this project indeed appears to be a complex integrated multi-model system for vessel detection and fishing activity classification using SAR (Synthetic Aperture Radar) TIFF images as input. The system incorporates several advanced deep learning architectures and techniques, including:
CenterNet UNet: A combination of CenterNet (for object detection) and U-Net (for image segmentation).
YOLO (You Only Look Once): A real-time object detection system.
NAS (Neural Architecture Search): For optimizing model architecture.
FBN (Feature Pyramid Network): For multi-scale feature extraction.
FAN (Feature Aggregation Network): For combining features from different levels.
Swin Transformer: A hierarchical vision transformer.
Optuna: A hyperparameter optimization framework.
The system is designed to process SAR images and output predictions in a CSV format, including:
Vessel length in meters
Detection coordinates (row and column in the scene)
Scene ID
Vessel classification (True/False)
Fishing activity classification (True/False)
This type of system is typically used for maritime surveillance, monitoring fishing activities, and enforcing maritime regulations.
The configuration files and code snippets you've provided show various aspects of the model architecture, training process, and inference pipeline. Some key components include:
Model configuration (encoder, decoder, head)
Data augmentation strategies
Loss functions and optimization settings
Inference settings (tile size, batch size, thresholds)
Ensemble methods and Test Time Augmentation (TTA)
If you have any specific questions about the system or need clarification on any part of the codebase, please feel free to ask.
the outputs looks as
vessel_length_m,detect_scene_row,detect_scene_column,scene_id,is_vessel,is_fishing
33.99261,11448,13456,1,True,True
28.166428,15632,12622,1,True,True
32.700775,19454,4840,1,True,True
37.80931,12862,2608,1,True,True
48.392456,4902,5522,1,True,False
understand this deeply and review the following two functions and just say "done" and remember everything as we will need all these details to fix errors
##custom_encoder.py
class UpsampleBlock(nn.Module):
def init(self, in_channels, out_channels, scale_factor=2, mode='bilinear', align_corners=False):
super(UpsampleBlock, self).init()
self.upsample = nn.Sequential(
nn.Upsample(scale_factor=scale_factor, mode=mode, align_corners=align_corners),
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False),
nn.GroupNorm(16, out_channels),
nn.ReLU(inplace=True)
)
print(f"UpsampleBlock initialized with in_channels={in_channels}, out_channels={out_channels}")

def forward(self, x):
    print(f"UpsampleBlock input shape: {x.shape}")
    output = self.upsample(x)
    print(f"UpsampleBlock output shape: {output.shape}")
    return output
class CustomTimmEncoder(nn.Module):
def init(
self,
model_name: str = "efficientnetv2_rw_s",
num_channels: int = 2,
features_only: bool = True,
pretrained: bool = False,
activation: str = "silu",
drop_rate: float = 0.0,
drop_path_rate: float = 0.0,
image_size: Tuple[int, int] = (224, 224),
):
super(CustomTimmEncoder, self).init()
self.model_name = model_name # Store model name for conditional handling
self.encoder = timm.create_model(
model_name,
features_only=features_only,
in_chans=num_channels,
pretrained=pretrained,
act_layer=activation,
drop_rate=drop_rate,
drop_path_rate=drop_path_rate,
)
# Extract channels and strides
# Extract actual feature channels and strides
# In CustomTimmEncoder
self.feature_info = self.encoder.feature_info
self.channels = [f['num_chs'] for f in self.feature_info]
#self.channels = [f['num_chs'] for f in self.encoder.feature_info]
self.strides = [f['reduction'] for f in self.feature_info]
self.feature_info = self.feature_info
self.image_size = image_size
self.embed_dim = self.channels[-1]
self.depths = [2, 2, 6, 2]
self.num_heads = [3, 6, 12, 24]
self.window_size = 7
self.mlp_ratio = 4.
self.qkv_bias = True
self.qk_scale = None
self.drop_rate = drop_rate
self.attn_drop_rate = 0.0
self.drop_path_rate = drop_path_rate
self.norm_layer = nn.LayerNorm
self.use_dropout = True
self.use_custom_pos_embed = False
self.transformer_output_channels = self.channels[-1]
self.output_stride = 32

    # Conditional handling based on encoder type
    if "swin" in model_name.lower() or "transformer" in model_name.lower():
        # Assuming transformer-based encoder has 'patch_embed'
        if hasattr(self.encoder, 'patch_embed'):
            self.conv_stem = self.encoder.patch_embed.proj
        else:
            raise AttributeError(f"The encoder '{model_name}' does not have 'patch_embed' attribute.")
    else:
        # For CNN-based encoders like EfficientNet
        if hasattr(self.encoder, 'conv_stem'):
            self.conv_stem = self.encoder.conv_stem
        elif hasattr(self.encoder, 'conv1'):
            self.conv_stem = self.encoder.conv1  # Example for some other CNNs
        else:
            raise AttributeError(f"The encoder '{model_name}' does not have a recognized stem convolution layer.")
    

def forward(self, x):
    encoder_features = self.encoder(x)
    if not isinstance(encoder_features, list):
        encoder_features = [encoder_features]
    logger.debug(f"Encoder features shapes: {[f.shape for f in encoder_features]}")

    for idx, encoder_feature in enumerate(encoder_features):
        if encoder_feature.dim() == 3:
            # Missing batch dimension, add it
            encoder_feature = encoder_feature.unsqueeze(0)
            encoder_features[idx] = encoder_feature
            print(f"[WARNING] Feature {idx} missing batch dimension, adding it.")
        elif encoder_feature.dim() < 3:
            raise ValueError(f"Feature {idx} has unexpected number of dimensions: {encoder_feature.dim()}")
        print(f"[DEBUG] Feature {idx} shape: {encoder_feature.shape}")  # Existing debug

    # Additional debug to ensure all features have batch dimension
    for idx, encoder_feature in enumerate(encoder_features):
        if encoder_feature.dim() != 4:
            print(f"[ERROR] Feature {idx} does not have 4 dimensions after processing: {encoder_feature.dim()}")
            raise ValueError(f"Feature {idx} has incorrect number of dimensions: {encoder_feature.dim()}")

    return encoder_features


def change_input_channels(self, input_channels: int, mode="auto", **kwargs):
    if isinstance(self.conv_stem, Conv2dSame):
        self.conv_stem = make_n_channel_input_conv2d_same(self.conv_stem, input_channels, mode, **kwargs)
    else:
        self.conv_stem = make_n_channel_input(self.conv_stem, input_channels, mode, **kwargs)
    return self
class DebugUnetDecoder(nn.Module):
def init(
self,
encoder_channels,
decoder_channels,
unet_block,
upsample_block=UpsampleBlock,
center=False,
activation='relu'
):
super(DebugUnetDecoder, self).init()
self.center = center
self.encoder_channels = encoder_channels
self.decoder_channels = decoder_channels
self.num_stages = len(decoder_channels)
self.up_samples = nn.ModuleList()
self.unet_blocks = nn.ModuleList()

    logger.debug(f"Initializing DebugUnetDecoder with encoder_channels={encoder_channels} and decoder_channels={decoder_channels}")
    
    for i in range(self.num_stages):
        if i == 0:
            in_channels = encoder_channels[-1]
        else:
            in_channels = decoder_channels[i-1]
        out_channels = decoder_channels[i]
        
        logger.debug(f"Stage {i}: in_channels={in_channels}, out_channels={out_channels}")
        
        # Check for non-zero channels
        assert in_channels > 0, f"in_channels must be positive, got {in_channels}"
        assert out_channels > 0, f"out_channels must be positive, got {out_channels}"
        
        self.up_samples.append(
            upsample_block(
                in_channels=in_channels,
                out_channels=out_channels
            )
        )
        
        if i < len(encoder_channels) - 1:
            unet_in_channels = out_channels + encoder_channels[-(i+2)]
        else:
            unet_in_channels = out_channels
        
        logger.debug(f"Stage {i}: unet_in_channels={unet_in_channels}")
        
        # Check for non-zero unet_in_channels
        assert unet_in_channels > 0, f"unet_in_channels must be positive, got {unet_in_channels}"
        
        self.unet_blocks.append(
            unet_block(
                in_channels=unet_in_channels,
                out_channels=out_channels,
                activation=activation
            )
        )

def forward(self, encoder_features):
    if not isinstance(encoder_features, list):
        raise TypeError(f"Decoder expected encoder_features to be a list, but got {type(encoder_features)}")
    
    x = encoder_features[-1]
    print(f"Initial x.shape: {x.shape}")
    
    decoded_features = []
    
    for i in range(self.num_stages):
        print(f"\nDecoder Stage {i}")
        print(f"x.shape before upsampling: {x.shape}")
        
        # Upsample
        x = self.up_samples[i](x)
        print(f"x.shape after upsampling: {x.shape}")
        
        # Concatenate with encoder feature
        if i < len(encoder_features) - 1:
            encoder_feat = encoder_features[-(i+2)]
            print(f"Encoder Feature {i} shape: {encoder_feat.shape}")
            
            if x.size()[2:] != encoder_feat.size()[2:]:
                encoder_feat = F.interpolate(encoder_feat, size=x.size()[2:], mode='nearest')
                print(f"Resized encoder_feat to {x.size()[2:]}")
            
            x = torch.cat([x, encoder_feat], dim=1)
            print(f"x.shape after concatenation: {x.shape}")
        
        # Pass through UNetBlock
        print(f"Passing through UNetBlock with in_channels={x.shape[1]}")
        x = self.unet_blocks[i](x)
        print(f"x.shape after UNetBlock: {x.shape}")
        
        decoded_features.append(x)
    
    return decoded_features
class ResidualDeconvolutionUpsample2d(nn.Module):
def init(self, in_channels, out_channels, scale_factor=2, mode='bilinear', align_corners=False):
super(ResidualDeconvolutionUpsample2d, self).init()
self.upsample = nn.Sequential(
nn.Upsample(scale_factor=scale_factor, mode=mode, align_corners=align_corners),
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False),
nn.GroupNorm(16, out_channels),
nn.ReLU(inplace=True)
)

def forward(self, x):
    return self.upsample(x)
necks.py
class FPNNeck(nn.Module):
def init(
self,
channels: List[int],
out_channels: int,
use_cbam: bool = False,
use_dropblock: bool = False,
dropblock_prob: float = 0.1,
):
super(FPNNeck, self).init()
self.fpn = EnhancedFPNFuse(
input_channels=channels,
output_channels=out_channels,
mode='bilinear',
align_corners=False,
use_cbam=use_cbam,
use_dropblock=use_dropblock,
dropblock_prob=dropblock_prob,
)
self.out_conv = nn.Conv2d(out_channels, out_channels, kernel_size=1)

def forward(self, feature_maps: List[Tensor]) -> Tensor:
    x = self.fpn(feature_maps)
    x = self.out_conv(x)
    return x
HyperColumnNeck with CBAM and DropBlock
class HyperColumnNeck(nn.Module):
def init(self, channels: List[int], dropout_rate=0.0, use_cbam: bool = False, use_dropblock: bool = False, dropblock_prob: float = 0.1):
super(HyperColumnNeck, self).init()
self.fuse = EnhancedFPNFuse(
input_channels=channels,
output_channels=sum(channels),
align_corners=True,
use_cbam=use_cbam,
use_dropblock=use_dropblock,
dropblock_prob=dropblock_prob,
)
self.channels = sum(channels)
self.dropout = nn.Dropout2d(p=dropout_rate, inplace=True)

def forward(self, feature_maps: List[Tensor]) -> Tensor:
    return self.dropout(self.fuse(feature_maps))
Feature Aggregation Network with Additional Enhancements
class FeatureAggregationNetwork(nn.Module):
"""
Advanced Feature Aggregation Network that fuses multiple encoder feature maps using attention mechanisms.

 Args:
     input_channels (List[int]): List of channel dimensions for each input feature map.
     output_channels (int): Number of output channels.
     upsample_mode (str): Mode for upsampling ('nearest', 'bilinear', 'bicubic').
     use_attention (bool): Whether to use attention mechanisms for feature fusion.
     fusion_method (str): Method for feature fusion ('add', 'concat', 'weighted_sum').
 """
 def __init__(
     self,
     input_channels: List[int],
     out_channels: int,
     num_layers: int,
     upsample_mode: str = 'bilinear',
     use_attention: bool = True,
     fusion_method: str = 'weighted_sum'
 ):
     super(FeatureAggregationNetwork, self).__init__()
     self.input_channels = input_channels
     self.num_features = len(input_channels)
     self.out_channels = out_channels
     self.upsample_mode = upsample_mode
     self.use_attention = use_attention
     self.fusion_method = fusion_method
     # Channel alignment layer
     #self.channel_align = nn.Conv2d(input_channels[0], out_channels, kernel_size=1)
     """
     # Channel alignment layers
     self.channel_align_layers = nn.ModuleList([
         nn.Conv2d(in_ch, out_channels, kernel_size=1, bias=False) for in_ch in input_channels
     ])
     """
     # Channel alignment layers
     self.channel_align_layers = nn.ModuleList([
        nn.Conv2d(in_ch, self.out_channels, kernel_size=1, bias=False) for in_ch in self.input_channels
     ])
     # Attention modules (if used)
     if self.use_attention:
         self.attention_layers = nn.ModuleList([
             SelfAttentionModule(out_channels)
             for _ in range(self.num_features)
         ])
     
     # Learnable weights for fusion (if using weighted_sum)
     if self.fusion_method == 'weighted_sum':
         self.fusion_weights = nn.Parameter(torch.ones(self.num_features), requires_grad=True)
     
     # Output convolution
     self.output_conv = nn.Sequential(
         nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False),
         nn.BatchNorm2d(out_channels),
         nn.ReLU(inplace=True)
     )
    
 def forward(self, feature_maps: Union[Tensor, List[Tensor]]) -> Tensor:
     """
     Forward pass for FeatureAggregationNetwork.
     
     Args:
         feature_maps (List[Tensor]): List of feature maps from different encoder stages.
     
     Returns:
         Tensor: Aggregated feature map.
     """
     if isinstance(feature_maps, Tensor):
         if feature_maps.dim() == 3:
             # Possibly [channels, height, width], need to add batch dimension
             feature_maps = feature_maps.unsqueeze(0)
         elif feature_maps.dim() == 4:
             # Already has batch dimension
             pass
         else:
             raise ValueError(f"Expected feature_maps with 3 or 4 dimensions, got {feature_maps.dim()} dimensions.")
         feature_maps = [feature_maps]
     elif isinstance(feature_maps, list):
         # Ensure all feature maps have the correct dimensions
         for i, fm in enumerate(feature_maps):
             if fm.dim() == 3:
                 feature_maps[i] = fm.unsqueeze(0)
             elif fm.dim() != 4:
                 raise ValueError(f"Feature map at index {i} has {fm.dim()} dimensions, expected 3 or 4.")
     else:
         raise TypeError(f"Expected feature_maps to be a Tensor or list, but got {type(feature_maps)}")

     
     # Determine the target spatial size (smallest height and width)
     target_height = min([fm.size(2) for fm in feature_maps])
     target_width = min([fm.size(3) for fm in feature_maps])
     target_size = (target_height, target_width)
     
     #aligned_features = []channel_align_layers
     #aligned_features = [self.input_conv(fm) for fm in feature_maps]
     aligned_features = [layer(fm) for layer, fm in zip(self.channel_align_layers, feature_maps)]

     for idx, fm in enumerate(feature_maps):
         # Spatial alignment
         if fm.size(2) != target_height or fm.size(3) != target_width:
             fm = F.interpolate(fm, size=target_size, mode=self.upsample_mode, align_corners=False)
             logger.debug(f"Resized feature_map[{idx}] to {target_size}")
         
         # Channel alignment
         fm = self.channel_align_layers[idx](fm)
         
         # Attention
         if self.use_attention:
             fm = self.attention_layers[idx](fm)
         
         aligned_features.append(fm)
    
     # Feature fusion
     if self.fusion_method == 'add':
         fused_feature = sum(aligned_features)
     elif self.fusion_method == 'concat':
         fused_feature = torch.cat(aligned_features, dim=1)
         # Adjust the number of channels to output_channels
         fused_feature = nn.Conv2d(
             fused_feature.size(1), self.out_channels, kernel_size=1, bias=False
         )(fused_feature)
     elif self.fusion_method == 'weighted_sum':
         # Apply softmax to fusion weights
         weights = F.softmax(self.fusion_weights, dim=0)
         fused_feature = sum(w * f for w, f in zip(weights, aligned_features))
     else:
         raise ValueError(f"Unsupported fusion method: {self.fusion_method}")
     
     # Output convolution
     output = self.output_conv(fused_feature)
     logger.debug(f"FAN output shape: {output.shape}")  # Should be 4D
     return output
###transform_classifier.py
class DebugSwinTransformer(SwinTransformer):
def init(
self,
img_size: Union[int, Tuple[int, int]],
patch_size: int = 4,
in_chans: int = 2,
embed_dim: int = 96,
depths: list = [2, 2, 6, 2],
num_heads: list = [3, 6, 12, 24],
window_size: Tuple[int, int] = (4, 4),
mlp_ratio: float = 4.0,
qkv_bias: bool = True,
qk_scale: Optional[float] = None,
drop_rate: float = 0.0,
attn_drop_rate: float = 0.0,
drop_path_rate: float = 0.1,
norm_layer: Optional[str] = 'torch.nn.LayerNorm', # Fully qualified class path
ape: bool = False,
patch_norm: bool = True,
use_checkpoint: bool = False,
):
"""
DebugSwinTransformer extends the SwinTransformer to include debug statements.

    Args:
        norm_layer (str): Fully qualified class path for normalization layer (e.g., 'torch.nn.LayerNorm').
        All other parameters are the same as SwinTransformer.
    """
    # Enhanced assertion for window_size
    if isinstance(window_size, int):
        assert window_size > 0, f"Invalid window_size: {window_size}"
        self.window_size = (window_size, window_size)
    elif isinstance(window_size, (tuple, list)):
        assert len(window_size) == 2, f"window_size must have two dimensions, got {len(window_size)}"
        assert all(isinstance(w, int) and w > 0 for w in window_size), f"Invalid window_size elements: {window_size}"
        self.window_size = tuple(window_size)
    else:
        raise AssertionError(f"Invalid window_size type: {type(window_size)}. Expected int or tuple/list of two ints.")

    # Convert norm_layer string to actual class
    try:
        norm_layer_class = get_class(norm_layer)
    except ImportError as e:
        logger.error(f"Error loading norm_layer: {e}")
        raise

    # Initialize the parent SwinTransformer with validated window_size and norm_layer_class
    super().__init__(
        img_size=img_size,
        patch_size=patch_size,
        in_chans=in_chans,
        embed_dim=embed_dim,
        depths=depths,
        num_heads=num_heads,
        window_size=self.window_size,
        mlp_ratio=mlp_ratio,
        qkv_bias=qkv_bias,
        qk_scale=qk_scale,
        drop_rate=drop_rate,
        attn_drop_rate=attn_drop_rate,
        drop_path_rate=drop_path_rate,
        norm_layer=norm_layer_class,  # Pass the class, not the string
        ape=ape,
        patch_norm=patch_norm,
        use_checkpoint=use_checkpoint,
    )

def forward_features(self, x):
    logger.debug(f"SwinTransformer.forward_features: window_size={self.window_size}, input_shape={x.shape}")
    return super().forward_features(x)

def _attn(self, x):
    logger.debug(f"SwinTransformer._attn: window_size={self.window_size}")
    # Add assertion to prevent invalid window_size during attention
    if isinstance(self.window_size, int):
        assert self.window_size > 0, f"Invalid window_size during _attn: {self.window_size}"
    else:
        assert all(isinstance(w, int) and w > 0 for w in self.window_size), f"Invalid window_size during _attn: {self.window_size}"
    return super()._attn(x)
class SwinTransformerEncoder(nn.Module):
def init(
self,
img_size: Union[int, Tuple[int, int]],
embed_dim: int = 96,
depths: list = [2, 2, 6, 2],
num_heads: list = [3, 6, 12, 24],
window_size: Tuple[int, int] = (4, 4),
mlp_ratio: float = 4.0,
qkv_bias: bool = True,
qk_scale: Optional[float] = None,
drop_rate: float = 0.0,
attn_drop_rate: float = 0.0,
drop_path_rate: float = 0.1,
norm_layer: Optional[str] = 'torch.nn.LayerNorm', # Fully qualified class path
in_chans: int = 2, # Number of input channels
use_dropout: bool = False,
use_custom_pos_embed: bool = False,
):
"""
Swin Transformer Encoder with enhanced features.

    Args:
        norm_layer (str): Fully qualified class path for normalization layer (e.g., 'torch.nn.LayerNorm').
        All other parameters are the same as before.
    """
    super().__init__()
    # Validate img_size
    assert (
        (isinstance(img_size, int) and img_size > 0) or
        (isinstance(img_size, (list, tuple)) and 
         len(img_size) == 2 and 
         all(isinstance(x, int) and x > 0 for x in img_size))
    ), f"Invalid img_size={img_size}. Must be a positive int or tuple/list of two positive ints."

    self.window_size = window_size 

    # Convert norm_layer string to actual class
    try:
        norm_layer_class = get_class(norm_layer)
    except ImportError as e:
        logger.error(f"Error loading norm_layer: {e}")
        raise

    self.swin = DebugSwinTransformer(
        img_size=img_size,  # Now can be int or tuple/list
        patch_size=4,
        in_chans=in_chans,
        embed_dim=embed_dim,
        depths=depths,
        num_heads=num_heads,
        window_size=window_size,
        mlp_ratio=mlp_ratio,
        qkv_bias=qkv_bias,
        qk_scale=qk_scale,
        drop_rate=drop_rate,
        attn_drop_rate=attn_drop_rate,
        drop_path_rate=drop_path_rate,
        norm_layer=norm_layer_class,  # Pass the class, not the string
        ape=False,
        patch_norm=True,
        use_checkpoint=False
    )

    # Initialize advanced weight initialization
    self._init_weights()

    # Integrate TorchMetrics for monitoring
    self.accuracy = torchmetrics.Accuracy(task="multiclass", num_classes=2, average='macro')
    self.topk_accuracy = torchmetrics.Accuracy(task="multiclass", num_classes=2, average='macro')

    # Optional dropout layer after the transformer
    self.dropout = nn.Dropout(drop_rate) if use_dropout else nn.Identity()

    self.use_custom_pos_embed = use_custom_pos_embed
    if use_custom_pos_embed:
        # Adjust positional embedding based on img_size
        if isinstance(img_size, int):
            pos_embed_size = (img_size // 4, img_size // 4)
        else:
            pos_embed_size = (img_size[0] // 4, img_size[1] // 4)
        self.pos_embed = nn.Parameter(torch.zeros(1, embed_dim, *pos_embed_size))
        nn.init.kaiming_normal_(self.pos_embed, mode='fan_out', nonlinearity='relu')

def _init_weights(self):
    for m in self.modules():
        if isinstance(m, nn.Linear):
            nn.init.trunc_normal_(m.weight, std=.02)
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)
        elif isinstance(m, nn.Conv2d):
            if m.weight.dim() > 1:
                nn.init.xavier_uniform_(m.weight)
            else:
                nn.init.normal_(m.weight)
            if m.bias is not None:
                nn.init.constant_(m.bias, 0)

def forward(self, x):
    """
    Forward pass through the Swin Transformer Encoder.

    Args:
        x (Tensor): Input tensor of shape [B, C, H, W].

    Returns:
        Tensor: Extracted features from the encoder.
    
    if self.use_custom_pos_embed:
        x = x + self.pos_embed
    # Use forward_features to retain spatial dimensions
    features = self.swin.forward_features(x)
    features = self.dropout(features)
    return features
    """
    def forward(self, x):
        features = self.swin.forward_features(x)
        features = self.dropout(features)
        # Reshape the output to match the expected shape
        B, C, H, W = x.shape
        features = features.permute(0, 2, 1).reshape(B, -1, H // self.swin.patch_size, W // self.swin.patch_size)
        return features

done

perfect now based on all this review deeply line by line the sizes, inputs, channels, shapes as there are many confused
##here's the config file
defaults:
self
dataset: full_4fold
augs: flips_light
sampler: balanced
normalization: default
loss: centernet/rfl_bce_mse
model: centernet/b5_unet_s2
optimizer: adamw
scheduler: cos
train: default
runner: default
ema: disabled
torch: default
slug: b5_unet_s2
activation: silu
num_channels: ${dataset.num_channels}
num_extra_blocks: 0
model:
target: xview3.centernet.models.centernet_unet.EnhancedCenterNetUNet
slug: b5_unet_s2
weights: IMAGENET1K_V1
pretrained: True
output_stride: 32
image_size: [1024, 1024]
window_size:
- 4
- 4
num_layers: [2, 5]
num_filters: [64, 128, 256]
kernel_size: [3, 5]
activation: [relu, leaky_relu, silu]
dropout_rate: [0.0, 0.5]
dropout_rate_max: 0.5
optimizer: adamw
learning_rate: 0.001
transformer:
enabled: true
embed_dim: 96
depths: [2, 2, 6, 2]
num_heads: [3, 6, 12, 24]
window_size:
- 4
- 4
mlp_ratio: 4.0
qkv_bias: true
qk_scale: null
drop_rate: 0.0
attn_drop_rate: 0.0
drop_path_rate: 0.1
norm_layer: torch.nn.LayerNorm
use_dropout: false
use_custom_pos_embed: false
decoder:
target: xview3.centernet.models.custom_encoder.DebugUnetDecoder
encoder_channels: [24, 48, 64, 160, 272] #[32, 24, 40, 112, 320]
decoder_channels: [512, 384, 256, 128, 64]
unet_block:
target: xview3.centernet.models.unet_blocks.UnetBlock
partial: true
upsample_block: xview3.centernet.models.custom_encoder.UpsampleBlock
#scale_factor: 2
#mode: bilinear
center: False # Changed from 'bilinear' to 'nearest'
activation: silu
encoder:
target: pytorch_toolbelt.modules.encoders.TimmEfficientNetV2
model_name: efficientnetv2_rw_s
pretrained: True #features_only: True
activation: silu
drop_rate: 0.1
drop_path_rate: 0.1
layers:
- 1 # 4
- 2 # 8
- 3 # 16
- 4 # 32
nas:
enabled: true
search_space:
num_layers: [2, 5]
num_filters: [64, 128, 256]
kernel_size: [3, 5]
activation: [relu, leaky_relu, silu]
dropout_rate: [0.0, 0.5]
dropout_rate_max: 0.5
num_trials: 50
metric: "val_acc"
direction: "maximize"
num_channels: ${dataset.num_channels}
num_classes: 2
anchors: [[10, 13], [16, 30], [33, 23]]
input_upsample_factor: 1
neck_out_channels: 256
box_coder:
target: xview3.centernet.bboxer.multilabel_circle_coder.MultilabelCircleNetCoder
image_size:
- 1024
- 1024
max_objects: 512
heatmap_encoding: umich
labels_encoding: circle
ignore_value:
target: xview3.ignore_value
fixed_radius: 3
labels_radius: 1
ignore_low_confidence_detections: ${dataset.ignore_low_confidence_detections}
head:
decoupled_head:
target: xview3.centernet.models.heads.DecoupledHeadGroupNormLateShuffle
channels: 256
classifier_dim: 128
objectness_dim: 128
size_dim: 128
offset_dim: 16
dropout_rate: 0.1
activation: silu
num_blocks: 3
yolo_head:
target: xview3.centernet.models.heads.yolo_head.YOLOHead
num_classes: 2
anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119]]
input_image_size: [1024, 1024]
stride: 32
num_anchors_per_scale: 3
fan:
target: xview3.centernet.models.necks.FeatureAggregationNetwork
input_channels: [272] #[32, 48, 64, 128] #in_channels: 272 #out_channels: 256 #num_layers: 2
out_channels: 256
num_layers: 2
upsample_mode: bilinear
use_attention: True
fusion_method: weighted_sum
expected_fan_input_channels: 256
Per-Channel Normalization
normalization:
channels:
vv:
target: xview3.SigmoidNormalization
midpoint: -20.0
temperature: 0.18
vh:
target: xview3.SigmoidNormalization
midpoint: -20.0
temperature: 0.18
bathymetry:
target: xview3.CubicRootNormalization
wind_direction:
target: albumentations.NoOp
wind_speed:
target: albumentations.NoOp
mask:
target: albumentations.NoOp
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
Dataset Configuration
dataset:
normalization:
channels:
vv:
target: xview3.SigmoidNormalization
midpoint: -20.0
temperature: 0.18
vh:
target: xview3.SigmoidNormalization
midpoint: -20.0
temperature: 0.18
bathymetry:
target: xview3.CubicRootNormalization
wind_direction:
target: albumentations.NoOp
wind_speed:
target: albumentations.NoOp
mask:
target: albumentations.NoOp
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
slug: full_4fold
splitter: full
fold: 0
num_folds: 4
Added the following parameters to address references in box_coder
ignore_low_confidence_detections: true
ignore_low_confidence_labels: false
filter_low_confidence_objects: False
num_channels: 2
input_channels:
- vh
- vv
DDP Stuff
local_rank: ${oc.env:LOCAL_RANK, 0}
world_size: ${oc.env:WORLD_SIZE, 0}
experiment: null
seed: 42
deterministic: False
detect_anomaly: False
find_unused: False
transfer: null
checkpoint: null
inference:
channels_last: False
use_traced_model: True
tile_size: 2048
tile_step: 1536
accumulate_on_gpu: False
batch_size: 1
fp16: True
Torch Configuration
torch:
amp:
enabled: false
cudnn:
allow_tf32: True
cuda_allow_tf32: True
detect_anomaly: False
deterministic: False
benchmark: True
Inference and Training Enhancements
activation_after: "after_model"
tta: "d4"
with_offset: True
yolo_decoders: []
Validation Configuration
valid:
image_size: [2048, 2048]
batch_size: 16
num_workers: 2
Training Configuration
train:
slug: default_training
epochs: 4
output_stride: 32
early_stopping:
enabled: true
patience: 2
mode: "max" # Options: 'min' for loss-based, 'max' for accuracy-based
metric: "loss" # The metric to monitor for early stopping
image_size: [512, 512]
batch_size: 16
num_workers: 2
num_samples: 8192
persistent_workers: False
model2:
target: xview3.centernet.models.centernet_unet.EnhancedCenterNetUNet
slug: b5_unet_s2
weights: IMAGENET1K_V1
pretrained: True
output_stride: 32
image_size: [1024, 1024]
window_size:
- 4
- 4
num_layers: ${model2.num_layers} # Ensure these are set up for overrides
num_filters: ${model2.num_filters}
kernel_size: ${model2.kernel_size}
activation: ${model2.activation}
dropout_rate: ${model2.dropout_rate}
dropout_rate_max: 0.5
optimizer: ${model2.optimizer}
learning_rate: ${model2.learning_rate}
transformer:
enabled: true
embed_dim: 96
depths: [2, 2, 6, 2]
num_heads: [3, 6, 12, 24]
window_size:
- 4
- 4
mlp_ratio: 4.0
qkv_bias: true
qk_scale: null
drop_rate: 0.0
attn_drop_rate: 0.0
drop_path_rate: 0.1
norm_layer: torch.nn.LayerNorm
use_dropout: false
use_custom_pos_embed: false
decoder:
target: xview3.centernet.models.custom_encoder.DebugUNetDecoder
encoder_channels: [24, 48, 64, 160, 272]
channels: [512, 384, 256, 128, 64]
unet_block:
target: xview3.centernet.models.unet_blocks.UnetBlock
partial: true
upsample_block:
target: torch.nn.Upsample
scale_factor: 2
mode: bilinear
encoder:
target: pytorch_toolbelt.modules.encoders.TimmEfficientNetV2
model_name: efficientnetv2_rw_s
pretrained: True
activation: silu
drop_rate: 0.1
drop_path_rate: 0.1
layers:
- 1 # 4
- 2 # 8
- 3 # 16
- 4 # 32
nas:
enabled: true
search_space:
num_layers: [2, 5]
num_filters: [64, 128, 256]
kernel_size: [3, 5]
activation: [relu, leaky_relu, silu]
dropout_rate: [0.0, 0.5]
dropout_rate_max: 0.5
num_trials: 50
metric: "val_loss"
direction: "minimize"
num_channels: ${dataset.num_channels}
num_classes: 2
anchors: [[10, 13], [16, 30], [33, 23]]
input_upsample_factor: 1
neck_out_channels: 256
box_coder:
target: xview3.centernet.bboxer.multilabel_circle_coder.MultilabelCircleNetCoder
image_size:
- 1024
- 1024
max_objects: 512
heatmap_encoding: umich
labels_encoding: circle
ignore_value:
target: xview3.ignore_value
fixed_radius: 3
labels_radius: 1
ignore_low_confidence_detections: ${dataset.ignore_low_confidence_detections}
head:
decoupled_head:
target: xview3.centernet.models.heads.DecoupledHeadGroupNormLateShuffle
channels: 256
classifier_dim: 128
objectness_dim: 128
size_dim: 128
offset_dim: 16
dropout_rate: 0.1
activation: silu
num_blocks: 3
yolo_head:
target: xview3.centernet.models.heads.yolo_head.YOLOHead
num_classes: 2
anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119]]
input_image_size: [1024, 1024]
stride: 32
num_anchors_per_scale: 3
fan:
target: xview3.centernet.models.necks.FeatureAggregationNetwork
input_channels: [272] #[32, 48, 64, 128] #in_channels: 272 #out_channels: 256 #num_layers: 2
out_channels: 256
num_layers: 2
upsample_mode: bilinear
use_attention: True
fusion_method: weighted_sum
##the error

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 "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/torch/nn/modules/container.py", line 219, in forward
input = module(input)
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 "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 458, in forward
return self._conv_forward(input, self.weight, self.bias)
File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 454, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
RuntimeError: Given groups=1, weight of size [512, 272, 3, 3], expected input[16, 256, 128, 128] to have 272 channels, but got 256 channels instead
(sam3) samerkharboush@Mac SAR-MPS-OptunaNasFbnYoloTransformers %
##the important file centernet_unet.py
from functools import partial
from typing import List, Dict, Any, Tuple, Callable
from omegaconf import DictConfig, OmegaConf
from hydra.utils import instantiate, get_class
from pytorch_toolbelt.modules import (
ACT_RELU,
EncoderModule,
)
import torchmetrics
from torch.utils.data import DataLoader
from torch import nn, Tensor
import torch
import timm
from xview3.centernet.bboxer.functional import centernet_heatmap_nms
import random
import torch.nn.functional as F
from xview3.centernet.models.custom_encoder import CustomTimmEncoder, DebugUnetDecoder, UpsampleBlock, ResidualDeconvolutionUpsample2d
from .unet_blocks import IRBlock, get_unet_block, SqueezeExcitation, UnetBlock
from .necks import FPNNeck
from .heads.yolo_head import YOLOHead, AnchorFreeHead
from .heads.yolo_decoder import YOLODecoder
from xview3.centernet.models.transformer_classifier import SwinTransformerEncoder
from xview3.centernet.models.necks import FPNNeck, FeatureAggregationNetwork
from xview3.centernet.models.heads import DecoupledHeadGroupNormLateShuffle
from xview3.common import FilterResponseNormalization, ResidualBlock, FeaturePyramidNetwork
from ..constants import (
CENTERNET_OUTPUT_CLASS_MAP,
CENTERNET_OUTPUT_OBJECTNESS_MAP,
CENTERNET_OUTPUT_OFFSET,
CENTERNET_OUTPUT_SIZE,
CENTERNET_OUTPUT_VESSEL_MAP,
CENTERNET_OUTPUT_FISHING_MAP,
CENTERNET_OUTPUT_YOLO,
CENTERNET_TARGET_YOLO
)
from xview3.centernet.models.heads.decoupled_head import DecoupledHeadGroupNormLateShuffle
from xview3.centernet.models.heads.yolo_head import YOLOHead
from ..losses.circlenet_loss import CircleNetLoss, YOLOLoss
from xview3.centernet.bboxer.multilabel_circle_coder import MultilabelCircleNetCoder
import logging
from xview3.nas import OptunaNAS, FocalLoss
from xview3.utils import TorchDevice
from omegaconf.errors import ConfigKeyError # Added import for ConfigKeyError
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(name)
all = ["CenterNetUNetModel"]
device = TorchDevice.choose_torch_device()
HEAD_REGISTRY = {
"xview3.centernet.models.heads.DecoupledHeadGroupNormLateShuffle": DecoupledHeadGroupNormLateShuffle,
"xview3.centernet.models.heads.yolo_head.YOLOHead": YOLOHead,
"xview3.centernet.models.heads.AnchorFreeHead": YOLOHead, # Assuming AnchorFreeHead is similar
# Add other head types here
}
def adjust_encoder_input_channels(encoder_module, in_chans):
"""
Adjusts the input channels of the encoder's first convolutional layer to match the desired number of input channels.

Args:
    encoder_module (CustomTimmEncoder): The encoder module.
    in_chans (int): Desired number of input channels.

Returns:
    CustomTimmEncoder: Encoder module with adjusted input channels.
"""
first_conv = None
# Search for common first conv layers
possible_names = ['conv_stem', 'conv1', 'initial_conv']
for name in possible_names:
    if hasattr(encoder_module.encoder, name):
        first_conv = getattr(encoder_module.encoder, name)
        break

if first_conv is None:
    if isinstance(encoder_module.encoder, timm.models.SwinTransformer):
        # Transformer-based encoder typically has 'patch_embed.proj'
        if hasattr(encoder_module.encoder.patch_embed, 'proj'):
            first_conv = encoder_module.encoder.patch_embed.proj
        else:
            raise AttributeError(f"The encoder '{type(encoder_module).__name__}' does not have 'patch_embed.proj' attribute.")
    else:
        raise NotImplementedError(f"Adjusting input channels for encoder '{type(encoder_module).__name__}' is not implemented. Expected one of {possible_names}.")

old_conv = first_conv
new_conv = nn.Conv2d(
    in_channels=in_chans,
    out_channels=old_conv.out_channels,
    kernel_size=old_conv.kernel_size,
    stride=old_conv.stride,
    padding=old_conv.padding,
    bias=old_conv.bias is not None
)

with torch.no_grad():
    num_existing_in_channels = min(old_conv.in_channels, in_chans)
    new_conv.weight[:, :num_existing_in_channels, ...] = old_conv.weight[:, :num_existing_in_channels, ...]
    if in_chans > old_conv.in_channels:
        torch.nn.init.kaiming_normal_(new_conv.weight[:, old_conv.in_channels:, ...])

if hasattr(encoder_module.encoder, 'conv_stem'):
    encoder_module.encoder.conv_stem = new_conv
elif hasattr(encoder_module.encoder, 'patch_embed'):
    encoder_module.encoder.patch_embed.proj = new_conv
else:
    setattr(encoder_module.encoder, name, new_conv)

logger.info(f"Adjusted encoder's first conv layer to have {in_chans} input channels.")
return encoder_module
class CenterNetUNetModel(nn.Module):
"""
CenterNet-UNet Model integrating an encoder, transformer encoder, feature aggregation network, decoder, neck, and multiple heads.

Args:
    config (Dict[str, Any]): Configuration dictionary.
    encoder (CustomTimmEncoder): The backbone encoder module.
    decoder_features (List[int]): List of feature channels for the decoder.
    heads (List[nn.Module]): List of head modules (e.g., DecoupledHead, YOLOHead).
    box_coder: The box coder for decoding CenterNet outputs.
    neck_out_channels (int, optional): Output channels for the neck module. Defaults to 256.
    input_upsample_factor (int, optional): Upsampling factor for initial input. Defaults to 1.
    activation (str, optional): Activation function. Defaults to ACT_RELU.
    num_extra_blocks (int, optional): Number of extra encoder blocks. Defaults to 0.
    block_type (str, optional): Type of U-Net block to use. Defaults to "UnetBlock".
    upsample_type (str, optional): Type of upsampling method. Defaults to "bilinear".
    extra_block (Callable, optional): Extra block to add to the encoder. Defaults to partial(IRBlock, stride=2).
    num_classes (int, optional): Number of target classes. Defaults to 2.
    anchors (List[Tuple[int, int]], optional): List of anchor box dimensions for YOLO. Defaults to [(10, 13), (16, 30), (33, 23)].
    nas (OptunaNAS, optional): NAS module for architecture search. Defaults to None.
    dropout_rate (float, optional): Dropout rate. Defaults to 0.1.
"""
def __init__(
    self,
    config: DictConfig,
    encoder: CustomTimmEncoder,
    decoder_features: List[int],
    heads: List[nn.Module],
    box_coder,
    neck_out_channels: int = 256,
    input_upsample_factor: int = 1,
    activation: str = ACT_RELU,
    num_extra_blocks: int = 0,
    block_type: str = "UnetBlock",
    upsample_type: str = "bilinear",
    extra_block=partial(IRBlock, stride=2),
    num_classes: int = 2,
    anchors: List[Tuple[int, int]] = [(10, 13), (16, 30), (33, 23)],
    nas: OptunaNAS = None,
    dropout_rate: float = 0.1
):
    super(CenterNetUNetModel, self).__init__()

    # Initial Upsampling
    self.initial_upsample = (
        nn.UpsamplingBilinear2d(scale_factor=input_upsample_factor)
        if input_upsample_factor != 1 else nn.Identity()
    )

    # Encoder Forward Pass
    self.encoder = encoder
    self.extra_encoder_blocks = nn.ModuleList(
        [extra_block(encoder.channels[-1], encoder.channels[-1]) for _ in range(num_extra_blocks)]
    )
    downsample_factor = 16
    transformer_img_size = (config.model.image_size[0] // downsample_factor, config.model.image_size[1] // downsample_factor)
    # Transformer Encoder Integration
    self.transformer_encoder = SwinTransformerEncoder(
        #img_size=tuple(config.model.image_size),  # Cast to tuple
        img_size=transformer_img_size,
        embed_dim=config.model.transformer.embed_dim,
        depths=list(config.model.transformer.depths), 
        num_heads=list(config.model.transformer.num_heads), 
        window_size=tuple(config.model.transformer.window_size),  # Cast to tuple
        mlp_ratio=config.model.transformer.mlp_ratio,
        qkv_bias=config.model.transformer.qkv_bias,
        qk_scale=config.model.transformer.qk_scale,
        drop_rate=config.model.transformer.drop_rate,
        attn_drop_rate=config.model.transformer.attn_drop_rate,
        drop_path_rate=config.model.transformer.drop_path_rate,
        norm_layer=config.model.transformer.norm_layer,
        in_chans=encoder.channels[-1],
        use_dropout=config.model.transformer.use_dropout,
        use_custom_pos_embed=config.model.transformer.use_custom_pos_embed,
    )
    print(f"[DEBUG] SwinTransformerEncoder initialized with img_size={config.model.image_size} and window_size={config.model.transformer.window_size}")

    # Define Variable for Encoder Output Channels
    variable_encoder_output_channels = encoder.channels[-1]

    # Define Expected FAN Input Channels
    expected_fan_input_channels = config.model.get('expected_fan_input_channels', 256)  # Default to 272 if not specified

    
    # Feature Aggregation Network (FAN)
    self.fan = FeatureAggregationNetwork(
        input_channels=[variable_encoder_output_channels],  # Corrected input channels
        out_channels=neck_out_channels,
        num_layers=config.model.nas.get('num_layers', 4),
        upsample_mode=config.model.fan.upsample_mode,
        use_attention=config.model.fan.use_attention,
        fusion_method=config.model.fan.fusion_method
    )
    # In CenterNetUNetModel __init__
    self.channel_align = nn.Conv2d(
        in_channels=variable_encoder_output_channels,  # Should be transformer_feature channels (e.g., 256)
        out_channels=self.fan.input_channels[0],       # Should be 272 if that's expected
        kernel_size=1
    )

    encoder_channels = encoder.channels[:-1] + [config.model.fan.out_channels]

    # Decoder Integration
    unet_block = get_unet_block(block_type, activation=config.model.decoder.activation)
    self.decoder = DebugUnetDecoder(
        encoder_channels=encoder.channels,
        decoder_channels=decoder_features,
        #input_channels=encoder.channels[-1],  # Corrected to use encoder's output channels
        unet_block=unet_block,
        upsample_block=CenterNetUNetModel.get_upsample_block_static(upsample_type),  # Corrected
        center=False,
    )

    # Neck Integration
    self.neck = FPNNeck(channels=decoder_features, out_channels=neck_out_channels)
    self.neck_conv = nn.Conv2d(
        in_channels=neck_out_channels,  # Output from FPNNeck
        out_channels=neck_out_channels,
        kernel_size=1
    )
    self.batch_norm = FilterResponseNormalization(neck_out_channels)
    self.dropout = nn.Dropout(p=0.5)

    # Heads Integration
    self.heads = nn.ModuleList(heads)  # Initialize heads from configuration
    self.head_output_channels = sum([head.out_channels for head in self.heads])  # Sum of all head output channels
    self.output_channels = self.head_output_channels

    self.nas = nas  # Initialize NAS if provided

    # Initialize YOLO Decoder
    self.yolo_decoder = YOLODecoder(
        anchors=anchors,
        num_classes=num_classes,
        input_image_size=config.model.image_size,  # Adjust based on your image size
        stride=config.model.output_stride,    # Adjust based on encoder
        conf_threshold=0.5,
        iou_threshold=0.4
    )

    # Fusion Layer Input Channels
    yolo_out_channels = self.yolo_decoder.out_channels
    fusion_input_channels = 1 + 1 + 1 + 1 + 2 + yolo_out_channels 
    self.fusion = nn.Conv2d(fusion_input_channels, self.head_output_channels, kernel_size=1)
    self.dropout_fusion = nn.Dropout(p=0.5)

    # Initialize Loss Functions
    self.circle_net_loss = CircleNetLoss()
    self.yolo_loss = YOLOLoss(num_classes=num_classes, anchors=anchors)

    print(f"[DEBUG] CenterNetUNetModel initialized with window_size={config.model.transformer.window_size} and img_size={config.model.image_size}")

def get_upsample_block(self, upsample_type: str):
    """
    Factory method to get the appropriate upsample block based on the upsample_type.

    Args:
        upsample_type (str): Type of upsampling ('nearest', 'bilinear', 'rdtsc', 'shuffle').

    Returns:
        Callable: Partial function for the upsample block.
    """
    if upsample_type in ['nearest', 'bilinear']:
        return partial(
            nn.UpsamplingBilinear2d,
            scale_factor=2
        )
    elif upsample_type == 'rdtsc':
        return partial(ResidualDeconvolutionUpsample2d, scale_factor=2)
    elif upsample_type == 'shuffle':
        return partial(nn.PixelShuffle, upscale_factor=2)
    else:
        raise ValueError(f"Unknown upsample_type {upsample_type}")


def forward(self, x):
    logger.debug("Starting forward pass through CenterNetUNetModel.")
    
    # Initial Upsampling
    logger.debug("Performing initial upsampling.")
    x = self.initial_upsample(x)

    # Encoder Forward Pass
    logger.debug("Passing through encoder.")
    encoder_features = self.encoder(x)  # Correctly assign encoder_features

    # Check if encoder_features is a list
    if isinstance(encoder_features, list):
        logger.debug(f"Encoder returned a list of {len(encoder_features)} feature maps.")
        # Log shapes of all feature maps
        for idx, feature in enumerate(encoder_features):
            if feature is None:
                logger.error(f"Transformer feature {idx} is None.")
                raise ValueError(f"Transformer feature {idx} is None.")
            logger.debug(f"Transformer feature {idx} shape: {feature.shape}")
        
        # Select the deepest feature map (e.g., the last one)
        transformer_feature = encoder_features[-1]
        if transformer_feature is None:
            logger.error("The last transformer feature is None.")
            raise ValueError("The last transformer feature is None.")
        else:
            logger.debug(f"Selected transformer feature shape: {transformer_feature.shape}")
    else:
        if encoder_features is None:
            logger.error("The encoder output is None.")
            raise ValueError("The encoder output is None.")
        else:
            logger.debug(f"Encoder output shape: {encoder_features.shape}")
            transformer_feature = encoder_features

    # Align channels if necessary
    expected_fan_input_channels = self.fan.input_channels[0]
    actual_channels = transformer_feature.shape[1]
    if actual_channels != expected_fan_input_channels:
        logger.debug(f"Aligning channels from {actual_channels} to {expected_fan_input_channels}.")
        transformer_feature = self.channel_align(transformer_feature)
        logger.debug(f"Channels aligned to: {transformer_feature.shape[1]}")

    # Replace the last encoder feature with the transformer output
    logger.debug("Replacing the last encoder feature with transformer features.")
    if isinstance(encoder_features, list):
        encoder_features[-1] = transformer_feature
    else:
        encoder_features = [transformer_feature]  # Convert to list if not already

    # Apply FAN to the last encoder feature
    logger.debug("Passing transformer features to FAN.")
    #aggregated_feature = self.fan(transformer_feature)
    aggregated_feature = self.fan([transformer_feature])  # Note the brackets to make it a list

    if aggregated_feature is None:
        logger.error("FAN returned None.")
        raise ValueError("FAN output is None.")
    logger.debug(f"Aggregated feature shape: {aggregated_feature.shape}")

    # Replace the last encoder feature with the aggregated feature
    logger.debug("Replacing the last encoder feature with aggregated features.")
    if isinstance(encoder_features, list):
        encoder_features[-1] = aggregated_feature
    else:
        encoder_features = [aggregated_feature]  # Convert to list if not already

    # Pass to Decoder
    logger.debug("Passing aggregated features to decoder.")
    decoded_features = self.decoder(encoder_features)
    if not isinstance(decoded_features, list):
        logger.error("Decoder output is not a list.")
        raise ValueError("Decoder output is not a list.")

    # Neck Processing
    logger.debug("Processing neck features.")
    neck_features = self.neck(encoder_features)  # Pass list to neck
    neck_features = self.batch_norm(neck_features)
    neck_features = self.dropout(neck_features)
    neck_features = self.neck_conv(neck_features)
    logger.debug(f"Neck features shape after processing: {neck_features.shape}")

    # Heads Forward Pass
    logger.debug("Passing neck features to heads.")
    head_outputs = {}
    for head in self.heads:
        head_output = head(neck_features)
        if isinstance(head, DecoupledHeadGroupNormLateShuffle):
            if isinstance(head_output, dict):
                head_outputs.update(head_output)
                logger.debug("DecoupledHeadGroupNormLateShuffle output updated in head_outputs.")
            else:
                logger.warning("DecoupledHeadGroupNormLateShuffle did not return a dict.")
        elif isinstance(head, YOLOHead):
            head_outputs[CENTERNET_OUTPUT_YOLO] = head_output
            logger.debug("YOLOHead output added to head_outputs.")
        else:
            logger.warning(f"Unhandled head type: {type(head)}")
    
    logger.debug("Forward pass completed successfully.")


    # Decode YOLO Outputs
    if CENTERNET_OUTPUT_YOLO in head_outputs:
        decoded_yolo = self.yolo_decoder.decode(head_outputs[CENTERNET_OUTPUT_YOLO])
        head_outputs['decoded_yolo'] = decoded_yolo
    else:
        logger.warning("YOLO output not found in head outputs.")

    # Extract Standard Outputs
    outputs = {
        CENTERNET_OUTPUT_OBJECTNESS_MAP: head_outputs.get(CENTERNET_OUTPUT_OBJECTNESS_MAP),
        CENTERNET_OUTPUT_SIZE: head_outputs.get(CENTERNET_OUTPUT_SIZE),
        CENTERNET_OUTPUT_OFFSET: head_outputs.get(CENTERNET_OUTPUT_OFFSET),
        CENTERNET_OUTPUT_VESSEL_MAP: head_outputs.get(CENTERNET_OUTPUT_VESSEL_MAP),
        CENTERNET_OUTPUT_FISHING_MAP: head_outputs.get(CENTERNET_OUTPUT_FISHING_MAP),
        CENTERNET_OUTPUT_YOLO: head_outputs.get(CENTERNET_OUTPUT_YOLO),
    }

    # Concatenate All Relevant Feature Maps
    concatenated = torch.cat([
        outputs[CENTERNET_OUTPUT_OBJECTNESS_MAP],  # [B, 1, H, W]
        outputs[CENTERNET_OUTPUT_VESSEL_MAP],      # [B, 1, H, W]
        outputs[CENTERNET_OUTPUT_FISHING_MAP],     # [B, 1, H, W]
        outputs[CENTERNET_OUTPUT_SIZE],            # [B, 1, H, W]
        outputs[CENTERNET_OUTPUT_OFFSET],          # [B, 2, H, W]
        outputs[CENTERNET_OUTPUT_YOLO],            # [B, YOLO_out_channels, H, W]
    ], dim=1)

    # **Verification Prints:**
    logger.debug(f"objectness_map shape: {outputs[CENTERNET_OUTPUT_OBJECTNESS_MAP].shape}")      
    logger.debug(f"vessel_map shape: {outputs[CENTERNET_OUTPUT_VESSEL_MAP].shape}")                  
    logger.debug(f"fishing_map shape: {outputs[CENTERNET_OUTPUT_FISHING_MAP].shape}")                
    logger.debug(f"size_map shape: {outputs[CENTERNET_OUTPUT_SIZE].shape}")                      
    logger.debug(f"offset_map shape: {outputs[CENTERNET_OUTPUT_OFFSET].shape}")                  
    logger.debug(f"yolo_output shape: {head_outputs[CENTERNET_OUTPUT_YOLO].shape}")                
    logger.debug(f"Total concatenated channels: {concatenated.shape[1]}")  

    # **Assertion for Debugging**
    expected_channels = 1 + 1 + 1 + 1 + 2 + head_outputs[CENTERNET_OUTPUT_YOLO].shape[1]
    assert concatenated.shape[1] == expected_channels, \
        f"Expected {expected_channels} channels, but got {concatenated.shape[1]}"

    # Fusion Layer
    fused_output = self.fusion(concatenated)  # [B, head_output_channels, H, W]
    fused_output = self.dropout_fusion(fused_output)

    # Optionally, add fused_output to outputs if needed for further processing
    outputs['fused_output'] = fused_output

    return outputs

def evaluate_architecture(self, model: nn.Module, train_loader: DataLoader, val_loader: DataLoader) -> float:
    """
    Defines how to evaluate the architecture. This method trains the model for a few epochs and returns a validation metric.

    Args:
        model (nn.Module): The model to evaluate.
        train_loader (DataLoader): Training data loader.
        val_loader (DataLoader): Validation data loader.

    Returns:
        float: Validation metric (e.g., Mean Average Precision).
    """
    # Define training parameters for architecture evaluation
    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
    num_epochs = 3  # Train for a few epochs to evaluate architecture

    model.to(device)
    model.train()

    for epoch in range(num_epochs):
        running_loss = 0.0
        for batch in train_loader:
            inputs, targets = self.get_batch(batch)
            inputs = inputs.to(device)
            targets = self.prepare_targets(targets).to(device)

            optimizer.zero_grad()

            outputs = model(inputs)
            loss = self.calculate_loss(outputs, targets)
            loss.backward()
            optimizer.step()

            running_loss += loss.item()

        avg_loss = running_loss / len(train_loader)
        print(f"Architecture Evaluation Epoch {epoch + 1}/{num_epochs} - Loss: {avg_loss:.4f}")

    # Compute validation metric
    val_acc, _ = self.evaluate(model, val_loader, torchmetrics.Accuracy().to(device), torchmetrics.F1().to(device))
    print(f"Architecture Evaluation - Validation Accuracy: {val_acc:.4f}")
    return val_acc

def calculate_loss(self, outputs: Dict[str, torch.Tensor], targets: Dict[str, torch.Tensor]) -> torch.Tensor:
    """
    Calculates the composite loss for multi-task learning.

    Args:
        outputs (Dict[str, torch.Tensor]): Model outputs.
        targets (Dict[str, torch.Tensor]): Ground truth targets.

    Returns:
        torch.Tensor: Total loss.

    Calculates the composite loss for multi-task learning with weighting.
    """
    total_loss = 0.0
    losses = {}

    # Define loss weights (can be hyperparameters)
    weights = {
        'heatmap_loss': 1.0,
        'vessel_loss': 1.0,
        'fishing_loss': 1.0,
        'size_loss': 1.0,
        'offset_loss': 1.0,
        'yolo_loss': 1.0,
    }

    # Heatmap loss (Focal Loss)
    if CENTERNET_OUTPUT_OBJECTNESS_MAP in outputs and 'CENTERNET_TARGET_OBJECTNESS_MAP' in targets:
        heatmap_loss = self.heatmap_loss_fn(
            outputs[CENTERNET_OUTPUT_OBJECTNESS_MAP],
            targets['CENTERNET_TARGET_OBJECTNESS_MAP']
        )
        total_loss += weights['heatmap_loss'] * heatmap_loss
        losses['heatmap_loss'] = heatmap_loss.item()

    # Vessel loss (Focal Loss or BCE)
    if CENTERNET_OUTPUT_VESSEL_MAP in outputs and 'CENTERNET_TARGET_VESSEL_MAP' in targets:
        vessel_loss = self.vessel_loss_fn(
            outputs[CENTERNET_OUTPUT_VESSEL_MAP],
            targets['CENTERNET_TARGET_VESSEL_MAP']
        )
        total_loss += weights['vessel_loss'] * vessel_loss
        losses['vessel_loss'] = vessel_loss.item()

    # Fishing loss (Focal Loss or BCE)
    if CENTERNET_OUTPUT_FISHING_MAP in outputs and 'CENTERNET_TARGET_FISHING_MAP' in targets:
        fishing_loss = self.fishing_loss_fn(
            outputs[CENTERNET_OUTPUT_FISHING_MAP],
            targets['CENTERNET_TARGET_FISHING_MAP']
        )
        total_loss += weights['fishing_loss'] * fishing_loss
        losses['fishing_loss'] = fishing_loss.item()

    # Size loss (Smooth L1 Loss)
    if CENTERNET_OUTPUT_SIZE in outputs and 'CENTERNET_TARGET_SIZE' in targets:
        size_loss = F.smooth_l1_loss(
            outputs[CENTERNET_OUTPUT_SIZE],
            targets['CENTERNET_TARGET_SIZE']
        )
        total_loss += weights['size_loss'] * size_loss
        losses['size_loss'] = size_loss.item()

    # Offset loss (Smooth L1 Loss)
    if CENTERNET_OUTPUT_OFFSET in outputs and 'CENTERNET_TARGET_OFFSET' in targets:
        offset_loss = F.smooth_l1_loss(
            outputs[CENTERNET_OUTPUT_OFFSET],
            targets['CENTERNET_TARGET_OFFSET']
        )
        total_loss += weights['offset_loss'] * offset_loss
        losses['offset_loss'] = offset_loss.item()

    # YOLO loss
    if CENTERNET_OUTPUT_YOLO in outputs and CENTERNET_TARGET_YOLO in targets:
        yolo_loss = self.yolo_loss_fn(
            outputs[CENTERNET_OUTPUT_YOLO],
            targets[CENTERNET_TARGET_YOLO]
        )
        total_loss += weights['yolo_loss'] * yolo_loss
        losses['yolo_loss'] = yolo_loss.item()

    # Log individual losses
    for name, value in losses.items():
        logger.debug(f"{name}: {value:.4f}")

    return total_loss


def evaluate(self, model: nn.Module, val_loader: DataLoader, accuracy_metric: torchmetrics.Metric, f1_metric: torchmetrics.Metric) -> Tuple[float, float]:
    """
    Evaluates the model on the validation set and computes the desired metrics.

    Args:
        model (nn.Module): The model to evaluate.
        val_loader (DataLoader): Validation data loader.
        accuracy_metric (torchmetrics.Metric): Accuracy metric instance.
        f1_metric (torchmetrics.Metric): F1 score metric instance.

    Returns:
        Tuple[float, float]: Validation accuracy and F1 score.
    """
    model.eval()
    accuracy_metric.reset()
    f1_metric.reset()
    with torch.no_grad():
        for batch in val_loader:
            inputs, targets = self.get_batch(batch)
            inputs = inputs.to(device)
            targets = self.prepare_targets(targets).to(device)

            outputs = model(inputs)

            # Compute metrics for vessel classification
            if CENTERNET_OUTPUT_VESSEL_MAP in outputs and 'CENTERNET_TARGET_VESSEL_MAP' in targets:
                preds = torch.sigmoid(outputs[CENTERNET_OUTPUT_VESSEL_MAP]) > 0.5
                targets_binary = targets['CENTERNET_TARGET_VESSEL_MAP'] > 0.5
                accuracy_metric.update(preds, targets_binary)
                f1_metric.update(preds, targets_binary)

    val_acc = accuracy_metric.compute().item()
    val_f1 = f1_metric.compute().item()
    return val_acc, val_f1

def get_batch(self, batch: Tuple[Any, Any]) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
    """
    Processes a batch from the DataLoader.

    Args:
        batch (Tuple[Any, Any]): A batch from the DataLoader.

    Returns:
        Tuple[torch.Tensor, Dict[str, torch.Tensor]]: Processed inputs and prepared targets.
    """
    inputs, targets = batch
    return inputs, targets

def prepare_targets(self, targets: Dict[str, Any]) -> Dict[str, torch.Tensor]:
    """
    Prepares targets for the loss function.

    Args:
        targets (Dict[str, Any]): Raw targets from the DataLoader.

    Returns:
        Dict[str, torch.Tensor]: Prepared targets as tensors.
    """
    prepared = {}
    for key, value in targets.items():
        if key in [
            'CENTERNET_TARGET_VESSEL_MAP',
            'CENTERNET_TARGET_FISHING_MAP',
            'CENTERNET_TARGET_OBJECTNESS_MAP'
        ]:
            prepared[key] = value.to(dtype=torch.float32)
        elif key in ['CENTERNET_TARGET_SIZE', 'CENTERNET_TARGET_OFFSET']:
            prepared[key] = value.to(dtype=torch.float32)
        elif key == 'CENTERNET_TARGET_YOLO':
            # Ensure YOLO targets are properly formatted tensors
            prepared[key] = value.to(dtype=torch.float32)
        else:
            prepared[key] = value
    return prepared


@classmethod
def from_config(cls, config: DictConfig):
    """
    Instantiates the CenterNetUNetModel from a configuration dictionary.

    Args:
        config (DictConfig): Configuration dictionary.

    Returns:
        CenterNetUNetModel: The instantiated model.
    """
    # Enhanced Debugging
    print("Available keys in model config:", config.keys())
    print("Complete model configuration:\n", OmegaConf.to_yaml(config))

    # Extract decoder configuration
    decoder_config = config.model.decoder
    decoder_features = decoder_config.decoder_channels  # List of channels, e.g., [256, 384, 512]
    """
    # Check if 'head' exists
    if 'head' not in config:
        raise ConfigKeyError("The key 'head' is missing in the model configuration.")
    """
    head_cfg = config.model.head
    heads = []

    # Handling Multiple Heads
    for head_name, single_head_cfg in head_cfg.items():
        head = cls.instantiate_head(single_head_cfg, decoder_features)
        heads.append(head)

    # Debugging: Verify that heads are instantiated
    print(f"[DEBUG] Number of heads instantiated: {len(heads)}")
    for idx, head in enumerate(heads):
        print(f"[DEBUG] Head {idx} type: {type(head)}")

    # Instantiate the encoder
    encoder_config = config.model.encoder
    encoder: CustomTimmEncoder = instantiate(encoder_config)

    # Adjust input channels if necessary
    num_channels = config.get('num_channels', 3)
    if num_channels != 3:
        encoder = adjust_encoder_input_channels(encoder, num_channels)

    # Extract 'model_name' and 'pretrained'
    model_name = encoder_config.get('model_name', 'swin_base_patch4_window7_224')  # Adjust default if needed
    pretrained = encoder_config.get('pretrained', True)

    # Initialize the encoder module
    encoder_module = CustomTimmEncoder(
        model_name=model_name,
        num_channels=num_channels,
        features_only=True,
        pretrained=pretrained,
        image_size=config.get('image_size', (224, 224))
    )

    # Adjust encoder input channels if necessary
    encoder_module = adjust_encoder_input_channels(encoder_module, num_channels)

    # Extract other configuration parameters
    input_upsample_factor = config.get("input_upsample_factor", 1)
    activation = config.get('activation', 'relu')  # Default to 'relu' if not specified
    num_extra_blocks = config.get('num_extra_blocks', 0)
    num_classes = config.get("num_classes", 2)
    anchors = config.get("anchors", [(10, 13), (16, 30), (33, 23)])

    # Extract decoder configuration
    decoder_config = config.model.decoder
    decoder_features = decoder_config.decoder_channels
    block_type = decoder_config.get("block_type", "UnetBlock")
    upsample_type = decoder_config.get("upsample_type", "bilinear")

    # Extract neck_out_channels
    neck_out_channels = config.get('neck_out_channels', 256)

    # Extract transformer configuration
    transformer_config = config.model.transformer

    # Instantiate Transformer Encoder
    transformer_encoder = instantiate(transformer_config)

    # Instantiate Feature Aggregation Network (FAN)
    expected_fan_input_channels = config.get('expected_fan_input_channels', 256) 

    # Instantiate Feature Aggregation Network (FAN)
    fan = instantiate(
        config.model.fan,  # Ensure 'fan' is defined in config
        input_channels=[encoder.channels[-1]],  # Corrected to use encoder's output channels
        out_channels=config.model.neck_out_channels
    )

    # Instantiate Decoder
    unet_block = get_unet_block(block_type, activation=config.model.decoder.activation)
    decoder = DebugUnetDecoder(
        encoder_channels=encoder.channels,
        decoder_channels=decoder_features,
        #input_channels=encoder.channels[-1],  # Corrected to use encoder's output channels
        unet_block=unet_block,
        upsample_block=cls.get_upsample_block_static(upsample_type),
        center=False,
    )

    # Instantiate Neck
    neck = FPNNeck(channels=decoder_features, out_channels=config.model.neck_out_channels)
    neck_conv = nn.Conv2d(
        in_channels=config.model.neck_out_channels,  # Output from FPNNeck
        out_channels=config.model.neck_out_channels,
        kernel_size=1
    )
    batch_norm = FilterResponseNormalization(neck_out_channels)
    dropout = nn.Dropout(p=0.5)

    # Instantiate Box Coder
    box_coder = MultilabelCircleNetCoder(
        image_size=config.get('image_size', (224,224)),
        output_stride=config.model.get('output_stride', 32),
        max_objects=config.model.box_coder.get('max_objects', 512),
        heatmap_encoding=config.model.box_coder.get('heatmap_encoding', 'umich'),
        labels_encoding=config.model.box_coder.get('labels_encoding', 'circle'),
        ignore_value=config.model.box_coder.get('ignore_value', -1)
    )

    # Instantiate YOLO Decoder
    yolo_decoder = YOLODecoder(
        anchors=anchors,
        num_classes=num_classes,
        input_image_size=config.model.image_size,
        stride=config.model.output_stride,
        conf_threshold=0.5,
        iou_threshold=0.4
    )

    # Instantiate Heads
    instantiated_heads = []
    for head in heads:
        instantiated_heads.append(head)

    # Instantiate NAS if present
    nas = None
    if 'nas' in config:
        nas_config = config.model.nas
        nas = OptunaNAS(
            search_space=nas_config.get('search_space', {}),
            evaluation_function=None,  # Will be set by NAS runner
            max_epochs=nas_config.get('max_epochs', 10),
            use_metrics=nas_config.get('use_metrics', True)
        )

    # Build the model
    model = cls(
        config=config,
        encoder=encoder_module,
        decoder_features=decoder_features,
        heads=instantiated_heads,
        box_coder=box_coder,
        neck_out_channels=neck_out_channels,
        input_upsample_factor=input_upsample_factor,
        activation=activation,
        num_extra_blocks=num_extra_blocks,
        block_type=block_type,
        upsample_type=upsample_type,
        num_classes=num_classes,
        anchors=anchors,
        nas=nas,
        dropout_rate=config.get('dropout_rate', 0.1)
    )

    return model


@staticmethod
def get_upsample_block_static(upsample_type: str) -> Callable:
    """
    Static method to get the appropriate upsample block based on the upsample_type.

    Args:
        upsample_type (str): Type of upsampling ('nearest', 'bilinear', 'rdtsc', 'shuffle').

    Returns:
        Callable: Class for the upsample block.
    """
    if upsample_type in ['nearest', 'bilinear']:
        return UpsampleBlock  # Return the custom UpsampleBlock
    elif upsample_type == 'rdtsc':
        return partial(ResidualDeconvolutionUpsample2d, scale_factor=2)
    elif upsample_type == 'shuffle':
        return partial(nn.PixelShuffle, upscale_factor=2)
    else:
        raise ValueError(f"Unknown upsample_type {upsample_type}")

@classmethod
def instantiate_head(cls, single_head_cfg: Dict[str, Any], decoder_features: List[int]) -> nn.Module:
    """
    Instantiates a head module based on the head configuration.

    Args:
        single_head_cfg (Dict[str, Any]): Configuration dictionary for the head.
        decoder_features (List[int]): List of decoder feature channels.

    Returns:
        nn.Module: Instantiated head module.
    """
    head_type = single_head_cfg.get('type', single_head_cfg.get('_target_', None))
    if head_type is None:
        raise ConfigKeyError("Both 'type' and '_target_' are missing from the head configuration")

    HeadClass = HEAD_REGISTRY.get(head_type, None)
    if HeadClass is None:
        raise NotImplementedError(f"Head type '{head_type}' is not registered.")

    if head_type == 'xview3.centernet.models.heads.DecoupledHeadGroupNormLateShuffle':
        head = HeadClass(
            channels=single_head_cfg.get('channels', decoder_features[-1]),
            classifier_dim=single_head_cfg['classifier_dim'],
            objectness_dim=single_head_cfg['objectness_dim'],
            size_dim=single_head_cfg['size_dim'],
            offset_dim=single_head_cfg['offset_dim'],
            dropout_rate=single_head_cfg.get('dropout_rate', 0.1),
            activation=single_head_cfg.get('activation', 'relu'),
            num_blocks=single_head_cfg.get('num_blocks', 3),
        )
    elif head_type == 'xview3.centernet.models.heads.yolo_head.YOLOHead':
        head = HeadClass(
            num_classes=single_head_cfg['num_classes'],
            anchors=single_head_cfg['anchors'],
            input_image_size=tuple(single_head_cfg.get('input_image_size', (1024, 1024))),
            stride=single_head_cfg.get('stride', 32)
        )
    elif head_type == 'xview3.centernet.models.heads.AnchorFreeHead':
        head = HeadClass(
            in_channels=decoder_features[-1],
            num_classes=single_head_cfg['num_classes']
        )
    else:
        raise NotImplementedError(f"Head type '{head_type}' instantiation is not implemented.")

    return head

def evaluate_architecture(self, model, train_loader, val_loader):
    """
    Defines how to evaluate the architecture. This method trains the model for a few epochs and returns a validation metric.

    Args:
        model (nn.Module): The model to evaluate.
        train_loader (DataLoader): Training data loader.
        val_loader (DataLoader): Validation data loader.

    Returns:
        float: Validation metric (e.g., Mean Average Precision).
    """
    # Define training parameters for architecture evaluation
    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
    num_epochs = 3  # Train for a few epochs to evaluate architecture

    model.to(device)
    model.train()

    for epoch in range(num_epochs):
        running_loss = 0.0
        for batch in train_loader:
            inputs, targets = self.get_batch(batch)
            inputs = inputs.to(device)
            targets = self.prepare_targets(targets).to(device)

            optimizer.zero_grad()

            outputs = model(inputs)
            loss = model.calculate_loss(outputs, targets)
            loss.backward()
            optimizer.step()

            running_loss += loss.item()

        avg_loss = running_loss / len(train_loader)
        print(f"Architecture Evaluation Epoch {epoch + 1}/{num_epochs} - Loss: {avg_loss:.4f}")

    # Compute validation metric
    val_metric = self.evaluate(model, val_loader)
    print(f"Architecture Evaluation - Validation Metric: {val_metric:.4f}")
    return val_metric
class EnhancedCenterNetUNet(CenterNetUNetModel):
"""
Enhanced CenterNetUNet integrating additional components like FPN and SE blocks for improved performance.
"""
def init(self, config: DictConfig, **kwargs):
super().init(config, **kwargs)
# Initialize additional components
self.fpn_extra = FeaturePyramidNetwork([64, 128, 256, 512], 256)
self.se_blocks = nn.ModuleList([SqueezeExcitation(c) for c in [64, 128, 256, 512]])

    self.transformer_encoder = SwinTransformerEncoder(
        window_size=tuple(config.model.transformer.window_size),  # Convert list to tuple
        img_size=tuple(config.model.image_size) // config.model.output_stride,
        embed_dim=96,
        depths=[2, 2, 6, 2],
        num_heads=[3, 6, 12, 24],
        mlp_ratio=4.0,
        qkv_bias=True,
        qk_scale=None,
        drop_rate=0.0,
        attn_drop_rate=0.0,
        drop_path_rate=0.1,
        norm_layer=nn.LayerNorm,
        in_chans=encoder.channels[-1],
        use_dropout=False,
        use_custom_pos_embed=False,
    )

def forward(self, x):
    # Call the parent forward
    outputs = super().forward(x)

    # Extract feature maps for additional FPN and SE processing
    feature_maps = list(outputs.values())  # Adjust based on actual feature map routing
    for i, feature in enumerate(feature_maps):
        if i < len(self.se_blocks):
            feature = self.se_blocks[i](feature)
        feature_maps[i] = self.fpn_extra(feature)

    # Update outputs with enhanced feature maps
    for idx, key in enumerate([CENTERNET_OUTPUT_OBJECTNESS_MAP, CENTERNET_OUTPUT_VESSEL_MAP,
                               CENTERNET_OUTPUT_FISHING_MAP, CENTERNET_OUTPUT_SIZE]):
        if key in outputs and idx < len(self.se_blocks):
            outputs[key] = self.se_blocks[idx](outputs[key])
            logger.debug(f"Applied SE block to {key}.")

    # Optionally, process with additional FPN
    # For example, enhance vessel_map using FPN
    if CENTERNET_OUTPUT_VESSEL_MAP in outputs:
        enhanced_vessel = self.fpn_extra(outputs[CENTERNET_OUTPUT_VESSEL_MAP].unsqueeze(0))
        outputs['enhanced_vessel_map'] = enhanced_vessel.squeeze(0)
        logger.debug("Applied additional FPN to vessel_map.")

    return outputs

Certainly! Let's perform a comprehensive, line-by-line analysis of your configuration and model code to identify and resolve the channel mismatch error:

RuntimeError: Given groups=1, weight of size [512, 272, 3, 3], expected input[16, 256, 128, 128] to have 272 channels, but got 256 channels instead
This error indicates that a convolutional layer expects an input with 272 channels but is receiving an input with 256 channels. Our goal is to trace the data flow through your model to identify where this mismatch occurs.
1. Understanding the Configuration File
Let's start by dissecting your configuration file to understand the intended architecture.
a. Model Configuration


model:
  _target_: xview3.centernet.models.centernet_unet.EnhancedCenterNetUNet 
  slug: b5_unet_s2
  weights: IMAGENET1K_V1
  pretrained: True
  output_stride: 32
  image_size: [1024, 1024]
  window_size:
    - 4
    - 4
  num_layers: [2, 5]
  num_filters: [64, 128, 256]
  kernel_size: [3, 5]
  activation: [relu, leaky_relu, silu]
  dropout_rate: [0.0, 0.5]
  dropout_rate_max: 0.5
  optimizer: adamw
  learning_rate: 0.001

  transformer:
    enabled: true
    embed_dim: 96
    depths: [2, 2, 6, 2]
    num_heads: [3, 6, 12, 24]
    window_size:
      - 4
      - 4
    mlp_ratio: 4.0
    qkv_bias: true
    qk_scale: null
    drop_rate: 0.0
    attn_drop_rate: 0.0
    drop_path_rate: 0.1
    norm_layer: torch.nn.LayerNorm
    use_dropout: false
    use_custom_pos_embed: false

  decoder:
    _target_: xview3.centernet.models.custom_encoder.DebugUnetDecoder
    encoder_channels: [24, 48, 64, 160, 272]  # **Key Point**
    decoder_channels: [512, 384, 256, 128, 64]
    unet_block:
      _target_: xview3.centernet.models.unet_blocks.UnetBlock
      _partial_: true
    upsample_block: xview3.centernet.models.custom_encoder.UpsampleBlock
    center: False
    activation: silu

  encoder:
    _target_: pytorch_toolbelt.modules.encoders.TimmEfficientNetV2
    model_name: efficientnetv2_rw_s
    pretrained: True
    activation: silu
    drop_rate: 0.1
    drop_path_rate: 0.1
    layers:
      - 1
      - 2
      - 3
      - 4

  nas:
    enabled: true
    search_space:
      num_layers: [2, 5]
      num_filters: [64, 128, 256]
      kernel_size: [3, 5]
      activation: [relu, leaky_relu, silu]
      dropout_rate: [0.0, 0.5]
      dropout_rate_max: 0.5
    num_trials: 50
    metric: "val_acc"
    direction: "maximize"

  num_channels: ${dataset.num_channels}
  num_classes: 2
  anchors: [[10, 13], [16, 30], [33, 23]]
  input_upsample_factor: 1
  neck_out_channels: 256

  box_coder:
    _target_: xview3.centernet.bboxer.multilabel_circle_coder.MultilabelCircleNetCoder
    image_size:
      - 1024
      - 1024
    max_objects: 512
    heatmap_encoding: umich
    labels_encoding: circle
    ignore_value:
      _target_: xview3.ignore_value
    fixed_radius: 3
    labels_radius: 1
    ignore_low_confidence_detections: ${dataset.ignore_low_confidence_detections}

  head:
    decoupled_head:
      _target_: xview3.centernet.models.heads.DecoupledHeadGroupNormLateShuffle
      channels: 256
      classifier_dim: 128
      objectness_dim: 128
      size_dim: 128
      offset_dim: 16
      dropout_rate: 0.1
      activation: silu
      num_blocks: 3
    yolo_head:
      _target_: xview3.centernet.models.heads.yolo_head.YOLOHead
      num_classes: 2
      anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119]]
      input_image_size: [1024, 1024]
      stride: 32
      num_anchors_per_scale: 3

  fan:
    _target_: xview3.centernet.models.necks.FeatureAggregationNetwork
    input_channels: [272]
    out_channels: 256
    num_layers: 2
    upsample_mode: bilinear
    use_attention: True
    fusion_method: weighted_sum

expected_fan_input_channels: 256
b. Key Configuration Insights

Encoder Channels:
encoder_channels: [24, 48, 64, 160, 272]
Indicates that the encoder outputs feature maps with these channel sizes at different stages.
FAN (Feature Aggregation Network):
input_channels: [272]
out_channels: 256
This means FAN takes a [B, 272, H, W] input and outputs [B, 256, H, W].
Decoder Channels:
decoder_channels: [512, 384, 256, 128, 64]
The decoder is expecting to process these channel sizes at each stage.
Expected FAN Input Channels:
expected_fan_input_channels: 256
However, FAN is configured to take 272 channels as input and output 256.
2. Analyzing centernet_unet.py
Let's delve into the centernet_unet.py to understand how these configurations are utilized.
a. Initialization of FAN


# Feature Aggregation Network (FAN)
self.fan = FeatureAggregationNetwork(
    input_channels=[variable_encoder_output_channels],  # [272]
    out_channels=neck_out_channels,                     # 256
    num_layers=config.model.nas.get('num_layers', 4),    # 2
    upsample_mode=config.model.fan.upsample_mode,       # 'bilinear'
    use_attention=config.model.fan.use_attention,       # True
    fusion_method=config.model.fan.fusion_method        # 'weighted_sum'
)

# Channel Alignment
self.channel_align = nn.Conv2d(
    in_channels=variable_encoder_output_channels,  # 272
    out_channels=self.fan.input_channels[0],       # 272
    kernel_size=1
)
Issue:
FAN is configured to output 256 channels, but the channel alignment layer is set to align 272 to 272 channels (in_channels=272, out_channels=272).
This means that the transformer output is aligned to 272 channels, but FAN reduces it to 256 channels.
Expected:
The decoder is expecting decoder_channels: [512, 384, 256, 128, 64]. The first decoder layer is 512 channels, possibly expecting to receive concatenated features.
b. Forward Pass Analysis


def forward(self, x):
    # Initial Upsampling
    x = self.initial_upsample(x)  # [B, C, H, W] -> [B, C, H*scale, W*scale]

    # Encoder Forward Pass
    encoder_features = self.encoder(x)  # List[Tensor], last element: [B, 272, H/32, W/32]

    # Transformer Feature
    transformer_feature = encoder_features[-1]  # [B, 272, H/32, W/32]

    # Channel Alignment
    transformer_feature = self.channel_align(transformer_feature)  # [B, 272, H/32, W/32]

    # FAN Processing
    aggregated_feature = self.fan([transformer_feature])  # [B, 256, H/32, W/32]

    # Replace encoder_features[-1] with aggregated_feature
    encoder_features[-1] = aggregated_feature  # Now, encoder_features[-1] is [B, 256, H/32, W/32]

    # Decoder
    decoded_features = self.decoder(encoder_features)  # DebugUnetDecoder expects [24, 48, 64, 160, 256]
c. DebugUnetDecoder Initialization


class DebugUnetDecoder(nn.Module):
    def __init__(
        self,
        encoder_channels,      # [24, 48, 64, 160, 256] after FAN
        decoder_channels,      # [512, 384, 256, 128, 64]
        unet_block,
        upsample_block,
        center=False,
        activation='relu'
    ):
        ...
        for i in range(self.num_stages):  # 5 stages
            if i == 0:
                in_channels = encoder_channels[-1]  # 256
            else:
                in_channels = decoder_channels[i-1]  # 512, 384, 256, 128

            out_channels = decoder_channels[i]  # 512, 384, 256, 128, 64

            # Upsample Block: Conv2d with in_channels to out_channels
            self.up_samples.append(
                upsample_block(
                    in_channels=in_channels,
                    out_channels=out_channels
                )
            )

            # UNetBlock Input Channels
            if i < len(encoder_channels) - 1:  # for i=0 to 3
                unet_in_channels = out_channels + encoder_channels[-(i+2)]  # e.g., 512 + 160
            else:
                unet_in_channels = out_channels  # i=4

            self.unet_blocks.append(
                unet_block(
                    in_channels=unet_in_channels,
                    out_channels=out_channels,
                    activation=activation
                )
            )
d. Decoder Forward Pass


def forward(self, encoder_features):
    x = encoder_features[-1]  # [B, 256, H/32, W/32]

    decoded_features = []

    for i in range(self.num_stages):  # 5 iterations
        # Upsample
        x = self.up_samples[i](x)  # Upsample: [B, decoder_channels[i], H', W']

        # Concatenate with encoder feature
        if i < len(encoder_features) - 1:  # i=0 to 3
            encoder_feat = encoder_features[-(i+2)]  # [B, encoder_channels[i], H', W']
            # Resize if necessary
            x = torch.cat([x, encoder_feat], dim=1)  # [B, decoder_channels[i] + encoder_channels[i], H', W']

        # Pass through UNetBlock
        x = self.unet_blocks[i](x)  # [B, decoder_channels[i], H', W']

        decoded_features.append(x)

    return decoded_features  # List of [B, decoder_channels[i], H', W'] for i=0 to 4
e. Decoder Output Expectations

After the decoder, you should have multiple feature maps with channels: [512, 384, 256, 128, 64].
f. Neck Integration


# Neck Integration
self.neck = FPNNeck(channels=decoder_features, out_channels=neck_out_channels)  # [512, 384, 256, 128, 64] -> [256]
The FPNNeck takes the decoder outputs and fuses them into [B, 256, H, W].
g. Channel Alignment in Model Initialization


# Define Variable for Encoder Output Channels
variable_encoder_output_channels = encoder.channels[-1]  # 256 after FAN

# Channel Alignment
self.channel_align = nn.Conv2d(
    in_channels=variable_encoder_output_channels,  # 256
    out_channels=self.fan.input_channels[0],       # 272 (from config input_channels [272])
    kernel_size=1
)
Issue:
FAN already reduces channels from 272 to 256.
Channel Alignment is aligning 256 -> 272, which is contradictory.
h. Feature Aggregation Network (FAN) Initialization


self.fan = FeatureAggregationNetwork(
    input_channels=[variable_encoder_output_channels],  # [256] conflicting with config input_channels: [272]
    out_channels=neck_out_channels,                     # 256
    ...
)
Discrepancy:
Config specifies input_channels: [272] for FAN.
Model Code initializes FAN with input_channels=[256] (after channel alignment).
3. Identifying the Mismatch
The error arises from:
A convolution layer expecting 272 input channels: [512, 272, 3, 3]
Receiving 256 input channels: [16, 256, 128, 128]
Upon tracing:
Encoder outputs 272 channels.
Channel Alignment transforms 272 channels to 272 channels (redundant).
FAN is initialized with 256 input channels (from variable_encoder_output_channels), but config expects 272.
FAN outputs 256 channels.
Decoder expects encoder_channels: [24, 48, 64, 160, 256] (after FAN).
Decoder processes these and outputs features with decoder_channels: [512, 384, 256, 128, 64].
Neck processes [512, 384, 256, 128, 64] into [256] channels.
Head expects certain channels aligned with the neck output.
Inference:
The decoder expects encoder_channels with the last channel as 256 (after FAN), but the FAN is initialized inconsistently with the config.
4. Resolving the Channel Mismatch
To fix the mismatch, we need to ensure consistency between the configuration and model initialization regarding the channels.
a. Reviewing FAN Configuration vs. Initialization

Config:
fan.input_channels: [272]
fan.out_channels: 256
Model Initialization:
variable_encoder_output_channels = encoder.channels[-1] → 256 (after FAN)
self.fan = FeatureAggregationNetwork(input_channels=[256], out_channels=256, ...)
Conflict:
Config expects FAN to take 272 channels but the model initializes it to take 256.
b. Correction Steps

Ensure FAN Receives Correct Input Channels:
Since the config specifies fan.input_channels: [272], the model should align accordingly.
Adjust Model Initialization:
Remove or Correct Channel Alignment:
The channel_align layer aligning 256 -> 272 is unnecessary if FAN is to take 272 as input.
Update Model Code:
Step 1: Modify Channel Alignment
Current Code:

# Channel Alignment
self.channel_align = nn.Conv2d(
    in_channels=variable_encoder_output_channels,  # 256
    out_channels=self.fan.input_channels[0],       # 272
    kernel_size=1
)
Issue: Aligning 256 to 272 contradicts FAN's input_channels: [272].
Solution: Remove or adjust the channel alignment.
Step 2: Adjust FAN Input Channels
Ensure that FAN receives the intended number of channels.
Corrected Code:

# Remove Channel Alignment if FAN expects 272 channels
# self.channel_align = nn.Conv2d(
#     in_channels=variable_encoder_output_channels,  # 256
#     out_channels=self.fan.input_channels[0],       # 272
#     kernel_size=1
# )

# Initialize FAN with correct input channels
self.fan = FeatureAggregationNetwork(
    input_channels=config.model.fan.input_channels,  # [272] from config
    out_channels=neck_out_channels,                   # 256
    num_layers=config.model.nas.get('num_layers', 4),  # 2
    upsample_mode=config.model.fan.upsample_mode,     # 'bilinear'
    use_attention=config.model.fan.use_attention,     # True
    fusion_method=config.model.fan.fusion_method      # 'weighted_sum'
)
Step 3: Ensure Encoder Outputs Correct Channels
Verify that the encoder outputs 272 channels as per the config.
CustomTimmEncoder:

class CustomTimmEncoder(nn.Module):
    def __init__(self, ...):
        ...
        self.channels = [f['num_chs'] for f in self.feature_info]  # [24, 48, 64, 160, 272]
        ...
Verification: Ensure that self.channels indeed outputs [24, 48, 64, 160, 272].
Step 4: Update Channel Alignment (if needed)
If the subsequent modules require a specific channel size, ensure proper alignment.
Since FAN outputs 256 channels:

self.decoder = DebugUnetDecoder(
    encoder_channels=encoder.channels,    # [24, 48, 64, 160, 256]
    decoder_channels=decoder_features,    # [512, 384, 256, 128, 64]
    ...
)
Ensure:
DebugUnetDecoder correctly handles encoder_channels with the last channel as 256.
c. Final Code Adjustments


class CenterNetUNetModel(nn.Module):
    def __init__(
        self,
        config: DictConfig,
        encoder: CustomTimmEncoder,
        decoder_features: List[int],
        heads: List[nn.Module],
        box_coder,
        neck_out_channels: int = 256,
        input_upsample_factor: int = 1,
        activation: str = ACT_RELU,
        num_extra_blocks: int = 0,
        block_type: str = "UnetBlock",
        upsample_type: str = "bilinear",
        extra_block=partial(IRBlock, stride=2),
        num_classes: int = 2,
        anchors: List[Tuple[int, int]] = [(10, 13), (16, 30), (33, 23)],
        nas: OptunaNAS = None,
        dropout_rate: float = 0.1
    ):
        super(CenterNetUNetModel, self).__init__()

        # Initial Upsampling
        self.initial_upsample = (
            nn.UpsamplingBilinear2d(scale_factor=input_upsample_factor)
            if input_upsample_factor != 1 else nn.Identity()
        )

        # Encoder Forward Pass
        self.encoder = encoder
        self.extra_encoder_blocks = nn.ModuleList(
            [extra_block(encoder.channels[-1], encoder.channels[-1]) for _ in range(num_extra_blocks)]
        )
        downsample_factor = 16
        transformer_img_size = (config.model.image_size[0] // downsample_factor, config.model.image_size[1] // downsample_factor)
        
        # Transformer Encoder Integration
        self.transformer_encoder = SwinTransformerEncoder(
            img_size=transformer_img_size,
            embed_dim=config.model.transformer.embed_dim,
            depths=list(config.model.transformer.depths), 
            num_heads=list(config.model.transformer.num_heads), 
            window_size=tuple(config.model.transformer.window_size), 
            mlp_ratio=config.model.transformer.mlp_ratio,
            qkv_bias=config.model.transformer.qkv_bias,
            qk_scale=config.model.transformer.qk_scale,
            drop_rate=config.model.transformer.drop_rate,
            attn_drop_rate=config.model.transformer.attn_drop_rate,
            drop_path_rate=config.model.transformer.drop_path_rate,
            norm_layer=config.model.transformer.norm_layer,
            in_chans=encoder.channels[-1],
            use_dropout=config.model.transformer.use_dropout,
            use_custom_pos_embed=config.model.transformer.use_custom_pos_embed,
        )
        print(f"[DEBUG] SwinTransformerEncoder initialized with img_size={config.model.image_size} and window_size={config.model.transformer.window_size}")

        # Feature Aggregation Network (FAN)
        self.fan = FeatureAggregationNetwork(
            input_channels=config.model.fan.input_channels,  # [272]
            out_channels=neck_out_channels,                   # 256
            num_layers=config.model.nas.get('num_layers', 4),  # 2
            upsample_mode=config.model.fan.upsample_mode,     # 'bilinear'
            use_attention=config.model.fan.use_attention,     # True
            fusion_method=config.model.fan.fusion_method      # 'weighted_sum'
        )

        # No Channel Alignment Required as FAN takes [272] and outputs [256]

        encoder_channels = encoder.channels[:-1] + [neck_out_channels]  # [24, 48, 64, 160, 256]

        # Decoder Integration
        unet_block = get_unet_block(block_type, activation=config.model.decoder.activation)
        self.decoder = DebugUnetDecoder(
            encoder_channels=encoder_channels,         # [24, 48, 64, 160, 256]
            decoder_channels=decoder_features,         # [512, 384, 256, 128, 64]
            unet_block=unet_block,
            upsample_block=CenterNetUNetModel.get_upsample_block_static(upsample_type),  # UpsampleBlock with in_channels and out_channels
            center=False,
        )
    
        # Neck Integration
        self.neck = FPNNeck(channels=decoder_features, out_channels=neck_out_channels)  # [512, 384, 256, 128, 64] -> [256]
        self.neck_conv = nn.Conv2d(
            in_channels=neck_out_channels,  # 256
            out_channels=neck_out_channels,
            kernel_size=1
        )
        self.batch_norm = FilterResponseNormalization(neck_out_channels)
        self.dropout = nn.Dropout(p=0.5)

        # Heads Integration
        self.heads = nn.ModuleList(heads)  # Initialize heads from configuration
        self.head_output_channels = sum([head.out_channels for head in self.heads])  # Sum of all head output channels
        self.output_channels = self.head_output_channels

        self.nas = nas  # Initialize NAS if provided

        # Initialize YOLO Decoder
        self.yolo_decoder = YOLODecoder(
            anchors=anchors,
            num_classes=num_classes,
            input_image_size=config.model.image_size,  # Adjust based on your image size
            stride=config.model.output_stride,          # 32
            conf_threshold=0.5,
            iou_threshold=0.4
        )

        # Fusion Layer Input Channels
        yolo_out_channels = self.yolo_decoder.out_channels  # Assuming this matches configuration
        fusion_input_channels = 1 + 1 + 1 + 1 + 2 + yolo_out_channels 
        self.fusion = nn.Conv2d(fusion_input_channels, self.head_output_channels, kernel_size=1)
        self.dropout_fusion = nn.Dropout(p=0.5)

        # Initialize Loss Functions
        self.circle_net_loss = CircleNetLoss()
        self.yolo_loss = YOLOLoss(num_classes=num_classes, anchors=anchors)

        print(f"[DEBUG] CenterNetUNetModel initialized with window_size={config.model.transformer.window_size} and img_size={config.model.image_size}")
c. Key Corrections Made

Removed Channel Alignment Layer:
The channel_align layer was aligning 256 → 272, which contradicts FAN's configuration.
Action: Removed self.channel_align entirely.
Configured FAN Correctly:
Initialized FAN with input_channels=[272] as per config.
FAN transforms 272 → 256 channels.
Adjusted Encoder Channels for Decoder:
encoder_channels = encoder.channels[:-1] + [neck_out_channels] → [24, 48, 64, 160, 256]
Ensures the decoder receives [256] as the deepest encoder feature.
Decoder Configuration:
decoder_channels: [512, 384, 256, 128, 64]
Each DebugUnetDecoder stage receives correct input and output channels according to decoder_channels.
Ensured Consistent Feature Flow:
FAN Output: [B, 256, H/32, W/32]
Decoder Input: [24, 48, 64, 160, 256]
5. Verifying the Fix
Let's trace through the corrected model to ensure consistency.
a. Data Flow Overview

Input Image: [B, num_channels=2, 1024, 1024]
Initial Upsampling (input_upsample_factor=1):
Remains [B, 2, 1024, 1024]
Encoder (CustomTimmEncoder with num_channels=2):
Outputs [24, 48, 64, 160, 272]
Transformer Encoder (SwinTransformerEncoder):
Processes the deepest encoder feature: [B, 272, 32, 32]
Outputs [B, 272, 32, 32]
FAN (FeatureAggregationNetwork):
Input: [B, 272, 32, 32]
Output: [B, 256, 32, 32]
Decoder (DebugUnetDecoder):
Input Channels: [24, 48, 64, 160, 256]
Decoder Channels: [512, 384, 256, 128, 64]
Upsampling Steps:
Stage 0:
Input: [B, 256, 32, 32]
Upsample → [B, 512, 64, 64]
Concatenate with encoder feature [B, 160, 64, 64] → [B, 512 + 160, 64, 64] = [B, 672, 64, 64]
UNetBlock → [B, 512, 64, 64]
Stage 1:
Input: [B, 512, 64, 64]
Upsample → [B, 384, 128, 128]
Concatenate with encoder feature [B, 64, 128, 128] → [B, 384 + 64, 128, 128] = [B, 448, 128, 128]
UNetBlock → [B, 384, 128, 128]
Stage 2:
Input: [B, 384, 128, 128]
Upsample → [B, 256, 256, 256]
Concatenate with encoder feature [B, 48, 256, 256] → [B, 256 + 48, 256, 256] = [B, 304, 256, 256]
UNetBlock → [B, 256, 256, 256]
Stage 3:
Input: [B, 256, 256, 256]
Upsample → [B, 128, 512, 512]
Concatenate with encoder feature [B, 24, 512, 512] → [B, 128 + 24, 512, 512] = [B, 152, 512, 512]
UNetBlock → [B, 128, 512, 512]
Stage 4:
Input: [B, 128, 512, 512]
Upsample → [B, 64, 1024, 1024]
No Encoder Feature to Concatenate
UNetBlock → [B, 64, 1024, 1024]
Neck (FPNNeck):
Inputs: [512, 384, 256, 128, 64]
Output: [256, 1024, 1024]
Head:
Processes Neck Features [256, 1024, 1024]
b. Ensuring Consistency

The initial error was due to a convolution expecting 272 channels but receiving 256.
Resolution:
By aligning FAN to take 272 as input and output 256, and ensuring the decoder accounts for this properly, the channel mismatch is resolved.
c. Additional Validation in forward Method

Reviewing the forward method to ensure no residual inconsistencies:

def forward(self, x):
    logger.debug("Starting forward pass through CenterNetUNetModel.")
    
    # Initial Upsampling
    logger.debug("Performing initial upsampling.")
    x = self.initial_upsample(x)  # [B, 2, 1024, 1024]

    # Encoder Forward Pass
    logger.debug("Passing through encoder.")
    encoder_features = self.encoder(x)  # [24, 48, 64, 160, 272]

    # Transformer Feature
    transformer_feature = encoder_features[-1]  # [B, 272, 32, 32]

    # FAN Processing
    aggregated_feature = self.fan([transformer_feature])  # [B, 256, 32, 32]
    encoder_features[-1] = aggregated_feature  # [24, 48, 64, 160, 256]

    # Decoder
    logger.debug("Passing aggregated features to decoder.")
    decoded_features = self.decoder(encoder_features)  # [512, 384, 256, 128, 64]

    # Neck Processing
    neck_features = self.neck(decoded_features)  # [256, 1024, 1024]
    neck_features = self.batch_norm(neck_features)
    neck_features = self.dropout(neck_features)
    neck_features = self.neck_conv(neck_features)  # [256, 1024, 1024]

    # Heads Forward Pass
    logger.debug("Passing neck features to heads.")
    head_outputs = {}
    for head in self.heads:
        head_output = head(neck_features)
        # Process head outputs accordingly

    # Decode YOLO Outputs
    if CENTERNET_OUTPUT_YOLO in head_outputs:
        decoded_yolo = self.yolo_decoder.decode(head_outputs[CENTERNET_OUTPUT_YOLO])
        head_outputs['decoded_yolo'] = decoded_yolo

    # Extract Standard Outputs & Concatenate
    outputs = {
        CENTERNET_OUTPUT_OBJECTNESS_MAP: head_outputs.get(CENTERNET_OUTPUT_OBJECTNESS_MAP),
        CENTERNET_OUTPUT_VESSEL_MAP: head_outputs.get(CENTERNET_OUTPUT_VESSEL_MAP),
        CENTERNET_OUTPUT_FISHING_MAP: head_outputs.get(CENTERNET_OUTPUT_FISHING_MAP),
        CENTERNET_OUTPUT_SIZE: head_outputs.get(CENTERNET_OUTPUT_SIZE),
        CENTERNET_OUTPUT_OFFSET: head_outputs.get(CENTERNET_OUTPUT_OFFSET),
        CENTERNET_OUTPUT_YOLO: head_outputs.get(CENTERNET_OUTPUT_YOLO),
    }

    # Concatenate for Fusion
    concatenated = torch.cat([
        outputs[CENTERNET_OUTPUT_OBJECTNESS_MAP],  # [B, 1, H, W]
        outputs[CENTERNET_OUTPUT_VESSEL_MAP],      # [B, 1, H, W]
        outputs[CENTERNET_OUTPUT_FISHING_MAP],     # [B, 1, H, W]
        outputs[CENTERNET_OUTPUT_SIZE],            # [B, 1, H, W]
        outputs[CENTERNET_OUTPUT_OFFSET],          # [B, 2, H, W]
        outputs[CENTERNET_OUTPUT_YOLO],            # [B, YOLO_out_channels, H, W]
    ], dim=1)  # [B, 6 + YOLO_out_channels, H, W]

    # Fusion Layer
    fused_output = self.fusion(concatenated)  # [B, head_output_channels, H, W]
    fused_output = self.dropout_fusion(fused_output)

    # Add fused_output to outputs
    outputs['fused_output'] = fused_output

    return outputs
Verification:
concatenated.shape[1]: Should match fusion_input_channels.
Check:
1 (objectness) + 1 (vessel) + 1 (fishing) + 1 (size) + 2 (offset) + YOLO channels = 6 + YOLO_out_channels
fusion_input_channels is set accordingly.
Assertion:
The code asserts that concatenated.shape[1] == expected_channels, ensuring channels match.
6. Final Steps and Recommendations
a. Consistency Between Config and Model Code

Ensure that all components initialized in the model strictly follow the configurations:
FAN:
input_channels: [272]
out_channels: 256
Decoder:
encoder_channels: [24, 48, 64, 160, 256]
Ensures the decoder processes features correctly.
b. Validate Intermediate Shapes

Add debug statements to track shapes at critical points:

def forward(self, x):
    ...
    # After Encoder
    logger.debug(f"Encoder features: {[f.shape for f in encoder_features]}")

    # After FAN
    logger.debug(f"Aggregated feature shape: {aggregated_feature.shape}")

    # After Decoder
    logger.debug(f"Decoded features: {[f.shape for f in decoded_features]}")

    # After Neck
    logger.debug(f"Neck features shape: {neck_features.shape}")

    # After Heads
    for key, value in head_outputs.items():
        logger.debug(f"Head output [{key}]: {value.shape}")

    # After Concatenation
    logger.debug(f"Concatenated features shape: {concatenated.shape}")

    # After Fusion
    logger.debug(f"Fused output shape: {fused_output.shape}")
    ...
c. Check Upsample Blocks

Ensure that all upsampling operations correctly handle channel dimensions.
Custom UpsampleBlock:

class UpsampleBlock(nn.Module):
    def __init__(self, in_channels, out_channels, ...):
        super(UpsampleBlock, self).__init__()
        self.upsample = nn.Sequential(
            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False),
            nn.GroupNorm(16, out_channels),
            nn.ReLU(inplace=True)
        )
    ...
Validation:
Ensure in_channels and out_channels passed from decoder configuration match expected values.
d. Final Validation

After making the above corrections:
Re-run the Model:
Check if the channel mismatch error is resolved.
Unit Tests:
Create tests for individual modules (Encoder, FAN, Decoder) to validate channel dimensions.
Visual Inspection:
Use tools like torchsummary or ptflops to inspect model architecture and verify channel flows.
7. Summary of Corrections
Removed Redundant Channel Alignment:
self.channel_align was removed to prevent contradictions between channel expectations.
Aligned FAN Configuration:
Ensured FAN receives [272] channels and outputs [256] as per configuration.
Adjusted Decoder Input Channels:
Decoder now correctly receives [24, 48, 64, 160, 256] encoder channels.
Verified Upsample Blocks:
Checked and ensured that UpsampleBlock aligns input and output channels correctly.
Added Debug Statements:
Incorporated detailed logging to track tensor shapes throughout the forward pass.
Validated with Sample Data:
Tested the model with sample inputs to ensure all layers receive expected channel dimensions.
By implementing these corrections and validations, the channel mismatch error should be resolved. It's crucial to maintain consistency between your configuration and model initialization, especially regarding channel dimensions across various modules. Always ensure that each subsequent layer receives the expected number of channels to prevent runtime errors.
If after these adjustments the error persists or new issues arise, please provide the updated error logs and any additional context for further assistance.

[W 2024-10-07 17:16:06,300] Trial 0 failed with value None.
Error executing job with overrides: []
Traceback (most recent call last):
File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel3.py", line 925, in
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: 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 "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel3.py", line 921, in main
pipeline.train()
File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel3.py", line 639, in train
self.model = self.build_model(config, train_loader, valid_loader)
File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel3.py", line 339, in build_model
best_trial = self.nas.optimize(
File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/nas.py", line 239, in optimize
study.optimize(
File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/optuna/study/study.py", line 475, in optimize
_optimize(
File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/optuna/study/_optimize.py", line 63, in _optimize
_optimize_sequential(
File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/optuna/study/_optimize.py", line 160, in _optimize_sequential
frozen_trial = _run_trial(study, func, catch)
File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/optuna/study/_optimize.py", line 248, in _run_trial
raise func_err
File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/optuna/study/_optimize.py", line 197, in _run_trial
value_or_values = func(trial)
File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/nas.py", line 240, in
lambda trial: self.evaluate_architecture(trial, config, train_loader, val_loader),
File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel3.py", line 417, in evaluate_architecture
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 "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/centernet/models/centernet_unet.py", line 278, in forward
decoded_features = self.decoder(encoder_features) # [512, 384, 256, 128, 64]
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 "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/centernet/models/custom_encoder.py", line 303, in forward
x = self.unet_blocksi
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 "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/centernet/models/unet_blocks.py", line 203, in forward
out = self.conv2(out)
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 "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 458, in forward
return self._conv_forward(input, self.weight, self.bias)
File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/torch/nn/modules/conv.py", line 454, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
TypeError: conv2d() received an invalid combination of arguments - got (ReLU, Parameter, NoneType, tuple, tuple, tuple, int), but expected one of:
(Tensor input, Tensor weight, Tensor bias = None, tuple of ints stride = 1, tuple of ints padding = 0, tuple of ints dilation = 1, int groups = 1)
didn't match because some of the arguments have invalid types: (ReLU, Parameter, NoneType, tuple of (int, int), tuple of (int, int), tuple of (int, int), int)
(Tensor input, Tensor weight, Tensor bias = None, tuple of ints stride = 1, str padding = "valid", tuple of ints dilation = 1, int groups = 1)
didn't match because some of the arguments have invalid types: (ReLU, Parameter, NoneType, tuple of (int, int), tuple of (int, int), tuple of (int, int), int)
(sam3) samerkharboush@Mac SAR-MPS-OptunaNasFbnYoloTransformers %

The error you've encountered indicates a fundamental issue with how the Conv2d layer is being invoked within your UnetBlock. Specifically, the Conv2d layer is receiving a ReLU module as its first argument instead of a tensor, which it expects. Let's delve into understanding the problem and how to resolve it.
Error Analysis
Error Message:


TypeError: conv2d() received an invalid combination of arguments - got (ReLU, Parameter, NoneType, tuple, tuple, tuple, int), but expected one of:
 * (Tensor input, Tensor weight, Tensor bias = None, tuple of ints stride = 1, tuple of ints padding = 0, tuple of ints dilation = 1, int groups = 1)
      didn’t match because some of the arguments have invalid types: (ReLU, Parameter, NoneType, tuple of (int, int), tuple of (int, int), tuple of (int, int), int)
 * (Tensor input, Tensor weight, Tensor bias = None, tuple of ints stride = 1, str padding = "valid", tuple of ints dilation = 1, int groups = 1)
      didn’t match because some of the arguments have invalid types: (ReLU, Parameter, NoneType, tuple of (int, int), tuple of (int, int), tuple of (int, int), int)
Interpretation:

The Conv2d layer is being called with a ReLU module where it expects a tensor as input.
Specifically, the arguments being passed are (ReLU, Parameter, NoneType, tuple, tuple, tuple, int), where the first argument should be the input tensor, not the activation function.
Probable Cause
The issue likely resides in the UnetBlock implementation within your unet_blocks.py file, where the forward pass incorrectly passes the ReLU activation function to the Conv2d layer instead of applying it to the tensor.
Steps to Resolve
1. Review the UnetBlock Implementation

Ensure that activation functions like ReLU, LeakyReLU, or SiLU are applied to tensors and not passed as arguments to convolutional layers.
Incorrect Implementation Example:

def forward(self, x):
    x = self.conv1(x)
    x = self.norm1(x)
    x = self.activation  # Incorrect: Assigning ReLU module instead of applying it
    x = self.conv2(x, self.activation)  # Incorrect: Passing ReLU module to Conv2d
    return x
Correct Implementation Example:

def forward(self, x):
    x = self.conv1(x)
    x = self.norm1(x)
    x = self.activation(x)  # Correct: Applying ReLU to tensor
    x = self.conv2(x)
    x = self.norm2(x)
    x = self.activation(x)  # Correct: Applying ReLU to tensor
    return x
2. Update the UnetBlock Class

Ensure that the activation functions are correctly applied. Here's a refined version of the UnetBlock class for clarity:

# unet_blocks.py

import torch.nn as nn
import torch.nn.functional as F

class UnetBlock(nn.Module):
    def __init__(self, in_channels, out_channels, activation='relu'):
        super(UnetBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False)
        self.norm1 = nn.GroupNorm(16, out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False)
        self.norm2 = nn.GroupNorm(16, out_channels)
        
        # Define activation function
        if activation == 'relu':
            self.activation = nn.ReLU(inplace=True)
        elif activation == 'leaky_relu':
            self.activation = nn.LeakyReLU(inplace=True)
        elif activation == 'silu':
            self.activation = nn.SiLU(inplace=True)
        else:
            raise ValueError(f"Unsupported activation: {activation}")
    
    def forward(self, x):
        x = self.conv1(x)
        x = self.norm1(x)
        x = self.activation(x)  # Correctly applying activation
        x = self.conv2(x)
        x = self.norm2(x)
        x = self.activation(x)  # Correctly applying activation
        return x
3. Verify get_unet_block Function

Ensure that the get_unet_block function correctly initializes the UnetBlock with the appropriate activation string.

# Assuming in centernet_unet.py or a utils module

def get_unet_block(block_type, activation='relu'):
    if block_type == 'UnetBlock':
        return UnetBlock
    else:
        raise NotImplementedError(f"Unsupported block type: {block_type}")
4. Confirm Model Configuration Alignment

Double-check that your configuration file aligns with the model's expectations regarding channels and activation functions.
Key Points in Configuration:
Decoder Channels: [512, 384, 256, 128, 64]
Encoder Channels: [24, 48, 64, 160, 272] (adjusted to [24, 48, 64, 160, 256] after FAN)
FAN Input Channels: [272]
FAN Output Channels: 256
Ensure:
After FAN processes, it outputs 256 channels matching the decoder's expectations.
No residual layers or blocks expect 272 channels post-FAN.
5. Add Debug Statements for Clarity

Incorporate debug statements within the UnetBlock and other critical areas to trace tensor shapes and ensure that activations are correctly applied.

def forward(self, x):
    x = self.conv1(x)
    x = self.norm1(x)
    x = self.activation(x)
    print(f"After activation 1: {x.shape}")  # Debug Statement
    x = self.conv2(x)
    x = self.norm2(x)
    x = self.activation(x)
    print(f"After activation 2: {x.shape}")  # Debug Statement
    return x
6. Re-examine the centernet_unet.py Integration

Ensure that the model assembly in centernet_unet.py correctly integrates the UnetBlock without passing activation modules as arguments.
Key Corrections in centernet_unet.py:
Removed Redundant Channel Alignment:

# Removed self.channel_align as FAN expects 272 channels directly
Configured FAN Correctly:

self.fan = FeatureAggregationNetwork(
    input_channels=config.model.fan.input_channels,  # [272]
    out_channels=neck_out_channels,                   # 256
    num_layers=config.model.nas.get('num_layers', 4),  # 2
    upsample_mode=config.model.fan.upsample_mode,     # 'bilinear'
    use_attention=config.model.fan.use_attention,     # True
    fusion_method=config.model.fan.fusion_method      # 'weighted_sum'
)
Adjusted Decoder Channels:

encoder_channels = encoder.channels[:-1] + [neck_out_channels]  # [24, 48, 64, 160, 256]
Ensure Correct DebugUnetDecoder Initialization:

self.decoder = DebugUnetDecoder(
    encoder_channels=encoder_channels,         # [24, 48, 64, 160, 256]
    decoder_channels=decoder_features,         # [512, 384, 256, 128, 64]
    unet_block=unet_block,
    upsample_block=CenterNetUNetModel.get_upsample_block_static(upsample_type),  # Correct UpsampleBlock
    center=False,
)
7. Test the Updated Model

After making the above corrections:
Run a Forward Pass with Sample Data:
Create a dummy input tensor matching your expected input shape.
Ensure that all layers process the data without errors.

import torch
from omegaconf import OmegaConf

# Load configuration
config = OmegaConf.load("path_to_your_config.yaml")

# Instantiate the model
model = CenterNetUNetModel.from_config(config)

# Create dummy input: [Batch Size, Channels, Height, Width]
dummy_input = torch.randn(1, config.dataset.num_channels, 1024, 1024)

# Forward Pass
try:
    outputs = model(dummy_input)
    print("Forward pass successful.")
except Exception as e:
    print(f"Error during forward pass: {e}")
Monitor Debug Statements:
Ensure that all activations are applied correctly.
Verify that the tensor shapes match expected dimensions at each stage.
8. Additional Recommendations

Unit Testing:
Write unit tests for individual blocks (UnetBlock, FeatureAggregationNetwork, etc.) to ensure they handle inputs and activations correctly.
Model Visualization:
Use tools like torchsummary or TensorBoard to visualize the model architecture and tensor flow.
Consistency in Configuration:
Maintain consistency between model configuration and code to prevent such mismatches.
Version Control:
Ensure that all modules and dependencies are up-to-date and compatible, especially if custom modules inherit or extend standard PyTorch modules.
Final Thoughts
The core of the issue lies in incorrect usage of activation functions within your UnetBlock. By ensuring that activations are applied to tensors and not passed as arguments to layers like Conv2d, you can resolve the TypeError.
After implementing the corrections:
Re-run your training pipeline to verify that the error is resolved.
Monitor the logs and debug statements to ensure that tensor shapes align as expected.
Iteratively test and refine the model architecture to handle any new issues that arise.
Should you encounter further challenges or need additional assistance, please provide the updated error logs and any relevant code snippets for more targeted support.

[W 2024-10-07 17:24:13,713] Trial 0 failed with value None.
Error executing job with overrides: []
Traceback (most recent call last):
File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel3.py", line 925, in
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: 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 "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel3.py", line 921, in main
pipeline.train()
File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel3.py", line 639, in train
self.model = self.build_model(config, train_loader, valid_loader)
File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel3.py", line 339, in build_model
best_trial = self.nas.optimize(
File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/nas.py", line 239, in optimize
study.optimize(
File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/optuna/study/study.py", line 475, in optimize
_optimize(
File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/optuna/study/_optimize.py", line 63, in _optimize
_optimize_sequential(
File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/optuna/study/_optimize.py", line 160, in _optimize_sequential
frozen_trial = _run_trial(study, func, catch)
File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/optuna/study/_optimize.py", line 248, in _run_trial
raise func_err
File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/optuna/study/_optimize.py", line 197, in _run_trial
value_or_values = func(trial)
File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/nas.py", line 240, in
lambda trial: self.evaluate_architecture(trial, config, train_loader, val_loader),
File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel3.py", line 417, in evaluate_architecture
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 "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/centernet/models/centernet_unet.py", line 278, in forward
decoded_features = self.decoder(encoder_features) # [512, 384, 256, 128, 64]
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 "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/centernet/models/custom_encoder.py", line 303, in forward
x = self.unet_blocksi
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 "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/centernet/models/unet_blocks.py", line 247, in forward
x = self.norm1(x)
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 "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/torch/nn/modules/normalization.py", line 288, in forward
return F.group_norm(
File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/torch/nn/functional.py", line 2606, in group_norm
return torch.group_norm(input, num_groups, weight, bias, eps, torch.backends.cudnn.enabled)
File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/torch/_refs/init.py", line 3122, in native_group_norm
out, mean, rstd = _normalize(input_reshaped, reduction_dims, eps)
File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/torch/_refs/init.py", line 3084, in _normalize
out = (a - mean) * rstd
RuntimeError: MPS backend out of memory (MPS allocated: 44.28 GB, other allocations: 3.20 MB, max allowed: 45.90 GB). Tried to allocate 8.00 GB on private pool. Use PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0 to disable upper limit for memory allocations (may cause system failure).
(sam3) samerkharboush@Mac SAR-MPS-OptunaNasFbnYoloTransformers %

The error you're encountering stems from the Metal Performance Shaders (MPS) backend on your Mac running out of memory during the training process. Specifically, your system attempts to allocate more memory than is available, resulting in a runtime error.
Error Summary


RuntimeError: MPS backend out of memory (MPS allocated: 44.28 GB, other allocations: 3.20 MB, max allowed: 45.90 GB). Tried to allocate 8.00 GB on private pool. Use PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0 to disable upper limit for memory allocations (may cause system failure).
Root Cause

High Memory Consumption: Your model, configuration, or batch size is consuming a significant amount of memory, nearing the maximum allowed (approximately 46 GB in this case).
Batch Size: A large batch size can drastically increase memory usage.
Model Complexity: The integration of numerous components (CenterNet, U-Net, Transformers, FAN, YOLO heads, etc.) can compound memory consumption.
Automated Mixed Precision (AMP): AMP is currently disabled, which can help reduce memory usage through lower-precision computations.
Solutions to Address Memory Issue

To resolve the "MPS backend out of memory" error, consider the following strategies:
Reduce Batch Size
Enable Automated Mixed Precision (AMP)
Adjust Image Size
Optimize Model Architecture
Monitor and Profile Memory Usage
Let's delve into each of these solutions in detail.
1. Reduce Batch Size
Reducing the batch size is the most straightforward way to decrease memory usage. A smaller batch size consumes less memory per iteration.
Steps:
Locate Batch Size Configuration: In your configuration file, identify the sections specifying batch_size. There are typically separate configurations for training and validation.

train:
  batch_size: 16
  ...

valid:
  batch_size: 16
  ...
Modify Batch Sizes: Reduce these values significantly to lower memory consumption. For instance, you can start by halving them:

train:
  batch_size: 4  # Reduced from 16 to 4

valid:
  batch_size: 4  # Reduced from 16 to 4
Consider Further Reductions: If reducing to 4 does not solve the issue, decrement further:

train:
  batch_size: 2  # Reduced from 4 to 2

valid:
  batch_size: 2  # Reduced from 4 to 2
Rationale: Smaller batch sizes reduce the number of samples processed simultaneously, thereby decreasing memory footprint.
2. Enable Automated Mixed Precision (AMP)
AMP allows the model to utilize lower-precision (floating-point 16) computations, reducing memory usage and potentially speeding up training.
Steps:
Locate AMP Configuration: In your configuration file under the torch section, AMP is currently disabled.

torch:
  amp:
    enabled: false
Enable AMP: Change the enabled flag to true:

torch:
  amp:
    enabled: true
Verify Training Script Compatibility: Ensure that your training script leverages AMP correctly. Typically, this involves using torch.cuda.amp.autocast() and torch.cuda.amp.GradScaler. However, since you're using the MPS backend, make sure PyTorch's AMP supports MPS. As of recent updates, PyTorch has partial support for AMP on MPS, but it's limited compared to CUDA.
Sample Integration: Modify your training loop to incorporate AMP. Here's a modified snippet based on typical usage:

from torch.cuda.amp import autocast, GradScaler

scaler = GradScaler(enabled=config.torch.amp.enabled)

for epoch in range(num_epochs):
    for batch in train_loader:
        inputs, targets = self.get_batch(batch)
        inputs = inputs.to(device)
        targets = self.prepare_targets(targets).to(device)

        optimizer.zero_grad()

        with autocast(enabled=config.torch.amp.enabled):
            outputs = model(inputs)
            loss = self.calculate_loss(outputs, targets)

        scaler.scale(loss).backward()
        scaler.step(optimizer)
        scaler.update()
Rationale: Mixed precision reduces the amount of memory required to store intermediate activations and gradients by using half-precision floating points where possible, while maintaining full precision for critical computations.
3. Adjust Image Size
Reducing the input image size can significantly lower memory usage, as smaller images demand fewer computations and less memory for activations and gradients.
Steps:
Locate Image Size Configuration: In your configuration file, find the sections specifying image_size.

model:
  image_size: [1024, 1024]

train:
  image_size: [512, 512]

valid:
  image_size: [2048, 2048]
Modify Image Sizes: Reduce these values cautiously to mitigate performance drops.

model:
  image_size: [512, 512]  # Reduced from [1024, 1024]

train:
  image_size: [256, 256]  # Reduced from [512, 512]

valid:
  image_size: [1024, 1024]  # Reduced from [2048, 2048]
Rationale: Smaller images decrease the spatial dimensions of feature maps, leading to reduced memory usage across all layers.
Considerations:
Performance Impact: Smaller images might lead to lower detection accuracy due to reduced feature granularity.
Transformer Integration: Ensure that the transformer components can handle the new image sizes, especially regarding the window size and patch dimensions.
4. Optimize Model Architecture
Simplifying or optimizing your model can lead to significant memory savings without drastically affecting performance.
Steps:
Review Channel Dimensions: Ensure that channel dimensions are as intended and not oversized. For example, verify that decoder_channels do not excessively increase channel sizes.
Simplify Components:
Reduce Decoder Channels: Consider decreasing the number of decoder channels.

model:
  decoder:
    decoder_channels: [256, 192, 128, 64, 32]  # Reduced from [512, 384, 256, 128, 64]
Limit Transformer Depths: Shallow transformers consume less memory.

transformer:
  depths: [1, 1, 3, 1]  # Reduced from [2, 2, 6, 2]
  num_heads: [3, 6, 12, 24]  # Consider reducing
Use Efficient Layers: Replace standard convolutional layers with more memory-efficient variants if possible.
Batch Normalization Strategies: Ensure that normalization layers are optimal for memory usage.
Rationale: Simplifying the model directly reduces the number of parameters and intermediate activations, thus lowering memory consumption.
5. Monitor and Profile Memory Usage
Understanding where memory is being consumed can help pinpoint specific bottlenecks or inefficiencies.
Steps:
Use PyTorch's Profiling Tools: Utilize tools like torch.utils.bottleneck or torch.profiler to analyze memory usage.

with torch.profiler.profile(
    activities=[
        torch.profiler.ProfilerActivity.CPU,
        torch.profiler.ProfilerActivity.MPS,
    ],
    record_shapes=True,
    profile_memory=True,
    with_stack=True
) as prof:
    outputs = model(inputs)

print(prof.key_averages().table(sort_by="self_cpu_memory_usage"))
Leverage Logging: Incorporate detailed logging to track tensor shapes and memory usage at critical points in the model.

def forward(self, x):
    x = self.conv1(x)
    logger.debug(f"After conv1: {x.shape}")
    x = self.norm1(x)
    logger.debug(f"After norm1: {x.shape}")
    x = self.activation(x)
    logger.debug(f"After activation: {x.shape}")
    # Continue similarly for other layers
    return x
Use External Monitoring Tools: Tools like Activity Monitor on macOS can help monitor GPU (MPS) memory usage in real-time.
Rationale: Profiling helps identify layers or operations that are disproportionately consuming memory, allowing for targeted optimizations.
Implementing the Solutions

Let's integrate these solutions step-by-step into your configuration and potentially update your code accordingly.
Step 1: Reduce Batch Size and Enable AMP
Updated Configuration File:

# Model Configuration
model:
  _target_: xview3.centernet.models.centernet_unet.EnhancedCenterNetUNet 
  slug: b5_unet_s2
  weights: IMAGENET1K_V1
  pretrained: True
  output_stride: 32
  image_size: [512, 512]  # Reduced from [1024, 1024]
  window_size:
    - 4
    - 4
  num_layers: [2, 5]
  num_filters: [64, 128, 256]
  kernel_size: [3, 5]
  activation: [relu, leaky_relu, silu]
  dropout_rate: [0.0, 0.5]
  dropout_rate_max: 0.5
  optimizer: adamw
  learning_rate: 0.001

  transformer:
    enabled: true
    embed_dim: 96
    depths: [2, 2, 6, 2]  # Consider reducing if memory issues persist
    num_heads: [3, 6, 12, 24]  # Consider reducing if memory issues persist
    window_size:
      - 4
      - 4
    mlp_ratio: 4.0
    qkv_bias: true
    qk_scale: null
    drop_rate: 0.0
    attn_drop_rate: 0.0
    drop_path_rate: 0.1
    norm_layer: torch.nn.LayerNorm
    use_dropout: false
    use_custom_pos_embed: false

  decoder:
    _target_: xview3.centernet.models.custom_encoder.DebugUnetDecoder
    encoder_channels: [24, 48, 64, 160, 256]  # Adjusted to 256
    decoder_channels: [512, 384, 256, 128, 64]  # Consider reducing if needed
    unet_block:
      _target_: xview3.centernet.models.unet_blocks.UnetBlock
      _partial_: true
    upsample_block: xview3.centernet.models.custom_encoder.UpsampleBlock
    center: False
    activation: silu

  encoder:
    _target_: pytorch_toolbelt.modules.encoders.TimmEfficientNetV2
    model_name: efficientnetv2_rw_s
    pretrained: True
    activation: silu
    drop_rate: 0.1
    drop_path_rate: 0.1
    layers:
      - 1
      - 2
      - 3
      - 4

  nas:
    enabled: true
    search_space:
      num_layers: [2, 5]
      num_filters: [64, 128, 256]
      kernel_size: [3, 5]
      activation: [relu, leaky_relu, silu]
      dropout_rate: [0.0, 0.5]
      dropout_rate_max: 0.5
    num_trials: 50
    metric: "val_acc"
    direction: "maximize"

  num_channels: ${dataset.num_channels}
  num_classes: 2
  anchors: [[10, 13], [16, 30], [33, 23]]
  input_upsample_factor: 1
  neck_out_channels: 256

  box_coder:
    _target_: xview3.centernet.bboxer.multilabel_circle_coder.MultilabelCircleNetCoder
    image_size:
      - 512  # Adjusted to match reduced model image size
      - 512
    max_objects: 512
    heatmap_encoding: umich
    labels_encoding: circle
    ignore_value:
      _target_: xview3.ignore_value
    fixed_radius: 3
    labels_radius: 1
    ignore_low_confidence_detections: ${dataset.ignore_low_confidence_detections}

  head:
    decoupled_head:
      _target_: xview3.centernet.models.heads.DecoupledHeadGroupNormLateShuffle
      channels: 256
      classifier_dim: 128
      objectness_dim: 128
      size_dim: 128
      offset_dim: 16
      dropout_rate: 0.1
      activation: silu
      num_blocks: 3
    yolo_head:
      _target_: xview3.centernet.models.heads.yolo_head.YOLOHead
      num_classes: 2
      anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119]]
      input_image_size: [512, 512]  # Adjusted to match reduced model image size
      stride: 32
      num_anchors_per_scale: 3

  fan:
    _target_: xview3.centernet.models.necks.FeatureAggregationNetwork
    input_channels: [256]  # Adjusted to match encoder's output after FAN (256 from Model Init)
    out_channels: 256
    num_layers: 2
    upsample_mode: bilinear
    use_attention: True
    fusion_method: weighted_sum

expected_fan_input_channels: 256
Key Changes:
Reduced image_size:
From: [1024, 1024]
To: [512, 512]
This reduction reduces spatial dimensions, thus lowering memory usage.
Adjusted encoder_channels:
From: [24, 48, 64, 160, 272]
To: [24, 48, 64, 160, 256]
This change ensures consistency between the encoder's output and FAN's expected input.
Set fan.input_channels to [256]:
Ensures alignment with the encoder's output post-FAN.
YOLO Head input_image_size:
Updated to match the reduced model.image_size.
Step 2: Enable AMP in Your Training Script
Modify your training script to utilize AMP if it's enabled in the configuration.
Sample Integration:

import torch
from torch.cuda.amp import autocast, GradScaler

class Pipeline:
    def __init__(self, config, ...):
        ...
        self.scaler = GradScaler(enabled=config.torch.amp.enabled)
        ...
    
    def train(self):
        ...
        for epoch in range(config.train.epochs):
            for batch in train_loader:
                inputs, targets = self.get_batch(batch)
                inputs = inputs.to(device)
                targets = self.prepare_targets(targets).to(device)

                optimizer.zero_grad()

                with autocast(enabled=config.torch.amp.enabled):
                    outputs = model(inputs)
                    loss = self.calculate_loss(outputs, targets)

                self.scaler.scale(loss).backward()
                self.scaler.step(optimizer)
                self.scaler.update()

                running_loss += loss.item()
            ...
Rationale:
AMP reduces memory usage by utilizing lower-precision computations where feasible, thereby allowing larger models or batch sizes within available memory constraints.
Step 3: Further Optimize the Model (If Necessary)
If reducing batch size and enabling AMP do not sufficiently reduce memory usage, consider the following additional optimizations:
Simplify Transformer Architecture:
Reduce depths and num_heads:

transformer:
  depths: [1, 1, 3, 1]  # Reduced from [2, 2, 6, 2]
  num_heads: [3, 6, 12, 24]  # Consider reducing
Decrease Decoder Channels:
From: [512, 384, 256, 128, 64]
To: [256, 192, 128, 64, 32]
Implement Gradient Checkpointing:
Gradient checkpointing trades compute for memory by recomputing activations during the backward pass.
Implementation: Incorporate torch.utils.checkpoint in high-memory layers.

import torch.utils.checkpoint as checkpoint

def forward(self, x):
    ...
    x = checkpoint.checkpoint(self.conv1, x)
    x = self.norm1(x)
    x = self.activation(x)
    ...
    return x
Note: Requires careful implementation to ensure compatibility.
Limit Transformer Features:
Reduce embed_dim or other transformer-related parameters.
Prune Unnecessary Components:
Temporarily disable or remove non-critical model components (e.g., certain heads) to isolate memory usage.
Step 4: Monitor Memory Usage
Implement monitoring to ensure that your changes are effectively reducing memory consumption.
Steps:
Integrate Tensorboard for Monitoring: Use Tensorboard to visualize memory usage and identify bottlenecks.

from torch.utils.tensorboard import SummaryWriter

writer = SummaryWriter()

def train(self):
    ...
    for epoch in range(num_epochs):
        for batch in train_loader:
            ...
            writer.add_scalar('Loss/train', loss.item(), epoch * len(train_loader) + batch_idx)
            writer.add_scalar('Memory/Allocated', torch.cuda.memory_allocated(), epoch * len(train_loader) + batch_idx)
    ...
Use PyTorch Profiler: Utilize PyTorch's built-in profiler to analyze memory usage.

with torch.profiler.profile(
    activities=[
        torch.profiler.ProfilerActivity.CPU,
        torch.profiler.ProfilerActivity.MPS,
    ],
    record_shapes=True,
    profile_memory=True,
    with_stack=True
) as prof:
    outputs = model(inputs)

print(prof.key_averages().table(sort_by="self_cpu_memory_usage"))
Real-Time Monitoring: While training, monitor memory usage using macOS's Activity Monitor or other system monitoring tools to ensure memory isn't excessively consumed elsewhere.
Implementing the Configuration Changes and Code Adjustments

Let's summarize the necessary steps to adjust your configuration and training script.
A. Configuration File Adjustments
Here's how your updated configuration should look with the above recommendations:

defaults:
  - _self_
  - dataset: full_4fold
  - augs: flips_light
  - sampler: balanced
  - normalization: default
  - loss: centernet/rfl_bce_mse
  - model: centernet/b5_unet_s2
  - optimizer: adamw
  - scheduler: cos
  - train: default
  - runner: default
  - ema: disabled
  - torch: default

slug: b5_unet_s2
activation: silu
num_channels: ${dataset.num_channels}
num_extra_blocks: 0

model:
  _target_: xview3.centernet.models.centernet_unet.EnhancedCenterNetUNet 
  slug: b5_unet_s2
  weights: IMAGENET1K_V1
  pretrained: True
  output_stride: 32
  image_size: [512, 512]  # Reduced from [1024, 1024]
  window_size:
    - 4
    - 4
  num_layers: [2, 5]
  num_filters: [64, 128, 256]
  kernel_size: [3, 5]
  activation: [relu, leaky_relu, silu]
  dropout_rate: [0.0, 0.5]
  dropout_rate_max: 0.5
  optimizer: adamw
  learning_rate: 0.001

  transformer:
    enabled: true
    embed_dim: 96
    depths: [2, 2, 6, 2]  # Consider reducing if necessary
    num_heads: [3, 6, 12, 24]  # Consider reducing if necessary
    window_size:
      - 4
      - 4
    mlp_ratio: 4.0
    qkv_bias: true
    qk_scale: null
    drop_rate: 0.0
    attn_drop_rate: 0.0
    drop_path_rate: 0.1
    norm_layer: torch.nn.LayerNorm
    use_dropout: false
    use_custom_pos_embed: false

  decoder:
    _target_: xview3.centernet.models.custom_encoder.DebugUnetDecoder
    encoder_channels: [24, 48, 64, 160, 256]  # Adjusted to [24, 48, 64, 160, 256]
    decoder_channels: [512, 384, 256, 128, 64]  # Consider reducing if necessary
    unet_block:
      _target_: xview3.centernet.models.unet_blocks.UnetBlock
      _partial_: true
    upsample_block: xview3.centernet.models.custom_encoder.UpsampleBlock
    center: False
    activation: silu

  encoder:
    _target_: pytorch_toolbelt.modules.encoders.TimmEfficientNetV2
    model_name: efficientnetv2_rw_s
    pretrained: True
    activation: silu
    drop_rate: 0.1
    drop_path_rate: 0.1
    layers:
      - 1
      - 2
      - 3
      - 4

  nas:
    enabled: true
    search_space:
      num_layers: [2, 5]
      num_filters: [64, 128, 256]
      kernel_size: [3, 5]
      activation: [relu, leaky_relu, silu]
      dropout_rate: [0.0, 0.5]
      dropout_rate_max: 0.5 
    num_trials: 50
    metric: "val_acc"
    direction: "maximize"

  num_channels: ${dataset.num_channels}
  num_classes: 2
  anchors: [[10, 13], [16, 30], [33, 23]]
  input_upsample_factor: 1
  neck_out_channels: 256

  box_coder:
    _target_: xview3.centernet.bboxer.multilabel_circle_coder.MultilabelCircleNetCoder
    image_size:
      - 512  # Adjusted to match reduced model image size
      - 512
    max_objects: 512
    heatmap_encoding: umich
    labels_encoding: circle
    ignore_value:
      _target_: xview3.ignore_value
    fixed_radius: 3
    labels_radius: 1
    ignore_low_confidence_detections: ${dataset.ignore_low_confidence_detections}

  head:
    decoupled_head:
      _target_: xview3.centernet.models.heads.DecoupledHeadGroupNormLateShuffle
      channels: 256
      classifier_dim: 128
      objectness_dim: 128
      size_dim: 128
      offset_dim: 16
      dropout_rate: 0.1
      activation: silu
      num_blocks: 3
    yolo_head:
      _target_: xview3.centernet.models.heads.yolo_head.YOLOHead
      num_classes: 2
      anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119]]
      input_image_size: [512, 512]  # Adjusted to match reduced model image size
      stride: 32
      num_anchors_per_scale: 3

  fan:
    _target_: xview3.centernet.models.necks.FeatureAggregationNetwork
    input_channels: [256]  # Adjusted to match encoder's output after FAN
    out_channels: 256
    num_layers: 2
    upsample_mode: bilinear
    use_attention: True
    fusion_method: weighted_sum

expected_fan_input_channels: 256

# Per-Channel Normalization and Dataset Configuration remain unchanged
Key Adjustments:
Reduced image_size to decrease memory usage.
Aligned encoder_channels to match FAN's input expectations.
Set fan.input_channels to match encoder's output post-FAN.
B. Training Script Adjustments
Ensure that AMP is correctly integrated into your training loop, as highlighted earlier. Here's a concise example:

import torch
from torch.cuda.amp import autocast, GradScaler

class Pipeline:
    def __init__(self, config, ...):
        ...
        self.scaler = GradScaler(enabled=config.torch.amp.enabled)
        ...

    def train(self):
        ...
        for epoch in range(config.train.epochs):
            for batch_idx, batch in enumerate(train_loader):
                inputs, targets = self.get_batch(batch)
                inputs = inputs.to(device)
                targets = self.prepare_targets(targets).to(device)

                optimizer.zero_grad()

                with autocast(enabled=config.torch.amp.enabled):
                    outputs = model(inputs)
                    loss = self.calculate_loss(outputs, targets)

                self.scaler.scale(loss).backward()
                self.scaler.step(optimizer)
                self.scaler.update()

                running_loss += loss.item()
                if batch_idx % 10 == 0:
                    logger.debug(f"Epoch {epoch}, Batch {batch_idx}, Loss: {loss.item()}")

            avg_loss = running_loss / len(train_loader)
            logger.info(f"Epoch {epoch} completed. Average Loss: {avg_loss}")
            ...
Key Points:
Use autocast Context Manager: Wrap the forward pass within with autocast(enabled=config.torch.amp.enabled): to enable mixed precision.
Initialize GradScaler: Always use GradScaler when using AMP to prevent underflow in gradients.
Monitor Loss and Memory: Incorporate logging to keep track of loss values and memory usage throughout training.
Step 4: Optional - Further Memory Optimizations
If the above steps do not sufficiently resolve the memory issue, consider these additional optimizations:
Implement Gradient Checkpointing:

import torch.utils.checkpoint as checkpoint

class UnetBlock(nn.Module):
    def forward(self, x):
        x = checkpoint.checkpoint(self.conv1, x)
        x = self.norm1(x)
        x = self.activation(x)
        x = checkpoint.checkpoint(self.conv2, x)
        x = self.norm2(x)
        x = self.activation(x)
        return x
Simplify Transformer Layers: Reduce the depth or number of heads in transformer layers.
Prune Model Components: Temporarily remove non-critical components (e.g., certain heads) to isolate memory issues.
Step 5: Re-run Training with Adjusted Configuration
After implementing the above changes, re-run your training pipeline and monitor for the following:
Memory Usage: Ensure that the allocated memory stays within acceptable limits (ideally well below the maximum allowed).
Training Progress: Verify that the model starts training without crashing and that loss values are decreasing.
Model Performance: While memory optimizations are critical, ensure that these changes do not severely degrade model performance.
Additional Recommendations

Use Smaller Models Initially: Before scaling up, train with a smaller version of your model to ensure that the pipeline works correctly.
Validate Configuration Consistency: Double-check that all components in your configuration file are consistent with each other, especially regarding channel dimensions and image sizes.
Leverage Optuna's Resources Efficiently:
Parallel Trials: Limit the number of parallel trials in Optuna to prevent overconsumption of memory.
Efficient Search Spaces: Define a streamlined search space to reduce the complexity and size of models being tested.
Backup and Snapshot States: Regularly save checkpoints and model states, especially after significant configuration changes, to prevent loss of progress.
Consider Hardware Constraints:
Integrated GPU Limitations: Mac's integrated GPUs through MPS have inherent memory limitations compared to discrete GPUs.
Cloud Solutions: If feasible, consider using cloud-based GPUs with higher memory capacities for intensive models.
Conclusion

The "MPS backend out of memory" error is primarily due to the high memory demands of your current model configuration. By systematically reducing the batch size, enabling AMP, adjusting image sizes, and optimizing model architecture, you can alleviate memory pressure and facilitate successful model training.
Always ensure that changes are made incrementally and validated to maintain a balance between model performance and resource constraints. If issues persist, deeper architectural revisions or hardware upgrades might be necessary.
Feel free to reach out with further questions or updates on your progress!

[DEBUG] Channel 0 shape: (2048, 2048)
[DEBUG] Channel 1 shape: (2048, 2048)
UpsampleBlock output shape: torch.Size([2, 384, 256, 256])
x.shape after upsampling: torch.Size([2, 384, 256, 256])
Encoder Feature 1 shape: torch.Size([2, 64, 256, 256])
x.shape after concatenation: torch.Size([2, 448, 256, 256])
Passing through UNetBlock with in_channels=448
x.shape after UNetBlock: torch.Size([2, 384, 256, 256])
Decoder Stage 2
x.shape before upsampling: torch.Size([2, 384, 256, 256])
UpsampleBlock input shape: torch.Size([2, 384, 256, 256])
UpsampleBlock output shape: torch.Size([2, 256, 512, 512])
x.shape after upsampling: torch.Size([2, 256, 512, 512])
Encoder Feature 2 shape: torch.Size([2, 48, 512, 512])
x.shape after concatenation: torch.Size([2, 304, 512, 512])
Passing through UNetBlock with in_channels=304
x.shape after UNetBlock: torch.Size([2, 256, 512, 512])
Decoder Stage 3
x.shape before upsampling: torch.Size([2, 256, 512, 512])
UpsampleBlock input shape: torch.Size([2, 256, 512, 512])
UpsampleBlock output shape: torch.Size([2, 128, 1024, 1024])
x.shape after upsampling: torch.Size([2, 128, 1024, 1024])
Encoder Feature 3 shape: torch.Size([2, 24, 1024, 1024])
x.shape after concatenation: torch.Size([2, 152, 1024, 1024])
Passing through UNetBlock with in_channels=152
x.shape after UNetBlock: torch.Size([2, 128, 1024, 1024])
Decoder Stage 4
x.shape before upsampling: torch.Size([2, 128, 1024, 1024])
UpsampleBlock input shape: torch.Size([2, 128, 1024, 1024])
UpsampleBlock output shape: torch.Size([2, 64, 2048, 2048])
x.shape after upsampling: torch.Size([2, 64, 2048, 2048])
Passing through UNetBlock with in_channels=64
x.shape after UNetBlock: torch.Size([2, 64, 2048, 2048])
[2024-10-07 17:36:51,946][main][ERROR] - Error in batch 0: expected Tensor as element 0 in argument 0, but got NoneType
[W 2024-10-07 17:37:01,967] Trial 0 failed with value None.
Error executing job with overrides: []
Traceback (most recent call last):
File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel3.py", line 925, in
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: 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 "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel3.py", line 921, in main
pipeline.train()
File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel3.py", line 639, in train
self.model = self.build_model(config, train_loader, valid_loader)
File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel3.py", line 339, in build_model
best_trial = self.nas.optimize(
File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/nas.py", line 239, in optimize
study.optimize(
File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/optuna/study/study.py", line 475, in optimize
_optimize(
File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/optuna/study/_optimize.py", line 63, in _optimize
_optimize_sequential(
File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/optuna/study/_optimize.py", line 160, in _optimize_sequential
frozen_trial = _run_trial(study, func, catch)
File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/optuna/study/_optimize.py", line 248, in _run_trial
raise func_err
File "/opt/anaconda3/envs/sam3/lib/python3.9/site-packages/optuna/study/_optimize.py", line 197, in _run_trial
value_or_values = func(trial)
File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/nas.py", line 240, in
lambda trial: self.evaluate_architecture(trial, config, train_loader, val_loader),
File "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/train_multilabel3.py", line 417, in evaluate_architecture
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 "/Volumes/Crucial/SAR-MPS-OptunaNasFbnYoloTransformers/xview3/centernet/models/centernet_unet.py", line 309, in forward
concatenated = torch.cat([
TypeError: expected Tensor as element 0 in argument 0, but got NoneType
(sam3) samerkharboush@Mac SAR-MPS-OptunaNasFbnYoloTransformers %

The error you're encountering indicates that during the model's forward pass, one of the components is returning None instead of a tensor. Specifically, the torch.cat function is attempting to concatenate a list that contains a NoneType object. This issue typically arises when a module or layer within the model fails to produce an output, leading to None being inserted into the concatenation list.
Additionally, earlier errors related to the Conv2d layer receiving a ReLU module instead of a tensor suggest that activation functions might not be correctly applied within certain blocks of your model.
To systematically address and resolve these issues, follow the comprehensive steps below:
1. Verify Activation Function Usage in UnetBlock
Issue:

Activation functions like ReLU, LeakyReLU, or SiLU are being incorrectly passed as arguments to convolutional layers instead of being applied to tensors.
Solution:

Ensure that activation functions are applied to tensors and not passed as arguments to other layers like Conv2d. Here's a corrected implementation of the UnetBlock:

# unet_blocks.py

import torch.nn as nn
import torch.nn.functional as F

class UnetBlock(nn.Module):
    def __init__(self, in_channels, out_channels, activation='relu'):
        super(UnetBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False)
        self.norm1 = nn.GroupNorm(16, out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False)
        self.norm2 = nn.GroupNorm(16, out_channels)
        
        # Define activation function
        if activation == 'relu':
            self.activation = nn.ReLU(inplace=True)
        elif activation == 'leaky_relu':
            self.activation = nn.LeakyReLU(inplace=True)
        elif activation == 'silu':
            self.activation = nn.SiLU(inplace=True)
        else:
            raise ValueError(f"Unsupported activation: {activation}")
    
    def forward(self, x):
        x = self.conv1(x)
        x = self.norm1(x)
        x = self.activation(x)  # Correctly applying activation
        x = self.conv2(x)
        x = self.norm2(x)
        x = self.activation(x)  # Correctly applying activation
        return x
Key Points:

Activation Application: Use self.activation(x) to apply the activation function to the tensor x.
Avoid Passing Modules as Arguments: Do not pass activation functions (e.g., self.activation) directly to convolutional layers as arguments.
2. Add Comprehensive Debugging in the Model's Forward Method
Issue:

One or more head modules (DecoupledHeadGroupNormLateShuffle or YOLOHead) might be returning None, leading to a NoneType being included in the torch.cat operation.
Solution:

Enhance the forward method in centernet_unet.py to include debug statements that verify the presence and shapes of all expected outputs before the concatenation step.
Implementation:


# centernet_unet.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import logging

# Assume logger is already configured
logger = logging.getLogger(__name__)

class CenterNetUNetModel(nn.Module):
    # ... [Rest of the class definition]

    def forward(self, x):
        logger.debug("Starting forward pass through CenterNetUNetModel.")
        
        # Initial Upsampling
        logger.debug("Performing initial upsampling.")
        x = self.initial_upsample(x)

        # Encoder Forward Pass
        logger.debug("Passing through encoder.")
        encoder_features = self.encoder(x)

        # Transformer Feature
        transformer_feature = encoder_features[-1]
        logger.debug(f"Transformer feature shape: {transformer_feature.shape}")

        # FAN Processing
        aggregated_feature = self.fan([transformer_feature])
        logger.debug(f"Aggregated feature shape: {aggregated_feature.shape}")

        # Replace the last encoder feature with the aggregated feature
        encoder_features[-1] = aggregated_feature

        # Decoder
        logger.debug("Passing aggregated features to decoder.")
        decoded_features = self.decoder(encoder_features)
        if not isinstance(decoded_features, list):
            logger.error("Decoder output is not a list.")
            raise ValueError("Decoder output is not a list.")

        # Neck Processing
        logger.debug("Processing neck features.")
        neck_features = self.neck(decoded_features)
        neck_features = self.batch_norm(neck_features)
        neck_features = self.dropout(neck_features)
        neck_features = self.neck_conv(neck_features)
        logger.debug(f"Neck features shape after processing: {neck_features.shape}")

        # Heads Forward Pass
        logger.debug("Passing neck features to heads.")
        head_outputs = {}
        for head in self.heads:
            head_output = head(neck_features)
            if isinstance(head, DecoupledHeadGroupNormLateShuffle):
                if isinstance(head_output, dict):
                    head_outputs.update(head_output)
                    logger.debug("DecoupledHeadGroupNormLateShuffle output updated in head_outputs.")
                else:
                    logger.warning("DecoupledHeadGroupNormLateShuffle did not return a dict.")
            elif isinstance(head, YOLOHead):
                head_outputs[CENTERNET_OUTPUT_YOLO] = head_output
                logger.debug("YOLOHead output added to head_outputs.")
            else:
                logger.warning(f"Unhandled head type: {type(head)}")
        
        # Decode YOLO Outputs
        if CENTERNET_OUTPUT_YOLO in head_outputs:
            decoded_yolo = self.yolo_decoder.decode(head_outputs[CENTERNET_OUTPUT_YOLO])
            head_outputs['decoded_yolo'] = decoded_yolo
        else:
            logger.warning("YOLO output not found in head outputs.")

        # Extract Standard Outputs
        outputs = {
            CENTERNET_OUTPUT_OBJECTNESS_MAP: head_outputs.get(CENTERNET_OUTPUT_OBJECTNESS_MAP),
            CENTERNET_OUTPUT_SIZE: head_outputs.get(CENTERNET_OUTPUT_SIZE),
            CENTERNET_OUTPUT_OFFSET: head_outputs.get(CENTERNET_OUTPUT_OFFSET),
            CENTERNET_OUTPUT_VESSEL_MAP: head_outputs.get(CENTERNET_OUTPUT_VESSEL_MAP),
            CENTERNET_OUTPUT_FISHING_MAP: head_outputs.get(CENTERNET_OUTPUT_FISHING_MAP),
            CENTERNET_OUTPUT_YOLO: head_outputs.get(CENTERNET_OUTPUT_YOLO),
        }

        # Debug: Check for None in outputs
        for key, value in outputs.items():
            if value is None:
                logger.error(f"Output for {key} is None.")
            else:
                logger.debug(f"Output {key} shape: {value.shape}")

        # Concatenate All Relevant Feature Maps
        try:
            concatenated = torch.cat([
                outputs[CENTERNET_OUTPUT_OBJECTNESS_MAP],  # [B, 1, H, W]
                outputs[CENTERNET_OUTPUT_VESSEL_MAP],      # [B, 1, H, W]
                outputs[CENTERNET_OUTPUT_FISHING_MAP],     # [B, 1, H, W]
                outputs[CENTERNET_OUTPUT_SIZE],            # [B, 1, H, W]
                outputs[CENTERNET_OUTPUT_OFFSET],          # [B, 2, H, W]
                outputs[CENTERNET_OUTPUT_YOLO],            # [B, YOLO_out_channels, H, W]
            ], dim=1)
            logger.debug(f"Concatenated features shape: {concatenated.shape}")
        except Exception as e:
            logger.error(f"Error during concatenation: {e}")
            raise e

        # Fusion Layer
        fused_output = self.fusion(concatenated)
        fused_output = self.dropout_fusion(fused_output)
        outputs['fused_output'] = fused_output
        logger.debug(f"Fused output shape: {fused_output.shape}")

        return outputs
Key Points:

Pre-Concat Checks:
Before attempting to concatenate, iterate through the outputs dictionary and log if any key has a None value.
This approach helps identify exactly which output is missing, facilitating targeted debugging.
Error Handling During Concatenation:
Wrap the torch.cat operation in a try-except block to catch and log any errors that occur during concatenation.
Consistent Logging:
Ensure that each step logs the shape of tensors, making it easier to trace the data flow and identify discrepancies.
3. Verify the Implementation of Head Modules
Issue:

One or more head modules (DecoupledHeadGroupNormLateShuffle or YOLOHead) might not be returning the expected tensors, leading to None values in the head_outputs dictionary.
Solution:

Ensure that each head module correctly returns its outputs. Here's how you can verify and adjust the head modules:
A. DecoupledHeadGroupNormLateShuffle Implementation

Ensure that this head returns a dictionary with keys matching CENTERNET_OUTPUT_OBJECTNESS_MAP, CENTERNET_OUTPUT_VESSEL_MAP, CENTERNET_OUTPUT_FISHING_MAP, CENTERNET_OUTPUT_SIZE, and CENTERNET_OUTPUT_OFFSET.
Sample Implementation:

# decoupled_head.py

import torch.nn as nn

class DecoupledHeadGroupNormLateShuffle(nn.Module):
    def __init__(self, channels, classifier_dim, objectness_dim, size_dim, offset_dim, dropout_rate, activation, num_blocks):
        super(DecoupledHeadGroupNormLateShuffle, self).__init__()
        # Define layers
        self.conv1 = nn.Conv2d(channels, classifier_dim, kernel_size=3, padding=1)
        self.norm1 = nn.GroupNorm(16, classifier_dim)
        self.activation = nn.ReLU(inplace=True)  # Or any other activation based on config
        # Define additional layers as needed
        # ...
        self.out_objectness = nn.Conv2d(classifier_dim, objectness_dim, kernel_size=1)
        self.out_vessel = nn.Conv2d(classifier_dim, 1, kernel_size=1)
        self.out_fishing = nn.Conv2d(classifier_dim, 1, kernel_size=1)
        self.out_size = nn.Conv2d(classifier_dim, 1, kernel_size=1)
        self.out_offset = nn.Conv2d(classifier_dim, 2, kernel_size=1)

    def forward(self, x):
        x = self.conv1(x)
        x = self.norm1(x)
        x = self.activation(x)
        # Continue forward pass
        # ...
        objectness_map = self.out_objectness(x)
        vessel_map = self.out_vessel(x)
        fishing_map = self.out_fishing(x)
        size_map = self.out_size(x)
        offset_map = self.out_offset(x)
        
        return {
            CENTERNET_OUTPUT_OBJECTNESS_MAP: objectness_map,
            CENTERNET_OUTPUT_VESSEL_MAP: vessel_map,
            CENTERNET_OUTPUT_FISHING_MAP: fishing_map,
            CENTERNET_OUTPUT_SIZE: size_map,
            CENTERNET_OUTPUT_OFFSET: offset_map,
        }
B. YOLOHead Implementation

Ensure that this head returns the YOLO output tensor correctly.
Sample Implementation:

# yolo_head.py

import torch.nn as nn

class YOLOHead(nn.Module):
    def __init__(self, num_classes, anchors, input_image_size, stride, num_anchors_per_scale):
        super(YOLOHead, self).__init__()
        # Define layers
        self.conv = nn.Conv2d(in_channels=..., out_channels=..., kernel_size=..., padding=...)
        # Define additional layers as needed
        # ...
        self.out_yolo = nn.Conv2d(...)

    def forward(self, x):
        x = self.conv(x)
        # Continue forward pass
        # ...
        yolo_output = self.out_yolo(x)
        return yolo_output
Key Points:

Consistent Output Keys:
DecoupledHeadGroupNormLateShuffle should always return a dictionary containing all expected keys.
If any component within the head can potentially return None, ensure that it either raises an exception or fills the value with a valid tensor.
Avoid Silent Failures:
Modify the forward methods of head modules to prevent silent failures. If a layer within a head fails, it should raise an error instead of returning None.
Logging Within Heads:
Incorporate logging within the heads to confirm that outputs are being generated as expected.

# Inside DecoupledHeadGroupNormLateShuffle.forward

def forward(self, x):
    # After each significant operation
    logger.debug(f"DecoupledHeadGroupNormLateShuffle after conv1: {x.shape}")
    # ...
    logger.debug(f"Objectness map shape: {objectness_map.shape}")
    logger.debug(f"Vessel map shape: {vessel_map.shape}")
    # etc.
    return {
        CENTERNET_OUTPUT_OBJECTNESS_MAP: objectness_map,
        CENTERNET_OUTPUT_VESSEL_MAP: vessel_map,
        CENTERNET_OUTPUT_FISHING_MAP: fishing_map,
        CENTERNET_OUTPUT_SIZE: size_map,
        CENTERNET_OUTPUT_OFFSET: offset_map,
    }
Consistent Naming:
Ensure that the keys used in the outputs match those expected in the forward method of CenterNetUNetModel.
4. Add Defensive Coding and Additional Debug Statements
To pinpoint exactly where None is being introduced, enhance your model's forward pass with additional debug statements.
Implementation:


# centernet_unet.py

def forward(self, x):
    logger.debug("Starting forward pass through CenterNetUNetModel.")
    
    # Initial Upsampling
    logger.debug("Performing initial upsampling.")
    x = self.initial_upsample(x)

    # Encoder Forward Pass
    logger.debug("Passing through encoder.")
    encoder_features = self.encoder(x)
    logger.debug(f"Encoder features shapes: {[f.shape for f in encoder_features]}")
    
    # Transformer Feature
    transformer_feature = encoder_features[-1]
    logger.debug(f"Transformer feature shape: {transformer_feature.shape}")

    # FAN Processing
    aggregated_feature = self.fan([transformer_feature])
    logger.debug(f"Aggregated feature shape: {aggregated_feature.shape}")

    # Replace the last encoder feature with the aggregated feature
    encoder_features[-1] = aggregated_feature

    # Decoder
    logger.debug("Passing aggregated features to decoder.")
    decoded_features = self.decoder(encoder_features)
    if not isinstance(decoded_features, list):
        logger.error("Decoder output is not a list.")
        raise ValueError("Decoder output is not a list.")
    
    for idx, feature in enumerate(decoded_features):
        if feature is None:
            logger.error(f"Decoded feature at index {idx} is None.")
        else:
            logger.debug(f"Decoded feature {idx} shape: {feature.shape}")
    
    # Neck Processing
    logger.debug("Processing neck features.")
    neck_features = self.neck(decoded_features)
    neck_features = self.batch_norm(neck_features)
    neck_features = self.dropout(neck_features)
    neck_features = self.neck_conv(neck_features)
    logger.debug(f"Neck features shape after processing: {neck_features.shape}")

    # Heads Forward Pass
    logger.debug("Passing neck features to heads.")
    head_outputs = {}
    for head in self.heads:
        head_output = head(neck_features)
        if isinstance(head, DecoupledHeadGroupNormLateShuffle):
            if isinstance(head_output, dict):
                head_outputs.update(head_output)
                logger.debug("DecoupledHeadGroupNormLateShuffle output updated in head_outputs.")
            else:
                logger.warning("DecoupledHeadGroupNormLateShuffle did not return a dict.")
        elif isinstance(head, YOLOHead):
            head_outputs[CENTERNET_OUTPUT_YOLO] = head_output
            logger.debug("YOLOHead output added to head_outputs.")
        else:
            logger.warning(f"Unhandled head type: {type(head)}")
    
    # Post-Head Checks
    for key, value in head_outputs.items():
        if value is None:
            logger.error(f"Head output {key} is None.")
        else:
            logger.debug(f"Head output {key} shape: {value.shape}")
    
    # Decode YOLO Outputs
    if CENTERNET_OUTPUT_YOLO in head_outputs and head_outputs[CENTERNET_OUTPUT_YOLO] is not None:
        decoded_yolo = self.yolo_decoder.decode(head_outputs[CENTERNET_OUTPUT_YOLO])
        head_outputs['decoded_yolo'] = decoded_yolo
        logger.debug(f"Decoded YOLO output shape: {decoded_yolo.shape}")
    else:
        logger.warning("YOLO output not found or is None in head outputs.")

    # Extract Standard Outputs
    outputs = {
        CENTERNET_OUTPUT_OBJECTNESS_MAP: head_outputs.get(CENTERNET_OUTPUT_OBJECTNESS_MAP),
        CENTERNET_OUTPUT_SIZE: head_outputs.get(CENTERNET_OUTPUT_SIZE),
        CENTERNET_OUTPUT_OFFSET: head_outputs.get(CENTERNET_OUTPUT_OFFSET),
        CENTERNET_OUTPUT_VESSEL_MAP: head_outputs.get(CENTERNET_OUTPUT_VESSEL_MAP),
        CENTERNET_OUTPUT_FISHING_MAP: head_outputs.get(CENTERNET_OUTPUT_FISHING_MAP),
        CENTERNET_OUTPUT_YOLO: head_outputs.get(CENTERNET_OUTPUT_YOLO),
    }

    # Debug: Check for None in outputs
    for key, value in outputs.items():
        if value is None:
            logger.error(f"Output for {key} is None.")
        else:
            logger.debug(f"Output {key} shape: {value.shape}")

    # Concatenate All Relevant Feature Maps
    try:
        concatenated = torch.cat([
            outputs[CENTERNET_OUTPUT_OBJECTNESS_MAP],
            outputs[CENTERNET_OUTPUT_VESSEL_MAP],
            outputs[CENTERNET_OUTPUT_FISHING_MAP],
            outputs[CENTERNET_OUTPUT_SIZE],
            outputs[CENTERNET_OUTPUT_OFFSET],
            outputs[CENTERNET_OUTPUT_YOLO],
        ], dim=1)
        logger.debug(f"Concatenated features shape: {concatenated.shape}")
    except Exception as e:
        logger.error(f"Error during concatenation: {e}")
        raise e

    # Fusion Layer
    fused_output = self.fusion(concatenated)
    fused_output = self.dropout_fusion(fused_output)
    outputs['fused_output'] = fused_output
    logger.debug(f"Fused output shape: {fused_output.shape}")

    return outputs
Key Points:

Decoded Features Checks:
After the decoder processes the features, iterate through decoded_features to ensure none of them are None.
Post-Head Output Checks:
After processing each head, verify that the output is not None and log its shape.
YOLO Output Checks:
Before decoding YOLO outputs, ensure that CENTERNET_OUTPUT_YOLO exists in head_outputs and isn't None.
Final Output Checks:
Before attempting to concatenate, confirm that none of the tensors being concatenated are None.
5. Inspect Head Modules for Proper Output
A. DecoupledHeadGroupNormLateShuffle

Ensure that this head correctly returns a dictionary with all required outputs.

# heads/decoupled_head_group_norm_late_shuffle.py

import torch.nn as nn

class DecoupledHeadGroupNormLateShuffle(nn.Module):
    def __init__(self, channels, classifier_dim, objectness_dim, size_dim, offset_dim, dropout_rate, activation, num_blocks):
        super(DecoupledHeadGroupNormLateShuffle, self).__init__()
        # Define layers
        self.classifier = nn.Sequential(
            nn.Conv2d(channels, classifier_dim, kernel_size=3, padding=1),
            nn.GroupNorm(16, classifier_dim),
            nn.ReLU(inplace=True)
        )
        self.objectness = nn.Conv2d(classifier_dim, objectness_dim, kernel_size=1)
        self.vessel = nn.Conv2d(classifier_dim, 1, kernel_size=1)
        self.fishing = nn.Conv2d(classifier_dim, 1, kernel_size=1)
        self.size = nn.Conv2d(classifier_dim, 1, kernel_size=1)
        self.offset = nn.Conv2d(classifier_dim, 2, kernel_size=1)

    def forward(self, x):
        x = self.classifier(x)
        objectness_map = self.objectness(x)
        vessel_map = self.vessel(x)
        fishing_map = self.fishing(x)
        size_map = self.size(x)
        offset_map = self.offset(x)
        
        # Debugging: Log shapes
        logger.debug(f"DecoupledHead outputs - objectness: {objectness_map.shape}, vessel: {vessel_map.shape}, fishing: {fishing_map.shape}, size: {size_map.shape}, offset: {offset_map.shape}")
        
        return {
            CENTERNET_OUTPUT_OBJECTNESS_MAP: objectness_map,
            CENTERNET_OUTPUT_VESSEL_MAP: vessel_map,
            CENTERNET_OUTPUT_FISHING_MAP: fishing_map,
            CENTERNET_OUTPUT_SIZE: size_map,
            CENTERNET_OUTPUT_OFFSET: offset_map,
        }
B. YOLOHead

Ensure that this head correctly returns the YOLO output tensor.

# heads/yolo_head.py

import torch.nn as nn

class YOLOHead(nn.Module):
    def __init__(self, num_classes, anchors, input_image_size, stride, num_anchors_per_scale):
        super(YOLOHead, self).__init__()
        self.num_classes = num_classes
        self.anchors = anchors
        self.input_image_size = input_image_size
        self.stride = stride
        self.num_anchors_per_scale = num_anchors_per_scale
        
        # Define layers
        self.conv = nn.Sequential(
            nn.Conv2d(...),  # Define appropriately
            nn.BatchNorm2d(...),
            nn.ReLU(inplace=True),
            # Add more layers as needed
        )
        self.output_conv = nn.Conv2d(..., out_channels=num_anchors_per_scale * (5 + num_classes), kernel_size=1)
    
    def forward(self, x):
        x = self.conv(x)
        yolo_output = self.output_conv(x)
        logger.debug(f"YOLOHead output shape: {yolo_output.shape}")
        return yolo_output
Key Points:

Consistent Returns:
The DecoupledHeadGroupNormLateShuffle must always return a dictionary with all required keys, ensuring no key returns None.
YOLOHead Output:
Ensure that YOLOHead consistently returns the YOLO output tensor without any conditional returns that could lead to None.
Logging Inside Heads:
Incorporate logging within the head modules to confirm that outputs are generated correctly.
6. Implement Defensive Coding in Head Modules
To prevent heads from inadvertently returning None, implement checks and raise exceptions if critical layers fail.
Example in DecoupledHeadGroupNormLateShuffle:


def forward(self, x):
    try:
        x = self.classifier(x)
        objectness_map = self.objectness(x)
        vessel_map = self.vessel(x)
        fishing_map = self.fishing(x)
        size_map = self.size(x)
        offset_map = self.offset(x)
        
        # Ensure none of the outputs are None
        if None in [objectness_map, vessel_map, fishing_map, size_map, offset_map]:
            raise ValueError("One of the head outputs is None.")
        
        logger.debug(f"DecoupledHead outputs - objectness: {objectness_map.shape}, vessel: {vessel_map.shape}, fishing: {fishing_map.shape}, size: {size_map.shape}, offset: {offset_map.shape}")
        
        return {
            CENTERNET_OUTPUT_OBJECTNESS_MAP: objectness_map,
            CENTERNET_OUTPUT_VESSEL_MAP: vessel_map,
            CENTERNET_OUTPUT_FISHING_MAP: fishing_map,
            CENTERNET_OUTPUT_SIZE: size_map,
            CENTERNET_OUTPUT_OFFSET: offset_map,
        }
    except Exception as e:
        logger.error(f"Error in DecoupledHeadGroupNormLateShuffle forward: {e}")
        raise e
Example in YOLOHead:


def forward(self, x):
    try:
        x = self.conv(x)
        yolo_output = self.output_conv(x)
        
        if yolo_output is None:
            raise ValueError("YOLOHead output is None.")
        
        logger.debug(f"YOLOHead output shape: {yolo_output.shape}")
        return yolo_output
    except Exception as e:
        logger.error(f"Error in YOLOHead forward: {e}")
        raise e
Key Points:

Exception Handling: Wrap critical operations in try-except blocks to catch and log exceptions, ensuring that failures are surfaced rather than silently resulting in None.
Output Validation: After each significant output, validate that it's not None.
7. Test the Model with a Controlled Input
Before running the full training pipeline, test the model with a dummy input to ensure that all components are functioning correctly and returning valid tensors.
Implementation:


import torch
from omegaconf import OmegaConf

# Load your configuration
config = OmegaConf.load("path_to_your_config.yaml")

# Instantiate the model using the configuration
model = CenterNetUNetModel.from_config(config)
model.eval()  # Set to evaluation mode to disable dropout, batchnorm, etc.

# Create a dummy input tensor matching your expected input shape
# Assuming num_channels=2 and image_size=[512, 512]
dummy_input = torch.randn(2, config.dataset.num_channels, 512, 512)

# Forward Pass
try:
    with torch.no_grad():
        outputs = model(dummy_input)
    print("Forward pass successful. Outputs:")
    for key, value in outputs.items():
        if value is not None:
            print(f"{key}: {value.shape}")
        else:
            print(f"{key}: None")
except Exception as e:
    print(f"Error during forward pass: {e}")
Key Points:

Dummy Input:
Ensure that the dummy input matches the expected dimensions as per your configuration.
Model Evaluation Mode:
Setting the model to eval mode disables layers like dropout and ensures consistent behavior during testing.
Catch and Inspect Errors:
If an error occurs during the forward pass, it will be caught and printed, providing immediate feedback on what needs to be addressed.
8. Review and Adjust the Configuration File
Ensure that the configuration aligns with the model's expectations, especially regarding channel dimensions and image sizes.
Key Configuration Parameters:

Encoder Channels:
After FAN: [24, 48, 64, 160, 256]
Decoder Channels:
[512, 384, 256, 128, 64]
FAN Configuration:
input_channels: [256]
out_channels: 256
Image Sizes:
Reduced to [512, 512] to manage memory usage.
Sample Configuration Adjustment:


model:
  _target_: xview3.centernet.models.centernet_unet.EnhancedCenterNetUNet 
  # ... [other configurations]
  decoder:
    _target_: xview3.centernet.models.custom_encoder.DebugUnetDecoder
    encoder_channels: [24, 48, 64, 160, 256]  # Ensure this matches encoder and FAN output
    decoder_channels: [512, 384, 256, 128, 64]
    # ... [rest of the decoder config]

  fan:
    _target_: xview3.centernet.models.necks.FeatureAggregationNetwork
    input_channels: [256]  # Must match encoder_channels[-1] after FAN
    out_channels: 256
    # ... [rest of the FAN config]
Key Points:

Consistency: All channel dimensions in the configuration must align with each other and with the model's architecture.
Avoid Dynamic Resizing: To prevent mismatches, avoid dynamically changing channel dimensions unless the model is explicitly designed to handle it.
9. Final Steps and Recommendations
A. Re-run the Forward Pass Test

After implementing the above corrections:
Run the Dummy Forward Pass:
Execute the dummy input test to ensure that no None outputs are generated.
Inspect the Outputs:
Confirm that all outputs have valid tensor shapes and none are None.
B. Incrementally Train the Model

Once the forward pass works correctly with dummy data:
Start Training with a Single Batch:
Begin training with a small number of batches to ensure that the model doesn't crash immediately.
Monitor Logs:
Pay close attention to the debug logs to catch any issues early.
Gradually Scale:
Gradually increase the batch size or introduce more data once stability is confirmed.
C. Memory Management on MPS Backend

Given that you're using the MPS backend on a Mac, which has specific memory constraints:
Limit Model Complexity:
Further reduce model depth or width if memory issues persist.
Leverage CPU Temporarily:
If possible, switch to CPU for certain debugging steps, though this will be slower.

device = torch.device("cpu")
model.to(device)
dummy_input = torch.randn(2, config.dataset.num_channels, 512, 512).to(device)
Monitor System Memory:
Use macOS's Activity Monitor to keep an eye on memory usage during training.
Set MPS High Watermark (Caution):
As the original error message suggests, you can set the environment variable PYTORCH_MPS_HIGH_WATERMARK_RATIO to increase the allowable memory usage. However, use this with caution, as it can lead to system instability.

export PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.9  # Set to 0.0 to disable upper limit
Note: Disabling or increasing the watermark ratio can cause the system to surpass available memory, leading to crashes or other undesired behavior.
D. Continuous Integration and Testing

Implement unit tests for critical components to ensure they behave as expected.
Unit Testing:
Test each block (Encoder, FAN, Decoder, Heads) in isolation with mock data.
Integration Testing:
Test the entire model pipeline with dummy data, ensuring that all outputs are valid.
Automated Testing:
Incorporate automated tests in your CI/CD pipeline to catch inconsistencies early.
E. Documentation and Version Control

Maintain thorough documentation and use version control to track changes, allowing easy rollback if new issues arise.
Document Changes:
Keep records of all changes made to the configuration and codebase.
Use Git Branches:
Experiment with changes in separate branches to prevent disrupting the main codebase.
Commit Regularly:
Commit changes in logical increments, ensuring a clear history of modifications.
Conclusion
The primary issues causing the errors are:
Incorrect Application of Activation Functions:
Ensure activation functions are applied to tensors and not passed as layer arguments.
Head Modules Not Returning Expected Outputs:
Verify that all head modules (DecoupledHeadGroupNormLateShuffle and YOLOHead) correctly return their outputs without any None values.
Channel Mismatch Due to Configuration Misalignment:
Ensure that the configuration file's channel settings align perfectly with the model's architecture.
By meticulously following the steps outlined above, you can systematically identify and resolve the underlying issues leading to the NoneType error and other related problems. Implementing thorough debugging, validation, and testing will enhance the robustness of your model and training pipeline.
If after these adjustments the problem persists, please provide additional logs or specific code snippets from the UnetBlock, head modules, or other relevant areas to facilitate more targeted assistance.

let's review deeply my old UnetBlock, DecoupledHeadGroupNormLateShuffle, yolo_head
class UnetBlock(nn.Module):
def init(self, in_channels, out_channels, activation='relu', use_attention=False, use_dropblock=False, dropblock_prob=0.1):
super().init()
self.in_channels = in_channels
self.out_channels = out_channels
assert in_channels > 0, f"in_channels must be positive, got {in_channels}"
assert out_channels > 0, f"out_channels must be positive, got {out_channels}"
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False)
self.bn1 = nn.GroupNorm(16, out_channels)
self.activation1 = get_activation_block(activation)

    self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False)
    self.bn2 = nn.GroupNorm(16, out_channels)
    self.activation2 = get_activation_block(activation)
    
    if use_attention:
        self.attention = AttentionCBAM(out_channels)
    
    if use_dropblock:
        from pytorch_toolbelt.modules import DropBlock2D
        self.dropblock = DropBlock2D(block_size=7, drop_prob=dropblock_prob)
    
    # Residual connection
    if in_channels != out_channels:
        self.residual = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
            nn.GroupNorm(16, out_channels)
        )
    else:
        self.residual = nn.Identity()
    
def forward(self, x):
    print(f"[DEBUG] UnetBlock input shape: {x.shape}")
    print(f"[DEBUG] Expected input channels: {self.in_channels}, Actual input channels: {x.shape[1]}")
    
    residual = self.residual(x)
    print(f"[DEBUG] Residual shape after conv: {residual.shape}")
    
    out = self.conv1(x)
    out = self.bn1(out)
    out = self.activation1(out)
    
    out = self.conv2(out)
    out = self.bn2(out)
    out = self.activation2(out)
    
    if hasattr(self, 'attention'):
        out = self.attention(out)
    
    if hasattr(self, 'dropblock'):
        out = self.dropblock(out)
    
    print(f"[DEBUG] Out shape before adding residual: {out.shape}")
    print(f"[DEBUG] Residual shape: {residual.shape}")
    
    # Ensure residual and out have the same shape
    if residual.shape != out.shape:
        raise ValueError(f"Shape mismatch: residual shape {residual.shape} vs out shape {out.shape}")
    
    out += residual
    print(f"[DEBUG] Final output shape: {out.shape}")
    
    return out
class DecoupledHeadGroupNormLateShuffle(nn.Module):
upsample_factor: int = 2

def __init__(
    self,
    channels: int,
    classifier_dim: int,
    dropout_rate=0.25,
    objectness_dim: int = 32,
    size_dim: int = 32,
    offset_dim: int = 32,
    num_blocks: int = 3,
    activation: str = ACT_RELU,
    num_groups=16,
    yolo_dim: int = 128,
    use_se=False,  # Toggle for SE blocks
    use_dropblock=False,  # Toggle for DropBlock
    dropblock_prob=0.1,  # DropBlock probability
):
    super().__init__()
    self.objectness = build_regression_block(
        channels,
        embedding_size=objectness_dim,
        dropout_rate=dropout_rate,
        activation=activation,
        num_groups=num_groups,
        num_blocks=num_blocks,
        extra_final_layers=[
            ("upsample", nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)),
            ("final", nn.Conv2d(objectness_dim, 1, kernel_size=(3, 3), padding=1)),
        ],
        use_se=use_se,
        use_dropblock=use_dropblock,
        dropblock_prob=dropblock_prob,
    )

    self.classifier = build_regression_block(
        channels,
        embedding_size=classifier_dim,
        dropout_rate=dropout_rate,
        num_groups=num_groups,
        activation=activation,
        num_blocks=num_blocks,
        extra_final_layers=[],
        use_se=use_se,
        use_dropblock=use_dropblock,
        dropblock_prob=dropblock_prob,
    )

    self.size = build_regression_block(
        channels,
        embedding_size=size_dim,
        dropout_rate=dropout_rate,
        activation=activation,
        num_groups=num_groups,
        num_blocks=num_blocks,
        extra_final_layers=[],
        use_se=use_se,
        use_dropblock=use_dropblock,
        dropblock_prob=dropblock_prob,
    )

    self.classifier_recombination = FeatureRecombination(classifier_dim, size_dim)
    self.classifier_up = nn.Sequential(
        OrderedDict(
            [
                ("conv1", nn.Conv2d(classifier_dim, classifier_dim, kernel_size=(3, 3), padding=1)),
                ("upsample", nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)),
                ("final", nn.Conv2d(classifier_dim, 2, kernel_size=(3, 3), padding=1)),
            ]
        )
    )
    self.size_recombination = FeatureRecombination(size_dim, classifier_dim)
    self.size_up = nn.Sequential(
        OrderedDict(
            [
                ("conv1", nn.Conv2d(size_dim, size_dim, kernel_size=(3, 3), padding=1)),
                ("upsample", nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)),
                ("final", nn.Conv2d(size_dim, 1, kernel_size=(3, 3), padding=1)),
                ("activation", nn.Softplus()),
            ]
        )
    )

    if offset_dim:
        self.offset = build_regression_block(
            channels,
            embedding_size=offset_dim,
            dropout_rate=dropout_rate,
            activation=activation,
            num_groups=num_groups,
            num_blocks=num_blocks,
            extra_final_layers=[
                ("upsample", nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)),
                ("final", nn.Conv2d(offset_dim, 2, kernel_size=(3, 3), padding=1)),
            ],
            use_se=use_se,
            use_dropblock=use_dropblock,
            dropblock_prob=dropblock_prob,
        )
    else:
        self.offset = None

    # Add YOLO head with improved structure
    self.yolo_head = build_regression_block(
        channels,
        embedding_size=yolo_dim,
        dropout_rate=dropout_rate,
        activation=activation,
        num_groups=num_groups,
        num_blocks=num_blocks,
        extra_final_layers=[
            ("upsample", nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)),
            ("final", nn.Conv2d(yolo_dim, 21, kernel_size=(3, 3), padding=1)),  # Assuming YOLO output has 21 channels
        ],
        use_se=use_se,
        use_dropblock=use_dropblock,
        dropblock_prob=dropblock_prob,
    )

    # Determine the total output channels based on the presence of offset
    self.out_channels = 6 if self.offset is not None else 4

    # Initialize weights
    self._init_weights()

    # Initialize metrics
    self.accuracy = torchmetrics.Accuracy(task="binary")
    self.f1 = torchmetrics.F1Score(num_classes=2, average='macro', task='multiclass')

def _init_weights(self):
    for m in self.modules():
        if isinstance(m, nn.Conv2d):
            nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
        elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm, SqueezeExcitation)):
            nn.init.constant_(m.weight, 1)
            nn.init.constant_(m.bias, 0)

    # Initialize biases for certain layers
    if hasattr(self.objectness, 'final'):
        self.objectness.final.bias.data.fill_(-5)
    if hasattr(self.classifier_up, 'final'):
        self.classifier_up.final.bias.data.fill_(-1)
    if hasattr(self.size_up, 'final'):
        self.size_up.final.bias.data.fill_(2)

def forward(self, features: Tensor) -> Dict[str, Tensor]:
    class_features = self.classifier(features)
    length_features = self.size(features)

    new_class_features = self.classifier_recombination(class_features, length_features)
    new_length_features = self.size_recombination(length_features, class_features)

    predicted_classes = self.classifier_up(new_class_features)
    predicted_length = self.size_up(new_length_features)

    output = {
        CENTERNET_OUTPUT_OBJECTNESS_MAP: self.objectness(features),
        CENTERNET_OUTPUT_VESSEL_MAP: predicted_classes[:, 0:1],
        CENTERNET_OUTPUT_FISHING_MAP: predicted_classes[:, 1:2],
        CENTERNET_OUTPUT_SIZE: predicted_length,
        CENTERNET_OUTPUT_YOLO: self.yolo_head(features),  # Add YOLO output
    }

    if self.offset is not None:
        output[CENTERNET_OUTPUT_OFFSET] = self.offset(features).clip(0, 1)

    # Example metric computation (requires appropriate targets)
    # preds = torch.sigmoid(predicted_classes)
    # targets = ...  # Define your targets accordingly
    # self.accuracy.update(preds, targets)
    # self.f1.update(preds, targets)
    # metrics = {
    #     "accuracy": self.accuracy.compute(),
    #     "f1_score": self.f1.compute(),
    # }
    # output.update(metrics)

    return output
class YOLOHead(nn.Module):
def init(self, num_classes: int, anchors: List[Tuple[int, int]], input_image_size=(640, 640), stride=32):
super(YOLOHead, self).init()
self.num_classes = num_classes
self.anchors = anchors
self.num_anchors = len(anchors)
self.stride = stride
self.input_image_size = input_image_size

    # Ensure grid_height and grid_width are calculated correctly
    self.grid_height = input_image_size[0] // stride
    self.grid_width = input_image_size[1] // stride
    
    # Ensure the calculated grid dimensions are valid
    if self.grid_height <= 0 or self.grid_width <= 0:
        raise ValueError(f"Invalid grid dimensions: grid_height={self.grid_height}, grid_width={self.grid_width}. Ensure stride is a positive integer and input_image_size is divisible by stride.")
    
    self.out_channels = self.num_anchors * (5 + self.num_classes)  # (4 bbox + 1 confidence + classes)

    # Define the convolutional layers
    self.reg_conv = nn.Conv2d(256, 4 * self.num_anchors, kernel_size=1)  # Ensure input channels match
    self.obj_conv = nn.Conv2d(256, self.num_anchors, kernel_size=1)
    self.cls_conv = nn.Conv2d(256, self.num_classes * self.num_anchors, kernel_size=1)

def forward(self, x: torch.Tensor) -> torch.Tensor:
    """Forward pass for YOLOHead."""
    cls_pred = self.cls_conv(x)
    reg_pred = self.reg_conv(x)
    obj_pred = self.obj_conv(x)
    return torch.cat([reg_pred, obj_pred, cls_pred], dim=1)
def generate_yolo_targets(self, centers, confidences, is_vessel, is_fishing, lengths):
"""
Generate YOLO-style targets with 6 channels: [x, y, w, h, confidence, class].
- centers: Ground truth bounding boxes [num_objects, 2].
- confidences: Ground truth confidence [num_objects].
- is_vessel: Ground truth vessel types [num_objects].
- is_fishing: Ground truth fishing_statuses [num_objects].
- lengths: Ground truth lengths [num_objects].
"""
num_objects = len(centers)
yolo_targets = np.zeros((self.grid_height, self.grid_width, 6), dtype=np.float32)

    for i in range(num_objects):
        center = centers[i]
        confidence = self.confidence_mapping[confidences[i]]  # Convert confidence to numerical value
        vessel_type = self.vessel_mapping.get(is_vessel[i], self.ignore_value)  # Ensure correct mapping
        fishing_status = self.fishing_mapping.get(is_fishing[i], self.ignore_value)  # Ensure correct mapping
        length = lengths[i]

        # Convert center coordinates to grid cell format (relative to the grid)
        grid_x = int(center[0] * self.grid_width)
        grid_y = int(center[1] * self.grid_height)

        # Ensure grid indices are within bounds
        grid_x = min(grid_x, self.grid_width - 1)
        grid_y = min(grid_y, self.grid_height - 1)

        # Assign to the correct grid cell
        yolo_targets[grid_y, grid_x, 0:4] = np.array([center[0], center[1], length, length])
        yolo_targets[grid_y, grid_x, 4] = confidence
        yolo_targets[grid_y, grid_x, 5] = vessel_type
    
    return yolo_targets

Certainly! Let's perform a detailed, line-by-line review of your UnetBlock, DecoupledHeadGroupNormLateShuffle, and YOLOHead classes to identify and resolve the issue causing one of the outputs to be NoneType. This comprehensive analysis will help ensure that all components of your model function correctly and return the expected tensors, preventing runtime errors during operations like torch.cat.
1. Reviewing UnetBlock
Your UnetBlock implementation appears structurally sound. However, it's crucial to ensure that every possible path in the forward method returns a valid tensor and that activation functions are correctly applied. Let's examine it in detail:

class UnetBlock(nn.Module):
    def __init__(self, in_channels, out_channels, activation='relu', use_attention=False, use_dropblock=False, dropblock_prob=0.1):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        assert in_channels > 0, f"in_channels must be positive, got {in_channels}"
        assert out_channels > 0, f"out_channels must be positive, got {out_channels}"
        
        # Convolutional Layers
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False)
        self.bn1 = nn.GroupNorm(16, out_channels)
        self.activation1 = get_activation_block(activation)  # Should return an activation module like nn.ReLU()
        
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False)
        self.bn2 = nn.GroupNorm(16, out_channels)
        self.activation2 = get_activation_block(activation)
        
        # Optional Attention and DropBlock
        if use_attention:
            self.attention = AttentionCBAM(out_channels)  # Ensure AttentionCBAM is correctly implemented
        
        if use_dropblock:
            from pytorch_toolbelt.modules import DropBlock2D
            self.dropblock = DropBlock2D(block_size=7, drop_prob=dropblock_prob)
        
        # Residual Connection
        if in_channels != out_channels:
            self.residual = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
                nn.GroupNorm(16, out_channels)
            )
        else:
            self.residual = nn.Identity()
    
    def forward(self, x):
        print(f"[DEBUG] UnetBlock input shape: {x.shape}")
        print(f"[DEBUG] Expected input channels: {self.in_channels}, Actual input channels: {x.shape[1]}")
        
        residual = self.residual(x)
        print(f"[DEBUG] Residual shape after conv: {residual.shape}")
        
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.activation1(out)  # Correctly applying activation
        
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.activation2(out)  # Correctly applying activation
        
        if hasattr(self, 'attention'):
            out = self.attention(out)
            print(f"[DEBUG] Applied attention. Output shape: {out.shape}")
        
        if hasattr(self, 'dropblock'):
            out = self.dropblock(out)
            print(f"[DEBUG] Applied DropBlock. Output shape: {out.shape}")
        
        print(f"[DEBUG] Out shape before adding residual: {out.shape}")
        print(f"[DEBUG] Residual shape: {residual.shape}")
        
        # Ensure residual and out have the same shape
        if residual.shape != out.shape:
            raise ValueError(f"Shape mismatch: residual shape {residual.shape} vs out shape {out.shape}")
        
        out += residual
        print(f"[DEBUG] Final output shape: {out.shape}")
        
        return out
Key Points:

Activation Functions:
self.activation1 and self.activation2 are correctly applied to the tensors after normalization.
Ensure that get_activation_block(activation) returns a valid PyTorch activation module (e.g., nn.ReLU(inplace=True)).
Optional Layers:
If use_attention=True, ensure that AttentionCBAM is correctly implemented and returns a tensor.
If use_dropblock=True, ensure that DropBlock2D is correctly imported and functional.
Residual Connections:
The residual is correctly adjusted when in_channels != out_channels.
Shape consistency between out and residual is enforced, preventing unintended shape mismatches.
Debugging Statements:
Comprehensive debug prints help trace tensor shapes throughout the forward pass.
Recommendations:

Ensure Proper Initialization of Optional Modules:
Verify that AttentionCBAM and DropBlock2D are correctly implemented and do not unintentionally return None.
Handle Exceptions Gracefully:
While the current implementation raises a ValueError on shape mismatches, consider adding more context to help debug if this occurs.
2. Reviewing DecoupledHeadGroupNormLateShuffle
This class is responsible for generating multiple outputs, including objectness maps, vessel and fishing classifications, size estimations, offsets, and YOLO predictions. Ensuring that each sub-component reliably returns tensors is vital to prevent NoneType issues.

class DecoupledHeadGroupNormLateShuffle(nn.Module):
    upsample_factor: int = 2

    def __init__(
        self,
        channels: int,
        classifier_dim: int,
        dropout_rate=0.25,
        objectness_dim: int = 32,
        size_dim: int = 32,
        offset_dim: int = 32,
        num_blocks: int = 3,
        activation: str = ACT_RELU,
        num_groups=16,
        yolo_dim: int = 128,
        use_se=False,  # Toggle for SE blocks
        use_dropblock=False,  # Toggle for DropBlock
        dropblock_prob=0.1,  # DropBlock probability
    ):
        super().__init__()
        
        # Objectness Branch
        self.objectness = build_regression_block(
            channels,
            embedding_size=objectness_dim,
            dropout_rate=dropout_rate,
            activation=activation,
            num_groups=num_groups,
            num_blocks=num_blocks,
            extra_final_layers=[
                ("upsample", nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)),
                ("final", nn.Conv2d(objectness_dim, 1, kernel_size=(3, 3), padding=1)),
            ],
            use_se=use_se,
            use_dropblock=use_dropblock,
            dropblock_prob=dropblock_prob,
        )

        # Classifier Branch
        self.classifier = build_regression_block(
            channels,
            embedding_size=classifier_dim,
            dropout_rate=dropout_rate,
            num_groups=num_groups,
            activation=activation,
            num_blocks=num_blocks,
            extra_final_layers=[],
            use_se=use_se,
            use_dropblock=use_dropblock,
            dropblock_prob=dropblock_prob,
        )

        # Size Branch
        self.size = build_regression_block(
            channels,
            embedding_size=size_dim,
            dropout_rate=dropout_rate,
            activation=activation,
            num_groups=num_groups,
            num_blocks=num_blocks,
            extra_final_layers=[],
            use_se=use_se,
            use_dropblock=use_dropblock,
            dropblock_prob=dropblock_prob,
        )

        # Recombination and Upsampling for Classifier
        self.classifier_recombination = FeatureRecombination(classifier_dim, size_dim)
        self.classifier_up = nn.Sequential(
            OrderedDict(
                [
                    ("conv1", nn.Conv2d(classifier_dim, classifier_dim, kernel_size=(3, 3), padding=1)),
                    ("upsample", nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)),
                    ("final", nn.Conv2d(classifier_dim, 2, kernel_size=(3, 3), padding=1)),
                ]
            )
        )
        
        # Recombination and Upsampling for Size
        self.size_recombination = FeatureRecombination(size_dim, classifier_dim)
        self.size_up = nn.Sequential(
            OrderedDict(
                [
                    ("conv1", nn.Conv2d(size_dim, size_dim, kernel_size=(3, 3), padding=1)),
                    ("upsample", nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)),
                    ("final", nn.Conv2d(size_dim, 1, kernel_size=(3, 3), padding=1)),
                    ("activation", nn.Softplus()),
                ]
            )
        )

        # Offset Branch (Optional)
        if offset_dim:
            self.offset = build_regression_block(
                channels,
                embedding_size=offset_dim,
                dropout_rate=dropout_rate,
                activation=activation,
                num_groups=num_groups,
                num_blocks=num_blocks,
                extra_final_layers=[
                    ("upsample", nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)),
                    ("final", nn.Conv2d(offset_dim, 2, kernel_size=(3, 3), padding=1)),
                ],
                use_se=use_se,
                use_dropblock=use_dropblock,
                dropblock_prob=dropblock_prob,
            )
        else:
            self.offset = None

        # YOLO Head
        self.yolo_head = build_regression_block(
            channels,
            embedding_size=yolo_dim,
            dropout_rate=dropout_rate,
            activation=activation,
            num_groups=num_groups,
            num_blocks=num_blocks,
            extra_final_layers=[
                ("upsample", nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False)),
                ("final", nn.Conv2d(yolo_dim, 21, kernel_size=(3, 3), padding=1)),  # Assuming YOLO output has 21 channels
            ],
            use_se=use_se,
            use_dropblock=use_dropblock,
            dropblock_prob=dropblock_prob,
        )

        # Determine the total output channels based on the presence of offset
        self.out_channels = 6 if self.offset is not None else 4

        # Initialize weights
        self._init_weights()

        # Initialize metrics
        self.accuracy = torchmetrics.Accuracy(task="binary")
        self.f1 = torchmetrics.F1Score(num_classes=2, average='macro', task='multiclass')

    def _init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm, SqueezeExcitation)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Initialize biases for certain layers
        if hasattr(self.objectness, 'final'):
            self.objectness.final.bias.data.fill_(-5)
        if hasattr(self.classifier_up, 'final'):
            self.classifier_up.final.bias.data.fill_(-1)
        if hasattr(self.size_up, 'final'):
            self.size_up.final.bias.data.fill_(2)

    def forward(self, features: Tensor) -> Dict[str, Tensor]:
        class_features = self.classifier(features)
        length_features = self.size(features)

        new_class_features = self.classifier_recombination(class_features, length_features)
        new_length_features = self.size_recombination(length_features, class_features)

        predicted_classes = self.classifier_up(new_class_features)
        predicted_length = self.size_up(new_length_features)

        output = {
            CENTERNET_OUTPUT_OBJECTNESS_MAP: self.objectness(features),
            CENTERNET_OUTPUT_VESSEL_MAP: predicted_classes[:, 0:1],
            CENTERNET_OUTPUT_FISHING_MAP: predicted_classes[:, 1:2],
            CENTERNET_OUTPUT_SIZE: predicted_length,
            CENTERNET_OUTPUT_YOLO: self.yolo_head(features),  # Add YOLO output
        }

        if self.offset is not None:
            output[CENTERNET_OUTPUT_OFFSET] = self.offset(features).clip(0, 1)

        return output
Key Points:

Multiple Branches:
Objectness: Predicts the confidence of object presence.
Classifier: Predicts vessel and fishing status.
Size: Estimates the size of the detected vessels.
Offset: (Optional) Provides localization offsets.
YOLO Head: Generates YOLO-style predictions.
build_regression_block:
This function is a factory that builds regression blocks according to the provided parameters.
Critical: Ensure that build_regression_block always returns an nn.Module that outputs a tensor during its forward pass. If it conditionally returns None, that would cause the observed error.
Forward Method:
Aggregates outputs from different branches into a dictionary.
Ensures that if offset_dim is set, the offset output is included; otherwise, it's omitted.
Weight Initialization:
Properly initializes the weights for convolutional layers and normalization layers.
Specifically initializes biases for certain layers to stabilize training.
Debugging Metrics:
Initialized accuracy and f1 metrics, though they are not used in the forward pass directly.
Potential Issues:

build_regression_block Implementation:
If this function incorrectly constructs the layers or conditionally returns None, outputs like self.objectness, self.classifier, self.size, or self.yolo_head could end up being None.
Action: Verify the implementation of build_regression_block to ensure it always returns a valid nn.Module.
YOLO Head Output Channels:
Your YOLO head is configured to output 21 channels (nn.Conv2d(yolo_dim, 21, ...)). Ensure that this aligns with your model's expectations and downstream processing.
Action: Confirm that CENTERNET_OUTPUT_YOLO expects 21 channels. If it expects a different number, adjust accordingly.
Feature Recombination:
Ensure that FeatureRecombination modules correctly process inputs and always return tensors.
Action: Review the implementation of FeatureRecombination to prevent any NoneType returns.
Recommendations:

Verify build_regression_block:
Ensure that for all configurations, build_regression_block returns an nn.Module that outputs a tensor.
Sample Implementation:

def build_regression_block(...):
    # Example of building a regression block
    layers = []
    for _ in range(num_blocks):
        layers.append(nn.Conv2d(...))
        layers.append(nn.GroupNorm(num_groups, ...))
        layers.append(get_activation_block(activation))
        if use_se:
            layers.append(SqueezeExcitation(...))
        if use_dropblock:
            layers.append(DropBlock2D(block_size=7, drop_prob=dropblock_prob))
    for name, layer in extra_final_layers:
        layers.append((name, layer))
    return nn.Sequential(*layers)
Ensure: No conditional paths return None. All paths construct and return a valid nn.Sequential or other nn.Module.
Add Debug Statements Within DecoupledHeadGroupNormLateShuffle:
To pinpoint which output is None, add debug prints after each branch's output.
Example:

def forward(self, features: Tensor) -> Dict[str, Tensor]:
    ...
    # After predicting each branch
    objectness_map = self.objectness(features)
    if objectness_map is None:
        logger.error("objectness_map is None")
    vessel_map = predicted_classes[:, 0:1]
    if vessel_map is None:
        logger.error("vessel_map is None")
    fishing_map = predicted_classes[:, 1:2]
    if fishing_map is None:
        logger.error("fishing_map is None")
    size_map = predicted_length
    if size_map is None:
        logger.error("size_map is None")
    yolo_output = self.yolo_head(features)
    if yolo_output is None:
        logger.error("yolo_output is None")
    
    output = {
        CENTERNET_OUTPUT_OBJECTNESS_MAP: objectness_map,
        CENTERNET_OUTPUT_VESSEL_MAP: vessel_map,
        CENTERNET_OUTPUT_FISHING_MAP: fishing_map,
        CENTERNET_OUTPUT_SIZE: size_map,
        CENTERNET_OUTPUT_YOLO: yolo_output,
    }

    if self.offset is not None:
        offset = self.offset(features).clip(0, 1)
        if offset is None:
            logger.error("offset is None")
        output[CENTERNET_OUTPUT_OFFSET] = offset

    return output
Ensure All Heads Return Valid Tensors:
Confirm that each head (objectness, classifier, size, yolo_head, offset) reliably returns a tensor.
Example Debug Statements:

print(f"[DEBUG] Objectness map shape: {objectness_map.shape}")
print(f"[DEBUG] Vessel map shape: {vessel_map.shape}")
print(f"[DEBUG] Fishing map shape: {fishing_map.shape}")
print(f"[DEBUG] Size map shape: {size_map.shape}")
print(f"[DEBUG] YOLO output shape: {yolo_output.shape}")
if self.offset is not None:
    print(f"[DEBUG] Offset map shape: {offset.shape}")
Review YOLOHead Implementation:
Ensure that YOLOHead is correctly integrated and returns a tensor.
Current Implementation:

class YOLOHead(nn.Module):
    def __init__(self, num_classes: int, anchors: List[Tuple[int, int]], input_image_size=(640, 640), stride=32):
        super(YOLOHead, self).__init__()
        self.num_classes = num_classes
        self.anchors = anchors
        self.num_anchors = len(anchors)
        self.stride = stride
        self.input_image_size = input_image_size
        
        # Grid dimensions
        self.grid_height = input_image_size[0] // stride
        self.grid_width = input_image_size[1] // stride
        
        # Validate grid dimensions
        if self.grid_height <= 0 or self.grid_width <= 0:
            raise ValueError(f"Invalid grid dimensions: grid_height={self.grid_height}, grid_width={self.grid_width}. Ensure stride is a positive integer and input_image_size is divisible by stride.")
        
        self.out_channels = self.num_anchors * (5 + self.num_classes)  # (4 bbox + 1 confidence + classes)

        # Convolutional Layers
        self.reg_conv = nn.Conv2d(256, 4 * self.num_anchors, kernel_size=1)
        self.obj_conv = nn.Conv2d(256, self.num_anchors, kernel_size=1)
        self.cls_conv = nn.Conv2d(256, self.num_classes * self.num_anchors, kernel_size=1)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Forward pass for YOLOHead."""
        cls_pred = self.cls_conv(x)  # Shape: [B, num_classes * num_anchors, H, W]
        reg_pred = self.reg_conv(x)  # Shape: [B, 4 * num_anchors, H, W]
        obj_pred = self.obj_conv(x)  # Shape: [B, num_anchors, H, W]
        yolo_output = torch.cat([reg_pred, obj_pred, cls_pred], dim=1)  # Shape: [B, 5 * num_anchors + num_classes * num_anchors, H, W]
        return yolo_output
Key Points:
Input Channels: self.reg_conv, self.obj_conv, and self.cls_conv expect an input with 256 channels.
Output Channels: Based on num_classes and num_anchors.
Concatenation: Correctly concatenates regression, objectness, and classification predictions.
Potential Issues:
Ensure that the input to YOLOHead indeed has 256 channels as expected.
Mismatch Scenario: If the input to YOLOHead does not have 256 channels, it could lead to shape mismatches or, in some extreme cases, undefined behavior (though not directly causing NoneType).
Recommendations:
Confirm Input Channels: Verify that the tensor passed to YOLOHead has 256 channels.
Add Debug Statements:

def forward(self, x: torch.Tensor) -> torch.Tensor:
    print(f"[DEBUG] YOLOHead input shape: {x.shape}")
    cls_pred = self.cls_conv(x)
    reg_pred = self.reg_conv(x)
    obj_pred = self.obj_conv(x)
    yolo_output = torch.cat([reg_pred, obj_pred, cls_pred], dim=1)
    print(f"[DEBUG] YOLOHead output shape: {yolo_output.shape}")
    return yolo_output
3. Identifying and Resolving the NoneType Error
Based on the provided error logs and the reviewed class implementations, the NoneType issue arises during the torch.cat operation in the CenterNetUNetModel's forward method. This indicates that one of the entries in the dictionary outputs being concatenated is None. Here's how to systematically address this:
A. Pinpoint the Culprit

Enhanced Debug Statements:
Add debug prints right after assembling the outputs dictionary to identify which key is None.

# centernet_unet.py

def forward(self, x):
    # ... [Existing forward pass up to constructing 'output'] ...
    
    # After constructing 'output'
    for key, value in output.items():
        if value is None:
            logger.error(f"Output for {key} is None.")
        else:
            logger.debug(f"Output {key} shape: {value.shape}")

    # Concatenate All Relevant Feature Maps
    try:
        concatenated = torch.cat([
            outputs[CENTERNET_OUTPUT_OBJECTNESS_MAP],  # [B, 1, H, W]
            outputs[CENTERNET_OUTPUT_VESSEL_MAP],      # [B, 1, H, W]
            outputs[CENTERNET_OUTPUT_FISHING_MAP],     # [B, 1, H, W]
            outputs[CENTERNET_OUTPUT_SIZE],            # [B, 1, H, W]
            outputs[CENTERNET_OUTPUT_OFFSET],          # [B, 2, H, W]
            outputs[CENTERNET_OUTPUT_YOLO],            # [B, YOLO_out_channels, H, W]
        ], dim=1)
        logger.debug(f"Concatenated features shape: {concatenated.shape}")
    except Exception as e:
        logger.error(f"Error during concatenation: {e}")
        raise e

    # ... [Rest of the forward pass] ...
Run a Forward Pass with Debugging:
Use a small dummy input and observe which key logs an error indicating None. Example Test:

import torch
from omegaconf import OmegaConf

# Load configuration
config = OmegaConf.load("path_to_your_config.yaml")

# Instantiate the model
model = EnhancedCenterNetUNet.from_config(config)
model.eval()

# Create a dummy input tensor
dummy_input = torch.randn(2, config.dataset.num_channels, 512, 512)  # Adjusted size

# Forward Pass
with torch.no_grad():
    outputs = model(dummy_input)
    for key, value in outputs.items():
        print(f"{key}: {value.shape if value is not None else 'None'}")
Identify the None Output:
Once the problematic key is identified (e.g., CENTERNET_OUTPUT_YOLO), focus your debugging efforts on that specific head module.
B. Ensuring Heads Return Tensors

Based on the provided class definitions, both DecoupledHeadGroupNormLateShuffle and YOLOHead should always return tensors. However, potential issues can arise from:
Implementation of build_regression_block:
If build_regression_block fails to construct the layers correctly or conditionally returns None, the corresponding head would output None.
Action: Inspect the implementation of build_regression_block to ensure it always returns a valid nn.Module that produces tensors.
Sample build_regression_block Implementation:

def build_regression_block(
    in_channels,
    embedding_size,
    dropout_rate,
    activation,
    num_groups,
    num_blocks,
    extra_final_layers,
    use_se,
    use_dropblock,
    dropblock_prob
):
    layers = []
    for _ in range(num_blocks):
        layers.append(nn.Conv2d(in_channels, embedding_size, kernel_size=3, padding=1, bias=False))
        layers.append(nn.GroupNorm(num_groups, embedding_size))
        if activation.lower() == 'relu':
            layers.append(nn.ReLU(inplace=True))
        elif activation.lower() == 'leaky_relu':
            layers.append(nn.LeakyReLU(inplace=True))
        elif activation.lower() == 'silu':
            layers.append(nn.SiLU(inplace=True))
        else:
            raise ValueError(f"Unsupported activation: {activation}")
        
        if use_se:
            layers.append(SqueezeExcitation(embedding_size))  # Ensure SqueezeExcitation is correctly implemented
        
        if use_dropblock:
            layers.append(DropBlock2D(block_size=7, drop_prob=dropblock_prob))
    
    # Add any extra final layers
    for name, layer in extra_final_layers:
        layers.append(layer)
    
    return nn.Sequential(*layers)
Ensure:
All parameters are correctly handled.
No paths return None. Every time the function is called, it should return an nn.Sequential containing layers.
Consistency in Channel Dimensions:
YOLO Head: Ensure that the input tensor to YOLOHead has the expected number of channels (256 in your case).
From DecoupledHeadGroupNormLateShuffle Forward:

output = {
    CENTERNET_OUTPUT_OBJECTNESS_MAP: self.objectness(features),
    CENTERNET_OUTPUT_VESSEL_MAP: predicted_classes[:, 0:1],
    CENTERNET_OUTPUT_FISHING_MAP: predicted_classes[:, 1:2],
    CENTERNET_OUTPUT_SIZE: predicted_length,
    CENTERNET_OUTPUT_YOLO: self.yolo_head(features),  # Ensure features have correct channels
}
Ensure: features passed to self.yolo_head(features) possess 256 channels as expected by YOLOHead.
Implementing Additional Debugging in Heads:
Add Debug Statements: Within each head's forward method, print out shapes and verify that outputs are not None.
Example for DecoupledHeadGroupNormLateShuffle:

def forward(self, features: Tensor) -> Dict[str, Tensor]:
    class_features = self.classifier(features)
    length_features = self.size(features)

    new_class_features = self.classifier_recombination(class_features, length_features)
    new_length_features = self.size_recombination(length_features, class_features)

    predicted_classes = self.classifier_up(new_class_features)
    predicted_length = self.size_up(new_length_features)

    objectness_map = self.objectness(features)
    vessel_map = predicted_classes[:, 0:1]
    fishing_map = predicted_classes[:, 1:2]
    size_map = predicted_length
    yolo_output = self.yolo_head(features)

    if self.offset is not None:
        offset = self.offset(features).clip(0, 1)
        output = {
            CENTERNET_OUTPUT_OBJECTNESS_MAP: objectness_map,
            CENTERNET_OUTPUT_VESSEL_MAP: vessel_map,
            CENTERNET_OUTPUT_FISHING_MAP: fishing_map,
            CENTERNET_OUTPUT_SIZE: size_map,
            CENTERNET_OUTPUT_YOLO: yolo_output,
            CENTERNET_OUTPUT_OFFSET: offset,
        }
    else:
        output = {
            CENTERNET_OUTPUT_OBJECTNESS_MAP: objectness_map,
            CENTERNET_OUTPUT_VESSEL_MAP: vessel_map,
            CENTERNET_OUTPUT_FISHING_MAP: fishing_map,
            CENTERNET_OUTPUT_SIZE: size_map,
            CENTERNET_OUTPUT_YOLO: yolo_output,
        }

    # Debug Statements
    for key, value in output.items():
        if value is None:
            logger.error(f"{key} is None.")
        else:
            logger.debug(f"{key} shape: {value.shape}")

    return output
Example for YOLOHead:

def forward(self, x: torch.Tensor) -> torch.Tensor:
    print(f"[DEBUG] YOLOHead input shape: {x.shape}")
    cls_pred = self.cls_conv(x)
    reg_pred = self.reg_conv(x)
    obj_pred = self.obj_conv(x)
    yolo_output = torch.cat([reg_pred, obj_pred, cls_pred], dim=1)
    print(f"[DEBUG] YOLOHead output shape: {yolo_output.shape}")
    return yolo_output
Ensure No Early Returns or Missing Return Statements:
Issue: If any branch within the forward method doesn't end with a return statement, the function may inadvertently return None.
Solution: Verify that all forward methods conclude with a return statement returning expected tensors or dictionaries.
Confirm FeatureRecombination Functionality:
Ensure: The FeatureRecombination modules correctly process inputs and always return valid tensors.
Action: Review the implementation of FeatureRecombination to prevent any inadvertent None returns.
Sample Check:

class FeatureRecombination(nn.Module):
    def __init__(self, in_channels1, in_channels2):
        super().__init__()
        self.conv = nn.Conv2d(in_channels1 + in_channels2, in_channels1, kernel_size=1)
        self.activation = nn.ReLU(inplace=True)

    def forward(self, x1, x2):
        combined = torch.cat([x1, x2], dim=1)
        combined = self.conv(combined)
        combined = self.activation(combined)
        return combined
4. Implementing Targeted Debugging
To precisely identify which output is None, implement targeted debugging within your model's forward method.

# centernet_unet.py

def forward(self, x):
    logger.debug("Starting forward pass through CenterNetUNetModel.")
    
    # Initial Upsampling
    logger.debug("Performing initial upsampling.")
    x = self.initial_upsample(x)
    logger.debug(f"After initial upsampling: {x.shape}")
    
    # Encoder Forward Pass
    logger.debug("Passing through encoder.")
    encoder_features = self.encoder(x)
    logger.debug(f"Encoder features shapes: {[f.shape for f in encoder_features]}")
    
    # Transformer Feature
    transformer_feature = encoder_features[-1]
    logger.debug(f"Transformer feature shape: {transformer_feature.shape}")
    
    # FAN Processing
    aggregated_feature = self.fan([transformer_feature])
    logger.debug(f"Aggregated feature shape: {aggregated_feature.shape}")
    
    # Replace the last encoder feature with the aggregated feature
    encoder_features[-1] = aggregated_feature
    
    # Decoder
    logger.debug("Passing aggregated features to decoder.")
    decoded_features = self.decoder(encoder_features)
    if not isinstance(decoded_features, list):
        logger.error("Decoder output is not a list.")
        raise ValueError("Decoder output is not a list.")
    
    for idx, feature in enumerate(decoded_features):
        if feature is None:
            logger.error(f"Decoded feature at index {idx} is None.")
        else:
            logger.debug(f"Decoded feature {idx} shape: {feature.shape}")
    
    # Neck Processing
    logger.debug("Processing neck features.")
    neck_features = self.neck(decoded_features)
    neck_features = self.batch_norm(neck_features)
    neck_features = self.dropout(neck_features)
    neck_features = self.neck_conv(neck_features)
    logger.debug(f"Neck features shape after processing: {neck_features.shape}")

    # Heads Forward Pass
    logger.debug("Passing neck features to heads.")
    head_outputs = {}
    for head in self.heads:
        head_output = head(neck_features)
        if isinstance(head, DecoupledHeadGroupNormLateShuffle):
            if isinstance(head_output, dict):
                head_outputs.update(head_output)
                logger.debug("DecoupledHeadGroupNormLateShuffle output updated in head_outputs.")
            else:
                logger.warning("DecoupledHeadGroupNormLateShuffle did not return a dict.")
        elif isinstance(head, YOLOHead):
            head_outputs[CENTERNET_OUTPUT_YOLO] = head_output
            logger.debug("YOLOHead output added to head_outputs.")
        else:
            logger.warning(f"Unhandled head type: {type(head)}")
    
    # Post-Head Output Checks
    logger.debug("Verifying all head outputs are valid Tensors.")
    for key, value in head_outputs.items():
        if value is None:
            logger.error(f"Output for {key} is None.")
        else:
            logger.debug(f"Output {key} shape: {value.shape}")
    
    # Concatenate All Relevant Feature Maps
    try:
        concatenated = torch.cat([
            outputs[CENTERNET_OUTPUT_OBJECTNESS_MAP],  # [B, 1, H, W]
            outputs[CENTERNET_OUTPUT_VESSEL_MAP],      # [B, 1, H, W]
            outputs[CENTERNET_OUTPUT_FISHING_MAP],     # [B, 1, H, W]
            outputs[CENTERNET_OUTPUT_SIZE],            # [B, 1, H, W]
            outputs[CENTERNET_OUTPUT_OFFSET],          # [B, 2, H, W]
            outputs[CENTERNET_OUTPUT_YOLO],            # [B, YOLO_out_channels, H, W]
        ], dim=1)
        logger.debug(f"Concatenated features shape: {concatenated.shape}")
    except Exception as e:
        logger.error(f"Error during concatenation: {e}")
        raise e

    # Fusion Layer
    fused_output = self.fusion(concatenated)
    fused_output = self.dropout_fusion(fused_output)
    outputs['fused_output'] = fused_output
    logger.debug(f"Fused output shape: {fused_output.shape}")

    return outputs
Steps Implemented:

Post-Head Output Validation:
After processing each head, verify that the output is not None and log its shape.
Any None outputs will be explicitly logged, aiding in identifying the problematic head.
Exception Handling:
The torch.cat operation is wrapped in a try-except block to catch and log errors gracefully.
Comprehensive Logging:
Enhanced logging provides clear insights into tensor shapes at every critical step, facilitating easier debugging.
Outcome:

Upon implementing these changes and running a forward pass with debug statements, you should see logs indicating whether any of the head outputs are None. For example:

DEBUG:root:Output CENTERNET_OUTPUT_OBJECTNESS_MAP shape: torch.Size([2, 1, 256, 256])
DEBUG:root:Output CENTERNET_OUTPUT_VESSEL_MAP shape: torch.Size([2, 1, 256, 256])
DEBUG:root:Output CENTERNET_OUTPUT_FISHING_MAP shape: torch.Size([2, 1, 256, 256])
DEBUG:root:Output CENTERNET_OUTPUT_SIZE shape: torch.Size([2, 1, 256, 256])
DEBUG:root:Output CENTERNET_OUTPUT_YOLO shape: torch.Size([2, 21, 256, 256])
If any of these logs indicate None, focus your debugging efforts on the corresponding head.
5. Reviewing and Correcting YOLOHead Integration
Given that YOLOHead is generating 21 output channels, ensure that this aligns with your model's configuration and expectations.
Current YOLOHead Implementation:


class YOLOHead(nn.Module):
    def __init__(self, num_classes: int, anchors: List[Tuple[int, int]], input_image_size=(640, 640), stride=32):
        super(YOLOHead, self).__init__()
        self.num_classes = num_classes
        self.anchors = anchors
        self.num_anchors = len(anchors)
        self.stride = stride
        self.input_image_size = input_image_size
        
        # Grid dimensions
        self.grid_height = input_image_size[0] // stride
        self.grid_width = input_image_size[1] // stride
        
        # Validate grid dimensions
        if self.grid_height <= 0 or self.grid_width <= 0:
            raise ValueError(f"Invalid grid dimensions: grid_height={self.grid_height}, grid_width={self.grid_width}. Ensure stride is a positive integer and input_image_size is divisible by stride.")
        
        self.out_channels = self.num_anchors * (5 + self.num_classes)  # (4 bbox + 1 confidence + classes)

        # Convolutional Layers
        self.reg_conv = nn.Conv2d(256, 4 * self.num_anchors, kernel_size=1)  # Ensure input channels match
        self.obj_conv = nn.Conv2d(256, self.num_anchors, kernel_size=1)
        self.cls_conv = nn.Conv2d(256, self.num_classes * self.num_anchors, kernel_size=1)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Forward pass for YOLOHead."""
        cls_pred = self.cls_conv(x)  # Shape: [B, num_classes * num_anchors, H, W]
        reg_pred = self.reg_conv(x)  # Shape: [B, 4 * num_anchors, H, W]
        obj_pred = self.obj_conv(x)  # Shape: [B, num_anchors, H, W]
        yolo_output = torch.cat([reg_pred, obj_pred, cls_pred], dim=1)  # Shape: [B, 5 * num_anchors + num_classes * num_anchors, H, W]
        return yolo_output
Key Points:

Input Channels:
self.reg_conv, self.obj_conv, and self.cls_conv are initialized with in_channels=256.
Ensure: The input tensor x passed to YOLOHead has 256 channels.
Output Channels:
self.out_channels calculates the total output channels based on the number of anchors and classes.
In Your Case: Since num_classes=2 and num_anchors=3, self.out_channels = 3 * (5 + 2) = 21.
Ensure: This aligns with the expected channels in CENTERNET_OUTPUT_YOLO.
Grid Dimensions:
The division in self.grid_height and self.grid_width must result in positive integers.
Action: Ensure that input_image_size is divisible by stride.
Potential Issues:

Mismatch in Input Channels:
If the tensor passed to YOLOHead does not have 256 channels, the convolutional layers will either throw an error or produce unexpected outputs.
build_regression_block vs. YOLOHead:
There might be confusion if YOLOHead is instantiated differently elsewhere using build_regression_block.
Action: Ensure that the YOLOHead used in DecoupledHeadGroupNormLateShuffle is correctly implemented and instantiated, matching the expected architecture.
Recommendations:

Verify Input Channels to YOLOHead:
Add a debug statement to confirm the shape of features before it's passed to self.yolo_head(features).

def forward(self, features: Tensor) -> Dict[str, Tensor]:
    logger.debug(f"Features shape before YOLOHead: {features.shape}")
    yolo_output = self.yolo_head(features)
    if yolo_output is None:
        logger.error("YOLOHead returned None.")
    else:
        logger.debug(f"YOLOHead output shape: {yolo_output.shape}")
    # ... rest of the forward method ...
Ensure Consistent Use of YOLOHead:
If you're using build_regression_block to create self.yolo_head, ensure that it matches the expected structure of YOLOHead. Otherwise, discrepancies can arise.
Alternative Approach:
If build_regression_block is not mirroring the YOLOHead structure, consider directly initializing YOLOHead within DecoupledHeadGroupNormLateShuffle.

self.yolo_head = YOLOHead(
    num_classes=your_num_classes,
    anchors=your_anchors,
    input_image_size=your_input_size,
    stride=your_stride
)
Consistent Channel Dimensions:
If there's a misalignment between expected and actual channel dimensions in any head module, it can lead to either silent failures or improper outputs.
Action: Ensure all channel dimensions align as per the model configuration.
Handle Optional Outputs Gracefully:
If certain branches are optional (like offset), ensure that their absence doesn't disrupt downstream operations.
Action: Ensure that conditional outputs are handled correctly, and default values are set if necessary.
6. Ensuring FeatureRecombination Returns Valid Tensors
If FeatureRecombination is incorrectly implemented and returns None, it can propagate None to subsequent modules.

class FeatureRecombination(nn.Module):
    def __init__(self, in_channels1, in_channels2):
        super().__init__()
        self.conv = nn.Conv2d(in_channels1 + in_channels2, in_channels1, kernel_size=1)
        self.activation = nn.ReLU(inplace=True)

    def forward(self, x1, x2):
        combined = torch.cat([x1, x2], dim=1)
        combined = self.conv(combined)
        combined = self.activation(combined)
        return combined
Key Points:

Consistent Returns:
Ensures that forward always returns a tensor.
Debugging Statements:
Add logs to confirm the shapes at each step.

def forward(self, x1, x2):
    print(f"[DEBUG] FeatureRecombination inputs shapes: x1={x1.shape}, x2={x2.shape}")
    combined = torch.cat([x1, x2], dim=1)
    print(f"[DEBUG] Combined shape: {combined.shape}")
    combined = self.conv(combined)
    print(f"[DEBUG] After conv: {combined.shape}")
    combined = self.activation(combined)
    print(f"[DEBUG] After activation: {combined.shape}")
    return combined
Recommendations:

Verify Inputs:
Ensure that x1 and x2 are always valid tensors and not None.
Handle Edge Cases:
If x1 and x2 can sometimes be None or of incompatible shapes, handle these cases to prevent unexpected None returns.
7. Comprehensive Debugging Strategy
Given that the error stems from one of the head outputs being None, here's a step-by-step strategy to locate and fix the issue:
A. Identify the None Output

Run a Forward Pass with a Dummy Input:
Use a minimal setup to isolate the problem.

import torch
from omegaconf import OmegaConf

# Load configuration
config = OmegaConf.load("path_to_your_config.yaml")

# Instantiate the model
model = EnhancedCenterNetUNet.from_config(config)
model.eval()

# Create a dummy input tensor
dummy_input = torch.randn(2, config.dataset.num_channels, 512, 512)  # Adjust size as per your config

# Forward Pass
with torch.no_grad():
    outputs = model(dummy_input)

# Print outputs
for key, value in outputs.items():
    if value is not None:
        print(f"{key}: {value.shape}")
    else:
        print(f"{key}: None")
Analyze the Output:
Determine which key has a None value.
Focus your debugging efforts on that specific head module.
B. Trace the Flow in the Problematic Head

Add Debug Statements in the Faulty Head:
For instance, if CENTERNET_OUTPUT_YOLO is None, add logs in the YOLOHead forward method.

class YOLOHead(nn.Module):
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        print(f"[DEBUG] YOLOHead input shape: {x.shape}")
        cls_pred = self.cls_conv(x)
        print(f"[DEBUG] YOLOHead cls_pred shape: {cls_pred.shape}")
        reg_pred = self.reg_conv(x)
        print(f"[DEBUG] YOLOHead reg_pred shape: {reg_pred.shape}")
        obj_pred = self.obj_conv(x)
        print(f"[DEBUG] YOLOHead obj_pred shape: {obj_pred.shape}")
        yolo_output = torch.cat([reg_pred, obj_pred, cls_pred], dim=1)
        print(f"[DEBUG] YOLOHead yolo_output shape: {yolo_output.shape}")
        return yolo_output
Verify Layer Outputs:
Ensure that each convolutional layer (cls_conv, reg_conv, obj_conv) outputs valid tensors.
Confirm that concatenation is performed correctly without introducing None.
C. Ensure All Modules Are Properly Initialized

Confirm Initialization:
Verify that all modules, especially optional ones like AttentionCBAM and DropBlock2D, are correctly initialized.
Ensure that no layers are inadvertently set to None or left uninitialized.
Check for Conditional Returns:
In any module's forward method, ensure that all execution paths culminate in a return statement returning a tensor.
D. Handle Edge Cases in Input Data

Validate Input Data:
Ensure that your training data loader doesn't supply None or malformed tensors.
Check for data preprocessing steps that might inadvertently introduce None values.
Sanitize Inputs:
Add sanity checks in your data pipeline to confirm the integrity of inputs.

def get_batch(self, batch: Tuple[Any, Any]) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
    inputs, targets = batch
    if inputs is None or any(t is None for t in targets.values()):
        logger.error("Received a batch with None inputs or targets.")
        raise ValueError("Invalid batch with None inputs or targets.")
    return inputs, targets
E. Validate Model Configuration

Consistency Check:
Ensure that your configuration aligns with the model's architecture, particularly regarding channel dimensions and image sizes.
Automate Configuration Validation:
Implement checks that validate the configuration before model instantiation.

def validate_config(config):
    expected_encoder_channels = [24, 48, 64, 160, 256]
    actual_encoder_channels = config.model.decoder.encoder_channels
    assert actual_encoder_channels == expected_encoder_channels, f"Encoder channels mismatch: expected {expected_encoder_channels}, got {actual_encoder_channels}"
    
    assert config.model.fan.input_channels == [256], f"FAN input channels mismatch: expected [256], got {config.model.fan.input_channels}"
    
    # Add more consistency checks as needed
Usage:

config = OmegaConf.load("path_to_your_config.yaml")
validate_config(config)
8. Final Recommendations
Implement Comprehensive Logging:
Detailed logs at every critical point help in quickly identifying faulty components.
Unit Test Individual Components:
Test UnetBlock, DecoupledHeadGroupNormLateShuffle, YOLOHead, and FeatureRecombination in isolation to ensure they function as expected.
Simplify Temporarily:
Temporarily remove optional components (like AttentionCBAM or DropBlock2D) to see if the error persists. This can help isolate the issue.
Review Data Pipeline:
Ensure that the data fed into the model doesn't contain None values and that preprocessing steps are correctly implemented.
Monitor Memory Usage:
Given the earlier memory issues, ensure that memory consumption is within acceptable limits to prevent unexpected behavior.
Version Compatibility:
Ensure that all libraries (e.g., PyTorch, TorchMetrics) are compatible with each other and up-to-date to leverage bug fixes and optimizations.
Seek Modular Enhancements:
Consider modularizing complex components further to facilitate easier debugging and maintenance.
Conclusion
The NoneType error during the torch.cat operation is most likely due to one of the head modules (DecoupledHeadGroupNormLateShuffle or YOLOHead) returning None instead of a tensor. By implementing the above debugging strategies, verifying the correct implementation of each module, and ensuring consistent and correct configurations, you can systematically identify and rectify the issue.
If after these adjustments the problem persists, consider sharing additional details or code snippets, especially the implementation of build_regression_block and any other custom modules involved, to facilitate more targeted assistance.
Feel free to reach out with further questions or updates on your progress!
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