distilled else 1
        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
        act_layer = act_layer or nn.GELU

        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.pos_embed = nn.Parameter(torch.zeros(1, in_c + 1, embed_dim))
        self.pos_drop = nn.Dropout(p=drop_ratio)

        self.se_block = SE_block(input_dim=embed_dim)


        self.patch_embed = embed_layer(img_size=img_size, patch_size=patch_size, in_c=256, embed_dim=768)
        num_patches = self.patch_embed.num_patches
        self.head = ClassificationHead(input_dim=embed_dim, target_dim=self.num_classes)
        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
        self.dist_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) if distilled else None
        # self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + self.num_tokens, embed_dim))
        self.pos_drop = nn.Dropout(p=drop_ratio)
        # self.IR = IR()
        self.eca_block = eca_block()


        # self.ir_back = Backbone(50, 0.0, 'ir')
        # ir_checkpoint = torch.load('./models/pretrain/ir50.pth', map_location=lambda storage, loc: storage)
        # # ir_checkpoint = ir_checkpoint["model"]
        # self.ir_back = load_pretrained_weights(self.ir_back, ir_checkpoint)

        self.CON1 = nn.Conv2d(256, 768, kernel_size=1, stride=1, bias=False)
        self.IRLinear1 = nn.Linear(1024, 768)
        self.IRLinear2 = nn.Linear(768, 512)
        self.eca_block = eca_block()
        dpr = [x.item() for x in torch.linspace(0, drop_path_ratio, depth)]  # stochastic depth decay rule
        self.blocks = nn.Sequential(*[
            Block(dim=embed_dim, in_chans=in_c, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
                  qk_scale=qk_scale,
                  drop_ratio=drop_ratio, attn_drop_ratio=attn_drop_ratio, drop_path_ratio=dpr[i],
                  norm_layer=norm_layer, act_layer=act_layer)
            for i in range(depth)
        ])
        self.norm = norm_layer(embed_dim)

        # Representation layer
        if representation_size and not distilled:
            self.has_logits = True
            self.num_features = representation_size
            self.pre_logits = nn.Sequential(OrderedDict([
                ("fc", nn.Linear(embed_dim, representation_size)),
                ("act", nn.Tanh())
            ]))
        else:
            self.has_logits = False
            self.pre_logits = nn.Identity()

        # Classifier head(s)
        # self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
        self.head_dist = None
        if distilled:
            self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()

        # Weight init
        nn.init.trunc_normal_(self.pos_embed, std=0.02)
        if self.dist_token is not None:
            nn.init.trunc_normal_(self.dist_token, std=0.02)

        nn.init.trunc_normal_(self.cls_token, std=0.02)
        self.apply(_init_vit_weights)

    def forward_features(self, x):
        # [B, C, H, W] -> [B, num_patches, embed_dim]
        # x = self.patch_embed(x)  # [B, 196, 768]
        # [1, 1, 768] -> [B, 1, 768]
        # print(x.shape)

        cls_token = self.cls_token.expand(x.shape[0], -1, -1)
        if self.dist_token is None:
            x = torch.cat((cls_token, x), dim=1)  # [B, 197, 768]
        else:
            x = torch.cat((cls_token, self.dist_token.expand(x.shape[0], -1, -1), x), dim=1)
        # print(x.shape)
        x = self.pos_drop(x + self.pos_embed)
        x = self.blocks(x)
        x = self.norm(x)
        if self.dist_token is None:
            return self.pre_logits(x[:, 0])
        else:
            return x[:, 0], x[:, 1]

    def forward(self, x):

        # B = x.shape[0]
        # print(x)
        # x = self.eca_block(x)
        # x = self.IR(x)
        # x = eca_block(x)
        # x = self.ir_back(x)
        # print(x.shape)
        # x = self.CON1(x)
        # x = x.view(-1, 196, 768)
        #
        # # print(x.shape)
        # # x = self.IRLinear1(x)
        # # print(x)
        # x_cls = torch.mean(x, 1).view(B, 1, -1)
        # x = torch.cat((x_cls, x), dim=1)
        # # print(x.shape)
        # x = self.pos_drop(x + self.pos_embed)
        # # print(x.shape)
        # x = self.blocks(x)
        # # print(x)
        # x = self.norm(x)
        # # print(x)
        # # x1 = self.IRLinear2(x)
        # x1 = x[:, 0, :]

        # print(x1)
        # print(x1.shape)

        x = self.forward_features(x)
        # # print(x.shape)
        # if self.head_dist is not None:
        #     x, x_dist = self.head(x[0]), self.head_dist(x[1])
        #     if self.training and not torch.jit.is_scripting():
        #         # during inference, return the average of both classifier predictions
        #         return x, x_dist
        #     else:
        #         return (x + x_dist) / 2
        # else:
        # print(x.shape)
        x = self.se_block(x)

        x1 = self.head(x)

        return x1


def _init_vit_weights(m):
    """
    ViT weight initialization
    :param m: module
    """
    if isinstance(m, nn.Linear):
        nn.init.trunc_normal_(m.weight, std=.01)
        if m.bias is not None:
            nn.init.zeros_(m.bias)
    elif isinstance(m, nn.Conv2d):
        nn.init.kaiming_normal_(m.weight, mode="fan_out")
        if m.bias is not None:
            nn.init.zeros_(m.bias)
    elif isinstance(m, nn.LayerNorm):
        nn.init.zeros_(m.bias)
        nn.init.ones_(m.weight)


def vit_base_patch16_224(num_classes: int = 7):
    """
    ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
    ImageNet-1k weights @ 224x224, source https://github.com/google-research/vision_transformer.
    weights ported from official Google JAX impl:
    链接: https://pan.baidu.com/s/1zqb08naP0RPqqfSXfkB2EA  密码: eu9f
    """
    model = VisionTransformer(img_size=224,
                              patch_size=16,
                              embed_dim=768,
                              depth=12,
                              num_heads=12,
                              representation_size=None,
                              num_classes=num_classes)

    return model


def vit_base_patch16_224_in21k(num_classes: int = 21843, has_logits: bool = True):
    """
    ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
    weights ported from official Google JAX impl:
    https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_patch16_224_in21k-e5005f0a.pth
    """
    model = VisionTransformer(img_size=224,
                              patch_size=16,
                              embed_dim=768,
                              depth=12,
                              num_heads=12,
                              representation_size=768 if has_logits else None,
                              num_classes=num_classes)
    return model


def vit_base_patch32_224(num_classes: int = 1000):
    """
    ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
    ImageNet-1k weights @ 224x224, source https://github.com/google-research/vision_transformer.
    weights ported from official Google JAX impl:
    链接: https://pan.baidu.com/s/1hCv0U8pQomwAtHBYc4hmZg  密码: s5hl
    """
    model = VisionTransformer(img_size=224,
                              patch_size=32,
                              embed_dim=768,
                              depth=12,
                              num_heads=12,
                              representation_size=None,
                              num_classes=num_classes)
    return model


def vit_base_patch32_224_in21k(num_classes: int = 21843, has_logits: bool = True):
    """
    ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
    weights ported from official Google JAX impl:
    https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_patch32_224_in21k-8db57226.pth
    """
    model = VisionTransformer(img_size=224,
                              patch_size=32,
                              embed_dim=768,
                              depth=12,
                              num_heads=12,
                              representation_size=768 if has_logits else None,
                              num_classes=num_classes)
    return model


def vit_large_patch16_224(num_classes: int = 1000):
    """
    ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
    ImageNet-1k weights @ 224x224, source https://github.com/google-research/vision_transformer.
    weights ported from official Google JAX impl:
    链接: https://pan.baidu.com/s/1cxBgZJJ6qUWPSBNcE4TdRQ  密码: qqt8
    """
    model = VisionTransformer(img_size=224,
                              patch_size=16,
                              embed_dim=1024,
                              depth=24,
                              num_heads=16,
                              representation_size=None,
                              num_classes=num_classes)
    return model


def vit_large_patch16_224_in21k(num_classes: int = 21843, has_logits: bool = True):
    """
    ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
    weights ported from official Google JAX impl:
    https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch16_224_in21k-606da67d.pth
    """
    model = VisionTransformer(img_size=224,
                              patch_size=16,
                              embed_dim=1024,
                              depth=24,
                              num_heads=16,
                              representation_size=1024 if has_logits else None,
                              num_classes=num_classes)
    return model


def vit_large_patch32_224_in21k(num_classes: int = 21843, has_logits: bool = True):
    """
    ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
    weights ported from official Google JAX impl:
    https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch32_224_in21k-9046d2e7.pth
    """
    model = VisionTransformer(img_size=224,
                              patch_size=32,
                              embed_dim=1024,
                              depth=24,
                              num_heads=16,
                              representation_size=1024 if has_logits else None,
                              num_classes=num_classes)
    return model


def vit_huge_patch14_224_in21k(num_classes: int = 21843, has_logits: bool = True):
    """
    ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929).
    ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
    NOTE: converted weights not currently available, too large for github release hosting.
    """
    model = VisionTransformer(img_size=224,
                              patch_size=14,
                              embed_dim=1280,
                              depth=32,
                              num_heads=16,
                              representation_size=1280 if has_logits else None,
                              num_classes=num_classes)
    return model