model_definition.py

# model_definition.py
# ============================================================================
# الاستيرادات الأساسية
# ============================================================================
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.optim import AdamW
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from functools import partial
from typing import Optional, List
from torch import Tensor

# مكتبات إضافية
import os
import json
import logging
import math
import copy
from pathlib import Path
from collections import OrderedDict

# مكتبات معالجة البيانات
import numpy as np
import cv2

# مكتبات اختيارية (يمكن تعطيلها إذا لم تكن متوفرة)
try:
    import wandb
    WANDB_AVAILABLE = True
except ImportError:
    WANDB_AVAILABLE = False
    
try:
    from tqdm import tqdm
except ImportError:
    # إذا لم تكن tqdm متوفرة، استخدم دالة بديلة
    def tqdm(iterable, *args, **kwargs):
        return iterable

# ============================================================================
# دوال مساعدة
# ============================================================================
def to_2tuple(x):
    """تحويل قيمة إلى tuple من عنصرين"""
    if isinstance(x, (list, tuple)):
        return tuple(x)
    return (x, x)
# ============================================================================
# ============================================================================

class HybridEmbed(nn.Module):
    def __init__(
        self,
        backbone,
        img_size=224,
        patch_size=1,
        feature_size=None,
        in_chans=3,
        embed_dim=768,
    ):
        super().__init__()
        assert isinstance(backbone, nn.Module)
        img_size = to_2tuple(img_size)
        patch_size = to_2tuple(patch_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.backbone = backbone
        if feature_size is None:
            with torch.no_grad():
                training = backbone.training
                if training:
                    backbone.eval()
                o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))
                if isinstance(o, (list, tuple)):
                    o = o[-1]  # last feature if backbone outputs list/tuple of features
                feature_size = o.shape[-2:]
                feature_dim = o.shape[1]
                backbone.train(training)
        else:
            feature_size = to_2tuple(feature_size)
            if hasattr(self.backbone, "feature_info"):
                feature_dim = self.backbone.feature_info.channels()[-1]
            else:
                feature_dim = self.backbone.num_features

        self.proj = nn.Conv2d(feature_dim, embed_dim, kernel_size=1, stride=1)

    def forward(self, x):
        x = self.backbone(x)
        if isinstance(x, (list, tuple)):
            x = x[-1]  # last feature if backbone outputs list/tuple of features
        x = self.proj(x)
        global_x = torch.mean(x, [2, 3], keepdim=False)[:, :, None]
        return x, global_x


class PositionEmbeddingSine(nn.Module):
    """
    This is a more standard version of the position embedding, very similar to the one
    used by the Attention is all you need paper, generalized to work on images.
    """

    def __init__(
        self, num_pos_feats=64, temperature=10000, normalize=False, scale=None
    ):
        super().__init__()
        self.num_pos_feats = num_pos_feats
        self.temperature = temperature
        self.normalize = normalize
        if scale is not None and normalize is False:
            raise ValueError("normalize should be True if scale is passed")
        if scale is None:
            scale = 2 * math.pi
        self.scale = scale

    def forward(self, tensor):
        x = tensor
        bs, _, h, w = x.shape
        not_mask = torch.ones((bs, h, w), device=x.device)
        y_embed = not_mask.cumsum(1, dtype=torch.float32)
        x_embed = not_mask.cumsum(2, dtype=torch.float32)
        if self.normalize:
            eps = 1e-6
            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale

        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)

        pos_x = x_embed[:, :, :, None] / dim_t
        pos_y = y_embed[:, :, :, None] / dim_t
        pos_x = torch.stack(
            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
        ).flatten(3)
        pos_y = torch.stack(
            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
        ).flatten(3)
        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
        return pos


class TransformerEncoder(nn.Module):
    def __init__(self, encoder_layer, num_layers, norm=None):
        super().__init__()
        self.layers = _get_clones(encoder_layer, num_layers)
        self.num_layers = num_layers
        self.norm = norm

    def forward(
        self,
        src,
        mask: Optional[Tensor] = None,
        src_key_padding_mask: Optional[Tensor] = None,
        pos: Optional[Tensor] = None,
    ):
        output = src

        for layer in self.layers:
            output = layer(
                output,
                src_mask=mask,
                src_key_padding_mask=src_key_padding_mask,
                pos=pos,
            )

        if self.norm is not None:
            output = self.norm(output)

        return output


class SpatialSoftmax(nn.Module):
    def __init__(self, height, width, channel, temperature=None, data_format="NCHW"):
        super().__init__()

        self.data_format = data_format
        self.height = height
        self.width = width
        self.channel = channel

        if temperature:
            self.temperature = Parameter(torch.ones(1) * temperature)
        else:
            self.temperature = 1.0

        pos_x, pos_y = np.meshgrid(
            np.linspace(-1.0, 1.0, self.height), np.linspace(-1.0, 1.0, self.width)
        )
        pos_x = torch.from_numpy(pos_x.reshape(self.height * self.width)).float()
        pos_y = torch.from_numpy(pos_y.reshape(self.height * self.width)).float()
        self.register_buffer("pos_x", pos_x)
        self.register_buffer("pos_y", pos_y)

    def forward(self, feature):
        # Output:
        #   (N, C*2) x_0 y_0 ...

        if self.data_format == "NHWC":
            feature = (
                feature.transpose(1, 3)
                .tranpose(2, 3)
                .view(-1, self.height * self.width)
            )
        else:
            feature = feature.view(-1, self.height * self.width)

        weight = F.softmax(feature / self.temperature, dim=-1)
        expected_x = torch.sum(
            torch.autograd.Variable(self.pos_x) * weight, dim=1, keepdim=True
        )
        expected_y = torch.sum(
            torch.autograd.Variable(self.pos_y) * weight, dim=1, keepdim=True
        )
        expected_xy = torch.cat([expected_x, expected_y], 1)
        feature_keypoints = expected_xy.view(-1, self.channel, 2)
        feature_keypoints[:, :, 1] = (feature_keypoints[:, :, 1] - 1) * 12
        feature_keypoints[:, :, 0] = feature_keypoints[:, :, 0] * 12
        return feature_keypoints


class MultiPath_Generator(nn.Module):
    def __init__(self, in_channel, embed_dim, out_channel):
        super().__init__()
        self.spatial_softmax = SpatialSoftmax(100, 100, out_channel)
        self.tconv0 = nn.Sequential(
            nn.ConvTranspose2d(in_channel, 256, 4, 2, 1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(True),
        )
        self.tconv1 = nn.Sequential(
            nn.ConvTranspose2d(256, 256, 4, 2, 1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(True),
        )
        self.tconv2 = nn.Sequential(
            nn.ConvTranspose2d(256, 192, 4, 2, 1, bias=False),
            nn.BatchNorm2d(192),
            nn.ReLU(True),
        )
        self.tconv3 = nn.Sequential(
            nn.ConvTranspose2d(192, 64, 4, 2, 1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(True),
        )
        self.tconv4_list = torch.nn.ModuleList(
            [
                nn.Sequential(
                    nn.ConvTranspose2d(64, out_channel, 8, 2, 3, bias=False),
                    nn.Tanh(),
                )
                for _ in range(6)
            ]
        )

        self.upsample = nn.Upsample(size=(50, 50), mode="bilinear")

    def forward(self, x, measurements):
        mask = measurements[:, :6]
        mask = mask.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1).repeat(1, 1, 1, 100, 100)
        velocity = measurements[:, 6:7].unsqueeze(-1).unsqueeze(-1)
        velocity = velocity.repeat(1, 32, 2, 2)

        n, d, c = x.shape
        x = x.transpose(1, 2)
        x = x.view(n, -1, 2, 2)
        x = torch.cat([x, velocity], dim=1)
        x = self.tconv0(x)
        x = self.tconv1(x)
        x = self.tconv2(x)
        x = self.tconv3(x)
        x = self.upsample(x)
        xs = []
        for i in range(6):
            xt = self.tconv4_list[i](x)
            xs.append(xt)
        xs = torch.stack(xs, dim=1)
        x = torch.sum(xs * mask, dim=1)
        x = self.spatial_softmax(x)
        return x


class LinearWaypointsPredictor(nn.Module):
    def __init__(self, input_dim, cumsum=True):
        super().__init__()
        self.cumsum = cumsum
        self.rank_embed = nn.Parameter(torch.zeros(1, 10, input_dim))
        self.head_fc1_list = nn.ModuleList([nn.Linear(input_dim, 64) for _ in range(6)])
        self.head_relu = nn.ReLU(inplace=True)
        self.head_fc2_list = nn.ModuleList([nn.Linear(64, 2) for _ in range(6)])

    def forward(self, x, measurements):
        # input shape: n 10 embed_dim
        bs, n, dim = x.shape
        x = x + self.rank_embed
        x = x.reshape(-1, dim)

        mask = measurements[:, :6]
        mask = torch.unsqueeze(mask, -1).repeat(n, 1, 2)

        rs = []
        for i in range(6):
            res = self.head_fc1_list[i](x)
            res = self.head_relu(res)
            res = self.head_fc2_list[i](res)
            rs.append(res)
        rs = torch.stack(rs, 1)
        x = torch.sum(rs * mask, dim=1)

        x = x.view(bs, n, 2)
        if self.cumsum:
            x = torch.cumsum(x, 1)
        return x


class GRUWaypointsPredictor(nn.Module):
    def __init__(self, input_dim, waypoints=10):
        super().__init__()
        # self.gru = torch.nn.GRUCell(input_size=input_dim, hidden_size=64)
        self.gru = torch.nn.GRU(input_size=input_dim, hidden_size=64, batch_first=True)
        self.encoder = nn.Linear(2, 64)
        self.decoder = nn.Linear(64, 2)
        self.waypoints = waypoints

    def forward(self, x, target_point):
        bs = x.shape[0]
        z = self.encoder(target_point).unsqueeze(0)
        output, _ = self.gru(x, z)
        output = output.reshape(bs * self.waypoints, -1)
        output = self.decoder(output).reshape(bs, self.waypoints, 2)
        output = torch.cumsum(output, 1)
        return output

class GRUWaypointsPredictorWithCommand(nn.Module):
    def __init__(self, input_dim, waypoints=10):
        super().__init__()
        # self.gru = torch.nn.GRUCell(input_size=input_dim, hidden_size=64)
        self.grus = nn.ModuleList([torch.nn.GRU(input_size=input_dim, hidden_size=64, batch_first=True) for _ in range(6)])
        self.encoder = nn.Linear(2, 64)
        self.decoders = nn.ModuleList([nn.Linear(64, 2) for _ in range(6)])
        self.waypoints = waypoints

    def forward(self, x, target_point, measurements):
        bs, n, dim = x.shape
        mask = measurements[:, :6, None, None]
        mask = mask.repeat(1, 1, self.waypoints, 2)

        z = self.encoder(target_point).unsqueeze(0)
        outputs = []
        for i in range(6):
            output, _ = self.grus[i](x, z)
            output = output.reshape(bs * self.waypoints, -1)
            output = self.decoders[i](output).reshape(bs, self.waypoints, 2)
            output = torch.cumsum(output, 1)
            outputs.append(output)
        outputs = torch.stack(outputs, 1)
        output = torch.sum(outputs * mask, dim=1)
        return output


class TransformerDecoder(nn.Module):
    def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):
        super().__init__()
        self.layers = _get_clones(decoder_layer, num_layers)
        self.num_layers = num_layers
        self.norm = norm
        self.return_intermediate = return_intermediate

    def forward(
        self,
        tgt,
        memory,
        tgt_mask: Optional[Tensor] = None,
        memory_mask: Optional[Tensor] = None,
        tgt_key_padding_mask: Optional[Tensor] = None,
        memory_key_padding_mask: Optional[Tensor] = None,
        pos: Optional[Tensor] = None,
        query_pos: Optional[Tensor] = None,
    ):
        output = tgt

        intermediate = []

        for layer in self.layers:
            output = layer(
                output,
                memory,
                tgt_mask=tgt_mask,
                memory_mask=memory_mask,
                tgt_key_padding_mask=tgt_key_padding_mask,
                memory_key_padding_mask=memory_key_padding_mask,
                pos=pos,
                query_pos=query_pos,
            )
            if self.return_intermediate:
                intermediate.append(self.norm(output))

        if self.norm is not None:
            output = self.norm(output)
            if self.return_intermediate:
                intermediate.pop()
                intermediate.append(output)

        if self.return_intermediate:
            return torch.stack(intermediate)

        return output.unsqueeze(0)


class TransformerEncoderLayer(nn.Module):
    def __init__(
        self,
        d_model,
        nhead,
        dim_feedforward=2048,
        dropout=0.1,
        activation=nn.ReLU(),
        normalize_before=False,
    ):
        super().__init__()
        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
        # Implementation of Feedforward model
        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(dim_feedforward, d_model)

        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)

        self.activation = activation()
        self.normalize_before = normalize_before

    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
        return tensor if pos is None else tensor + pos

    def forward_post(
        self,
        src,
        src_mask: Optional[Tensor] = None,
        src_key_padding_mask: Optional[Tensor] = None,
        pos: Optional[Tensor] = None,
    ):
        q = k = self.with_pos_embed(src, pos)
        src2 = self.self_attn(
            q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask
        )[0]
        src = src + self.dropout1(src2)
        src = self.norm1(src)
        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
        src = src + self.dropout2(src2)
        src = self.norm2(src)
        return src

    def forward_pre(
        self,
        src,
        src_mask: Optional[Tensor] = None,
        src_key_padding_mask: Optional[Tensor] = None,
        pos: Optional[Tensor] = None,
    ):
        src2 = self.norm1(src)
        q = k = self.with_pos_embed(src2, pos)
        src2 = self.self_attn(
            q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask
        )[0]
        src = src + self.dropout1(src2)
        src2 = self.norm2(src)
        src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))
        src = src + self.dropout2(src2)
        return src

    def forward(
        self,
        src,
        src_mask: Optional[Tensor] = None,
        src_key_padding_mask: Optional[Tensor] = None,
        pos: Optional[Tensor] = None,
    ):
        if self.normalize_before:
            return self.forward_pre(src, src_mask, src_key_padding_mask, pos)
        return self.forward_post(src, src_mask, src_key_padding_mask, pos)


class TransformerDecoderLayer(nn.Module):
    def __init__(
        self,
        d_model,
        nhead,
        dim_feedforward=2048,
        dropout=0.1,
        activation=nn.ReLU(),
        normalize_before=False,
    ):
        super().__init__()
        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
        self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
        # Implementation of Feedforward model
        self.linear1 = nn.Linear(d_model, dim_feedforward)
        self.dropout = nn.Dropout(dropout)
        self.linear2 = nn.Linear(dim_feedforward, d_model)

        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.norm3 = nn.LayerNorm(d_model)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        self.dropout3 = nn.Dropout(dropout)

        self.activation = activation()
        self.normalize_before = normalize_before

    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
        return tensor if pos is None else tensor + pos

    def forward_post(
        self,
        tgt,
        memory,
        tgt_mask: Optional[Tensor] = None,
        memory_mask: Optional[Tensor] = None,
        tgt_key_padding_mask: Optional[Tensor] = None,
        memory_key_padding_mask: Optional[Tensor] = None,
        pos: Optional[Tensor] = None,
        query_pos: Optional[Tensor] = None,
    ):
        q = k = self.with_pos_embed(tgt, query_pos)
        tgt2 = self.self_attn(
            q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask
        )[0]
        tgt = tgt + self.dropout1(tgt2)
        tgt = self.norm1(tgt)
        tgt2 = self.multihead_attn(
            query=self.with_pos_embed(tgt, query_pos),
            key=self.with_pos_embed(memory, pos),
            value=memory,
            attn_mask=memory_mask,
            key_padding_mask=memory_key_padding_mask,
        )[0]
        tgt = tgt + self.dropout2(tgt2)
        tgt = self.norm2(tgt)
        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
        tgt = tgt + self.dropout3(tgt2)
        tgt = self.norm3(tgt)
        return tgt

    def forward_pre(
        self,
        tgt,
        memory,
        tgt_mask: Optional[Tensor] = None,
        memory_mask: Optional[Tensor] = None,
        tgt_key_padding_mask: Optional[Tensor] = None,
        memory_key_padding_mask: Optional[Tensor] = None,
        pos: Optional[Tensor] = None,
        query_pos: Optional[Tensor] = None,
    ):
        tgt2 = self.norm1(tgt)
        q = k = self.with_pos_embed(tgt2, query_pos)
        tgt2 = self.self_attn(
            q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask
        )[0]
        tgt = tgt + self.dropout1(tgt2)
        tgt2 = self.norm2(tgt)
        tgt2 = self.multihead_attn(
            query=self.with_pos_embed(tgt2, query_pos),
            key=self.with_pos_embed(memory, pos),
            value=memory,
            attn_mask=memory_mask,
            key_padding_mask=memory_key_padding_mask,
        )[0]
        tgt = tgt + self.dropout2(tgt2)
        tgt2 = self.norm3(tgt)
        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
        tgt = tgt + self.dropout3(tgt2)
        return tgt

    def forward(
        self,
        tgt,
        memory,
        tgt_mask: Optional[Tensor] = None,
        memory_mask: Optional[Tensor] = None,
        tgt_key_padding_mask: Optional[Tensor] = None,
        memory_key_padding_mask: Optional[Tensor] = None,
        pos: Optional[Tensor] = None,
        query_pos: Optional[Tensor] = None,
    ):
        if self.normalize_before:
            return self.forward_pre(
                tgt,
                memory,
                tgt_mask,
                memory_mask,
                tgt_key_padding_mask,
                memory_key_padding_mask,
                pos,
                query_pos,
            )
        return self.forward_post(
            tgt,
            memory,
            tgt_mask,
            memory_mask,
            tgt_key_padding_mask,
            memory_key_padding_mask,
            pos,
            query_pos,
        )


def _get_clones(module, N):
    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])


def _get_activation_fn(activation):
    """Return an activation function given a string"""
    if activation == "relu":
        return F.relu
    if activation == "gelu":
        return F.gelu
    if activation == "glu":
        return F.glu
    raise RuntimeError(f"activation should be relu/gelu, not {activation}.")


def build_attn_mask(mask_type):
    mask = torch.ones((151, 151), dtype=torch.bool).cuda()
    if mask_type == "seperate_all":
        mask[:50, :50] = False
        mask[50:67, 50:67] = False
        mask[67:84, 67:84] = False
        mask[84:101, 84:101] = False
        mask[101:151, 101:151] = False
    elif mask_type == "seperate_view":
        mask[:50, :50] = False
        mask[50:67, 50:67] = False
        mask[67:84, 67:84] = False
        mask[84:101, 84:101] = False
        mask[101:151, :] = False
        mask[:, 101:151] = False
    return mask
# class InterfuserModel(nn.Module):

class InterfuserModel(nn.Module):
    def __init__(
        self,
        img_size=224,
        multi_view_img_size=112,
        patch_size=8,
        in_chans=3,
        embed_dim=768,
        enc_depth=6,
        dec_depth=6,
        dim_feedforward=2048,
        normalize_before=False,
        rgb_backbone_name="r50",
        lidar_backbone_name="r50",
        num_heads=8,
        norm_layer=None,
        dropout=0.1,
        end2end=False,
        direct_concat=False,
        separate_view_attention=False,
        separate_all_attention=False,
        act_layer=None,
        weight_init="",
        freeze_num=-1,
        with_lidar=False,
        with_right_left_sensors=False,
        with_center_sensor=False,
        traffic_pred_head_type="det",
        waypoints_pred_head="heatmap",
        reverse_pos=True,
        use_different_backbone=False,
        use_view_embed=False,
        use_mmad_pretrain=None,
    ):
        super().__init__()
        self.traffic_pred_head_type = traffic_pred_head_type
        self.num_features = (
            self.embed_dim
        ) = embed_dim  # num_features for consistency with other models
        norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
        act_layer = act_layer or nn.GELU

        self.reverse_pos = reverse_pos
        self.waypoints_pred_head = waypoints_pred_head
        self.with_lidar = with_lidar
        self.with_right_left_sensors = with_right_left_sensors
        self.with_center_sensor = with_center_sensor

        self.direct_concat = direct_concat
        self.separate_view_attention = separate_view_attention
        self.separate_all_attention = separate_all_attention
        self.end2end = end2end
        self.use_view_embed = use_view_embed

        if self.direct_concat:
            in_chans = in_chans * 4
            self.with_center_sensor = False
            self.with_right_left_sensors = False

        if self.separate_view_attention:
            self.attn_mask = build_attn_mask("seperate_view")
        elif self.separate_all_attention:
            self.attn_mask = build_attn_mask("seperate_all")
        else:
            self.attn_mask = None

        if use_different_backbone:
            if rgb_backbone_name == "r50":
                self.rgb_backbone = resnet50d(
                    pretrained=True,
                    in_chans=in_chans,
                    features_only=True,
                    out_indices=[4],
                )
            elif rgb_backbone_name == "r26":
                self.rgb_backbone = resnet26d(
                    pretrained=True,
                    in_chans=in_chans,
                    features_only=True,
                    out_indices=[4],
                )
            elif rgb_backbone_name == "r18":
                self.rgb_backbone = resnet18d(
                    pretrained=True,
                    in_chans=in_chans,
                    features_only=True,
                    out_indices=[4],
                )
            if lidar_backbone_name == "r50":
                self.lidar_backbone = resnet50d(
                    pretrained=False,
                    in_chans=in_chans,
                    features_only=True,
                    out_indices=[4],
                )
            elif lidar_backbone_name == "r26":
                self.lidar_backbone = resnet26d(
                    pretrained=False,
                    in_chans=in_chans,
                    features_only=True,
                    out_indices=[4],
                )
            elif lidar_backbone_name == "r18":
                self.lidar_backbone = resnet18d(
                    pretrained=False, in_chans=3, features_only=True, out_indices=[4]
                )
            rgb_embed_layer = partial(HybridEmbed, backbone=self.rgb_backbone)
            lidar_embed_layer = partial(HybridEmbed, backbone=self.lidar_backbone)

            if use_mmad_pretrain:
                params = torch.load(use_mmad_pretrain)["state_dict"]
                updated_params = OrderedDict()
                for key in params:
                    if "backbone" in key:
                        updated_params[key.replace("backbone.", "")] = params[key]
                self.rgb_backbone.load_state_dict(updated_params)

            self.rgb_patch_embed = rgb_embed_layer(
                img_size=img_size,
                patch_size=patch_size,
                in_chans=in_chans,
                embed_dim=embed_dim,
            )
            self.lidar_patch_embed = lidar_embed_layer(
                img_size=img_size,
                patch_size=patch_size,
                in_chans=3,
                embed_dim=embed_dim,
            )
        else:
            if rgb_backbone_name == "r50":
                self.rgb_backbone = resnet50d(
                    pretrained=True, in_chans=3, features_only=True, out_indices=[4]
                )
            elif rgb_backbone_name == "r101":
                self.rgb_backbone = resnet101d(
                    pretrained=True, in_chans=3, features_only=True, out_indices=[4]
                )
            elif rgb_backbone_name == "r26":
                self.rgb_backbone = resnet26d(
                    pretrained=True, in_chans=3, features_only=True, out_indices=[4]
                )
            elif rgb_backbone_name == "r18":
                self.rgb_backbone = resnet18d(
                    pretrained=True, in_chans=3, features_only=True, out_indices=[4]
                )
            embed_layer = partial(HybridEmbed, backbone=self.rgb_backbone)

            self.rgb_patch_embed = embed_layer(
                img_size=img_size,
                patch_size=patch_size,
                in_chans=in_chans,
                embed_dim=embed_dim,
            )
            self.lidar_patch_embed = embed_layer(
                img_size=img_size,
                patch_size=patch_size,
                in_chans=in_chans,
                embed_dim=embed_dim,
            )

        self.global_embed = nn.Parameter(torch.zeros(1, embed_dim, 5))
        self.view_embed = nn.Parameter(torch.zeros(1, embed_dim, 5, 1))

        if self.end2end:
            self.query_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, 4))
            self.query_embed = nn.Parameter(torch.zeros(4, 1, embed_dim))
        elif self.waypoints_pred_head == "heatmap":
            self.query_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, 5))
            self.query_embed = nn.Parameter(torch.zeros(400 + 5, 1, embed_dim))
        else:
            self.query_pos_embed = nn.Parameter(torch.zeros(1, embed_dim, 11))
            self.query_embed = nn.Parameter(torch.zeros(400 + 11, 1, embed_dim))

        if self.end2end:
            self.waypoints_generator = GRUWaypointsPredictor(embed_dim, 4)
        elif self.waypoints_pred_head == "heatmap":
            self.waypoints_generator = MultiPath_Generator(
                embed_dim + 32, embed_dim, 10
            )
        elif self.waypoints_pred_head == "gru":
            self.waypoints_generator = GRUWaypointsPredictor(embed_dim)
        elif self.waypoints_pred_head == "gru-command":
            self.waypoints_generator = GRUWaypointsPredictorWithCommand(embed_dim)
        elif self.waypoints_pred_head == "linear":
            self.waypoints_generator = LinearWaypointsPredictor(embed_dim)
        elif self.waypoints_pred_head == "linear-sum":
            self.waypoints_generator = LinearWaypointsPredictor(embed_dim, cumsum=True)

        self.junction_pred_head = nn.Linear(embed_dim, 2)
        self.traffic_light_pred_head = nn.Linear(embed_dim, 2)
        self.stop_sign_head = nn.Linear(embed_dim, 2)

        if self.traffic_pred_head_type == "det":
            self.traffic_pred_head = nn.Sequential(
                *[
                    nn.Linear(embed_dim + 32, 64),
                    nn.ReLU(),
                    nn.Linear(64, 7),
                    # nn.Sigmoid(),
                ]
            )
        elif self.traffic_pred_head_type == "seg":
            self.traffic_pred_head = nn.Sequential(
                *[nn.Linear(embed_dim, 64), nn.ReLU(), nn.Linear(64, 1), nn.Sigmoid()]
            )

        self.position_encoding = PositionEmbeddingSine(embed_dim // 2, normalize=True)

        encoder_layer = TransformerEncoderLayer(
            embed_dim, num_heads, dim_feedforward, dropout, act_layer, normalize_before
        )
        self.encoder = TransformerEncoder(encoder_layer, enc_depth, None)

        decoder_layer = TransformerDecoderLayer(
            embed_dim, num_heads, dim_feedforward, dropout, act_layer, normalize_before
        )
        decoder_norm = nn.LayerNorm(embed_dim)
        self.decoder = TransformerDecoder(
            decoder_layer, dec_depth, decoder_norm, return_intermediate=False
        )
        self.reset_parameters()

    def reset_parameters(self):
        nn.init.uniform_(self.global_embed)
        nn.init.uniform_(self.view_embed)
        nn.init.uniform_(self.query_embed)
        nn.init.uniform_(self.query_pos_embed)

    def forward_features(
        self,
        front_image,
        left_image,
        right_image,
        front_center_image,
        lidar,
        measurements,
    ):
        features = []

        # Front view processing
        front_image_token, front_image_token_global = self.rgb_patch_embed(front_image)
        if self.use_view_embed:
            front_image_token = (
                front_image_token
                + self.view_embed[:, :, 0:1, :]
                + self.position_encoding(front_image_token)
            )
        else:
            front_image_token = front_image_token + self.position_encoding(
                front_image_token
            )
        front_image_token = front_image_token.flatten(2).permute(2, 0, 1)
        front_image_token_global = (
            front_image_token_global
            + self.view_embed[:, :, 0, :]
            + self.global_embed[:, :, 0:1]
        )
        front_image_token_global = front_image_token_global.permute(2, 0, 1)
        features.extend([front_image_token, front_image_token_global])

        if self.with_right_left_sensors:
            # Left view processing
            left_image_token, left_image_token_global = self.rgb_patch_embed(left_image)
            if self.use_view_embed:
                left_image_token = (
                    left_image_token
                    + self.view_embed[:, :, 1:2, :]
                    + self.position_encoding(left_image_token)
                )
            else:
                left_image_token = left_image_token + self.position_encoding(
                    left_image_token
                )
            left_image_token = left_image_token.flatten(2).permute(2, 0, 1)
            left_image_token_global = (
                left_image_token_global
                + self.view_embed[:, :, 1, :]
                + self.global_embed[:, :, 1:2]
            )
            left_image_token_global = left_image_token_global.permute(2, 0, 1)

            # Right view processing
            right_image_token, right_image_token_global = self.rgb_patch_embed(
                right_image
            )
            if self.use_view_embed:
                right_image_token = (
                    right_image_token
                    + self.view_embed[:, :, 2:3, :]
                    + self.position_encoding(right_image_token)
                )
            else:
                right_image_token = right_image_token + self.position_encoding(
                    right_image_token
                )
            right_image_token = right_image_token.flatten(2).permute(2, 0, 1)
            right_image_token_global = (
                right_image_token_global
                + self.view_embed[:, :, 2, :]
                + self.global_embed[:, :, 2:3]
            )
            right_image_token_global = right_image_token_global.permute(2, 0, 1)

            features.extend(
                [
                    left_image_token,
                    left_image_token_global,
                    right_image_token,
                    right_image_token_global,
                ]
            )

        if self.with_center_sensor:
            # Front center view processing
            (
                front_center_image_token,
                front_center_image_token_global,
            ) = self.rgb_patch_embed(front_center_image)
            if self.use_view_embed:
                front_center_image_token = (
                    front_center_image_token
                    + self.view_embed[:, :, 3:4, :]
                    + self.position_encoding(front_center_image_token)
                )
            else:
                front_center_image_token = (
                    front_center_image_token
                    + self.position_encoding(front_center_image_token)
                )

            front_center_image_token = front_center_image_token.flatten(2).permute(
                2, 0, 1
            )
            front_center_image_token_global = (
                front_center_image_token_global
                + self.view_embed[:, :, 3, :]
                + self.global_embed[:, :, 3:4]
            )
            front_center_image_token_global = front_center_image_token_global.permute(
                2, 0, 1
            )
            features.extend([front_center_image_token, front_center_image_token_global])

        if self.with_lidar:
            lidar_token, lidar_token_global = self.lidar_patch_embed(lidar)
            if self.use_view_embed:
                lidar_token = (
                    lidar_token
                    + self.view_embed[:, :, 4:5, :]
                    + self.position_encoding(lidar_token)
                )
            else:
                lidar_token = lidar_token + self.position_encoding(lidar_token)
            lidar_token = lidar_token.flatten(2).permute(2, 0, 1)
            lidar_token_global = (
                lidar_token_global
                + self.view_embed[:, :, 4, :]
                + self.global_embed[:, :, 4:5]
            )
            lidar_token_global = lidar_token_global.permute(2, 0, 1)
            features.extend([lidar_token, lidar_token_global])

        features = torch.cat(features, 0)
        return features

    def forward(self, x):
        front_image = x["rgb"]
        left_image = x["rgb_left"]
        right_image = x["rgb_right"]
        front_center_image = x["rgb_center"]
        measurements = x["measurements"]
        target_point = x["target_point"]
        lidar = x["lidar"]

        if self.direct_concat:
            img_size = front_image.shape[-1]
            left_image = torch.nn.functional.interpolate(
                left_image, size=(img_size, img_size)
            )
            right_image = torch.nn.functional.interpolate(
                right_image, size=(img_size, img_size)
            )
            front_center_image = torch.nn.functional.interpolate(
                front_center_image, size=(img_size, img_size)
            )
            front_image = torch.cat(
                [front_image, left_image, right_image, front_center_image], dim=1
            )
        features = self.forward_features(
            front_image,
            left_image,
            right_image,
            front_center_image,
            lidar,
            measurements,
        )

        bs = front_image.shape[0]

        if self.end2end:
            tgt = self.query_pos_embed.repeat(bs, 1, 1)
        else:
            tgt = self.position_encoding(
                torch.ones((bs, 1, 20, 20), device=x["rgb"].device)
            )
            tgt = tgt.flatten(2)
            tgt = torch.cat([tgt, self.query_pos_embed.repeat(bs, 1, 1)], 2)
        tgt = tgt.permute(2, 0, 1)

        memory = self.encoder(features, mask=self.attn_mask)
        hs = self.decoder(self.query_embed.repeat(1, bs, 1), memory, query_pos=tgt)[0]

        hs = hs.permute(1, 0, 2)  # Batchsize ,  N, C
        if self.end2end:
            waypoints = self.waypoints_generator(hs, target_point)
            return waypoints

        if self.waypoints_pred_head != "heatmap":
            traffic_feature = hs[:, :400]
            is_junction_feature = hs[:, 400]
            traffic_light_state_feature = hs[:, 400]
            stop_sign_feature = hs[:, 400]
            waypoints_feature = hs[:, 401:411]
        else:
            traffic_feature = hs[:, :400]
            is_junction_feature = hs[:, 400]
            traffic_light_state_feature = hs[:, 400]
            stop_sign_feature = hs[:, 400]
            waypoints_feature = hs[:, 401:405]

        if self.waypoints_pred_head == "heatmap":
            waypoints = self.waypoints_generator(waypoints_feature, measurements)
        elif self.waypoints_pred_head == "gru":
            waypoints = self.waypoints_generator(waypoints_feature, target_point)
        elif self.waypoints_pred_head == "gru-command":
            waypoints = self.waypoints_generator(waypoints_feature, target_point, measurements)
        elif self.waypoints_pred_head == "linear":
            waypoints = self.waypoints_generator(waypoints_feature, measurements)
        elif self.waypoints_pred_head == "linear-sum":
            waypoints = self.waypoints_generator(waypoints_feature, measurements)

        is_junction = self.junction_pred_head(is_junction_feature)
        traffic_light_state = self.traffic_light_pred_head(traffic_light_state_feature)
        stop_sign = self.stop_sign_head(stop_sign_feature)

        velocity = measurements[:, 6:7].unsqueeze(-1)
        velocity = velocity.repeat(1, 400, 32)
        traffic_feature_with_vel = torch.cat([traffic_feature, velocity], dim=2)
        traffic = self.traffic_pred_head(traffic_feature_with_vel)
        return traffic, waypoints, is_junction, traffic_light_state, stop_sign, traffic_feature
    def load_pretrained(self, model_path, strict=False):
        """
        تحميل الأوزان المدربة مسبقاً - نسخة محسنة
        
        Args:
            model_path (str): مسار ملف الأوزان
            strict (bool): إذا كان True، يتطلب تطابق تام للمفاتيح
        """
        if not model_path or not Path(model_path).exists():
            logging.warning(f"ملف الأوزان غير موجود: {model_path}")
            logging.info("سيتم استخدام أوزان عشوائية")
            return False
            
        try:
            logging.info(f"محاولة تحميل الأوزان من: {model_path}")
            
            # تحميل الملف مع معالجة أنواع مختلفة من ملفات الحفظ
            checkpoint = torch.load(model_path, map_location='cpu', weights_only=False)
            
            # استخراج state_dict من أنواع مختلفة من ملفات الحفظ
            if isinstance(checkpoint, dict):
                if 'model_state_dict' in checkpoint:
                    state_dict = checkpoint['model_state_dict']
                    logging.info("تم العثور على 'model_state_dict' في الملف")
                elif 'state_dict' in checkpoint:
                    state_dict = checkpoint['state_dict']
                    logging.info("تم العثور على 'state_dict' في الملف")
                elif 'model' in checkpoint:
                    state_dict = checkpoint['model']
                    logging.info("تم العثور على 'model' في الملف")
                else:
                    state_dict = checkpoint
                    logging.info("استخدام الملف كـ state_dict مباشرة")
            else:
                state_dict = checkpoint
                logging.info("استخدام الملف كـ state_dict مباشرة")
            
            # تنظيف أسماء المفاتيح (إزالة 'module.' إذا كانت موجودة)
            clean_state_dict = OrderedDict()
            for k, v in state_dict.items():
                # إزالة 'module.' من بداية اسم المفتاح إذا كان موجوداً
                clean_key = k[7:] if k.startswith('module.') else k
                clean_state_dict[clean_key] = v
            
            # تحميل الأوزان
            missing_keys, unexpected_keys = self.load_state_dict(clean_state_dict, strict=strict)
            
            # تقرير حالة التحميل
            if missing_keys:
                logging.warning(f"مفاتيح مفقودة ({len(missing_keys)}): {missing_keys[:5]}..." if len(missing_keys) > 5 else f"مفاتيح مفقودة: {missing_keys}")
            
            if unexpected_keys:
                logging.warning(f"مفاتيح غير متوقعة ({len(unexpected_keys)}): {unexpected_keys[:5]}..." if len(unexpected_keys) > 5 else f"مفاتيح غير متوقعة: {unexpected_keys}")
            
            if not missing_keys and not unexpected_keys:
                logging.info("✅ تم تحميل جميع الأوزان بنجاح تام")
            elif not strict:
                logging.info("✅ تم تحميل الأوزان بنجاح (مع تجاهل عدم التطابق)")
            
            return True
            
        except Exception as e:
            logging.error(f"❌ خطأ في تحميل الأوزان: {str(e)}")
            logging.info("سيتم استخدام أوزان عشوائية")
            return False


# ============================================================================
# دوال مساعدة لتحميل النموذج
# ============================================================================

def load_and_prepare_model(config, device):
    """
    يقوم بإنشاء النموذج وتحميل الأوزان المدربة مسبقًا.
    
    Args:
        config (dict): إعدادات النموذج والمسارات
        device (torch.device): الجهاز المستهدف (CPU/GPU)
    
    Returns:
        InterfuserModel: النموذج المحمل
    """
    try:
        # إنشاء النموذج
        model = InterfuserModel(**config.get('model_params', {})).to(device)
        logging.info(f"تم إنشاء النموذج على الجهاز: {device}")
        
        # تحميل الأوزان إذا كان المسار محدد
        checkpoint_path = config.get('paths', {}).get('pretrained_weights')
        if checkpoint_path:
            success = model.load_pretrained(checkpoint_path, strict=False)
            if success:
                logging.info("✅ تم تحميل النموذج والأوزان بنجاح")
            else:
                logging.warning("⚠️ تم إنشاء النموذج بأوزان عشوائية")
        else:
            logging.info("لم يتم تحديد مسار الأوزان، سيتم استخدام أوزان عشوائية")
        
        # وضع النموذج في وضع التقييم
        model.eval()
        
        return model
        
    except Exception as e:
        logging.error(f"خطأ في إنشاء النموذج: {str(e)}")
        raise


def create_model_config(model_path="model/best_model.pth", **model_params):
    """
    إنشاء إعدادات النموذج باستخدام الإعدادات الصحيحة من التدريب
    
    Args:
        model_path (str): مسار ملف الأوزان
        **model_params: معاملات النموذج الإضافية
    
    Returns:
        dict: إعدادات النموذج
    """
    # الإعدادات الصحيحة من كونفيج التدريب الأصلي
    training_config_params = {
        "img_size": 224,
        "embed_dim": 256,  # مهم: هذه القيمة من التدريب الأصلي
        "enc_depth": 6,
        "dec_depth": 6,
        "rgb_backbone_name": 'r50',
        "lidar_backbone_name": 'r18',
        "waypoints_pred_head": 'gru',
        "use_different_backbone": True,
        "with_lidar": False,
        "with_right_left_sensors": False,
        "with_center_sensor": False,
        
        # إعدادات إضافية من الكونفيج الأصلي
        "multi_view_img_size": 112,
        "patch_size": 8,
        "in_chans": 3,
        "dim_feedforward": 2048,
        "normalize_before": False,
        "num_heads": 8,
        "dropout": 0.1,
        "end2end": False,
        "direct_concat": False,
        "separate_view_attention": False,
        "separate_all_attention": False,
        "freeze_num": -1,
        "traffic_pred_head_type": "det",
        "reverse_pos": True,
        "use_view_embed": False,
        "use_mmad_pretrain": None,
    }
    
    # دمج المعاملات المخصصة مع الإعدادات من التدريب
    training_config_params.update(model_params)
    
    config = {
        'model_params': training_config_params,
        'paths': {
            'pretrained_weights': model_path
        },
        
        # إضافة إعدادات الشبكة من التدريب
        'grid_conf': {
            'h': 20, 'w': 20,
            'x_res': 1.0, 'y_res': 1.0,
            'y_min': 0.0, 'y_max': 20.0,
            'x_min': -10.0, 'x_max': 10.0,
        },
        
        # معلومات إضافية عن التدريب
        'training_info': {
            'original_project': 'Interfuser_Finetuning',
            'run_name': 'Finetune_Focus_on_Detection_v5',
            'focus': 'traffic_detection_and_iou',
            'backbone': 'ResNet50 + ResNet18',
            'trained_on': 'PDM_Lite_Carla'
        }
    }
    
    return config


def get_training_config():
    """
    إرجاع إعدادات التدريب الأصلية للمرجع
    هذه الإعدادات توضح كيف تم تدريب النموذج
    """
    return {
        'project_info': {
            'project': 'Interfuser_Finetuning',
            'entity': None,
            'run_name': 'Finetune_Focus_on_Detection_v5'
        },
        'training': {
            'epochs': 50,
            'batch_size': 8,
            'num_workers': 2,
            'learning_rate': 1e-4,  # معدل تعلم منخفض للـ Fine-tuning
            'weight_decay': 1e-2,
            'patience': 15,
            'clip_grad_norm': 1.0,
        },
        'loss_weights': {
            'iou': 2.0,              # أولوية قصوى لدقة الصناديق
            'traffic_map': 25.0,     # تركيز عالي على اكتشاف الكائنات
            'waypoints': 1.0,        # مرجع أساسي
            'junction': 0.25,        # مهام متقنة بالفعل
            'traffic_light': 0.5,
            'stop_sign': 0.25,
        },
        'data_split': {
            'strategy': 'interleaved',
            'segment_length': 100,
            'validation_frequency': 10,
        },
        'transforms': {
            'use_data_augmentation': False,  # معطل للتركيز على البيانات الأصلية
        }
    }