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import torch
import torch.nn as nn
from transformers import PreTrainedModel
from segformer_plusplus.model.backbone.mit import MixVisionTransformer # Backbone
from mix_vision_transformer_config import MySegformerConfig # Config
from segformer_plusplus.model.head.segformer_head import SegformerHead # <-- dein Head
class MySegformerForSemanticSegmentation(PreTrainedModel):
config_class = MySegformerConfig
base_model_prefix = "my_segformer"
def __init__(self, config):
super().__init__(config)
# Backbone (MixVisionTransformer)
self.backbone = MixVisionTransformer(
embed_dims=config.embed_dims, # z.B. [64, 128, 320, 512]
num_stages=config.num_stages,
num_layers=config.num_layers,
num_heads=config.num_heads,
patch_sizes=config.patch_sizes,
strides=config.strides,
sr_ratios=config.sr_ratios,
mlp_ratio=config.mlp_ratio,
qkv_bias=config.qkv_bias,
drop_rate=config.drop_rate,
attn_drop_rate=config.attn_drop_rate,
drop_path_rate=config.drop_path_rate,
out_indices=config.out_indices
)
# Head direkt importieren
in_channels = [64, 128, 320, 512]
self.segmentation_head = SegformerHead(
in_channels=in_channels, # Liste der Embeddings aus Backbone
in_index=list(config.out_indices), # welche Feature Maps genutzt werden
out_channels=getattr(config, "num_classes", 19), # Anzahl Klassen
dropout_ratio=0.1,
align_corners=False
)
self.post_init()
def forward(self, x):
# Backbone → Features (Liste von Tensors)
features = self.backbone(x)
# Debug: Ausgabe der Shapes der Backbone-Features
for i, f in enumerate(features):
print(f"Feature {i}: shape = {f.shape}")
# Head → logits
logits = self.segmentation_head(features)
return {"logits": logits}
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