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import logging
import torch
logger = logging.getLogger(__name__)
class ContextualWeightOverrideAgent:
def __init__(self):
self.context_overrides = {
# Example: when image is outdoor, model_X is penalized, model_Y is boosted
"outdoor": {
"model_1": 0.8, # Example: Reduce weight of model_1 by 20% for outdoor scenes
"model_5": 1.2, # Example: Boost weight of model_5 by 20% for outdoor scenes
},
"low_light": {
"model_2": 0.7,
"model_7": 1.3,
},
"sunny": {
"model_3": 0.9,
"model_4": 1.1,
}
# Add more contexts and their specific model weight adjustments here
}
def get_overrides(self, context_tags: list[str]) -> dict:
"""Returns combined weight overrides for given context tags."""
combined_overrides = {}
for tag in context_tags:
if tag in self.context_overrides:
for model_id, multiplier in self.context_overrides[tag].items():
# If a model appears in multiple contexts, we can decide how to combine (e.g., multiply, average, take max)
# For now, let's just take the last one if there are conflicts, or multiply for simple cumulative effect.
combined_overrides[model_id] = combined_overrides.get(model_id, 1.0) * multiplier
return combined_overrides
class ModelWeightManager:
def __init__(self):
self.base_weights = {
"model_1": 0.15, # SwinV2 Based
"model_2": 0.15, # ViT Based
"model_3": 0.15, # SDXL Dataset
"model_4": 0.15, # SDXL + FLUX
"model_5": 0.15, # ViT Based
"model_5b": 0.10, # ViT Based, Newer Dataset
"model_6": 0.10, # Swin, Midj + SDXL
"model_7": 0.05 # ViT
}
self.situation_weights = {
"high_confidence": 1.2, # Boost weights for high confidence predictions
"low_confidence": 0.8, # Reduce weights for low confidence
"conflict": 0.5, # Reduce weights when models disagree
"consensus": 1.5 # Boost weights when models agree
}
self.context_override_agent = ContextualWeightOverrideAgent()
def adjust_weights(self, predictions, confidence_scores, context_tags: list[str] = None):
"""Dynamically adjust weights based on prediction patterns and optional context."""
adjusted_weights = self.base_weights.copy()
# 1. Apply contextual overrides first
if context_tags:
overrides = self.context_override_agent.get_overrides(context_tags)
for model_id, multiplier in overrides.items():
adjusted_weights[model_id] = adjusted_weights.get(model_id, 0.0) * multiplier
# 2. Apply situation-based adjustments (consensus, conflict, confidence)
# Check for consensus
if self._has_consensus(predictions):
for model in adjusted_weights:
adjusted_weights[model] *= self.situation_weights["consensus"]
# Check for conflicts
if self._has_conflicts(predictions):
for model in adjusted_weights:
adjusted_weights[model] *= self.situation_weights["conflict"]
# Adjust based on confidence
for model, confidence in confidence_scores.items():
if confidence > 0.8:
adjusted_weights[model] *= self.situation_weights["high_confidence"]
elif confidence < 0.5:
adjusted_weights[model] *= self.situation_weights["low_confidence"]
return self._normalize_weights(adjusted_weights)
def _has_consensus(self, predictions):
"""Check if models agree on prediction"""
# Ensure all predictions are not None before checking for consensus
non_none_predictions = [p.get("Label") for p in predictions.values() if p is not None and isinstance(p, dict) and p.get("Label") is not None and p.get("Label") != "Error"]
return len(non_none_predictions) > 0 and len(set(non_none_predictions)) == 1
def _has_conflicts(self, predictions):
"""Check if models have conflicting predictions"""
# Ensure all predictions are not None before checking for conflicts
non_none_predictions = [p.get("Label") for p in predictions.values() if p is not None and isinstance(p, dict) and p.get("Label") is not None and p.get("Label") != "Error"]
return len(non_none_predictions) > 1 and len(set(non_none_predictions)) > 1
def _normalize_weights(self, weights):
"""Normalize weights to sum to 1"""
total = sum(weights.values())
if total == 0:
# Handle case where all weights became zero due to aggressive multipliers
# This could assign equal weights or revert to base weights
logger.warning("All weights became zero after adjustments. Reverting to base weights.")
return {k: 1.0/len(self.base_weights) for k in self.base_weights}
return {k: v/total for k, v in weights.items()} |