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modeling_interfuser.py

@@ -1,7 +1,6 @@
 
 # -*- coding: utf-8 -*-
 # This file contains all custom class definitions required to run the Interfuser model.
-
 import torch
 from torch import nn
 import torch.nn.functional as F
@@ -198,7 +197,33 @@ class GRUWaypointsPredictor(nn.Module):
 
 # --- The ORIGINAL Interfuser Model Class ---
 class Interfuser(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="r26", lidar_backbone_name="r26", num_heads=8, norm_layer=None, dropout=0.1, end2end=False, direct_concat=True, separate_view_attention=False, separate_all_attention=False, act_layer=None, weight_init="", freeze_num=-1, with_lidar=False, with_right_left_sensors=True, with_center_sensor=False, traffic_pred_head_type="det", waypoints_pred_head="heatmap", reverse_pos=True, use_different_backbone=False, use_view_embed=True, use_mmad_pretrain=None):
+    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="r26", 
+       lidar_backbone_name="r26", 
+       num_heads=8, norm_layer=None, 
+       dropout=0.1, end2end=False, 
+       direct_concat=True,
+       separate_view_attention=False,
+       separate_all_attention=False, 
+       act_layer=None,
+       weight_init="", 
+       freeze_num=-1,
+       with_lidar=False, 
+       with_right_left_sensors=True,
+       with_center_sensor=False, 
+       traffic_pred_head_type="det",
+       waypoints_pred_head="heatmap",
+       reverse_pos=True,
+       use_different_backbone=False,
+       use_view_embed=True,
+       use_mmad_pretrain=None):
         super().__init__()
         self.num_features = self.embed_dim = embed_dim
         norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6)
@@ -295,6 +320,8 @@ class InterfuserConfig(PretrainedConfig):
 
     model_type = "interfuser"
 
+
+
     def __init__(
         self,
         embed_dim=256,
@@ -336,6 +363,7 @@ class InterfuserForHuggingFace(PreTrainedModel):
         # The parameters are taken from our config object.
         # This requires the original 'Interfuser' class to be defined in the notebook.
         self.interfuser_model = Interfuser(
+            in_chans=self.config.in_chans,  # هنا تُمرّر القيمه
             embed_dim=self.config.embed_dim,
             enc_depth=self.config.enc_depth,
             dec_depth=self.config.dec_depth,
@@ -346,7 +374,7 @@ class InterfuserForHuggingFace(PreTrainedModel):
             waypoints_pred_head=self.config.waypoints_pred_head,
             use_different_backbone=self.config.use_different_backbone
         )
-
+        
     def forward(self, rgb, rgb_left, rgb_right, rgb_center, lidar, measurements, target_point, **kwargs):
     
         # The original model expects a dictionary, so we create one.
@@ -364,5 +392,4 @@ class InterfuserForHuggingFace(PreTrainedModel):
         # The output is already a tuple, which is what HF expects.
         return self.interfuser_model.forward(inputs_dict)
 
-# --- رسالة تأكيد ---
-print("✅ Hugging Face wrapper classes (InterfuserConfig, InterfuserForHuggingFace) are now defined.")
+

pytorch_model.bin

@@ -1,3 +1,3 @@
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+oid sha256:b96a031dc969bdb3c572e0945311632e6c7737489bec69df1c418308d04506ea
 size 212282626