I've applied your suggestions

#this

def forward(self, features):
    x = features[-1]
    x = self.center_block(x)
    num_features = len(features)
    print(f"[DEBUG] Number of encoder features: {num_features}")
    for i in range(self.num_stages):
        print(f"[DEBUG] Decoder stage {i}")
        x = self.up_samples[i](x)
        # Adjust the indexing logic
        if i < num_features:
            encoder_feature = features[-(i + 2)]
        else:
            raise IndexError(f"No encoder feature corresponding to decoder stage {i}")

        print(f"x.shape after upsampling: {x.shape}")
        print(f"encoder_feature.shape: {encoder_feature.shape}")
        # Adjust encoder_feature if necessary
        if x.size()[2:] != encoder_feature.size()[2:]:
            encoder_feature = self.center_crop(encoder_feature, x.size()[2:])
            print(f"encoder_feature.shape after cropping: {encoder_feature.shape}")

        x = torch.cat([x, encoder_feature], dim=1)
        x = self.unet_blocks[i](x)
    return x

#then modified to that


    def forward(self, features):
        x = features[-1]
        x = self.center_block(x)
        num_features = len(features)
        print(f"[DEBUG] Number of encoder features: {num_features}")
        for i in range(self.num_stages):
            print(f"[DEBUG] Decoder stage {i}")
            x = self.up_samples[i](x)
            encoder_index = num_features - i - 2  # Adjusted indexing
            if encoder_index >= 0:
                encoder_feature = features[encoder_index]
                print(f"Using encoder feature at index {encoder_index}")
                print(f"x.shape after upsampling: {x.shape}")
                print(f"encoder_feature.shape: {encoder_feature.shape}")
                # Adjust encoder_feature if necessary
                if x.size()[2:] != encoder_feature.size()[2:]:
                    encoder_feature = self.center_crop(encoder_feature, x.size()[2:])
                    print(f"encoder_feature.shape after cropping: {encoder_feature.shape}")
                x = torch.cat([x, encoder_feature], dim=1)
            else:
                print(f"No encoder feature for decoder stage {i}, skipping concatenation")
            x = self.unet_blocks[i](x)
        return x

#but  I faced this general error which requires more attention and professionally in reviewing both code and error while thinking more deeply to solve it totally



DEBUG:hydra.core.utils:Setting JobRuntime:name=UNKNOWN_NAME
DEBUG:hydra.core.utils:Setting JobRuntime:name=train_multilabel
Dataset valid_sar_cv
        ignore_low_confidence_detections False
        ignore_low_confidence_labels     False
        filter_low_confidence_objects    False
Train dataset
        Unknown vessels 90
        Unknown fishing 11242
Valid dataset
        Unknown vessels 20
        Unknown fishing 2778
Preparing training dataset: 100%|████████| 35/35 [00:00<00:00, 161.35it/s]
[DEBUG] self.box_coder in dataset.__init__: MultilabelCircleNetCoder(image_size=(512, 512), stride=4, max_objects=512, labels_encoding=circle, heatmap_encoding=umich, fixed_radius=None, ignore_index=-1, ignore_low_confidence_detections=False)
Preparing validation dataset: 100%|████████| 7/7 [00:00<00:00, 562.07it/s]
Train instances 13933 dataset 35 sampler 8192
Valid instances 3655 dataset 1039
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel.py", line 528, in <module>
    main()
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/main.py", line 94, in decorated_main
    _run_hydra(
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/_internal/utils.py", line 394, in _run_hydra
    _run_app(
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/_internal/utils.py", line 457, in _run_app
    run_and_report(
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/_internal/utils.py", line 220, in run_and_report
    return func()
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/_internal/utils.py", line 458, in <lambda>
    lambda: hydra.run(
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/_internal/hydra.py", line 119, in run
    ret = run_job(
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/core/utils.py", line 186, in run_job
    ret.return_value = task_function(task_cfg)
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel.py", line 524, in main
    CenternetPipeline(cfg).train()
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel.py", line 415, in train
    model = self.build_model(config, self.loaders)
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel.py", line 202, in build_model
    model = super().build_model(config)
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/pipeline.py", line 395, in build_model
    model = self.get_model(config["model"])
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel.py", line 379, in get_model
    model: nn.Module = get_detection_model(model_config)
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/factory.py", line 27, in get_detection_model
    return instantiate(model_config, _recursive_=False)
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 226, in instantiate
    return instantiate_node(
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 347, in instantiate_node
    return _call_target(_target_, partial, args, kwargs, full_key)
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/_internal/instantiate/_instantiate2.py", line 92, in _call_target
    return _target_(*args, **kwargs)
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/centernet/models/centernet_unet.py", line 210, in from_config
    encoder_module = CustomTimmEncoder(
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/centernet/models/custom_encoder.py", line 10, in __init__
    self.encoder = timm.create_model(
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/timm/models/_factory.py", line 117, in create_model
    model = create_fn(
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/timm/models/_registry.py", line 143, in _fn
    warnings.warn(f'Mapping deprecated model name {deprecated_name} to current {current_name}.', stacklevel=2)
  File "/opt/anaconda3/envs/sam3/lib/python3.9/warnings.py", line 109, in _showwarnmsg
    sw(msg.message, msg.category, msg.filename, msg.lineno,
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel.py", line 53, in warn_with_traceback
    traceback.print_stack(file=log)
/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/timm/models/_factory.py:117: UserWarning:

Mapping deprecated model name tf_efficientnet_b5_ns to current tf_efficientnet_b5.ns_jft_in1k.

[DEBUG] After adjustment, encoder.conv_stem.in_channels: 2
[DEBUG] model.box_coder after initialization: MultilabelCircleNetCoder(image_size=[768, 768], stride=1, max_objects=512, labels_encoding=circle, heatmap_encoding=umich, fixed_radius=None, ignore_index=-1, ignore_low_confidence_detections=False)
BoxCoder
MultilabelCircleNetCoder(image_size=[768, 768], stride=1, max_objects=512, labels_encoding=circle, heatmap_encoding=umich, fixed_radius=None, ignore_index=-1, ignore_low_confidence_detections=False)

...
DEBUG:rasterio.env:Starting outermost env
DEBUG:rasterio.env:No GDAL environment exists
DEBUG:rasterio.env:New GDAL environment <rasterio._env.GDALEnv object at 0x313c72280> created
DEBUG:rasterio._env:GDAL_DATA found in environment.
DEBUG:rasterio._env:PROJ_DATA found in environment.
DEBUG:rasterio._env:Started GDALEnv: self=<rasterio._env.GDALEnv object at 0x313c72280>.
DEBUG:rasterio.env:Entered env context: <rasterio.env.Env object at 0x320732bb0>
DEBUG:rasterio._io:Output nodata value read from file: -32768.0
DEBUG:rasterio._io:Output nodata values: [-32768.0]
DEBUG:rasterio._io:all_valid: False
DEBUG:rasterio._io:mask_flags: ([<MaskFlags.nodata: 8>],)
DEBUG:rasterio._io:Jump straight to _read()
DEBUG:rasterio._io:Window: Window(col_off=22789, row_off=9869, width=512, height=512)
DEBUG:rasterio._io:IO window xoff=22789.0 yoff=9869.0 width=512.0 height=512.0
DEBUG:rasterio.env:Exiting env context: <rasterio.env.Env object at 0x3398afb50>
DEBUG:rasterio.env:Cleared existing <rasterio._env.GDALEnv object at 0x3398afd60> options
DEBUG:rasterio._env:Stopped GDALEnv <rasterio._env.GDALEnv object at 0x3398afd60>.
DEBUG:rasterio.env:Exiting outermost env
DEBUG:rasterio.env:Exited env context: <rasterio.env.Env object at 0x3398afb50>
[DEBUG] Channel 0 shape: (512, 512)
[DEBUG] Channel 1 shape: (512, 512)
(293.0, 451.0, 0.0) [<class 'float'>, <class 'float'>, <class 'float'>]
(203.0, 355.0, 0.0) [<class 'float'>, <class 'float'>, <class 'float'>]
(95.0, 122.0, 0.0) [<class 'float'>, <class 'float'>, <class 'float'>]
(161.0, 481.0, 0.0) [<class 'float'>, <class 'float'>, <class 'float'>]
(242.0, 510.0, 0.0) [<class 'float'>, <class 'float'>, <class 'float'>]
(120.0, 394.0, 0.0) [<class 'float'>, <class 'float'>, <class 'float'>]
(118.0, 280.0, 0.0) [<class 'float'>, <class 'float'>, <class 'float'>]
(173.0, 135.0, 0.0) [<class 'float'>, <class 'float'>, <class 'float'>]
(142.0, 262.0, 0.0) [<class 'float'>, <class 'float'>, <class 'float'>]
(115.0, 232.0, 0.0) [<class 'float'>, <class 'float'>, <class 'float'>]
(256.0, 251.0, 0.0) [<class 'float'>, <class 'float'>, <class 'float'>]
(249.0, 321.0, 0.0) [<class 'float'>, <class 'float'>, <class 'float'>]
(62.0, 459.0, 0.0) [<class 'float'>, <class 'float'>, <class 'float'>]
(365.0, 384.0, 0.0) [<class 'float'>, <class 'float'>, <class 'float'>]
DEBUG:rasterio.env:Exiting env context: <rasterio.env.Env object at 0x320732bb0>
DEBUG:rasterio.env:Cleared existing <rasterio._env.GDALEnv object at 0x313c72280> options
DEBUG:rasterio._env:Stopped GDALEnv <rasterio._env.GDALEnv object at 0x313c72280>.
DEBUG:rasterio.env:Exiting outermost env
DEBUG:rasterio.env:Exited env context: <rasterio.env.Env object at 0x320732bb0>
[DEBUG] Channel 0 shape: (512, 512)
[DEBUG] Channel 1 shape: (512, 512)
(198.0, 273.0, 0.0) [<class 'float'>, <class 'float'>, <class 'float'>]
(78.0, 235.0, 0.0) [<class 'float'>, <class 'float'>, <class 'float'>]
(256.0, 242.0, 0.0) [<class 'float'>, <class 'float'>, <class 'float'>]
Error executing job with overrides: ['+config=configs/multilabel_circle_net.yaml']
Traceback (most recent call last):
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel.py", line 528, in <module>
    main()
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/main.py", line 94, in decorated_main
    _run_hydra(
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/_internal/utils.py", line 394, in _run_hydra
    _run_app(
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/_internal/utils.py", line 457, in _run_app
    run_and_report(
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/_internal/utils.py", line 223, in run_and_report
    raise ex
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/_internal/utils.py", line 220, in run_and_report
    return func()
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/_internal/utils.py", line 458, in <lambda>
    lambda: hydra.run(
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/_internal/hydra.py", line 132, in run
    _ = ret.return_value
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/core/utils.py", line 260, in return_value
    raise self._return_value
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/hydra/core/utils.py", line 186, in run_job
    ret.return_value = task_function(task_cfg)
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel.py", line 524, in main
    CenternetPipeline(cfg).train()
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel.py", line 415, in train
    model = self.build_model(config, self.loaders)
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel.py", line 206, in build_model
    model = nas.search(config.nas.num_iterations, loaders['train'], loaders['valid'])
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/nas.py", line 36, in search
    scores = [self.evaluate_model(model, train_loader, val_loader) for model in population]
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/nas.py", line 36, in <listcomp>
    scores = [self.evaluate_model(model, train_loader, val_loader) for model in population]
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/nas.py", line 112, in evaluate_model
    outputs = model(inputs)
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/centernet/models/centernet_unet.py", line 171, in forward
    decoded_features = self.decoder(feature_maps)
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
  File "/Users/samerkharboush/Downloads/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/centernet/models/custom_encoder.py", line 82, in forward
    x = torch.cat([x, encoder_feature], dim=1)
RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 256 but got size 1 for tensor number 1 in the list.
(sam3) samerkharboush@Samers-MBP SAR-MPS-OptunaNasFbnYoloTransformers %