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Single Target Strategy Implementations
Handles different feature extraction strategies for single-target classification
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from enum import Enum
from typing import Union, Tuple, Optional
class SingleTargetStrategy(Enum):
"""Enumeration of single-target classification strategies"""
DIRECT_CLASSIFICATION_HEAD = "Direct classification head"
CLS_TOKEN_CLASSIFICATION = "CLS token classification"
GLOBAL_AVERAGE_POOLING = "Global average pooling"
class FeatureExtractor(nn.Module):
"""Base class for feature extraction strategies"""
def __init__(self, strategy: SingleTargetStrategy):
super().__init__()
self.strategy = strategy
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Extract features based on the strategy"""
raise NotImplementedError
class DirectClassificationHeadExtractor(FeatureExtractor):
"""
Direct classification head strategy for CNN-based models
Uses global average pooling followed by classification head
"""
def __init__(self, input_dim: int, feature_dim: int = 512, dropout: float = 0.1):
super().__init__(SingleTargetStrategy.DIRECT_CLASSIFICATION_HEAD)
# Global average pooling for spatial features
self.global_pool = nn.AdaptiveAvgPool2d((1, 1))
# Feature processing layers
self.feature_processor = nn.Sequential(
nn.Flatten(),
nn.Linear(input_dim, feature_dim),
nn.ReLU(inplace=True),
nn.Dropout(dropout),
nn.BatchNorm1d(feature_dim)
)
self.output_dim = feature_dim
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Extract features using direct classification head approach
Args:
x: Input features [B, C, H, W] (spatial features from CNN) or [B, input_dim] (flattened features)
Returns:
Processed features [B, feature_dim]
"""
# Check if input is spatial (4D) or flattened (2D)
if x.dim() == 4:
# Spatial features: apply global average pooling first
pooled = self.global_pool(x) # [B, C, 1, 1]
features = self.feature_processor(pooled) # [B, feature_dim]
elif x.dim() == 2:
# Flattened features: skip global pooling, go directly to feature processing
# But we need to adjust the input dimension for the linear layer
# The input_dim in the constructor was for spatial features, but now we have flattened features
# We need to create a new feature processor for the flattened input
if not hasattr(self, 'flattened_processor'):
# Create a new processor for flattened features
self.flattened_processor = nn.Sequential(
nn.Linear(x.size(1), self.output_dim), # x.size(1) is the actual flattened dimension
nn.ReLU(inplace=True),
nn.Dropout(self.feature_processor[3].p), # Use same dropout rate
nn.LayerNorm(self.output_dim) # Use LayerNorm instead of BatchNorm to avoid single-sample issues
)
# Move to the same device as the input tensor
self.flattened_processor = self.flattened_processor.to(x.device)
features = self.flattened_processor(x) # [B, feature_dim]
else:
raise ValueError(f"Expected 2D or 4D input tensor, got {x.dim()}D tensor with shape {x.shape}")
return features
class CLSTokenClassificationExtractor(FeatureExtractor):
"""
CLS token classification strategy for Transformer-based models
Extracts the CLS token from transformer output
"""
def __init__(self, feature_dim: int = 768, dropout: float = 0.1):
super().__init__(SingleTargetStrategy.CLS_TOKEN_CLASSIFICATION)
# Feature processing for CLS token
self.feature_processor = nn.Sequential(
nn.Linear(feature_dim, feature_dim),
nn.ReLU(inplace=True),
nn.Dropout(dropout),
nn.LayerNorm(feature_dim)
)
self.output_dim = feature_dim
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Extract CLS token features from transformer output
Args:
x: Transformer output [B, seq_len, feature_dim] or [B, feature_dim]
Returns:
CLS token features [B, feature_dim]
"""
# Handle different input shapes
if x.dim() == 3: # [B, seq_len, feature_dim]
# Extract CLS token (first token)
cls_token = x[:, 0, :] # [B, feature_dim]
elif x.dim() == 2: # [B, feature_dim] - already extracted
cls_token = x
else:
raise ValueError(f"Unexpected input shape: {x.shape}")
# Process CLS token features
features = self.feature_processor(cls_token) # [B, feature_dim]
return features
class GlobalAveragePoolingExtractor(FeatureExtractor):
"""
Global average pooling strategy for models with spatial feature maps
Used for VAE encoders and other models that output spatial features
"""
def __init__(self, input_dim: int, feature_dim: int = 512, dropout: float = 0.1):
super().__init__(SingleTargetStrategy.GLOBAL_AVERAGE_POOLING)
# Global average pooling
self.global_pool = nn.AdaptiveAvgPool2d((1, 1))
# Feature processing layers
self.feature_processor = nn.Sequential(
nn.Flatten(),
nn.Linear(input_dim, feature_dim),
nn.ReLU(inplace=True),
nn.Dropout(dropout),
nn.BatchNorm1d(feature_dim)
)
self.output_dim = feature_dim
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Extract features using global average pooling
Args:
x: Input features [B, C, H, W] (spatial features)
Returns:
Processed features [B, feature_dim]
"""
# Apply global average pooling
pooled = self.global_pool(x) # [B, C, 1, 1]
# Process through feature layers
features = self.feature_processor(pooled) # [B, feature_dim]
return features
def create_feature_extractor(
strategy: Union[str, SingleTargetStrategy],
input_dim: int,
feature_dim: int = 512,
dropout: float = 0.1
) -> FeatureExtractor:
"""
Factory function to create appropriate feature extractor
Args:
strategy: Single-target strategy (string or enum)
input_dim: Input feature dimension
feature_dim: Output feature dimension
dropout: Dropout rate
Returns:
Appropriate FeatureExtractor instance
"""
# Convert string to enum if needed
if isinstance(strategy, str):
strategy = SingleTargetStrategy(strategy)
if strategy == SingleTargetStrategy.DIRECT_CLASSIFICATION_HEAD:
return DirectClassificationHeadExtractor(input_dim, feature_dim, dropout)
elif strategy == SingleTargetStrategy.CLS_TOKEN_CLASSIFICATION:
return CLSTokenClassificationExtractor(feature_dim, dropout)
elif strategy == SingleTargetStrategy.GLOBAL_AVERAGE_POOLING:
return GlobalAveragePoolingExtractor(input_dim, feature_dim, dropout)
else:
raise ValueError(f"Unknown single-target strategy: {strategy}")
def extract_features_from_model_output(
model_output: torch.Tensor,
strategy: Union[str, SingleTargetStrategy],
input_dim: Optional[int] = None,
feature_dim: int = 512,
dropout: float = 0.1
) -> torch.Tensor:
"""
Extract features from model output based on strategy
Args:
model_output: Raw output from backbone model
strategy: Single-target strategy to use
input_dim: Input dimension (required for some strategies)
feature_dim: Output feature dimension
dropout: Dropout rate
Returns:
Extracted features [B, feature_dim]
"""
# Convert string to enum if needed
if isinstance(strategy, str):
strategy = SingleTargetStrategy(strategy)
if strategy == SingleTargetStrategy.DIRECT_CLASSIFICATION_HEAD:
if input_dim is None:
raise ValueError("input_dim required for DIRECT_CLASSIFICATION_HEAD strategy")
extractor = DirectClassificationHeadExtractor(input_dim, feature_dim, dropout)
return extractor(model_output)
elif strategy == SingleTargetStrategy.CLS_TOKEN_CLASSIFICATION:
extractor = CLSTokenClassificationExtractor(feature_dim, dropout)
return extractor(model_output)
elif strategy == SingleTargetStrategy.GLOBAL_AVERAGE_POOLING:
if input_dim is None:
raise ValueError("input_dim required for GLOBAL_AVERAGE_POOLING strategy")
extractor = GlobalAveragePoolingExtractor(input_dim, feature_dim, dropout)
return extractor(model_output)
else:
raise ValueError(f"Unknown single-target strategy: {strategy}")
# Strategy mapping from config string values to enum values
STRATEGY_MAPPING = {
"Direct classification head": SingleTargetStrategy.DIRECT_CLASSIFICATION_HEAD,
"CLS token classification": SingleTargetStrategy.CLS_TOKEN_CLASSIFICATION,
"Global average pooling": SingleTargetStrategy.GLOBAL_AVERAGE_POOLING,
}
def get_strategy_from_name(strategy_name: str) -> SingleTargetStrategy:
"""
Convert strategy string value to SingleTargetStrategy enum.
Args:
strategy_name: Strategy string from config/checkpoint
Returns:
SingleTargetStrategy enum value
"""
if strategy_name not in STRATEGY_MAPPING:
raise ValueError(
f"Unknown strategy: {strategy_name}. Available: {list(STRATEGY_MAPPING.keys())}"
)
return STRATEGY_MAPPING[strategy_name]
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