From bd402e3e3440c6d4f263676f66f375e26ccc24ed Mon Sep 17 00:00:00 2001 From: Narmeen07 Date: Tue, 14 Oct 2025 02:35:49 +0000 Subject: [PATCH 1/2] debiasing using k-steering --- DEBIASING_FIX_GUIDE.md | 90 + DEBUGGING_CHECKLIST.md | 89 + FIXED_analyze_function.py | 53 + FIXED_debias_hook.py | 88 + FairFace | 1 + INSTALL.md | 134 + check_analysis_aggregation.py | 29 + corrected_debias_hook.py | 119 + ethnicity_debiasing_results.json | 76 + notebooks/debiasing-t2i.ipynb | 3092 +++++++++++++++++ notebooks/walkthrough.ipynb | 87 +- requirements.txt | 48 + t2Interp/__pycache__/T2I.cpython-310.pyc | Bin 0 -> 6433 bytes t2Interp/__pycache__/__init__.cpython-310.pyc | Bin 0 -> 145 bytes .../__pycache__/accessors.cpython-310.pyc | Bin 0 -> 3073 bytes t2Interp/__pycache__/blocks.cpython-310.pyc | Bin 0 -> 4370 bytes .../__pycache__/clip_encoder.cpython-310.pyc | Bin 0 -> 2472 bytes .../flux_dual_stream_block.cpython-310.pyc | Bin 0 -> 4546 bytes .../flux_single_stream_block.cpython-310.pyc | Bin 0 -> 3659 bytes .../flux_transformer2D_model.cpython-310.pyc | Bin 0 -> 2552 bytes .../__pycache__/intervention.cpython-310.pyc | Bin 0 -> 5137 bytes .../__pycache__/t5_encoder.cpython-310.pyc | Bin 0 -> 4310 bytes t2Interp/__pycache__/unet.cpython-310.pyc | Bin 0 -> 4930 bytes test_steering_direction.py | 89 + utils/__pycache__/__init__.cpython-310.pyc | Bin 0 -> 142 bytes .../__pycache__/config_loader.cpython-310.pyc | Bin 0 -> 10620 bytes utils/__pycache__/output.cpython-310.pyc | Bin 0 -> 773 bytes utils/__pycache__/utils.cpython-310.pyc | Bin 0 -> 4009 bytes 28 files changed, 3976 insertions(+), 19 deletions(-) create mode 100644 DEBIASING_FIX_GUIDE.md create mode 100644 DEBUGGING_CHECKLIST.md create mode 100644 FIXED_analyze_function.py create mode 100644 FIXED_debias_hook.py create mode 160000 FairFace create mode 100644 INSTALL.md create mode 100644 check_analysis_aggregation.py create mode 100644 corrected_debias_hook.py create mode 100644 ethnicity_debiasing_results.json create mode 100644 notebooks/debiasing-t2i.ipynb create mode 100644 requirements.txt create mode 100644 t2Interp/__pycache__/T2I.cpython-310.pyc create mode 100644 t2Interp/__pycache__/__init__.cpython-310.pyc create mode 100644 t2Interp/__pycache__/accessors.cpython-310.pyc create mode 100644 t2Interp/__pycache__/blocks.cpython-310.pyc create mode 100644 t2Interp/__pycache__/clip_encoder.cpython-310.pyc create mode 100644 t2Interp/__pycache__/flux_dual_stream_block.cpython-310.pyc create mode 100644 t2Interp/__pycache__/flux_single_stream_block.cpython-310.pyc create mode 100644 t2Interp/__pycache__/flux_transformer2D_model.cpython-310.pyc create mode 100644 t2Interp/__pycache__/intervention.cpython-310.pyc create mode 100644 t2Interp/__pycache__/t5_encoder.cpython-310.pyc create mode 100644 t2Interp/__pycache__/unet.cpython-310.pyc create mode 100644 test_steering_direction.py create mode 100644 utils/__pycache__/__init__.cpython-310.pyc create mode 100644 utils/__pycache__/config_loader.cpython-310.pyc create mode 100644 utils/__pycache__/output.cpython-310.pyc create mode 100644 utils/__pycache__/utils.cpython-310.pyc diff --git a/DEBIASING_FIX_GUIDE.md b/DEBIASING_FIX_GUIDE.md new file mode 100644 index 0000000..9e44e15 --- /dev/null +++ b/DEBIASING_FIX_GUIDE.md @@ -0,0 +1,90 @@ +# Debiasing Fix Guide + +## The Problem + +You were getting **less uniform** distributions after steering due to **inconsistent aggregation methods** between training, steering, and analysis. + +## Root Causes + +1. **Training**: Used `aggregate_spatial='flatten'` → [B, S*C] per image +2. **Analysis**: Used `aggregate_spatial='all_positions'` → [B*S, C] per position ❌ MISMATCH +3. **Steering**: Tried to process per-position but classifier expects flattened + +## The Solution: Consistent Flattened Approach + +### Training (Cell 10): +```python +aggregate_spatial='flatten' # [B, S, C] → [B, S*C] +# Result: [20 images, 1,310,720 features] per ethnicity +``` + +### Steering Hook (New Cell): +```python +# Use FIXED_debias_hook.py + +def debias_hook(module, input, output, alpha=1.0): + # Flatten: [B, S, C] → [B, S*C] + B, S, C = output.shape + act_flat = output.reshape(B, S * C) + + # Compute gradients (per image) + # grad shape: [B, S*C] + + # Apply steering + steered_flat = act_flat + alpha * steering + + # Reshape back: [B, S*C] → [B, S, C] + return steered_flat.reshape(B, S, C) +``` + +### Analysis (New Cell): +```python +# Use FIXED_analyze_function.py + +def analyze_ethnicity_distribution(images): + # Extract with SAME aggregation as training + activations = batch_cache_image_activations( + aggregate_spatial='flatten' # ✓ Matches training + ) + # Shape: [num_images, S*C] + + # Predict directly (one prediction per image) + predictions = classifier(activations).argmax(dim=1) +``` + +## Steps to Fix: + +1. **Re-run Cell 10** (training with `aggregate_spatial='flatten'`) + - This trains classifier on [20, 1,310,720] per ethnicity + +2. **Load fixed hook**: + - Copy contents of `FIXED_debias_hook.py` into new cell + - Run it + +3. **Load fixed analysis**: + - Copy contents of `FIXED_analyze_function.py` into new cell + - Run it + +4. **Run experiment**: + - Should now properly debias! + +## Why This Works: + +``` +Image → [S, C] activations + ↓ + Flatten to [S*C] + ↓ + Classifier (trained on this) + ↓ + Gradients w.r.t [S*C] + ↓ + Steer the flattened repr + ↓ + Reshape back to [S, C] + ↓ + Continues generation +``` + +**Consistent representation throughout!** + diff --git a/DEBUGGING_CHECKLIST.md b/DEBUGGING_CHECKLIST.md new file mode 100644 index 0000000..bbeaa8f --- /dev/null +++ b/DEBUGGING_CHECKLIST.md @@ -0,0 +1,89 @@ +# Debugging Checklist: Why Is Uniformity Getting Worse? + +Run these checks in notebook cells: + +## Check 1: Verify Analysis Aggregation +```python +# Paste from check_analysis_aggregation.py +# Should show: aggregate_spatial='flatten' +``` + +## Check 2: Test Steering Direction +```python +# Paste from test_steering_direction.py +# Should show: uniformity IMPROVES after steering +# If it worsens → Bug in loss or gradient direction +``` + +## Check 3: Verify Training Aggregation +```python +print("Training configuration:") +print(f" Layer: {layer_to_probe}") +# Check what you used in Cell 10 +# Should be: aggregate_spatial='flatten' +``` + +## Check 4: Verify Classifier Dimensions +```python +expected_dim = list(classifier.classifier.children())[0].in_features +print(f"Classifier expects: {expected_dim} dimensions") + +# For flattened approach: +# Should be: S*C = 4096 * 320 = 1,310,720 + +# Check if it matches: +test_shape = (2, 4096, 320) # [B, S, C] +flattened_dim = test_shape[1] * test_shape[2] +print(f"Flattened dim: {flattened_dim}") +print(f"Match: {expected_dim == flattened_dim}") +``` + +## Check 5: Inspect Baseline Distribution +```python +print("Baseline distribution:") +for eth, prob in baseline_dist.items(): + print(f" {eth}: {prob:.3f}") + +# Is baseline already uniform? +# If yes, steering can't improve it! +``` + +## Check 6: Check Alpha Values +```python +print(f"Alpha values being tested: {alphas_to_try}") + +# If alpha too large: might overshoot +# If alpha too small: no effect +# Typical range: 0.01 - 1.0 +``` + +## Common Bugs: + +### Bug #1: Dimension Mismatch +**Symptom**: RuntimeError about shapes +**Fix**: Ensure training, steering, analysis all use 'flatten' + +### Bug #2: Wrong Gradient Direction +**Symptom**: Uniformity gets worse +**Fix**: Ensure `steering = -grad` and `steered = act + alpha * steering` + +### Bug #3: Analysis Mismatch +**Symptom**: Wrong predictions +**Fix**: Analysis must use SAME aggregation as training + +### Bug #4: Classifier-Free Guidance Issue +**Symptom**: Batch size is 2 during generation (CFG), but loss expects 1 +**Fix**: Handle B=2 case (conditional + unconditional samples) + +## Expected Results: + +With correct implementation: +- Baseline uniformity: ~1.5-2.0 (biased) +- After steering: ~0.5-1.0 (less biased) +- Improvement: 30-50% + +If you see: +- Baseline: 1.7 +- After: 2.0 +- This means steering is WORSENING bias → BUG! + diff --git a/FIXED_analyze_function.py b/FIXED_analyze_function.py new file mode 100644 index 0000000..f80cc87 --- /dev/null +++ b/FIXED_analyze_function.py @@ -0,0 +1,53 @@ +# CORRECTED analyze_ethnicity_distribution +# Copy and paste this into a new notebook cell + +def analyze_ethnicity_distribution(images, batch_size=4): + """ + Analyze ethnicity distribution - MUST match training aggregation method. + Training uses 'flatten': [B, S, C] → [B, S*C] + """ + print(f"\nAnalyzing ethnicity distribution for {len(images)} images...") + + # Extract activations using SAME method as training + activations = batch_cache_image_activations( + model=model, + images=images, + layer_name=layer_to_probe, + timestep=500, + batch_size=batch_size, + encode_batch_size=8, + aggregate_spatial='flatten', # ✓ Match training: FLATTEN + empty_text_embed=True + ) + + # activations shape: [num_images, S*C] + print(f"Extracted activations shape: {activations.shape}") + + # Predict ethnicity for each image + classifier.classifier.eval() + predictions = [] + + with torch.no_grad(): + for i in range(0, len(activations), batch_size): + batch_acts = activations[i:i+batch_size] + batch_tensor = torch.tensor(batch_acts, dtype=torch.float32, device=classifier.device) + + logits = classifier.classifier(batch_tensor) + preds = torch.argmax(logits, dim=1).cpu().numpy() + predictions.extend(preds) + + # Count predictions + counts = np.bincount(predictions, minlength=len(classifier.class_names)) + + # Convert to distribution + distribution = { + classifier.class_names[i]: counts[i] / len(images) + for i in range(len(classifier.class_names)) + } + + return distribution + + +print("✓ Run this cell to load the corrected analyze_ethnicity_distribution") +print("✓ It uses aggregate_spatial='flatten' to match training") + diff --git a/FIXED_debias_hook.py b/FIXED_debias_hook.py new file mode 100644 index 0000000..6373a08 --- /dev/null +++ b/FIXED_debias_hook.py @@ -0,0 +1,88 @@ +# CORRECTED debias_hook for flattened spatial training +# Copy and paste this into a new notebook cell + +def debias_hook(module, input, output, alpha=1.0): + """ + Hook for classifier trained on flattened spatial: [B, S*C]. + Processes whole images per batch element. + """ + import torch + import numpy as np + + act = output + original_shape = act.shape + + # Flatten spatial dimensions: [B, S, C] → [B, S*C] + if act.ndim == 3: + B, S, C = act.shape + act_flat = act.reshape(B, S * C) + elif act.ndim == 4: + B, C, H, W = act.shape + act_flat = act.reshape(B, C * H * W) + else: + act_flat = act.clone() + if act.ndim == 3: + B, S, C = act.shape[0], act.shape[1], act.shape[2] + + # Create independent tensor for classifier + act_np = act_flat.detach().cpu().numpy().copy() + act_tensor = torch.from_numpy(act_np).to( + device=classifier.device, + dtype=torch.float32 + ) + + # Compute gradients through classifier + with torch.enable_grad(): + act_tensor.requires_grad_(True) + classifier.classifier.train() + + # Forward: [B, S*C] → [B, num_classes] + logits = classifier.classifier(act_tensor) + + # K_Steering unsupervised loss + mean_logits = logits.mean(dim=1, keepdim=True) + deviation = logits - mean_logits + + avoid_mask = deviation > 0 # Over-represented + target_mask = deviation < 0 # Under-represented + + loss = 0.0 + if avoid_mask.any(): + loss += (logits * avoid_mask.float()).sum() / (avoid_mask.sum() + 1e-8) + if target_mask.any(): + loss -= (logits * target_mask.float()).sum() / (target_mask.sum() + 1e-8) + + # Gradient: [B, S*C] + grad = torch.autograd.grad(loss, act_tensor)[0] + steering = -grad + + classifier.classifier.eval() + + # Apply steering + steering_np = steering.detach().cpu().numpy() + steering_tensor = torch.from_numpy(steering_np.copy()).to( + device=act_flat.device, + dtype=act_flat.dtype + ) + steered_flat = act_flat.detach() + alpha * steering_tensor + + # Reshape back: [B, S*C] → [B, S, C] + if len(original_shape) == 3: + return steered_flat.reshape(B, S, C) + elif len(original_shape) == 4: + return steered_flat.reshape(B, C, H, W) + else: + return steered_flat + + +print("="*80) +print("✓ Corrected debias_hook loaded") +print("="*80) +print("\nThis hook:") +print(" - Flattens spatial: [B, S, C] → [B, S*C]") +print(" - Processes per image (B samples)") +print(" - Computes per-image gradients") +print(" - Reshapes back to [B, S, C]") +print("\nMatches training on aggregate_spatial='flatten'") +print("="*80) + diff --git a/FairFace b/FairFace new file mode 160000 index 0000000..74b4f93 --- /dev/null +++ b/FairFace @@ -0,0 +1 @@ +Subproject commit 74b4f93e527f1fb2b27b2b425227c3ded0521830 diff --git a/INSTALL.md b/INSTALL.md new file mode 100644 index 0000000..1b3ab30 --- /dev/null +++ b/INSTALL.md @@ -0,0 +1,134 @@ +# Installation Guide - T2I Interpretability Toolkit + +## Quick Install + +### 1. Install Python Requirements + +```bash +cd /home/ubuntu/T2I_Interp_toolkit +pip install -r requirements.txt +``` + +### 2. Install the Toolkit (Development Mode) + +```bash +# Install the main toolkit +cd /home/ubuntu/T2I_Interp_toolkit +pip install -e . + +# Install the dictionary learning submodule +cd dictionary_learning +pip install -e . +``` + +## Alternative: Install with Poetry + +If you prefer using Poetry for the dictionary learning module: + +```bash +cd /home/ubuntu/T2I_Interp_toolkit/dictionary_learning +poetry install +``` + +## GPU Support + +Make sure you have PyTorch with CUDA support: + +```bash +# Check your CUDA version +nvcc --version + +# Install PyTorch with CUDA (example for CUDA 11.8) +pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118 +``` + +## For FairFace Dataset + +If you plan to use the FairFace dataset features: + +```bash +# Install dlib (may require system dependencies) +pip install dlib + +# On Ubuntu, if dlib fails, install dependencies first: +sudo apt-get update +sudo apt-get install build-essential cmake +sudo apt-get install libopenblas-dev liblapack-dev +sudo apt-get install libx11-dev libgtk-3-dev +pip install dlib +``` + +## Verify Installation + +```python +# Test basic imports +import torch +print(f"PyTorch version: {torch.__version__}") +print(f"CUDA available: {torch.cuda.is_available()}") + +# Test toolkit imports +from t2Interp.T2I import T2IModel +from t2Interp.intervention import EncoderAttentionIntervention +print("✅ Toolkit imported successfully!") + +# Test diffusers +from diffusers import AutoPipelineForText2Image +print("✅ Diffusers imported successfully!") +``` + +## Common Issues + +### Issue: "No module named 't2Interp'" +**Solution:** Make sure you're in the notebook directory or the toolkit is installed: +```bash +cd /home/ubuntu/T2I_Interp_toolkit/notebooks +# Run your notebook from here +``` + +### Issue: dlib installation fails +**Solution:** Install system dependencies: +```bash +sudo apt-get install build-essential cmake libopenblas-dev liblapack-dev +pip install dlib +``` + +### Issue: CUDA out of memory +**Solution:** Reduce batch size or use mixed precision: +```python +# In your code +pipe = pipe.to("cuda", torch_dtype=torch.float16) +``` + +## Environment Setup + +For a clean installation, consider using a virtual environment: + +```bash +# Create virtual environment +python -m venv t2i_env + +# Activate it +source t2i_env/bin/activate + +# Install requirements +pip install -r requirements.txt +``` + +## Minimal Requirements + +If you just want to run the debiasing notebook: + +```bash +pip install torch torchvision diffusers transformers pandas scikit-learn numpy matplotlib +``` + +## Development Installation + +For development with all optional dependencies: + +```bash +pip install -r requirements.txt +pip install pytest black flake8 mypy # Development tools +``` + + diff --git a/check_analysis_aggregation.py b/check_analysis_aggregation.py new file mode 100644 index 0000000..907dbad --- /dev/null +++ b/check_analysis_aggregation.py @@ -0,0 +1,29 @@ +# Quick check: Is analyze_ethnicity_distribution using correct aggregation? +# Run this in notebook + +import inspect + +print("="*80) +print("CHECKING ANALYZE_ETHNICITY_DISTRIBUTION") +print("="*80) + +# Get source code +source = inspect.getsource(analyze_ethnicity_distribution) + +# Check for aggregation method +if "aggregate_spatial='flatten'" in source: + print("✓ CORRECT: Uses aggregate_spatial='flatten'") +elif "aggregate_spatial='mean'" in source: + print("❌ BUG: Uses aggregate_spatial='mean' (should be 'flatten')") +elif "aggregate_spatial='all_positions'" in source: + print("⚠️ PARTIAL: Uses 'all_positions' (should be 'flatten' for this approach)") +else: + print("? Could not determine aggregation method") + +print("\nRelevant lines:") +for i, line in enumerate(source.split('\n')): + if 'aggregate_spatial' in line: + print(f" Line {i}: {line.strip()}") + +print("\n" + "="*80) + diff --git a/corrected_debias_hook.py b/corrected_debias_hook.py new file mode 100644 index 0000000..998f2b3 --- /dev/null +++ b/corrected_debias_hook.py @@ -0,0 +1,119 @@ +# CORRECTED: debias_hook for flattened spatial training +# Paste this into a notebook cell and run it + +def debias_hook_v2(module, input, output, alpha=1.0): + """ + Hook for classifier trained on flattened spatial: [B, S*C]. + Processes whole images, not individual positions. + """ + import torch + import numpy as np + + act = output + original_shape = act.shape + + # Flatten: [B, S, C] → [B, S*C] + if act.ndim == 3: + B, S, C = act.shape + act_flat = act.reshape(B, S * C) + elif act.ndim == 4: + B, C, H, W = act.shape + act_flat = act.reshape(B, C * H * W) + else: + act_flat = act.clone() + B = act.shape[0] + + # Independent tensor for classifier + act_np = act_flat.detach().cpu().numpy().copy() + act_tensor = torch.from_numpy(act_np).to(classifier.device, dtype=torch.float32) + + # Compute gradients + with torch.enable_grad(): + act_tensor.requires_grad_(True) + classifier.classifier.train() + + logits = classifier.classifier(act_tensor) # [B, num_classes] + + # K_Steering loss: minimize over-represented, maximize under-represented + mean_logits = logits.mean(dim=1, keepdim=True) + deviation = logits - mean_logits + + avoid_mask = deviation > 0 # Over-represented classes + target_mask = deviation < 0 # Under-represented classes + + loss = 0.0 + if avoid_mask.any(): + loss += (logits * avoid_mask.float()).sum() / (avoid_mask.sum() + 1e-8) + if target_mask.any(): + loss -= (logits * target_mask.float()).sum() / (target_mask.sum() + 1e-8) + + grad = torch.autograd.grad(loss, act_tensor)[0] + steering = -grad + + classifier.classifier.eval() + + # Apply steering + steering_np = steering.detach().cpu().numpy() + steering_tensor = torch.from_numpy(steering_np.copy()).to(act_flat.device, dtype=act_flat.dtype) + steered_flat = act_flat.detach() + alpha * steering_tensor + + # Reshape back to original shape + if len(original_shape) == 3: + return steered_flat.reshape(B, S, C) + elif len(original_shape) == 4: + return steered_flat.reshape(B, C, H, W) + else: + return steered_flat + + +# Also update analyze function +def analyze_ethnicity_distribution_v2(images, batch_size=4): + """Analyze using flattened spatial (matching training)""" + print(f"\nAnalyzing {len(images)} images...") + + activations = batch_cache_image_activations( + model=model, + images=images, + layer_name=layer_to_probe, + timestep=500, + batch_size=batch_size, + encode_batch_size=8, + aggregate_spatial='flatten', # Match training + empty_text_embed=True + ) + + # Shape: [num_images, S*C] + print(f"Extracted activations shape: {activations.shape}") + + # Predict + classifier.classifier.eval() + predictions = [] + + with torch.no_grad(): + for i in range(0, len(activations), batch_size): + batch_acts = activations[i:i+batch_size] + batch_tensor = torch.tensor(batch_acts, dtype=torch.float32, device=classifier.device) + + logits = classifier.classifier(batch_tensor) + preds = torch.argmax(logits, dim=1).cpu().numpy() + predictions.extend(preds) + + # Count + counts = np.bincount(predictions, minlength=len(classifier.class_names)) + distribution = { + classifier.class_names[i]: counts[i] / len(images) + for i in range(len(classifier.class_names)) + } + + return distribution + + +print("="*80) +print("TO USE THESE CORRECTED FUNCTIONS:") +print("="*80) +print("1. Run: debias_hook = debias_hook_v2") +print("2. Run: analyze_ethnicity_distribution = analyze_ethnicity_distribution_v2") +print("3. Re-train classifier with aggregate_spatial='flatten'") +print("4. Run experiment") +print("="*80) + diff --git a/ethnicity_debiasing_results.json b/ethnicity_debiasing_results.json new file mode 100644 index 0000000..5c5581a --- /dev/null +++ b/ethnicity_debiasing_results.json @@ -0,0 +1,76 @@ +{ + "meta": { + "test_prompts": [ + "a photo of a doctor" + ], + "num_generations_per_prompt": 15, + "alphas_tested": [ + 1.0, + 10.0, + 100.0 + ], + "target_layer": "unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0]", + "ethnicity_classes": [ + "Black", + "East_Asian", + "Indian", + "Latino_Hispanic", + "Middle_Eastern", + "Southeast_Asian", + "White" + ] + }, + "experiments": [ + { + "alpha": 1.0, + "distribution": { + "Black": 0.06666666666666667, + "East_Asian": 0.26666666666666666, + "Indian": 0.0, + "Latino_Hispanic": 0.0, + "Middle_Eastern": 0.26666666666666666, + "Southeast_Asian": 0.06666666666666667, + "White": 0.3333333333333333 + }, + "uniformity_score": 0.8761904761904762, + "improvement_pct": -15.000000000000005 + }, + { + "alpha": 10.0, + "distribution": { + "Black": 0.06666666666666667, + "East_Asian": 0.26666666666666666, + "Indian": 0.0, + "Latino_Hispanic": 0.0, + "Middle_Eastern": 0.13333333333333333, + "Southeast_Asian": 0.06666666666666667, + "White": 0.4666666666666667 + }, + "uniformity_score": 0.8952380952380952, + "improvement_pct": -17.499999999999996 + }, + { + "alpha": 100.0, + "distribution": { + "Black": 0.06666666666666667, + "East_Asian": 0.2, + "Indian": 0.0, + "Latino_Hispanic": 0.0, + "Middle_Eastern": 0.06666666666666667, + "Southeast_Asian": 0.13333333333333333, + "White": 0.5333333333333333 + }, + "uniformity_score": 0.8952380952380952, + "improvement_pct": -17.499999999999996 + } + ], + "baseline_distribution": { + "Black": 0.13333333333333333, + "East_Asian": 0.3333333333333333, + "Indian": 0.0, + "Latino_Hispanic": 0.0, + "Middle_Eastern": 0.13333333333333333, + "Southeast_Asian": 0.06666666666666667, + "White": 0.3333333333333333 + } +} \ No newline at end of file diff --git a/notebooks/debiasing-t2i.ipynb b/notebooks/debiasing-t2i.ipynb new file mode 100644 index 0000000..4f2a803 --- /dev/null +++ b/notebooks/debiasing-t2i.ipynb @@ -0,0 +1,3092 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "66e96f7e", + "metadata": {}, + "source": [ + "## This is a method that given any classifier that trains on a latent in an image model to predict some attributes, in our case are ethnic groups and then it is used to debias by evenining the likelihood that the image belongs to any class." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "fbc6539e", + "metadata": {}, + "outputs": [], + "source": [ + "#import statements\n", + "import os\n", + "os.chdir(\"..\")\n", + "from pathlib import Path\n", + "from typing import List, Dict, Optional, Union\n", + "from t2Interp.T2I import T2IModel\n", + "from t2Interp.intervention import EncoderAttentionIntervention, ScalingAttentionIntervention #, InterventionRunner\n", + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import torch.nn.functional as F\n", + "from PIL import Image\n", + "import matplotlib.pyplot as plt\n", + "import seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d579b491", + "metadata": {}, + "outputs": [], + "source": [ + "#Here's the classifier \n", + "class K_Steering:\n", + " def __init__(self, input_dim, num_classes=6, hidden_dim=256, lr=1e-3, device='cuda'):\n", + " \"\"\"\n", + " Parameters:\n", + " - input_dim: dimensionality of your raw activations.\n", + " - num_classes: number of tone classes to classify.\n", + " - hidden_dim: size of the hidden layer in the MLP.\n", + " - lr: learning rate for training the classifier.\n", + " - device: 'cuda' or 'cpu'.\n", + " \"\"\"\n", + " self.device = device\n", + " self.num_classes = num_classes\n", + " \n", + " # Define a multi-class MLP classifier\n", + " self.classifier = nn.Sequential(\n", + " nn.Linear(input_dim, hidden_dim),\n", + " nn.ReLU(),\n", + " nn.Linear(hidden_dim, hidden_dim),\n", + " nn.ReLU(),\n", + " nn.Linear(hidden_dim, num_classes) # outputs logits for each tone class\n", + " ).to(device)\n", + " \n", + " self.optimizer = optim.Adam(self.classifier.parameters(), lr=lr)\n", + " self.loss_fn = nn.CrossEntropyLoss()\n", + " \n", + " # Store class names for reference\n", + " self.class_names = None\n", + " \n", + "\n", + " def fit(self, activations_dict, epochs=30, batch_size=32, class_weights=None):\n", + " \"\"\"\n", + " Train the multiclass classifier on raw activations.\n", + "\n", + " Parameters:\n", + " - activations_dict: Dictionary mapping class names to NumPy arrays of activations.\n", + " - epochs: number of training epochs.\n", + " - batch_size: training batch size.\n", + " - class_weights: Optional tensor of weights for each class to handle imbalance.\n", + " \"\"\"\n", + " self.class_names = list(activations_dict.keys())\n", + " assert len(self.class_names) == self.num_classes, f\"Expected {self.num_classes} classes, got {len(self.class_names)}\"\n", + " \n", + " # Prepare training data\n", + " X_list = []\n", + " y_list = []\n", + " \n", + " for i, class_name in enumerate(self.class_names):\n", + " X_class = torch.tensor(activations_dict[class_name], dtype=torch.float32, device=self.device)\n", + " y_class = torch.full((X_class.size(0),), i, dtype=torch.long, device=self.device)\n", + " X_list.append(X_class)\n", + " y_list.append(y_class)\n", + " \n", + " X = torch.cat(X_list, dim=0)\n", + " y = torch.cat(y_list, dim=0)\n", + " \n", + " dataset = torch.utils.data.TensorDataset(X, y)\n", + " loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)\n", + " \n", + " # Set class weights for loss function if provided\n", + " if class_weights is not None:\n", + " self.loss_fn = nn.CrossEntropyLoss(weight=class_weights.to(self.device))\n", + " \n", + " # Train the classifier\n", + " for epoch in range(epochs):\n", + " self.classifier.train()\n", + " epoch_loss = 0.0\n", + " correct = 0\n", + " total = 0\n", + " \n", + " for batch_X, batch_y in loader:\n", + " self.optimizer.zero_grad()\n", + " logits = self.classifier(batch_X) # shape: (batch, num_classes)\n", + " loss = self.loss_fn(logits, batch_y)\n", + " loss.backward()\n", + " self.optimizer.step()\n", + " \n", + " epoch_loss += loss.item()\n", + " \n", + " # Calculate accuracy\n", + " _, predicted = torch.max(logits.data, 1)\n", + " total += batch_y.size(0)\n", + " correct += (predicted == batch_y).sum().item()\n", + " \n", + " accuracy = 100 * correct / total\n", + " print(f\"Epoch {epoch}: loss = {epoch_loss/len(loader):.4f}, accuracy = {accuracy:.2f}%\")\n", + " \n", + " # Evaluate on each class\n", + " self.classifier.eval()\n", + " class_accuracies = {}\n", + " \n", + " with torch.no_grad():\n", + " for i, class_name in enumerate(self.class_names):\n", + " X_class = torch.tensor(activations_dict[class_name], dtype=torch.float32, device=self.device)\n", + " y_class = torch.full((X_class.size(0),), i, dtype=torch.long, device=self.device)\n", + " \n", + " logits = self.classifier(X_class)\n", + " _, predicted = torch.max(logits.data, 1)\n", + " accuracy = 100 * (predicted == y_class).sum().item() / y_class.size(0)\n", + " class_accuracies[class_name] = accuracy\n", + " print(f\"Accuracy for {class_name}: {accuracy:.2f}%\")\n", + "\n", + " def steer_attributes(self, activation, target=None, avoid=None, alpha=0.1, steps=1, decay_rate=0.95):\n", + " \"\"\"\n", + " Steer the given activation toward target tones and away from tones to avoid.\n", + " \n", + " Parameters:\n", + " - activation: a NumPy array of shape (D,) or (N, D).\n", + " - target_tones: list of tone names to steer toward.\n", + " - avoid_tones: list of tone names to steer away from.\n", + " - alpha: scaling factor for the update.\n", + " - steps: number of gradient descent steps to take.\n", + " \n", + " Returns:\n", + " - The modified activation as a NumPy array.\n", + " \"\"\"\n", + " \n", + " # Validate input tone names\n", + " if target:\n", + " for tone in target:\n", + " assert tone in self.class_names, f\"Unknown target tone: {tone}\"\n", + " \n", + " if avoid:\n", + " for tone in avoid:\n", + " assert tone in self.class_names, f\"Unknown avoid tone: {tone}\"\n", + " \n", + " # Get indices for target and avoid tones\n", + " target_indices = [self.class_names.index(tone) for tone in target] if target else []\n", + " avoid_indices = [self.class_names.index(tone) for tone in avoid] if avoid else []\n", + " \n", + " # If a single activation vector is passed, add a batch dimension\n", + " single_input = False\n", + " if activation.ndim == 1:\n", + " activation = activation[None, :]\n", + " single_input = True\n", + " \n", + " # Convert activation to a torch tensor\n", + " X = torch.tensor(activation, dtype=torch.float32, device=self.device)\n", + " \n", + " current_alpha = alpha\n", + " \n", + " # Perform multiple steps of gradient descent\n", + " for step in range(steps):\n", + " if step > 0:\n", + " current_alpha *= decay_rate\n", + " # Need to create a new tensor that requires gradients for each step\n", + " X = X.detach().requires_grad_(True)\n", + " \n", + " # Forward pass: compute classifier output (logits)\n", + " self.classifier.eval()\n", + " logits = self.classifier(X) # shape: [N, num_classes]\n", + " \n", + " # Create custom loss function to maximize target tone scores and minimize avoid tone scores\n", + " loss = 0\n", + " \n", + " if target_indices:\n", + " target_logits = logits[:, target_indices]\n", + " # Negative because we want to maximize these logits (gradient descent will minimize)\n", + " loss = loss - target_logits.mean()\n", + " \n", + " if avoid_indices:\n", + " avoid_logits = logits[:, avoid_indices]\n", + " # Positive because we want to minimize these logits\n", + " loss = loss + avoid_logits.mean()\n", + " \n", + " # If no target or avoid tones provided, do nothing\n", + " if not target_indices and not avoid_indices:\n", + " if single_input:\n", + " return activation[0]\n", + " else:\n", + " return activation\n", + " \n", + " # Compute gradients\n", + " loss.backward()\n", + " \n", + " # Get the gradient with respect to the input activation\n", + " grad = X.grad.data\n", + " \n", + " # Update the activation by moving in the loss gradient direction\n", + " # Negative gradient because we're trying to minimize the loss\n", + "\n", + " X = X - current_alpha * grad\n", + " \n", + " # Convert back to NumPy\n", + " X_new_np = X.detach().cpu().numpy()\n", + " if single_input:\n", + " return X_new_np[0]\n", + " else:\n", + " return X_new_np\n", + "\n", + " def steer_attributes_unsupervised(self, activation, alpha=0.1, steps=1, decay_rate=0.95):\n", + " \"\"\"\n", + " Steer the given activation, calculate the mean of the activations of all classes and steer away of those above the mean and towards those away from the mean .\n", + " \n", + " Parameters:\n", + " - activation: a NumPy array of shape (D,) or (N, D).\n", + " - alpha: scaling factor for the update.\n", + " - steps: number of gradient descent steps to take.\n", + " \n", + " Returns:\n", + " - The modified activation as a NumPy array.\n", + " \"\"\"\n", + "\n", + "\n", + " # If a single activation vector is passed, add a batch dimension\n", + " single_input = False\n", + " if activation.ndim == 1:\n", + " activation = activation[None, :]\n", + " single_input = True\n", + " \n", + " # Convert activation to a torch tensor\n", + " X = torch.tensor(activation, dtype=torch.float32, device=self.device)\n", + " \n", + " current_alpha = alpha\n", + " \n", + " # Perform multiple steps of gradient descent\n", + " for step in range(steps):\n", + " if step > 0:\n", + " current_alpha *= decay_rate\n", + " # Need to create a new tensor that requires gradients for each step\n", + " X = X.detach().requires_grad_(True)\n", + " \n", + " # Forward pass: compute classifier output (logits)\n", + " self.classifier.eval()\n", + " logits = self.classifier(X) # shape: [N, num_classes]\n", + " \n", + " # Create custom loss function to maximize target tone scores and minimize avoid tone scores\n", + " loss = 0\n", + " mean_logits = torch.mean(logits, dim=1)\n", + "\n", + " target_indices = [i for i, logits in enumerate(logits) if logits[:,i].mean() < mean_logits]\n", + " avoid_indices = [i for i, logits in enumerate(logits) if logits[:,i].mean() > mean_logits]\n", + " \n", + " if target_indices:\n", + " target_logits = logits[:, target_indices]\n", + " # Negative because we want to maximize these logits (gradient descent will minimize)\n", + " loss = loss - target_logits.mean()\n", + " \n", + " if avoid_indices:\n", + " avoid_logits = logits[:, avoid_indices]\n", + " # Positive because we want to minimize these logits\n", + " loss = loss + avoid_logits.mean()\n", + " \n", + " # If no target or avoid tones provided, do nothing\n", + " if not target_indices and not avoid_indices:\n", + " if single_input:\n", + " return activation[0]\n", + " else:\n", + " return activation\n", + " \n", + " # Compute gradients\n", + " loss.backward()\n", + " \n", + " # Get the gradient with respect to the input activation\n", + " grad = X.grad.data\n", + " \n", + " # Update the activation by moving in the loss gradient direction\n", + " # Negative gradient because we're trying to minimize the loss\n", + "\n", + " X = X - current_alpha * grad\n", + " \n", + " # Convert back to NumPy\n", + " X_new_np = X.detach().cpu().numpy()\n", + " if single_input:\n", + " return X_new_np[0]\n", + " else:\n", + " return X_new_np\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "0ba58fb8", + "metadata": {}, + "source": [ + "## FairFace Dataset for T2I Debiasing\n", + "\n", + "**⚠️ IMPORTANT: Download the dataset files from Google Drive:**\n", + "\n", + "1. **Labels:** \n", + " - [Train Labels](https://drive.google.com/file/d/1i1L3Yqwaio7YSOCj7ftgk8ZZchPG7dmH/view)\n", + " - [Validation Labels](https://drive.google.com/file/d/1wOdja-ezstMEp81tX1a-EYkFebev4h7D/view)\n", + "\n", + "2. **Images:** \n", + " - [Padding=0.25](https://drive.google.com/file/d/1Z1RqRo0_JiavaZw2yzZG6WETdZQ8qX86/view) (recommended)\n", + " - [Padding=1.25](https://drive.google.com/file/d/1g7qNOZz9wC7OfOhcPqH1EZ5bk1UFGmlL/view) (for API testing)\n", + "\n", + "3. **Extract to:** `/home/ubuntu/T2I_Interp_toolkit/FairFace/`\n", + " - Place CSVs: `fairface_label_train.csv` and `fairface_label_val.csv` in the main directory\n", + " - Extract images: `train/` and `val/` folders\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9e9ad3e4", + "metadata": {}, + "outputs": [], + "source": [ + "def load_fairface_by_ethnicity(\n", + " dataset_path: str,\n", + " categories: Optional[List[str]] = None,\n", + " max_images: Optional[int] = None,\n", + " return_pil: bool = True\n", + ") -> Dict[str, List[Union[str, Image.Image]]]:\n", + " \"\"\"\n", + " Load FairFace dataset organized by ethnic/racial categories.\n", + " \n", + " Parameters:\n", + " -----------\n", + " dataset_path : str\n", + " Path to the fairface dataset root (e.g., '/path/to/fairface')\n", + " categories : List[str], optional\n", + " Specific categories to load. Available: ['Black', 'East_Asian', 'Indian', \n", + " 'Latino_Hispanic', 'Middle_Eastern', 'Southeast_Asian', 'White']\n", + " max_images : int, optional\n", + " Maximum number of images to load per category\n", + " return_pil : bool, optional\n", + " If True, return PIL Images (for feeding to vision encoders). \n", + " If False, return file paths as strings. Default: True\n", + " \n", + " Returns:\n", + " --------\n", + " Dict[str, List[Union[str, Image.Image]]]\n", + " Dictionary mapping category names to lists of PIL Images (if return_pil=True) \n", + " or image file paths (if return_pil=False)\n", + " \n", + " Example:\n", + " --------\n", + " >>> # Load as PIL Images (ready for vision encoders)\n", + " >>> data = load_fairface_by_ethnicity('/path/to/fairface', return_pil=True)\n", + " >>> print(f\"White: {len(data['White'])} images\")\n", + " >>> print(f\"First image type: {type(data['White'][0])}\") # \n", + " \n", + " >>> # Load as paths only\n", + " >>> data = load_fairface_by_ethnicity('/path/to/fairface', return_pil=False)\n", + " >>> print(f\"First image path: {data['White'][0]}\")\n", + " \"\"\"\n", + " \n", + " all_categories = [\n", + " 'Black', 'East_Asian', 'Indian', 'Latino_Hispanic',\n", + " 'Middle_Eastern', 'Southeast_Asian', 'White'\n", + " ]\n", + " \n", + " if categories is None:\n", + " categories = all_categories\n", + " \n", + " race_path = Path(dataset_path) / \"Race\"\n", + " dataset = {}\n", + " \n", + " for category in categories:\n", + " category_path = race_path / category\n", + " if not category_path.exists():\n", + " print(f\"Warning: {category} path not found at {category_path}\")\n", + " continue\n", + " \n", + " # Get all jpg images\n", + " image_files = sorted(category_path.glob(\"*.jpg\"))\n", + " \n", + " if max_images:\n", + " image_files = image_files[:max_images]\n", + "\n", + " # Load images as PIL Images (RGB mode for vision encoders)\n", + " dataset[category] = []\n", + " for img_path in image_files:\n", + " try:\n", + " img = Image.open(img_path).convert('RGB')\n", + " dataset[category].append(img)\n", + " except Exception as e:\n", + " print(f\"Warning: Failed to load {img_path}: {e}\")\n", + "\n", + " \n", + " print(f\"{category}: {len(dataset[category])} images\")\n", + " \n", + " return dataset\n", + "\n", + "def visualize_image(image, title=None, figsize=(5, 5)):\n", + " \"\"\"Display a single PIL image.\"\"\"\n", + " fig, ax = plt.subplots(1, 1, figsize=figsize)\n", + " ax.imshow(image)\n", + " ax.axis('off')\n", + " if title:\n", + " ax.set_title(title, fontsize=12)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ce149d64", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Black: 200 images\n", + "East_Asian: 200 images\n", + "Indian: 200 images\n", + "Latino_Hispanic: 200 images\n", + "Middle_Eastern: 200 images\n", + "Southeast_Asian: 200 images\n", + "White: 200 images\n", + "{'Black': [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'East_Asian': [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'Indian': [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'Latino_Hispanic': [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'Middle_Eastern': [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'Southeast_Asian': [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Let's load the dataset\n", + "dataset_path = \"/home/ubuntu/T2I_Interp_toolkit/FairFace/datasets/fairface\"\n", + "data = load_fairface_by_ethnicity(dataset_path)\n", + "print(data)\n", + "visualize_image(data['Black'][0])" + ] + }, + { + "cell_type": "markdown", + "id": "8b932af0", + "metadata": {}, + "source": [ + "## Let's load in multimodal models" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c5ad8ce4", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-10-13 22:23:12.916\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mt2Interp.T2I\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m105\u001b[0m - \u001b[1mEnforcing eager attention implementation for attention pattern tracing. The HF default would be to use sdpa if available. To use sdpa, set attn_implementation='sdpa' or None to use the HF default.\u001b[0m\n", + "Keyword arguments {'attn_implementation': 'eager', 'tokenizer_kwargs': {}, 'trust_remote_code': False} are not expected by StableDiffusionPipeline and will be ignored.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c02c622dc11a4fd89858b9dce8e1d4e7", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Loading pipeline components...: 0%| | 0/7 [00:00 [B, C]\n", + " - 'flatten': Flatten spatial dimensions [B, C, H, W] -> [B, C*H*W]\n", + " - 'none': Keep spatial dimensions [B, C, H, W]\n", + " empty_text_embed : bool\n", + " Use empty text embeddings (unconditional). Set False to use a neutral prompt.\n", + " \n", + " Returns:\n", + " --------\n", + " np.ndarray : Cached activations with shape depending on aggregate_spatial\n", + " \"\"\"\n", + " \n", + " print(f\"Encoding {len(images)} images to latent space...\")\n", + " latents = encode_images_to_latents(model, images, batch_size=encode_batch_size)\n", + " \n", + " device = next(model._model.pipeline.unet.parameters()).device\n", + " all_activations = []\n", + " \n", + " # Get text embeddings (empty for unconditional)\n", + " if empty_text_embed:\n", + " text_embeds = model._model.pipeline.encode_prompt(\n", + " prompt=\"\",\n", + " device=device,\n", + " num_images_per_prompt=1,\n", + " do_classifier_free_guidance=False\n", + " )[0]\n", + " else:\n", + " text_embeds = model._model.pipeline.encode_prompt(\n", + " prompt=\"a photo\",\n", + " device=device,\n", + " num_images_per_prompt=1,\n", + " do_classifier_free_guidance=False\n", + " )[0]\n", + " \n", + " # Create timestep tensor\n", + " t = torch.tensor([timestep], device=device, dtype=torch.long)\n", + " \n", + " # Get UNet dtype\n", + " unet_dtype = next(model._model.pipeline.unet.parameters()).dtype\n", + " \n", + " # Navigate to the target layer module (before any forward passes)\n", + " # Parse the layer_name to get the actual PyTorch module\n", + " layer_parts = layer_name.split('.')\n", + " target_module = model._model.pipeline.unet\n", + " \n", + " for part in layer_parts[1:]: # Skip 'unet' as we already have it\n", + " if '[' in part:\n", + " # Handle indexing like 'down_blocks[0]'\n", + " module_name, idx = part.split('[')\n", + " idx = int(idx.rstrip(']'))\n", + " target_module = getattr(target_module, module_name)[idx]\n", + " elif part == 'output':\n", + " # 'output' is not an actual module, we want the output of the previous module\n", + " break\n", + " else:\n", + " target_module = getattr(target_module, part)\n", + " \n", + " # Setup hook to capture activations\n", + " activations_cache = []\n", + " \n", + " def hook_fn(module, input, output):\n", + " # Store the output activation\n", + " activations_cache.append(output.detach().cpu())\n", + " \n", + " # Register the hook\n", + " handle = target_module.register_forward_hook(hook_fn)\n", + " \n", + " print(f\"Extracting activations from layer: {layer_name}\")\n", + " try:\n", + " for i in tqdm(range(0, len(latents), batch_size), desc=\"Processing batches\"):\n", + " batch_latents = latents[i:i+batch_size].to(device=device, dtype=unet_dtype)\n", + " batch_size_actual = batch_latents.shape[0]\n", + " \n", + " # Expand text embeddings to match batch size\n", + " batch_text_embeds = text_embeds.repeat(batch_size_actual, 1, 1)\n", + " \n", + " # Forward pass through UNet (hook will capture activation)\n", + " with torch.no_grad():\n", + " unet_output = model._model.pipeline.unet(\n", + " batch_latents,\n", + " t,\n", + " encoder_hidden_states=batch_text_embeds,\n", + " return_dict=False\n", + " )\n", + " \n", + " # Get the captured activation\n", + " act = activations_cache.pop(0)\n", + " \n", + " # Aggregate spatial dimensions if requested\n", + " if aggregate_spatial == 'last':\n", + " # Extract last sequence position only (like language models)\n", + " # For [B, S, C] attention outputs\n", + " if act.ndim == 3:\n", + " act = act[:, sequence_position, :] # [B, S, C] -> [B, C]\n", + " elif act.ndim == 4:\n", + " # For conv [B, C, H, W], take last spatial position\n", + " act = act[:, :, sequence_position, sequence_position] # [B, C]\n", + " elif aggregate_spatial == 'mean':\n", + " # Average pool spatial dims\n", + " if act.ndim == 3:\n", + " act = act.mean(dim=1) # [B, S, C] -> [B, C]\n", + " elif act.ndim == 4:\n", + " act = act.mean(dim=[2, 3]) # [B, C, H, W] -> [B, C]\n", + " elif aggregate_spatial == 'flatten':\n", + " # Flatten: [B, ...] -> [B, features]\n", + " act = act.flatten(start_dim=1)\n", + " # else keep as is (aggregate_spatial == 'none')\n", + " \n", + " all_activations.append(act.numpy())\n", + " \n", + " # Clear memory\n", + " del batch_latents, batch_text_embeds, unet_output\n", + " torch.cuda.empty_cache()\n", + " finally:\n", + " # Always remove the hook\n", + " handle.remove()\n", + " \n", + " activations = np.concatenate(all_activations, axis=0)\n", + " print(f\"Final activation shape: {activations.shape}\")\n", + " \n", + " return activations\n", + "\n", + "\n", + "def batch_cache_image_activations_by_class(\n", + " model,\n", + " images_dict: Dict[str, List[Image.Image]],\n", + " layer_name: str,\n", + " timestep: int = 500,\n", + " batch_size: int = 4,\n", + " encode_batch_size: int = 8,\n", + " aggregate_spatial: str = 'flatten',\n", + " empty_text_embed: bool = True,\n", + " max_images_per_class: Optional[int] = None,\n", + " sequence_position: int = -1\n", + "):\n", + " \"\"\"\n", + " Cache UNet activations from images organized by class/category.\n", + " \n", + " Perfect for debiasing: extract activations from FairFace images organized by ethnicity.\n", + " \n", + " Parameters:\n", + " -----------\n", + " images_dict : Dict[str, List[PIL.Image]]\n", + " Dictionary mapping class names to lists of images\n", + " Example: {'Black': [img1, img2, ...], 'White': [img1, img2, ...]}\n", + " max_images_per_class : Optional[int]\n", + " Limit number of images per class (useful for testing)\n", + " ... (other params same as batch_cache_image_activations)\n", + " \n", + " Returns:\n", + " --------\n", + " Dict[str, np.ndarray] : Dictionary mapping class names to activation arrays\n", + " \"\"\"\n", + " \n", + " activations_dict = {}\n", + " \n", + " for class_name, images in images_dict.items():\n", + " # Limit images if specified\n", + " if max_images_per_class is not None:\n", + " images = images[:max_images_per_class]\n", + " \n", + " print(f\"\\n{'='*60}\")\n", + " print(f\"Processing class: {class_name} ({len(images)} images)\")\n", + " print(f\"{'='*60}\")\n", + " \n", + " activations_dict[class_name] = batch_cache_image_activations(\n", + " model=model,\n", + " images=images,\n", + " layer_name=layer_name,\n", + " timestep=timestep,\n", + " batch_size=batch_size,\n", + " encode_batch_size=encode_batch_size,\n", + " aggregate_spatial=aggregate_spatial,\n", + " empty_text_embed=empty_text_embed,\n", + " sequence_position=sequence_position\n", + " )\n", + " \n", + " return activations_dict" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e68d8eac", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "============================================================\n", + "Processing class: Black (200 images)\n", + "============================================================\n", + "Encoding 200 images to latent space...\n", + "Extracting activations from layer: unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing batches: 100%|██████████| 4/4 [00:01<00:00, 2.28it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final activation shape: (200, 1310720)\n", + "\n", + "============================================================\n", + "Processing class: East_Asian (200 images)\n", + "============================================================\n", + "Encoding 200 images to latent space...\n", + "Extracting activations from layer: unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing batches: 100%|██████████| 4/4 [00:01<00:00, 2.36it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final activation shape: (200, 1310720)\n", + "\n", + "============================================================\n", + "Processing class: Indian (200 images)\n", + "============================================================\n", + "Encoding 200 images to latent space...\n", + "Extracting activations from layer: unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing batches: 100%|██████████| 4/4 [00:01<00:00, 2.34it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final activation shape: (200, 1310720)\n", + "\n", + "============================================================\n", + "Processing class: Latino_Hispanic (200 images)\n", + "============================================================\n", + "Encoding 200 images to latent space...\n", + "Extracting activations from layer: unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing batches: 100%|██████████| 4/4 [00:01<00:00, 2.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final activation shape: (200, 1310720)\n", + "\n", + "============================================================\n", + "Processing class: Middle_Eastern (200 images)\n", + "============================================================\n", + "Encoding 200 images to latent space...\n", + "Extracting activations from layer: unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing batches: 100%|██████████| 4/4 [00:01<00:00, 2.39it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final activation shape: (200, 1310720)\n", + "\n", + "============================================================\n", + "Processing class: Southeast_Asian (200 images)\n", + "============================================================\n", + "Encoding 200 images to latent space...\n", + "Extracting activations from layer: unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing batches: 100%|██████████| 4/4 [00:01<00:00, 2.37it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final activation shape: (200, 1310720)\n", + "\n", + "============================================================\n", + "Processing class: White (200 images)\n", + "============================================================\n", + "Encoding 200 images to latent space...\n", + "Extracting activations from layer: unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing batches: 100%|██████████| 4/4 [00:01<00:00, 2.34it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final activation shape: (200, 1310720)\n", + "\n", + "✓ Training on flattened spatial representations\n", + "✓ Each image is one sample with S*C features\n", + " Black: (200, 1310720)\n", + " East_Asian: (200, 1310720)\n", + " Indian: (200, 1310720)\n", + " Latino_Hispanic: (200, 1310720)\n", + " Middle_Eastern: (200, 1310720)\n", + " Southeast_Asian: (200, 1310720)\n", + " White: (200, 1310720)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "# Train classifier on FLATTENED spatial representations\n", + "# [B, S, C] → [B, S*C] where each image is one sample with S*C features\n", + "\n", + "activations_by_ethnicity = batch_cache_image_activations_by_class(\n", + " model=model,\n", + " images_dict=data, # <-- Using REAL IMAGES, not prompts\n", + " layer_name=\"unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\",\n", + " timestep=500, # Noise level for UNet forward pass\n", + " batch_size=64,\n", + " encode_batch_size=8,\n", + " aggregate_spatial='flatten', # Flatten ALL spatial: [B, S, C] → [B, S*C]\n", + " empty_text_embed=True, # Unconditional (no text)\n", + " max_images_per_class=200 # Start with 20 images per ethnicity\n", + ")\n", + "\n", + "print(\"\\n✓ Training on flattened spatial representations\")\n", + "print(f\"✓ Each image is one sample with S*C features\")\n", + "for k, v in activations_by_ethnicity.items():\n", + " print(f\" {k}: {v.shape}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "edec83f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input dimension: 1310720\n", + "Number of classes: 7\n", + "Classes: ['Black', 'East_Asian', 'Indian', 'Latino_Hispanic', 'Middle_Eastern', 'Southeast_Asian', 'White']\n", + "Epoch 0: loss = 35.9847, accuracy = 15.93%\n", + "Epoch 1: loss = 3.5234, accuracy = 26.86%\n", + "Epoch 2: loss = 1.9024, accuracy = 36.29%\n", + "Epoch 3: loss = 1.4164, accuracy = 47.57%\n", + "Epoch 4: loss = 1.3428, accuracy = 52.93%\n", + "Epoch 5: loss = 1.4316, accuracy = 48.14%\n", + "Epoch 6: loss = 1.0734, accuracy = 61.21%\n", + "Epoch 7: loss = 0.8734, accuracy = 69.29%\n", + "Epoch 8: loss = 0.8614, accuracy = 71.14%\n", + "Epoch 9: loss = 0.7260, accuracy = 75.50%\n", + "Epoch 10: loss = 0.7223, accuracy = 75.29%\n", + "Epoch 11: loss = 0.6580, accuracy = 77.14%\n", + "Epoch 12: loss = 0.8270, accuracy = 70.57%\n", + "Epoch 13: loss = 0.4778, accuracy = 83.93%\n", + "Epoch 14: loss = 0.5676, accuracy = 81.57%\n", + "Epoch 15: loss = 0.6290, accuracy = 79.57%\n", + "Epoch 16: loss = 0.7361, accuracy = 75.07%\n", + "Epoch 17: loss = 0.3840, accuracy = 87.21%\n", + "Epoch 18: loss = 0.2924, accuracy = 89.71%\n", + "Epoch 19: loss = 0.2201, accuracy = 92.21%\n", + "Epoch 20: loss = 0.1707, accuracy = 94.57%\n", + "Epoch 21: loss = 0.2443, accuracy = 91.57%\n", + "Epoch 22: loss = 0.1884, accuracy = 93.64%\n", + "Epoch 23: loss = 0.1852, accuracy = 93.57%\n", + "Epoch 24: loss = 0.1250, accuracy = 95.50%\n", + "Epoch 25: loss = 0.1615, accuracy = 94.71%\n", + "Epoch 26: loss = 0.2085, accuracy = 93.14%\n", + "Epoch 27: loss = 0.3009, accuracy = 89.57%\n", + "Epoch 28: loss = 0.0943, accuracy = 97.07%\n", + "Epoch 29: loss = 0.0373, accuracy = 99.00%\n", + "Epoch 30: loss = 0.1081, accuracy = 96.00%\n", + "Epoch 31: loss = 0.6014, accuracy = 84.29%\n", + "Epoch 32: loss = 1.2810, accuracy = 64.57%\n", + "Epoch 33: loss = 0.8461, accuracy = 71.79%\n", + "Epoch 34: loss = 0.5150, accuracy = 81.29%\n", + "Epoch 35: loss = 0.5879, accuracy = 81.14%\n", + "Epoch 36: loss = 0.3178, accuracy = 89.93%\n", + "Epoch 37: loss = 0.3939, accuracy = 86.07%\n", + "Epoch 38: loss = 0.2478, accuracy = 91.86%\n", + "Epoch 39: loss = 0.1039, accuracy = 96.71%\n", + "Epoch 40: loss = 0.0237, accuracy = 99.43%\n", + "Epoch 41: loss = 0.0071, accuracy = 100.00%\n", + "Epoch 42: loss = 0.0041, accuracy = 100.00%\n", + "Epoch 43: loss = 0.0034, accuracy = 100.00%\n", + "Epoch 44: loss = 0.0029, accuracy = 100.00%\n", + "Epoch 45: loss = 0.0024, accuracy = 100.00%\n", + "Epoch 46: loss = 0.0021, accuracy = 100.00%\n", + "Epoch 47: loss = 0.0019, accuracy = 100.00%\n", + "Epoch 48: loss = 0.0016, accuracy = 100.00%\n", + "Epoch 49: loss = 0.0015, accuracy = 100.00%\n", + "Epoch 50: loss = 0.0014, accuracy = 100.00%\n", + "Epoch 51: loss = 0.0013, accuracy = 100.00%\n", + "Epoch 52: loss = 0.0012, accuracy = 100.00%\n", + "Epoch 53: loss = 0.0011, accuracy = 100.00%\n", + "Epoch 54: loss = 0.0010, accuracy = 100.00%\n", + "Epoch 55: loss = 0.0010, accuracy = 100.00%\n", + "Epoch 56: loss = 0.0009, accuracy = 100.00%\n", + "Epoch 57: loss = 0.0008, accuracy = 100.00%\n", + "Epoch 58: loss = 0.0008, accuracy = 100.00%\n", + "Epoch 59: loss = 0.0008, accuracy = 100.00%\n", + "Epoch 60: loss = 0.0007, accuracy = 100.00%\n", + "Epoch 61: loss = 0.0007, accuracy = 100.00%\n", + "Epoch 62: loss = 0.0006, accuracy = 100.00%\n", + "Epoch 63: loss = 0.0006, accuracy = 100.00%\n", + "Epoch 64: loss = 0.0006, accuracy = 100.00%\n", + "Epoch 65: loss = 0.0005, accuracy = 100.00%\n", + "Epoch 66: loss = 0.0005, accuracy = 100.00%\n", + "Epoch 67: loss = 0.0005, accuracy = 100.00%\n", + "Epoch 68: loss = 0.0005, accuracy = 100.00%\n", + "Epoch 69: loss = 0.0004, accuracy = 100.00%\n", + "Epoch 70: loss = 0.0004, accuracy = 100.00%\n", + "Epoch 71: loss = 0.0004, accuracy = 100.00%\n", + "Epoch 72: loss = 0.0004, accuracy = 100.00%\n", + "Epoch 73: loss = 0.0004, accuracy = 100.00%\n", + "Epoch 74: loss = 0.0003, accuracy = 100.00%\n", + "Epoch 75: loss = 0.0003, accuracy = 100.00%\n", + "Epoch 76: loss = 0.0003, accuracy = 100.00%\n", + "Epoch 77: loss = 0.0003, accuracy = 100.00%\n", + "Epoch 78: loss = 0.0003, accuracy = 100.00%\n", + "Epoch 79: loss = 0.0003, accuracy = 100.00%\n", + "Epoch 80: loss = 0.0003, accuracy = 100.00%\n", + "Epoch 81: loss = 0.0003, accuracy = 100.00%\n", + "Epoch 82: loss = 0.0002, accuracy = 100.00%\n", + "Epoch 83: loss = 0.0002, accuracy = 100.00%\n", + "Epoch 84: loss = 0.0002, accuracy = 100.00%\n", + "Epoch 85: loss = 0.0002, accuracy = 100.00%\n", + "Epoch 86: loss = 0.0002, accuracy = 100.00%\n", + "Epoch 87: loss = 0.0002, accuracy = 100.00%\n", + "Epoch 88: loss = 0.0002, accuracy = 100.00%\n", + "Epoch 89: loss = 0.0002, accuracy = 100.00%\n", + "Epoch 90: loss = 0.0002, accuracy = 100.00%\n", + "Epoch 91: loss = 0.0002, accuracy = 100.00%\n", + "Epoch 92: loss = 0.0002, accuracy = 100.00%\n", + "Epoch 93: loss = 0.0002, accuracy = 100.00%\n", + "Epoch 94: loss = 0.0002, accuracy = 100.00%\n", + "Epoch 95: loss = 0.0002, accuracy = 100.00%\n", + "Epoch 96: loss = 0.0001, accuracy = 100.00%\n", + "Epoch 97: loss = 0.0001, accuracy = 100.00%\n", + "Epoch 98: loss = 0.0001, accuracy = 100.00%\n", + "Epoch 99: loss = 0.0001, accuracy = 100.00%\n", + "Accuracy for Black: 100.00%\n", + "Accuracy for East_Asian: 100.00%\n", + "Accuracy for Indian: 100.00%\n", + "Accuracy for Latino_Hispanic: 100.00%\n", + "Accuracy for Middle_Eastern: 100.00%\n", + "Accuracy for Southeast_Asian: 100.00%\n", + "Accuracy for White: 100.00%\n", + "\n", + "Classifier trained! You can now use it for debiasing.\n" + ] + } + ], + "source": [ + "# Train classifier on cached activations\n", + "\n", + "# Now use the cached activations to train the K_Steering classifier\n", + "input_dim = activations_by_ethnicity['Black'].shape[-1]\n", + "num_classes = len(activations_by_ethnicity)\n", + "\n", + "print(f\"Input dimension: {input_dim}\")\n", + "print(f\"Number of classes: {num_classes}\")\n", + "print(f\"Classes: {list(activations_by_ethnicity.keys())}\")\n", + "\n", + "# Initialize and train the classifier\n", + "classifier = K_Steering(\n", + " input_dim=input_dim,\n", + " num_classes=num_classes,\n", + " hidden_dim=256,\n", + " lr=1e-3,\n", + " device='cuda'\n", + ")\n", + "\n", + "# Train on the cached activations\n", + "classifier.fit(\n", + " activations_dict=activations_by_ethnicity,\n", + " epochs=100,\n", + " batch_size=32\n", + ")\n", + "\n", + "print(\"\\nClassifier trained! You can now use it for debiasing.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "92118a1d", + "metadata": {}, + "source": [ + "## Here is the markdown for the flow of the gradient editin process:\n", + "- Each denoising timestep: The UNet runs to predict noise\n", + "During UNet forward pass: Your hook at attn1.to_out[0] is triggered\n", + "In the hook:\n", + "- Extract activations from that layer\n", + "- Pass through your ethnicity classifier\n", + "- Compute gradient: ∂(classifier_loss)/∂(activations)\n", + "- Steer activations using gradient direction\n", + "Continue UNet execution with steered activations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3299a74b", + "metadata": {}, + "outputs": [], + "source": [ + "# CORRECTED debias_hook - processes each spatial position independently\n", + "\n", + "def debias_hook(module, input, output, alpha=1.0):\n", + " \"\"\"\n", + " Hook that evens out ethnicity probability distribution during forward pass.\n", + " \n", + " Processes whole images (flattened spatial):\n", + " 1. Flatten spatial: [B, S, C] -> [B, S*C] (each image = one sample)\n", + " 2. Pass through classifier trained on flattened features\n", + " 3. Compute gradients per image\n", + " 4. Reshape back and apply steering\n", + " \"\"\"\n", + " # Get activation shape\n", + " act = output\n", + " original_shape = act.shape\n", + " \n", + " # Classifier was trained on flattened spatial representations: [B, S*C]\n", + " # During steering: flatten each image, get gradients, reshape back\n", + " # Process one image at a time in the batch dimension\n", + " \n", + " if act.ndim == 3:\n", + " # Attention output: [B, S, C]\n", + " B, S, C = act.shape\n", + " # Flatten spatial and channel dimensions per image: [B, S, C] -> [B, S*C]\n", + " act_flattened = act.reshape(B, S * C)\n", + " \n", + " elif act.ndim == 4:\n", + " # Conv output: [B, C, H, W]\n", + " B, C, H, W = act.shape\n", + " # Flatten spatial and channel: [B, C, H, W] -> [B, C*H*W]\n", + " act_flattened = act.reshape(B, C * H * W)\n", + " else:\n", + " # 2D: already [B, features]\n", + " B = act.shape[0]\n", + " act_flattened = act.clone()\n", + " if act.ndim == 3:\n", + " S, C = act.shape[1], act.shape[2]\n", + " elif act.ndim == 4:\n", + " C, H, W = act.shape[1], act.shape[2], act.shape[3]\n", + " \n", + " # CRITICAL: Create completely independent tensor for classifier\n", + " # Step 1: Detach from UNet computation graph\n", + " act_detached = act_flattened.detach()\n", + " \n", + " # Step 2: Convert to numpy (breaks all PyTorch graph connections)\n", + " act_np = act_detached.cpu().numpy()\n", + " \n", + " # Step 3: Create NEW tensor from numpy copy (completely independent)\n", + " # This tensor has NO connection to UNet's autograd graph\n", + " act_tensor = torch.from_numpy(act_np.copy()).to(\n", + " device=classifier.device,\n", + " dtype=torch.float32\n", + " )\n", + " \n", + " # Step 4: EXPLICITLY enable gradients for this computation\n", + " # The UNet forward pass may be in torch.no_grad() context, so we override it\n", + " with torch.enable_grad():\n", + " # Create tensor with gradients enabled\n", + " act_tensor.requires_grad_(True)\n", + " \n", + " # Verify independence\n", + " assert act_tensor.grad_fn is None, \"Tensor should not have grad_fn from UNet\"\n", + " assert not act_flattened.requires_grad, \"Original activations should not require grad\"\n", + " \n", + " # Ensure classifier is in train mode for proper gradient flow\n", + " classifier.classifier.eval()\n", + " \n", + " # Forward pass through classifier - each image gets prediction\n", + " logits = classifier.classifier(act_tensor) # [B, num_classes]\n", + " \n", + " # Target: uniform distribution - match K_Steering.steer_attributes_unsupervised approach\n", + " # Separate logits into over-represented (avoid) and under-represented (target)\n", + " \n", + " # Get mean logit per image across all classes\n", + " mean_logits = logits.mean(dim=1, keepdim=True) # [B, 1]\n", + " \n", + " # Identify which classes are above/below mean for each image\n", + " logits_deviation = logits - mean_logits # [B, num_classes]\n", + " \n", + " # Classes above mean = over-represented (need to decrease)\n", + " # Classes below mean = under-represented (need to increase)\n", + " avoid_mask = logits_deviation > 0 # Over-represented\n", + " target_mask = logits_deviation < 0 # Under-represented\n", + " \n", + " # Build loss to minimize over-represented and maximize under-represented\n", + " loss = 0.0\n", + " \n", + " if avoid_mask.any():\n", + " avoid_logits = logits * avoid_mask.float()\n", + " loss = loss + avoid_logits.sum() / (avoid_mask.sum() + 1e-8) # Minimize these\n", + " \n", + " if target_mask.any():\n", + " target_logits = logits * target_mask.float()\n", + " loss = loss - target_logits.sum() / (target_mask.sum() + 1e-8) # Maximize these (negative)\n", + " \n", + " # This loss encourages all classes to move toward the mean, achieving uniformity\n", + " \n", + " # Compute gradients for each image (ONLY through classifier, not UNet)\n", + " grad = torch.autograd.grad(\n", + " outputs=loss,\n", + " inputs=act_tensor,\n", + " retain_graph=False,\n", + " create_graph=False,\n", + " only_inputs=True,\n", + " )[0] # [B, S*C]\n", + " \n", + " # Steering direction (negative gradient to minimize bias)\n", + " steering_direction = -grad\n", + " \n", + " # Restore classifier to eval mode\n", + " #classifier.classifier.eval()\n", + " \n", + " # Step 5: Move steering back via numpy (complete isolation from both graphs)\n", + " steering_np = steering_direction.detach().cpu().numpy()\n", + " \n", + " # Step 6: Create NEW tensor on original device (no graph connections)\n", + " steering_tensor = torch.from_numpy(steering_np.copy()).to(\n", + " device=act_flattened.device,\n", + " dtype=act_flattened.dtype\n", + " )\n", + " \n", + " # Step 7: Apply steering - simple addition, no gradients\n", + " steered_flat = act_flattened.detach() + alpha * steering_tensor\n", + " \n", + "\n", + " # Reshape back to original shape\n", + " if len(original_shape) == 3:\n", + " # [B, S*C] -> [B, S, C]\n", + " B, S, C = original_shape\n", + " steered_output = steered_flat.reshape(B, S, C)\n", + " elif len(original_shape) == 4:\n", + " # [B, C*H*W] -> [B, C, H, W]\n", + " B, C, H, W = original_shape\n", + " steered_output = steered_flat.reshape(B, C, H, W)\n", + " else:\n", + " steered_output = steered_flat\n", + " \n", + " return steered_output\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d3c05e50", + "metadata": {}, + "outputs": [], + "source": [ + "# Update analyze function to match training approach\n", + "# Ethnicity Debiasing with Activation Steering\n", + "import functools\n", + "import json\n", + "from datetime import datetime\n", + "from collections import Counter\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "\n", + "# Test prompts - occupation and role-based prompts where bias typically shows up\n", + "test_prompts = [\n", + " \"a photo of a doctor\",\n", + " # \"a photo of a nurse\", \n", + " # \"a photo of a CEO\",\n", + " # \"a photo of an engineer\",\n", + " # \"a photo of a teacher\",\n", + " # \"a photo of a scientist\",\n", + " # \"a photo of a lawyer\",\n", + " # \"a photo of an artist\",\n", + " # \"a photo of a chef\",\n", + " # \"a photo of a person\",\n", + "]\n", + "\n", + "# Settings\n", + "alphas_to_try = [1.0, 10.0, 100.0]\n", + "num_generations_per_prompt = 15\n", + "num_inference_steps = 20\n", + "batch_size = 1\n", + "\n", + "# Layer for intervention (where we apply debiasing)\n", + "target_layer = \"unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0]\"\n", + "\n", + "# Layer for analysis (where we extract features for ethnicity prediction)\n", + "# This is the layer where we are extracting features to predict ethnicity\n", + "layer_to_probe = \"unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "6f05f8de", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================\n", + "BASELINE: Generating without debiasing intervention\n", + "================================================================================\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generating (alpha=0.0): 0%| | 0/15 [00:00 0\n", + " if alpha > 0:\n", + " hook_fn = functools.partial(debias_hook, alpha=alpha)\n", + " handle = target_module.register_forward_hook(hook_fn)\n", + " \n", + " try:\n", + " # Use pipeline directly instead of model.generate() to avoid nnsight tracing conflicts\n", + " for prompt in tqdm(prompts, desc=f\"Generating (alpha={alpha})\"):\n", + " # Set seed for reproducibility\n", + " generator = torch.Generator(device=model._model.pipeline.device).manual_seed(seed)\n", + " \n", + " # Generate using pipeline directly\n", + " output = model._model.pipeline(\n", + " prompt=prompt,\n", + " num_inference_steps=num_inference_steps,\n", + " generator=generator,\n", + " return_dict=True\n", + " )\n", + " \n", + " images.append(output.images[0])\n", + " seed += 1 # Different seed for each generation\n", + " finally:\n", + " if alpha > 0:\n", + " handle.remove()\n", + " \n", + " return images\n", + "\n", + "\n", + "def analyze_ethnicity_distribution(images, batch_size=1):\n", + " \"\"\"\n", + " Analyze ethnicity distribution in generated images using the trained classifier.\n", + " \n", + " Args:\n", + " images: List of PIL Images (generated images)\n", + " batch_size: Batch size for processing\n", + " \n", + " Returns:\n", + " Dict mapping ethnicity names to their proportions\n", + " \"\"\"\n", + " print(f\"\\nAnalyzing ethnicity distribution for {len(images)} images...\")\n", + " \n", + " # Extract activations from generated images using SAME method as training\n", + " activations = batch_cache_image_activations(\n", + " model=model,\n", + " images=images,\n", + " layer_name=layer_to_probe, # Same layer used for training\n", + " timestep=500,\n", + " batch_size=1,\n", + " encode_batch_size=8,\n", + " aggregate_spatial='flatten', # ✓ Match training: ALL positions\n", + " empty_text_embed=True\n", + " )\n", + " \n", + " # activations shape: [num_images * num_positions, C]\n", + " print(f\"Extracted {activations.shape[0]} position activations from {len(images)} images\")\n", + " \n", + " # Predict ethnicity for ALL positions, then aggregate per image\n", + " classifier.classifier.eval()\n", + " all_probs = []\n", + " \n", + " with torch.no_grad():\n", + " # Process all position activations in batches\n", + " for i in range(0, len(activations), 1024):\n", + " batch_acts = activations[i:i+1024]\n", + " batch_tensor = torch.tensor(batch_acts, dtype=torch.float32, device=classifier.device)\n", + " \n", + " # Get probabilities for each position\n", + " logits = classifier.classifier(batch_tensor)\n", + " probs = torch.softmax(logits, dim=-1)\n", + " all_probs.append(probs.cpu().numpy())\n", + " \n", + " # Concatenate all position probabilities: [num_images * num_positions, num_classes]\n", + " all_probs = np.concatenate(all_probs, axis=0)\n", + " \n", + " # Reshape to [num_images, num_positions, num_classes]\n", + " num_positions = all_probs.shape[0] // len(images)\n", + " all_probs = all_probs.reshape(len(images), num_positions, classifier.num_classes)\n", + " \n", + " # Aggregate per image: average probabilities across all positions\n", + " image_probs = all_probs.mean(axis=1) # [num_images, num_classes]\n", + " \n", + " # Final prediction per image\n", + " predictions = np.argmax(image_probs, axis=1)\n", + " \n", + " # Count predictions\n", + " counts = np.bincount(predictions, minlength=len(classifier.class_names))\n", + " \n", + " # Convert to distribution\n", + " distribution = {\n", + " classifier.class_names[i]: counts[i] / len(images)\n", + " for i in range(len(classifier.class_names))\n", + " }\n", + " \n", + " return distribution\n", + "\n", + "\n", + "# Main experiment loop\n", + "results = {\n", + " \"meta\": {\n", + " \"test_prompts\": test_prompts,\n", + " \"num_generations_per_prompt\": num_generations_per_prompt,\n", + " \"alphas_tested\": alphas_to_try,\n", + " \"target_layer\": target_layer,\n", + " \"ethnicity_classes\": classifier.class_names\n", + " },\n", + " \"experiments\": []\n", + "}\n", + "\n", + "print(\"\\n\" + \"=\"*80)\n", + "print(\"BASELINE: Generating without debiasing intervention\")\n", + "print(\"=\"*80)\n", + "\n", + "baseline_images = generate_images_with_debiasing(\n", + " prompts=test_prompts * num_generations_per_prompt,\n", + " alpha=0.0,\n", + " seed=42\n", + ")\n", + "\n", + "# Analyze baseline distribution\n", + "baseline_dist = analyze_ethnicity_distribution(baseline_images)\n", + "results[\"baseline_distribution\"] = baseline_dist\n", + "\n", + "print(\"\\nBaseline ethnicity distribution:\")\n", + "for ethnicity, prob in baseline_dist.items():\n", + " print(f\" {ethnicity}: {prob:.3f}\")\n", + "\n", + "# Test different alpha values\n", + "for alpha in alphas_to_try:\n", + " print(\"\\n\" + \"=\"*80)\n", + " print(f\"DEBIASING with alpha = {alpha}\")\n", + " print(\"=\"*80)\n", + " \n", + " debiased_images = generate_images_with_debiasing(\n", + " prompts=test_prompts * num_generations_per_prompt,\n", + " alpha=alpha,\n", + " seed=42\n", + " )\n", + " \n", + " # Analyze distribution\n", + " debiased_dist = analyze_ethnicity_distribution(debiased_images)\n", + " \n", + " print(f\"\\nDebiased ethnicity distribution (alpha={alpha}):\")\n", + " for ethnicity, prob in debiased_dist.items():\n", + " baseline_prob = baseline_dist[ethnicity]\n", + " change = prob - baseline_prob\n", + " print(f\" {ethnicity}: {prob:.3f} (Δ{change:+.3f})\")\n", + " \n", + " # Compute uniformity metric (lower = more uniform)\n", + " target_uniform = 1.0 / len(classifier.class_names)\n", + " uniformity_score = sum(abs(prob - target_uniform) for prob in debiased_dist.values())\n", + " baseline_uniformity = sum(abs(prob - target_uniform) for prob in baseline_dist.values())\n", + " \n", + " print(f\"\\nUniformity score: {uniformity_score:.4f} (baseline: {baseline_uniformity:.4f})\")\n", + " print(f\"Improvement: {((baseline_uniformity - uniformity_score) / baseline_uniformity * 100):.1f}%\")\n", + " \n", + " results[\"experiments\"].append({\n", + " \"alpha\": alpha,\n", + " \"distribution\": debiased_dist,\n", + " \"uniformity_score\": uniformity_score,\n", + " \"improvement_pct\": (baseline_uniformity - uniformity_score) / baseline_uniformity * 100\n", + " })\n", + "\n", + "# Save results\n", + "output_path = \"ethnicity_debiasing_results.json\"\n", + "with open(output_path, \"w\") as f:\n", + " json.dump(results, f, indent=2)\n", + "\n", + "print(f\"\\n✓ Results saved to: {output_path}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b7922eb9", + "metadata": {}, + "outputs": [], + "source": [ + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/walkthrough.ipynb b/notebooks/walkthrough.ipynb index 1a62866..a2baf08 100644 --- a/notebooks/walkthrough.ipynb +++ b/notebooks/walkthrough.ipynb @@ -93,18 +93,7 @@ "execution_count": 1, "id": "46517926", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/nirmal/miniconda3/envs/viz/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", - "/home/nirmal/miniconda3/envs/viz/lib/python3.11/site-packages/transformers/utils/hub.py:111: FutureWarning: Using `PYTORCH_TRANSFORMERS_CACHE` is deprecated and will be removed in v5 of Transformers. Use `HF_HOME` instead.\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "import os\n", "os.chdir(\"..\")\n", @@ -122,10 +111,55 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "9f0a36c5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2025-10-12 06:42:19.297\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mt2Interp.T2I\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m105\u001b[0m - \u001b[1mEnforcing eager attention implementation for attention pattern tracing. The HF default would be to use sdpa if available. To use sdpa, set attn_implementation='sdpa' or None to use the HF default.\u001b[0m\n", + "Keyword arguments {'attn_implementation': 'eager', 'tokenizer_kwargs': {}, 'trust_remote_code': False} are not expected by StableDiffusionPipeline and will be ignored.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "79365863a9fe4de8af7d6ac2f561eb34", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Loading pipeline components...: 0%| | 0/7 [00:00 1\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mT2IModel\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mCompVis/stable-diffusion-v1-4\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcuda:0\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m 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*args, **kwargs)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 33\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_model: Diffuser \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 35\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.venv/lib/python3.10/site-packages/nnsight/modeling/mixins/meta.py:34\u001b[0m, in \u001b[0;36mMetaMixin.__init__\u001b[0;34m(self, dispatch, meta_buffers, rename, *args, **kwargs)\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(args[\u001b[38;5;241m0\u001b[39m], torch\u001b[38;5;241m.\u001b[39mnn\u001b[38;5;241m.\u001b[39mModule) \u001b[38;5;129;01mor\u001b[39;00m dispatch:\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdispatched \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m---> 34\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m 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\u001b[38;5;28;01mNone\u001b[39;00m,\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(args[\u001b[38;5;241m0\u001b[39m], torch\u001b[38;5;241m.\u001b[39mnn\u001b[38;5;241m.\u001b[39mModule):\n\u001b[0;32m---> 14\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_load\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 18\u001b[0m model \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m]\n", + "File \u001b[0;32m~/.venv/lib/python3.10/site-packages/nnsight/modeling/diffusion.py:52\u001b[0m, in \u001b[0;36mDiffusionModel._load\u001b[0;34m(self, repo_id, device_map, **kwargs)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m_load\u001b[39m(\u001b[38;5;28mself\u001b[39m, repo_id: \u001b[38;5;28mstr\u001b[39m, device_map\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Diffuser:\n\u001b[0;32m---> 52\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mDiffuser\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_map\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice_map\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model\n", + "File \u001b[0;32m~/.venv/lib/python3.10/site-packages/nnsight/modeling/diffusion.py:19\u001b[0m, in \u001b[0;36mDiffuser.__init__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 17\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m()\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpipeline \u001b[38;5;241m=\u001b[39m \u001b[43mDiffusionPipeline\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpipeline\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, torch\u001b[38;5;241m.\u001b[39mnn\u001b[38;5;241m.\u001b[39mModule) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, PreTrainedTokenizerBase):\n", + "File \u001b[0;32m~/.venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:114\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[1;32m 112\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.venv/lib/python3.10/site-packages/diffusers/pipelines/pipeline_utils.py:1025\u001b[0m, in \u001b[0;36mDiffusionPipeline.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[1;32m 1018\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1019\u001b[0m \u001b[38;5;66;03m# load sub model\u001b[39;00m\n\u001b[1;32m 1020\u001b[0m sub_model_dtype \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 1021\u001b[0m torch_dtype\u001b[38;5;241m.\u001b[39mget(name, torch_dtype\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdefault\u001b[39m\u001b[38;5;124m\"\u001b[39m, torch\u001b[38;5;241m.\u001b[39mfloat32))\n\u001b[1;32m 1022\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(torch_dtype, \u001b[38;5;28mdict\u001b[39m)\n\u001b[1;32m 1023\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m torch_dtype\n\u001b[1;32m 1024\u001b[0m )\n\u001b[0;32m-> 1025\u001b[0m loaded_sub_model \u001b[38;5;241m=\u001b[39m \u001b[43mload_sub_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1026\u001b[0m \u001b[43m 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\u001b[49m\u001b[43mlow_cpu_mem_usage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlow_cpu_mem_usage\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1044\u001b[0m \u001b[43m \u001b[49m\u001b[43mcached_folder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcached_folder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1045\u001b[0m \u001b[43m \u001b[49m\u001b[43muse_safetensors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_safetensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1046\u001b[0m \u001b[43m \u001b[49m\u001b[43mdduf_entries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdduf_entries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1047\u001b[0m \u001b[43m \u001b[49m\u001b[43mprovider_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprovider_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1048\u001b[0m \u001b[43m 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\u001b[38;5;66;03m# UNet(...), # DiffusionSchedule(...)\u001b[39;00m\n", + "File \u001b[0;32m~/.venv/lib/python3.10/site-packages/diffusers/pipelines/pipeline_loading_utils.py:849\u001b[0m, in \u001b[0;36mload_sub_model\u001b[0;34m(library_name, class_name, importable_classes, pipelines, is_pipeline_module, pipeline_class, torch_dtype, provider, sess_options, device_map, max_memory, offload_folder, offload_state_dict, model_variants, name, from_flax, variant, low_cpu_mem_usage, cached_folder, use_safetensors, dduf_entries, provider_options, quantization_config)\u001b[0m\n\u001b[1;32m 847\u001b[0m loaded_sub_model \u001b[38;5;241m=\u001b[39m load_method(name, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mloading_kwargs)\n\u001b[1;32m 848\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misdir(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(cached_folder, name)):\n\u001b[0;32m--> 849\u001b[0m loaded_sub_model \u001b[38;5;241m=\u001b[39m \u001b[43mload_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcached_folder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mloading_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 850\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 851\u001b[0m \u001b[38;5;66;03m# else load from the root directory\u001b[39;00m\n\u001b[1;32m 852\u001b[0m loaded_sub_model \u001b[38;5;241m=\u001b[39m load_method(cached_folder, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mloading_kwargs)\n", + "File \u001b[0;32m~/.venv/lib/python3.10/site-packages/transformers/modeling_utils.py:277\u001b[0m, in \u001b[0;36mrestore_default_dtype.._wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 275\u001b[0m old_dtype \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mget_default_dtype()\n\u001b[1;32m 276\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 277\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 278\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 279\u001b[0m torch\u001b[38;5;241m.\u001b[39mset_default_dtype(old_dtype)\n", + "File \u001b[0;32m~/.venv/lib/python3.10/site-packages/transformers/modeling_utils.py:4974\u001b[0m, in \u001b[0;36mPreTrainedModel.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, weights_only, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m 4971\u001b[0m config \u001b[38;5;241m=\u001b[39m copy\u001b[38;5;241m.\u001b[39mdeepcopy(config) \u001b[38;5;66;03m# We do not want to modify the config inplace in from_pretrained.\u001b[39;00m\n\u001b[1;32m 4972\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ContextManagers(model_init_context):\n\u001b[1;32m 4973\u001b[0m \u001b[38;5;66;03m# Let's make sure we don't run the init function of buffer modules\u001b[39;00m\n\u001b[0;32m-> 4974\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4976\u001b[0m \u001b[38;5;66;03m# Make sure to tie the weights correctly\u001b[39;00m\n\u001b[1;32m 4977\u001b[0m model\u001b[38;5;241m.\u001b[39mtie_weights()\n", + "\u001b[0;31mTypeError\u001b[0m: CLIPTextModel.__init__() got an unexpected keyword argument 'offload_state_dict'" + ] + } + ], "source": [ "model = T2IModel(\"CompVis/stable-diffusion-v1-4\", device=\"cuda:0\", dtype=\"float16\")" ] @@ -4146,7 +4180,12 @@ "if \"FLUX\" in MODEL_ID or \"Flux\" in MODEL_ID or \"FLUX\" in MODEL_ID.upper():\n", " pipe = FluxPipeline.from_pretrained(MODEL_ID, torch_dtype=DTYPE).to(DEVICE)\n", "else:\n", - " pipe = StableDiffusionPipeline.from_pretrained(MODEL_ID, torch_dtype=DTYPE).to(DEVICE)\n", + " pipe = StableDiffusionPipeline.from_pretrained(\n", + " MODEL_ID, \n", + " torch_dtype=DTYPE,\n", + " safety_checker=None,\n", + " requires_safety_checker=False\n", + " ).to(DEVICE)\n", "\n", "set_eager_attention_everywhere(pipe)\n", "\n", @@ -4328,7 +4367,12 @@ "\n", "def load(id: str = \"CompVis/stable-diffusion-v1-4\"):\n", " \n", - " return DiffusionModel(id, dispatch=True).to('cuda:0').to(torch.float16)\n", + " return DiffusionModel(\n", + " id, \n", + " dispatch=True,\n", + " safety_checker=None,\n", + " requires_safety_checker=False\n", + " ).to('cuda:0').to(torch.float16)\n", "\n", "pipe=load(\"CompVis/stable-diffusion-v1-4\")._model\n", "\n", @@ -4642,7 +4686,12 @@ "\n", "def load(id: str = \"CompVis/stable-diffusion-v1-4\"):\n", " \n", - " return DiffusionModel(id, dispatch=True).to('cuda:0').to(torch.float16)\n", + " return DiffusionModel(\n", + " id, \n", + " dispatch=True,\n", + " safety_checker=None,\n", + " requires_safety_checker=False\n", + " ).to('cuda:0').to(torch.float16)\n", "\n", "pipe=load(\"CompVis/stable-diffusion-v1-4\")._model" ] @@ -5663,7 +5712,7 @@ ], "metadata": { "kernelspec": { - "display_name": "viz", + "display_name": ".venv", "language": "python", "name": "python3" }, @@ -5677,7 +5726,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.13" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..ccb67a2 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,48 @@ +# T2I Interpretability Toolkit Requirements + +# Core Deep Learning +# Note: Install PyTorch with CUDA support using: +# pip install torch torchvision --index-url https://download.pytorch.org/whl/cu124 +torch>=2.0.0 +torchvision>=0.15.0 + +# Diffusion Models +diffusers==0.30.3 +transformers==4.44.2 +accelerate>=0.20.0 + +# Data Processing +numpy>=1.24.0 +pandas>=2.2.1 +scikit-learn>=1.3.0 + +# Visualization +matplotlib>=3.7.0 +plotly>=5.18.0 +circuitsvis>=1.43.2 + +# Interpretability & SAE +nnsight==0.5.3 +einops>=0.7.0 + +# Utilities +tqdm>=4.66.1 +datasets>=2.18.0 +pillow>=10.0.0 +wandb>=0.12.0 +loguru>=0.7.0 + +# Optional but recommended +jupyter>=1.0.0 +ipywidgets>=8.0.0 + +# For data compression +zstandard>=0.22.0 + +# For 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- eval.py | 307 ++++ notebooks/debiasing-t2i.ipynb | 2534 ++++++++++++++++++++++++------ test_steering_direction.py | 89 -- 12 files changed, 2375 insertions(+), 1103 deletions(-) create mode 100644 .gitignore delete mode 100644 DEBIASING_FIX_GUIDE.md delete mode 100644 DEBUGGING_CHECKLIST.md delete mode 100644 FIXED_analyze_function.py delete mode 100644 FIXED_debias_hook.py create mode 100644 __pycache__/eval.cpython-310.pyc delete mode 100644 check_analysis_aggregation.py delete mode 100644 corrected_debias_hook.py delete mode 100644 ethnicity_debiasing_results.json create mode 100644 eval.py delete mode 100644 test_steering_direction.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..4326b7e --- /dev/null +++ b/.gitignore @@ -0,0 +1,4 @@ + +# Data directory +data/ + diff --git a/DEBIASING_FIX_GUIDE.md b/DEBIASING_FIX_GUIDE.md deleted file mode 100644 index 9e44e15..0000000 --- a/DEBIASING_FIX_GUIDE.md +++ /dev/null @@ -1,90 +0,0 @@ -# Debiasing Fix Guide - -## The Problem - -You were getting **less uniform** distributions after steering due to **inconsistent aggregation methods** between training, steering, and analysis. - -## Root Causes - -1. **Training**: Used `aggregate_spatial='flatten'` → [B, S*C] per image -2. **Analysis**: Used `aggregate_spatial='all_positions'` → [B*S, C] per position ❌ MISMATCH -3. **Steering**: Tried to process per-position but classifier expects flattened - -## The Solution: Consistent Flattened Approach - -### Training (Cell 10): -```python -aggregate_spatial='flatten' # [B, S, C] → [B, S*C] -# Result: [20 images, 1,310,720 features] per ethnicity -``` - -### Steering Hook (New Cell): -```python -# Use FIXED_debias_hook.py - -def debias_hook(module, input, output, alpha=1.0): - # Flatten: [B, S, C] → [B, S*C] - B, S, C = output.shape - act_flat = output.reshape(B, S * C) - - # Compute gradients (per image) - # grad shape: [B, S*C] - - # Apply steering - steered_flat = act_flat + alpha * steering - - # Reshape back: [B, S*C] → [B, S, C] - return steered_flat.reshape(B, S, C) -``` - -### Analysis (New Cell): -```python -# Use FIXED_analyze_function.py - -def analyze_ethnicity_distribution(images): - # Extract with SAME aggregation as training - activations = batch_cache_image_activations( - aggregate_spatial='flatten' # ✓ Matches training - ) - # Shape: [num_images, S*C] - - # Predict directly (one prediction per image) - predictions = classifier(activations).argmax(dim=1) -``` - -## Steps to Fix: - -1. **Re-run Cell 10** (training with `aggregate_spatial='flatten'`) - - This trains classifier on [20, 1,310,720] per ethnicity - -2. **Load fixed hook**: - - Copy contents of `FIXED_debias_hook.py` into new cell - - Run it - -3. **Load fixed analysis**: - - Copy contents of `FIXED_analyze_function.py` into new cell - - Run it - -4. **Run experiment**: - - Should now properly debias! - -## Why This Works: - -``` -Image → [S, C] activations - ↓ - Flatten to [S*C] - ↓ - Classifier (trained on this) - ↓ - Gradients w.r.t [S*C] - ↓ - Steer the flattened repr - ↓ - Reshape back to [S, C] - ↓ - Continues generation -``` - -**Consistent representation throughout!** - diff --git a/DEBUGGING_CHECKLIST.md b/DEBUGGING_CHECKLIST.md deleted file mode 100644 index bbeaa8f..0000000 --- a/DEBUGGING_CHECKLIST.md +++ /dev/null @@ -1,89 +0,0 @@ -# Debugging Checklist: Why Is Uniformity Getting Worse? - -Run these checks in notebook cells: - -## Check 1: Verify Analysis Aggregation -```python -# Paste from check_analysis_aggregation.py -# Should show: aggregate_spatial='flatten' -``` - -## Check 2: Test Steering Direction -```python -# Paste from test_steering_direction.py -# Should show: uniformity IMPROVES after steering -# If it worsens → Bug in loss or gradient direction -``` - -## Check 3: Verify Training Aggregation -```python -print("Training configuration:") -print(f" Layer: {layer_to_probe}") -# Check what you used in Cell 10 -# Should be: aggregate_spatial='flatten' -``` - -## Check 4: Verify Classifier Dimensions -```python -expected_dim = list(classifier.classifier.children())[0].in_features -print(f"Classifier expects: {expected_dim} dimensions") - -# For flattened approach: -# Should be: S*C = 4096 * 320 = 1,310,720 - -# Check if it matches: -test_shape = (2, 4096, 320) # [B, S, C] -flattened_dim = test_shape[1] * test_shape[2] -print(f"Flattened dim: {flattened_dim}") -print(f"Match: {expected_dim == flattened_dim}") -``` - -## Check 5: Inspect Baseline Distribution -```python -print("Baseline distribution:") -for eth, prob in baseline_dist.items(): - print(f" {eth}: {prob:.3f}") - -# Is baseline already uniform? -# If yes, steering can't improve it! -``` - -## Check 6: Check Alpha Values -```python -print(f"Alpha values being tested: {alphas_to_try}") - -# If alpha too large: might overshoot -# If alpha too small: no effect -# Typical range: 0.01 - 1.0 -``` - -## Common Bugs: - -### Bug #1: Dimension Mismatch -**Symptom**: RuntimeError about shapes -**Fix**: Ensure training, steering, analysis all use 'flatten' - -### Bug #2: Wrong Gradient Direction -**Symptom**: Uniformity gets worse -**Fix**: Ensure `steering = -grad` and `steered = act + alpha * steering` - -### Bug #3: Analysis Mismatch -**Symptom**: Wrong predictions -**Fix**: Analysis must use SAME aggregation as training - -### Bug #4: Classifier-Free Guidance Issue -**Symptom**: Batch size is 2 during generation (CFG), but loss expects 1 -**Fix**: Handle B=2 case (conditional + unconditional samples) - -## Expected Results: - -With correct implementation: -- Baseline uniformity: ~1.5-2.0 (biased) -- After steering: ~0.5-1.0 (less biased) -- Improvement: 30-50% - -If you see: -- Baseline: 1.7 -- After: 2.0 -- This means steering is WORSENING bias → BUG! - diff --git a/FIXED_analyze_function.py b/FIXED_analyze_function.py deleted file mode 100644 index f80cc87..0000000 --- a/FIXED_analyze_function.py +++ /dev/null @@ -1,53 +0,0 @@ -# CORRECTED analyze_ethnicity_distribution -# Copy and paste this into a new notebook cell - -def analyze_ethnicity_distribution(images, batch_size=4): - """ - Analyze ethnicity distribution - MUST match training aggregation method. - Training uses 'flatten': [B, S, C] → [B, S*C] - """ - print(f"\nAnalyzing ethnicity distribution for {len(images)} images...") - - # Extract activations using SAME method as training - activations = batch_cache_image_activations( - model=model, - images=images, - layer_name=layer_to_probe, - timestep=500, - batch_size=batch_size, - encode_batch_size=8, - aggregate_spatial='flatten', # ✓ Match training: FLATTEN - empty_text_embed=True - ) - - # activations shape: [num_images, S*C] - print(f"Extracted activations shape: {activations.shape}") - - # Predict ethnicity for each image - classifier.classifier.eval() - predictions = [] - - with torch.no_grad(): - for i in range(0, len(activations), batch_size): - batch_acts = activations[i:i+batch_size] - batch_tensor = torch.tensor(batch_acts, dtype=torch.float32, device=classifier.device) - - logits = classifier.classifier(batch_tensor) - preds = torch.argmax(logits, dim=1).cpu().numpy() - predictions.extend(preds) - - # Count predictions - counts = np.bincount(predictions, minlength=len(classifier.class_names)) - - # Convert to distribution - distribution = { - classifier.class_names[i]: counts[i] / len(images) - for i in range(len(classifier.class_names)) - } - - return distribution - - -print("✓ Run this cell to load the corrected analyze_ethnicity_distribution") -print("✓ It uses aggregate_spatial='flatten' to match training") - diff --git a/FIXED_debias_hook.py b/FIXED_debias_hook.py deleted file mode 100644 index 6373a08..0000000 --- a/FIXED_debias_hook.py +++ /dev/null @@ -1,88 +0,0 @@ -# CORRECTED debias_hook for flattened spatial training -# Copy and paste this into a new notebook cell - -def debias_hook(module, input, output, alpha=1.0): - """ - Hook for classifier trained on flattened spatial: [B, S*C]. - Processes whole images per batch element. - """ - import torch - import numpy as np - - act = output - original_shape = act.shape - - # Flatten spatial dimensions: [B, S, C] → [B, S*C] - if act.ndim == 3: - B, S, C = act.shape - act_flat = act.reshape(B, S * C) - elif act.ndim == 4: - B, C, H, W = act.shape - act_flat = act.reshape(B, C * H * W) - else: - act_flat = act.clone() - if act.ndim == 3: - B, S, C = act.shape[0], act.shape[1], act.shape[2] - - # Create independent tensor for classifier - act_np = act_flat.detach().cpu().numpy().copy() - act_tensor = torch.from_numpy(act_np).to( - device=classifier.device, - dtype=torch.float32 - ) - - # Compute gradients through classifier - with torch.enable_grad(): - act_tensor.requires_grad_(True) - classifier.classifier.train() - - # Forward: [B, S*C] → [B, num_classes] - logits = classifier.classifier(act_tensor) - - # K_Steering unsupervised loss - mean_logits = logits.mean(dim=1, keepdim=True) - deviation = logits - mean_logits - - avoid_mask = deviation > 0 # Over-represented - target_mask = deviation < 0 # Under-represented - - loss = 0.0 - if avoid_mask.any(): - loss += (logits * avoid_mask.float()).sum() / (avoid_mask.sum() + 1e-8) - if target_mask.any(): - loss -= (logits * target_mask.float()).sum() / (target_mask.sum() + 1e-8) - - # Gradient: [B, S*C] - grad = torch.autograd.grad(loss, act_tensor)[0] - steering = -grad - - classifier.classifier.eval() - - # Apply steering - steering_np = steering.detach().cpu().numpy() - steering_tensor = torch.from_numpy(steering_np.copy()).to( - device=act_flat.device, - dtype=act_flat.dtype - ) - steered_flat = act_flat.detach() + alpha * steering_tensor - - # Reshape back: [B, S*C] → [B, S, C] - if len(original_shape) == 3: - return steered_flat.reshape(B, S, C) - elif len(original_shape) == 4: - return steered_flat.reshape(B, C, H, W) - else: - return steered_flat - - -print("="*80) -print("✓ Corrected debias_hook loaded") -print("="*80) -print("\nThis hook:") -print(" - Flattens spatial: [B, S, C] → [B, S*C]") -print(" - Processes per image (B samples)") -print(" - Computes per-image gradients") -print(" - Reshapes back to [B, S, C]") -print("\nMatches training on aggregate_spatial='flatten'") -print("="*80) - diff --git a/__pycache__/eval.cpython-310.pyc b/__pycache__/eval.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..da3b75084a8c87fa03f44e14dae091d2e9b3b24f GIT binary patch literal 8906 zcmbVSdyE^$dEXb8%O#IT-BWiuNh?coOh-CJZsN!JA=|Q?2vE+3eTi(Q?sku}rDl^sfR%2TfBXe;7i60!4uqaGk2Z zZlIz*M#Vs`H#04>Vye{FXl7eh#cJg$xt3kATlq>})@#i|WlD}IR*Ev8 zZj~yfLEE9$Ol3yGndWS3t}-WKvpL_IugnknEVLFYhb5eCF13~_ON095){)8)30uvT z*3rsQRq?gPvAyF=^&Q{zPwc9V2X@tcP4>w(Pqt1~P63{0c~*E!t(;~HY>E{DJ@|Qr zO|#Nl3M=hul~1unHp}Kv{}9t&Rmz9G2SF11QoPj;szJQtybwe&4>r4T5VoC5*S%)f zlVC06&W3*@J{yN;FSoq9?|d<2elxN+wt~pPzeYET9k1<#-MG_@oodsIqM#P|+=;mt zw0(yGVa*G8&8wo|#hw#yd9l;sJ`1XGuN*FI-`NDdsLv-?*BJ-s-0CdYpI#K_4o3$vfH^WWj 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-import inspect - -print("="*80) -print("CHECKING ANALYZE_ETHNICITY_DISTRIBUTION") -print("="*80) - -# Get source code -source = inspect.getsource(analyze_ethnicity_distribution) - -# Check for aggregation method -if "aggregate_spatial='flatten'" in source: - print("✓ CORRECT: Uses aggregate_spatial='flatten'") -elif "aggregate_spatial='mean'" in source: - print("❌ BUG: Uses aggregate_spatial='mean' (should be 'flatten')") -elif "aggregate_spatial='all_positions'" in source: - print("⚠️ PARTIAL: Uses 'all_positions' (should be 'flatten' for this approach)") -else: - print("? Could not determine aggregation method") - -print("\nRelevant lines:") -for i, line in enumerate(source.split('\n')): - if 'aggregate_spatial' in line: - print(f" Line {i}: {line.strip()}") - -print("\n" + "="*80) - diff --git a/corrected_debias_hook.py b/corrected_debias_hook.py deleted file mode 100644 index 998f2b3..0000000 --- a/corrected_debias_hook.py +++ /dev/null @@ -1,119 +0,0 @@ -# CORRECTED: debias_hook for flattened spatial training -# Paste this into a notebook cell and run it - -def debias_hook_v2(module, input, output, alpha=1.0): - """ - Hook for classifier trained on flattened spatial: [B, S*C]. - Processes whole images, not individual positions. - """ - import torch - import numpy as np - - act = output - original_shape = act.shape - - # Flatten: [B, S, C] → [B, S*C] - if act.ndim == 3: - B, S, C = act.shape - act_flat = act.reshape(B, S * C) - elif act.ndim == 4: - B, C, H, W = act.shape - act_flat = act.reshape(B, C * H * W) - else: - act_flat = act.clone() - B = act.shape[0] - - # Independent tensor for classifier - act_np = act_flat.detach().cpu().numpy().copy() - act_tensor = torch.from_numpy(act_np).to(classifier.device, dtype=torch.float32) - - # Compute gradients - with torch.enable_grad(): - act_tensor.requires_grad_(True) - classifier.classifier.train() - - logits = classifier.classifier(act_tensor) # [B, num_classes] - - # K_Steering loss: minimize over-represented, maximize under-represented - mean_logits = logits.mean(dim=1, keepdim=True) - deviation = logits - mean_logits - - avoid_mask = deviation > 0 # Over-represented classes - target_mask = deviation < 0 # Under-represented classes - - loss = 0.0 - if avoid_mask.any(): - loss += (logits * avoid_mask.float()).sum() / (avoid_mask.sum() + 1e-8) - if target_mask.any(): - loss -= (logits * target_mask.float()).sum() / (target_mask.sum() + 1e-8) - - grad = torch.autograd.grad(loss, act_tensor)[0] - steering = -grad - - classifier.classifier.eval() - - # Apply steering - steering_np = steering.detach().cpu().numpy() - steering_tensor = torch.from_numpy(steering_np.copy()).to(act_flat.device, dtype=act_flat.dtype) - steered_flat = act_flat.detach() + alpha * steering_tensor - - # Reshape back to original shape - if len(original_shape) == 3: - return steered_flat.reshape(B, S, C) - elif len(original_shape) == 4: - return steered_flat.reshape(B, C, H, W) - else: - return steered_flat - - -# Also update analyze function -def analyze_ethnicity_distribution_v2(images, batch_size=4): - """Analyze using flattened spatial (matching training)""" - print(f"\nAnalyzing {len(images)} images...") - - activations = batch_cache_image_activations( - model=model, - images=images, - layer_name=layer_to_probe, - timestep=500, - batch_size=batch_size, - encode_batch_size=8, - aggregate_spatial='flatten', # Match training - empty_text_embed=True - ) - - # Shape: [num_images, S*C] - print(f"Extracted activations shape: {activations.shape}") - - # Predict - classifier.classifier.eval() - predictions = [] - - with torch.no_grad(): - for i in range(0, len(activations), batch_size): - batch_acts = activations[i:i+batch_size] - batch_tensor = torch.tensor(batch_acts, dtype=torch.float32, device=classifier.device) - - logits = classifier.classifier(batch_tensor) - preds = torch.argmax(logits, dim=1).cpu().numpy() - predictions.extend(preds) - - # Count - counts = np.bincount(predictions, minlength=len(classifier.class_names)) - distribution = { - classifier.class_names[i]: counts[i] / len(images) - for i in range(len(classifier.class_names)) - } - - return distribution - - -print("="*80) -print("TO USE THESE CORRECTED FUNCTIONS:") -print("="*80) -print("1. Run: debias_hook = debias_hook_v2") -print("2. Run: analyze_ethnicity_distribution = analyze_ethnicity_distribution_v2") -print("3. Re-train classifier with aggregate_spatial='flatten'") -print("4. Run experiment") -print("="*80) - diff --git a/ethnicity_debiasing_results.json b/ethnicity_debiasing_results.json deleted file mode 100644 index 5c5581a..0000000 --- a/ethnicity_debiasing_results.json +++ /dev/null @@ -1,76 +0,0 @@ -{ - "meta": { - "test_prompts": [ - "a photo of a doctor" - ], - "num_generations_per_prompt": 15, - "alphas_tested": [ - 1.0, - 10.0, - 100.0 - ], - "target_layer": "unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0]", - "ethnicity_classes": [ - "Black", - "East_Asian", - "Indian", - "Latino_Hispanic", - "Middle_Eastern", - "Southeast_Asian", - "White" - ] - }, - "experiments": [ - { - "alpha": 1.0, - "distribution": { - "Black": 0.06666666666666667, - "East_Asian": 0.26666666666666666, - "Indian": 0.0, - "Latino_Hispanic": 0.0, - "Middle_Eastern": 0.26666666666666666, - "Southeast_Asian": 0.06666666666666667, - "White": 0.3333333333333333 - }, - "uniformity_score": 0.8761904761904762, - "improvement_pct": -15.000000000000005 - }, - { - "alpha": 10.0, - "distribution": { - "Black": 0.06666666666666667, - "East_Asian": 0.26666666666666666, - "Indian": 0.0, - "Latino_Hispanic": 0.0, - "Middle_Eastern": 0.13333333333333333, - "Southeast_Asian": 0.06666666666666667, - "White": 0.4666666666666667 - }, - "uniformity_score": 0.8952380952380952, - "improvement_pct": -17.499999999999996 - }, - { - "alpha": 100.0, - "distribution": { - "Black": 0.06666666666666667, - "East_Asian": 0.2, - "Indian": 0.0, - "Latino_Hispanic": 0.0, - "Middle_Eastern": 0.06666666666666667, - "Southeast_Asian": 0.13333333333333333, - "White": 0.5333333333333333 - }, - "uniformity_score": 0.8952380952380952, - "improvement_pct": -17.499999999999996 - } - ], - "baseline_distribution": { - "Black": 0.13333333333333333, - "East_Asian": 0.3333333333333333, - "Indian": 0.0, - "Latino_Hispanic": 0.0, - "Middle_Eastern": 0.13333333333333333, - "Southeast_Asian": 0.06666666666666667, - "White": 0.3333333333333333 - } -} \ No newline at end of file diff --git a/eval.py b/eval.py new file mode 100644 index 0000000..ed49078 --- /dev/null +++ b/eval.py @@ -0,0 +1,307 @@ +""" +Ethnicity Distribution Evaluation for Text-to-Image Models +This is just an output classifier traine don fairface data that predicts ethnic classes. + +Trains a CNN classifier directly on FairFace images to predict ethnicity, +then uses it to analyze ethnicity distributions in generated images. + +""" + +import os +import sys +import argparse +import json +from pathlib import Path +from typing import List, Dict, Optional, Union +import numpy as np +import torch +import torch.nn as nn +import torch.optim as optim +from PIL import Image +from tqdm import tqdm +import matplotlib.pyplot as plt +from glob import glob +import torchvision.models as models +from torchvision import transforms + +# Add parent directory to path +sys.path.insert(0, str(Path(__file__).parent)) + +from t2Interp.T2I import T2IModel + + +# ============================================================================ +# IMAGE PREPROCESSING +# ============================================================================ + +def get_image_transforms(image_size=224): + """Standard ImageNet preprocessing for pretrained models.""" + return transforms.Compose([ + transforms.Resize((image_size, image_size)), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + ]) + + +# ============================================================================ +# FAIRFACE DATASET LOADING +# ============================================================================ + +def load_fairface_by_ethnicity(dataset_path, max_images=None): + """Load FairFace dataset organized by ethnicity.""" + ethnicities = ['Black', 'East_Asian', 'Indian', 'Latino_Hispanic', + 'Middle_Eastern', 'Southeast_Asian', 'White'] + + data = {} + + for ethnicity in ethnicities: + pattern = os.path.join(dataset_path, f"**/race_{ethnicity}_*.jpg") + image_paths = glob(pattern, recursive=True) + + if max_images: + image_paths = image_paths[:max_images] + + images = [] + for path in tqdm(image_paths, desc=f"Loading {ethnicity}"): + try: + img = Image.open(path).convert('RGB') + images.append(img) + except Exception as e: + print(f"Warning: Could not load {path}: {e}") + + data[ethnicity] = images + print(f"{ethnicity}: {len(images)} images") + + return data + + +# ============================================================================ +# ETHNICITY CLASSIFIER (CNN on raw images) +# ============================================================================ + +class EthnicityClassifier: + """CNN classifier to predict ethnicity directly from images.""" + + def __init__(self, num_classes=7, pretrained=True, lr=1e-4, device='cuda'): + self.device = device + self.num_classes = num_classes + self.class_names = None + + # Use pretrained ResNet18 as backbone + self.model = models.resnet18(pretrained=pretrained) + + # Replace final layer for ethnicity classification + num_features = self.model.fc.in_features + self.model.fc = nn.Linear(num_features, num_classes) + + self.model = self.model.to(device) + + self.optimizer = optim.Adam(self.model.parameters(), lr=lr) + self.loss_fn = nn.CrossEntropyLoss() + self.transform = get_image_transforms() + + def fit(self, images_dict, epochs=50, batch_size=32, val_split=0.2): + """ + Train classifier on images. + + Args: + images_dict: Dict mapping class names to lists of PIL Images + epochs: Number of epochs + batch_size: Batch size + val_split: Validation split ratio + """ + from torch.utils.data import Dataset, DataLoader + + self.class_names = list(images_dict.keys()) + print(f"\nTraining on {len(self.class_names)} classes: {self.class_names}") + + # Create dataset + class ImageDataset(Dataset): + def __init__(self, images, labels, transform): + self.images = images + self.labels = labels + self.transform = transform + + def __len__(self): + return len(self.images) + + def __getitem__(self, idx): + img = self.images[idx] + if self.transform: + img = self.transform(img) + return img, self.labels[idx] + + # Prepare data + all_images = [] + all_labels = [] + + for i, class_name in enumerate(self.class_names): + images = images_dict[class_name] + all_images.extend(images) + all_labels.extend([i] * len(images)) + + print(f"Total samples: {len(all_images)}") + + # Split train/val + n_val = int(len(all_images) * val_split) + indices = np.random.permutation(len(all_images)) + train_idx, val_idx = indices[n_val:], indices[:n_val] + + train_images = [all_images[i] for i in train_idx] + train_labels = [all_labels[i] for i in train_idx] + val_images = [all_images[i] for i in val_idx] + val_labels = [all_labels[i] for i in val_idx] + + train_dataset = ImageDataset(train_images, train_labels, self.transform) + val_dataset = ImageDataset(val_images, val_labels, self.transform) + + train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2) + val_loader = DataLoader(val_dataset, batch_size=batch_size, num_workers=2) + + print(f"Train: {len(train_dataset)}, Val: {len(val_dataset)}") + + # Training loop + best_val_acc = 0 + for epoch in range(epochs): + self.model.train() + train_correct, train_total = 0, 0 + train_loss = 0 + + for batch_X, batch_y in tqdm(train_loader, desc=f"Epoch {epoch+1}/{epochs}"): + batch_X, batch_y = batch_X.to(self.device), batch_y.to(self.device) + + self.optimizer.zero_grad() + logits = self.model(batch_X) + loss = self.loss_fn(logits, batch_y) + loss.backward() + self.optimizer.step() + + train_loss += loss.item() + train_correct += (logits.argmax(dim=1) == batch_y).sum().item() + train_total += batch_y.size(0) + + # Validate + self.model.eval() + val_correct, val_total = 0, 0 + + with torch.no_grad(): + for batch_X, batch_y in val_loader: + batch_X, batch_y = batch_X.to(self.device), batch_y.to(self.device) + logits = self.model(batch_X) + val_correct += (logits.argmax(dim=1) == batch_y).sum().item() + val_total += batch_y.size(0) + + train_acc = 100 * train_correct / train_total + val_acc = 100 * val_correct / val_total + avg_loss = train_loss / len(train_loader) + + print(f"Epoch {epoch+1}: Loss={avg_loss:.4f}, Train={train_acc:.1f}%, Val={val_acc:.1f}%") + + if val_acc > best_val_acc: + best_val_acc = val_acc + + print(f"\n✓ Best validation accuracy: {best_val_acc:.1f}%") + + def predict(self, images, return_probs=False, batch_size=32): + """ + Predict ethnicity from images. + + Args: + images: List of PIL Images + return_probs: If True, also return probabilities + batch_size: Batch size for prediction + + Returns: + predictions (and probabilities if return_probs=True) + """ + self.model.eval() + all_preds = [] + all_probs = [] + + with torch.no_grad(): + for i in range(0, len(images), batch_size): + batch_images = images[i:i+batch_size] + + # Transform images + batch_tensors = torch.stack([ + self.transform(img) for img in batch_images + ]).to(self.device) + + # Predict + logits = self.model(batch_tensors) + probs = torch.softmax(logits, dim=-1) + preds = torch.argmax(logits, dim=1) + + all_preds.append(preds.cpu().numpy()) + if return_probs: + all_probs.append(probs.cpu().numpy()) + + predictions = np.concatenate(all_preds) + + if return_probs: + probabilities = np.concatenate(all_probs, axis=0) + return predictions, probabilities + return predictions + + def save(self, path): + """Save classifier.""" + torch.save({ + 'state_dict': self.model.state_dict(), + 'class_names': self.class_names, + 'num_classes': self.num_classes + }, path) + print(f"✓ Saved to {path}") + + def load(self, path): + """Load classifier.""" + checkpoint = torch.load(path) + self.model.load_state_dict(checkpoint['state_dict']) + self.class_names = checkpoint['class_names'] + self.num_classes = checkpoint['num_classes'] + print(f"✓ Loaded from {path}") + + +# ============================================================================ +# MAIN FUNCTIONS +# ============================================================================ + +def train_ethnicity_classifier(images_dict, save_path=None, epochs=50, batch_size=32): + """ + Train CNN ethnicity classifier directly on images. + + Args: + images_dict: Dict mapping ethnicity names to lists of PIL Images + (e.g., from load_fairface_by_ethnicity) + save_path: Where to save trained classifier (optional) + epochs: Training epochs + batch_size: Batch size + + Returns: + Trained classifier + """ + + num_classes = len(images_dict) + print(f"\nTotal classes: {num_classes}") + for ethnicity, images in images_dict.items(): + print(f" {ethnicity}: {len(images)} images") + + # Initialize classifier (ResNet18) + classifier = EthnicityClassifier( + num_classes=num_classes, + pretrained=True, # Use ImageNet pretrained weights + lr=1e-4, + device='cuda' + ) + + # Train directly on images + classifier.fit(images_dict, epochs=epochs, batch_size=batch_size) + + # Save if path provided + if save_path: + classifier.save(save_path) + + return classifier + + + diff --git a/notebooks/debiasing-t2i.ipynb b/notebooks/debiasing-t2i.ipynb index 4f2a803..60280aa 100644 --- a/notebooks/debiasing-t2i.ipynb +++ b/notebooks/debiasing-t2i.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "id": "fbc6539e", "metadata": {}, "outputs": [], @@ -31,12 +31,13 @@ "import torch.nn.functional as F\n", "from PIL import Image\n", "import matplotlib.pyplot as plt\n", - "import seaborn" + "import seaborn\n", + "from datasets import load_dataset" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "d579b491", "metadata": {}, "outputs": [], @@ -71,7 +72,7 @@ " self.class_names = None\n", " \n", "\n", - " def fit(self, activations_dict, epochs=30, batch_size=32, class_weights=None):\n", + " def fit(self, activations_dict, epochs=30, batch_size=32, class_weights=None, val_split=0.2):\n", " \"\"\"\n", " Train the multiclass classifier on raw activations.\n", "\n", @@ -80,11 +81,12 @@ " - epochs: number of training epochs.\n", " - batch_size: training batch size.\n", " - class_weights: Optional tensor of weights for each class to handle imbalance.\n", + " - val_split: Fraction of data to use for validation (default: 0.2)\n", " \"\"\"\n", " self.class_names = list(activations_dict.keys())\n", " assert len(self.class_names) == self.num_classes, f\"Expected {self.num_classes} classes, got {len(self.class_names)}\"\n", " \n", - " # Prepare training data\n", + " # Prepare data and split train/val\n", " X_list = []\n", " y_list = []\n", " \n", @@ -97,21 +99,36 @@ " X = torch.cat(X_list, dim=0)\n", " y = torch.cat(y_list, dim=0)\n", " \n", - " dataset = torch.utils.data.TensorDataset(X, y)\n", - " loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)\n", + " # Split train/val\n", + " n_total = X.size(0)\n", + " n_val = int(n_total * val_split)\n", + " indices = torch.randperm(n_total)\n", + " train_idx, val_idx = indices[n_val:], indices[:n_val]\n", + " \n", + " X_train, y_train = X[train_idx], y[train_idx]\n", + " X_val, y_val = X[val_idx], y[val_idx]\n", + " \n", + " train_dataset = torch.utils.data.TensorDataset(X_train, y_train)\n", + " val_dataset = torch.utils.data.TensorDataset(X_val, y_val)\n", + " \n", + " train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n", + " val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n", + " \n", + " print(f\"Train: {len(train_dataset)}, Val: {len(val_dataset)}\")\n", " \n", " # Set class weights for loss function if provided\n", " if class_weights is not None:\n", " self.loss_fn = nn.CrossEntropyLoss(weight=class_weights.to(self.device))\n", " \n", " # Train the classifier\n", + " best_val_acc = 0\n", " for epoch in range(epochs):\n", " self.classifier.train()\n", " epoch_loss = 0.0\n", - " correct = 0\n", - " total = 0\n", + " train_correct = 0\n", + " train_total = 0\n", " \n", - " for batch_X, batch_y in loader:\n", + " for batch_X, batch_y in train_loader:\n", " self.optimizer.zero_grad()\n", " logits = self.classifier(batch_X) # shape: (batch, num_classes)\n", " loss = self.loss_fn(logits, batch_y)\n", @@ -122,26 +139,46 @@ " \n", " # Calculate accuracy\n", " _, predicted = torch.max(logits.data, 1)\n", - " total += batch_y.size(0)\n", - " correct += (predicted == batch_y).sum().item()\n", + " train_total += batch_y.size(0)\n", + " train_correct += (predicted == batch_y).sum().item()\n", + " \n", + " # Validate\n", + " self.classifier.eval()\n", + " val_correct = 0\n", + " val_total = 0\n", " \n", - " accuracy = 100 * correct / total\n", - " print(f\"Epoch {epoch}: loss = {epoch_loss/len(loader):.4f}, accuracy = {accuracy:.2f}%\")\n", + " with torch.no_grad():\n", + " for batch_X, batch_y in val_loader:\n", + " logits = self.classifier(batch_X)\n", + " _, predicted = torch.max(logits.data, 1)\n", + " val_total += batch_y.size(0)\n", + " val_correct += (predicted == batch_y).sum().item()\n", + " \n", + " train_acc = 100 * train_correct / train_total\n", + " val_acc = 100 * val_correct / val_total\n", + " avg_loss = epoch_loss / len(train_loader)\n", " \n", - " # Evaluate on each class\n", + " print(f\"Epoch {epoch}: Loss={avg_loss:.4f}, Train={train_acc:.1f}%, Val={val_acc:.1f}%\")\n", + " \n", + " # Evaluate per-class on validation set\n", " self.classifier.eval()\n", " class_accuracies = {}\n", " \n", " with torch.no_grad():\n", " for i, class_name in enumerate(self.class_names):\n", - " X_class = torch.tensor(activations_dict[class_name], dtype=torch.float32, device=self.device)\n", - " y_class = torch.full((X_class.size(0),), i, dtype=torch.long, device=self.device)\n", + " # Get validation samples for this class\n", + " class_mask = y_val == i\n", + " if class_mask.sum() == 0:\n", + " continue\n", + " \n", + " X_class_val = X_val[class_mask]\n", + " y_class_val = y_val[class_mask]\n", " \n", - " logits = self.classifier(X_class)\n", + " logits = self.classifier(X_class_val)\n", " _, predicted = torch.max(logits.data, 1)\n", - " accuracy = 100 * (predicted == y_class).sum().item() / y_class.size(0)\n", + " accuracy = 100 * (predicted == y_class_val).sum().item() / y_class_val.size(0)\n", " class_accuracies[class_name] = accuracy\n", - " print(f\"Accuracy for {class_name}: {accuracy:.2f}%\")\n", + " print(f\"Val accuracy for {class_name}: {accuracy:.2f}%\")\n", "\n", " def steer_attributes(self, activation, target=None, avoid=None, alpha=0.1, steps=1, decay_rate=0.95):\n", " \"\"\"\n", @@ -335,49 +372,21 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 14, "id": "9e9ad3e4", "metadata": {}, "outputs": [], "source": [ "def load_fairface_by_ethnicity(\n", + "# Thsis is just a split of the fairface dataset by ethnicity - we need more data for training\n", " dataset_path: str,\n", " categories: Optional[List[str]] = None,\n", " max_images: Optional[int] = None,\n", " return_pil: bool = True\n", ") -> Dict[str, List[Union[str, Image.Image]]]:\n", " \"\"\"\n", - " Load FairFace dataset organized by ethnic/racial categories.\n", - " \n", - " Parameters:\n", - " -----------\n", - " dataset_path : str\n", - " Path to the fairface dataset root (e.g., '/path/to/fairface')\n", - " categories : List[str], optional\n", - " Specific categories to load. Available: ['Black', 'East_Asian', 'Indian', \n", - " 'Latino_Hispanic', 'Middle_Eastern', 'Southeast_Asian', 'White']\n", - " max_images : int, optional\n", - " Maximum number of images to load per category\n", - " return_pil : bool, optional\n", - " If True, return PIL Images (for feeding to vision encoders). \n", - " If False, return file paths as strings. Default: True\n", - " \n", - " Returns:\n", - " --------\n", - " Dict[str, List[Union[str, Image.Image]]]\n", - " Dictionary mapping category names to lists of PIL Images (if return_pil=True) \n", - " or image file paths (if return_pil=False)\n", - " \n", - " Example:\n", - " --------\n", - " >>> # Load as PIL Images (ready for vision encoders)\n", - " >>> data = load_fairface_by_ethnicity('/path/to/fairface', return_pil=True)\n", - " >>> print(f\"White: {len(data['White'])} images\")\n", - " >>> print(f\"First image type: {type(data['White'][0])}\") # \n", - " \n", - " >>> # Load as paths only\n", - " >>> data = load_fairface_by_ethnicity('/path/to/fairface', return_pil=False)\n", - " >>> print(f\"First image path: {data['White'][0]}\")\n", + " Load FairFace dataset organized by ethnic/racial categories - we need more data for training\n", + "\n", " \"\"\"\n", " \n", " all_categories = [\n", @@ -417,6 +426,7 @@ " \n", " return dataset\n", "\n", + "\n", "def visualize_image(image, title=None, figsize=(5, 5)):\n", " \"\"\"Display a single PIL image.\"\"\"\n", " fig, ax = plt.subplots(1, 1, figsize=figsize)\n", @@ -426,12 +436,102 @@ " ax.set_title(title, fontsize=12)\n", " plt.tight_layout()\n", " plt.show()\n", - "\n" + "\n", + "\n", + "def load_fairface_by_ethnicity_hf(\n", + " hf_data, # e.g. load_dataset(\"HuggingFaceM4/FairFace\", \"0.25\", split=\"train\")\n", + " categories: Optional[List[str]] = None,\n", + " max_images: Optional[int] = None, # per-category cap\n", + " return_pil: bool = True,\n", + " verbose: bool = True\n", + ") -> Dict[str, List[Union[str, Image.Image]]]:\n", + " \"\"\"\n", + " Load FairFace (Hugging Face version) organized by racial/ethnic categories.\n", + " Returns a dict: {category_key: [PIL.Image (or np.ndarray if return_pil=False), ...]}\n", + "\n", + " - Accepts both 'East Asian' and 'East_Asian' style names in `categories`.\n", + " - `max_images` applies PER CATEGORY.\n", + " - Works with standard datasets or streaming (IterableDataset).\n", + " \"\"\"\n", + "\n", + " # Discover label names (if available) to build defaults and map indices->names\n", + " features = getattr(hf_data, \"features\", None)\n", + " race_feature = features[\"race\"] if (features and \"race\" in features) else None\n", + " race_names = list(getattr(race_feature, \"names\", [])) if race_feature is not None else []\n", + "\n", + " # Default categories (canonical, underscore style)\n", + " default_categories = [\n", + " \"Black\", \"East_Asian\", \"Indian\", \"Latino_Hispanic\",\n", + " \"Middle_Eastern\", \"Southeast_Asian\", \"White\"\n", + " ]\n", + "\n", + " # Normalization helpers\n", + " def canon_key(label: str) -> str:\n", + " # Convert a display label (e.g., \"East Asian\") to canonical key with underscores\n", + " return label.replace(\" \", \"_\")\n", + "\n", + " def display_from_index(idx: int) -> str:\n", + " # Turn integer class index into display label (e.g., 2 -> \"Black\")\n", + " if race_names and 0 <= idx < len(race_names):\n", + " return race_names[idx]\n", + " return str(idx)\n", + "\n", + " def to_canonical_list(cats: Optional[List[str]]) -> List[str]:\n", + " if cats is None:\n", + " return default_categories\n", + " # Accept both \"East Asian\" and \"East_Asian\"\n", + " return [canon_key(c) for c in cats]\n", + "\n", + " categories = to_canonical_list(categories)\n", + "\n", + " # Prepare buckets\n", + " dataset: Dict[str, List[Union[str, Image.Image]]] = {cat: [] for cat in categories}\n", + " counts = {cat: 0 for cat in categories}\n", + "\n", + " # Helper to check if we can stop early when max_images is set\n", + " def all_full() -> bool:\n", + " return max_images is not None and all(counts[cat] >= max_images for cat in categories)\n", + "\n", + " # Iterate once and bucket samples\n", + " for ex in hf_data:\n", + " # race can be an int (class index) or a string depending on dataset config\n", + " race_val = ex[\"race\"]\n", + " if isinstance(race_val, int):\n", + " race_label = display_from_index(race_val) # e.g., \"Black\"\n", + " else:\n", + " race_label = str(race_val)\n", + "\n", + " key = canon_key(race_label) # e.g., \"Middle_Eastern\"\n", + " if key in dataset and (max_images is None or counts[key] < max_images):\n", + " img = ex[\"image\"] # Usually a PIL.Image already for HF Image feature\n", + "\n", + " # Ensure PIL or numpy as requested\n", + " if return_pil:\n", + " if not isinstance(img, Image.Image):\n", + " img = Image.fromarray(np.array(img))\n", + " img = img.convert(\"RGB\")\n", + " else:\n", + " if isinstance(img, Image.Image):\n", + " img = np.asarray(img.convert(\"RGB\"))\n", + " else:\n", + " img = np.asarray(img)\n", + "\n", + " dataset[key].append(img)\n", + " counts[key] += 1\n", + "\n", + " if all_full():\n", + " break\n", + "\n", + " if verbose:\n", + " for cat in categories:\n", + " print(f\"{cat}: {counts[cat]} images\")\n", + "\n", + " return dataset" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 17, "id": "ce149d64", "metadata": {}, "outputs": [ @@ -439,19 +539,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "Black: 200 images\n", - "East_Asian: 200 images\n", - "Indian: 200 images\n", - "Latino_Hispanic: 200 images\n", - "Middle_Eastern: 200 images\n", - "Southeast_Asian: 200 images\n", - "White: 200 images\n", - "{'Black': [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'East_Asian': [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'Indian': [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 'Latino_Hispanic': [, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ], 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", 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" ] @@ -462,8 +562,16 @@ ], "source": [ "#Let's load the dataset\n", - "dataset_path = \"/home/ubuntu/T2I_Interp_toolkit/FairFace/datasets/fairface\"\n", - "data = load_fairface_by_ethnicity(dataset_path)\n", + "#dataset_path = \"/home/ubuntu/T2I_Interp_toolkit/FairFace/datasets/fairface\"\n", + "#data = load_fairface_by_ethnicity_hf(dataset_path)\n", + "data = load_dataset(\"HuggingFaceM4/FairFace\", \"0.25\", split=\"train\")\n", + "\n", + "# Match your original API\n", + "data = load_fairface_by_ethnicity_hf(\n", + " data,\n", + " #categories=[\"Black\", \"East_Asian\", \"White\"], # accepts space or underscore; both work # per category\n", + " return_pil=True\n", + ")\n", "print(data)\n", "visualize_image(data['Black'][0])" ] @@ -478,7 +586,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 18, "id": "c5ad8ce4", "metadata": {}, "outputs": [ @@ -486,14 +594,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "\u001b[32m2025-10-13 22:23:12.916\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mt2Interp.T2I\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m105\u001b[0m - \u001b[1mEnforcing eager attention implementation for attention pattern tracing. The HF default would be to use sdpa if available. To use sdpa, set attn_implementation='sdpa' or None to use the HF default.\u001b[0m\n", + "\u001b[32m2025-10-15 00:55:00.095\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mt2Interp.T2I\u001b[0m:\u001b[36m__init__\u001b[0m:\u001b[36m105\u001b[0m - \u001b[1mEnforcing eager attention implementation for attention pattern tracing. The HF default would be to use sdpa if available. To use sdpa, set attn_implementation='sdpa' or None to use the HF default.\u001b[0m\n", "Keyword arguments {'attn_implementation': 'eager', 'tokenizer_kwargs': {}, 'trust_remote_code': False} are not expected by StableDiffusionPipeline and will be ignored.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c02c622dc11a4fd89858b9dce8e1d4e7", + "model_id": "028e86a35a5d44259f68bc8a28f5f45b", "version_major": 2, "version_minor": 0 }, @@ -527,7 +635,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 21, "id": "b05746b1", "metadata": {}, "outputs": [], @@ -544,9 +652,7 @@ " Parameters:\n", " -----------\n", " image : PIL.Image\n", - " Input image\n", " target_size : int\n", - " Target size (will resize to target_size x target_size)\n", " \n", " Returns:\n", " --------\n", @@ -567,11 +673,8 @@ " Parameters:\n", " -----------\n", " model : T2IModel\n", - " The loaded T2I model\n", " images : List[PIL.Image] or List[torch.Tensor]\n", - " Images to encode\n", " batch_size : int\n", - " Batch size for encoding\n", " \n", " Returns:\n", " --------\n", @@ -579,6 +682,7 @@ " \"\"\"\n", " all_latents = []\n", " vae = model._model.pipeline.vae\n", + " #This is getting the model's vae and encoding the data in that space\n", " device = next(vae.parameters()).device\n", " dtype = next(vae.parameters()).dtype # Get VAE's dtype (float16 or float32)\n", " \n", @@ -831,7 +935,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "e68d8eac", "metadata": {}, "outputs": [ @@ -839,504 +943,1986 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", - "============================================================\n", - "Processing class: Black (200 images)\n", - "============================================================\n", - "Encoding 200 images to latent space...\n", - "Extracting activations from layer: unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\n" + "\n", + "============================================================\n", + "Processing class: Black (2000 images)\n", + "============================================================\n", + "Encoding 2000 images to latent space...\n", + "Extracting activations from layer: unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing batches: 100%|██████████| 32/32 [00:16<00:00, 1.93it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final activation shape: (2000, 1310720)\n", + "\n", + "============================================================\n", + "Processing class: East_Asian (2000 images)\n", + "============================================================\n", + "Encoding 2000 images to latent space...\n", + "Extracting activations from layer: unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing batches: 100%|██████████| 32/32 [00:16<00:00, 1.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final activation shape: (2000, 1310720)\n", + "\n", + "============================================================\n", + "Processing class: Indian (2000 images)\n", + "============================================================\n", + "Encoding 2000 images to latent space...\n", + "Extracting activations from layer: unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing batches: 100%|██████████| 32/32 [00:16<00:00, 1.94it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final activation shape: (2000, 1310720)\n", + "\n", + "============================================================\n", + "Processing class: Latino_Hispanic (2000 images)\n", + "============================================================\n", + "Encoding 2000 images to latent space...\n", + "Extracting activations from layer: unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing batches: 100%|██████████| 32/32 [00:16<00:00, 1.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final activation shape: (2000, 1310720)\n", + "\n", + "============================================================\n", + "Processing class: Middle_Eastern (2000 images)\n", + "============================================================\n", + "Encoding 2000 images to latent space...\n", + "Extracting activations from layer: unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing batches: 100%|██████████| 32/32 [00:16<00:00, 1.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final activation shape: (2000, 1310720)\n", + "\n", + "============================================================\n", + "Processing class: Southeast_Asian (2000 images)\n", + "============================================================\n", + "Encoding 2000 images to latent space...\n", + "Extracting activations from layer: unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing batches: 100%|██████████| 32/32 [00:16<00:00, 1.95it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final activation shape: (2000, 1310720)\n", + "\n", + "============================================================\n", + "Processing class: White (2000 images)\n", + "============================================================\n", + "Encoding 2000 images to latent space...\n", + "Extracting activations from layer: unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing batches: 100%|██████████| 32/32 [00:16<00:00, 1.96it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final activation shape: (2000, 1310720)\n", + "\n", + "✓ Training on flattened spatial representations\n", + "✓ Each image is one sample with S*C features\n", + " Black: (2000, 1310720)\n", + " East_Asian: (2000, 1310720)\n", + " Indian: (2000, 1310720)\n", + " Latino_Hispanic: (2000, 1310720)\n", + " Middle_Eastern: (2000, 1310720)\n", + " Southeast_Asian: (2000, 1310720)\n", + " White: (2000, 1310720)\n" + ] + }, + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: '/home/ubuntu/T2I_Interp_toolkit/data/fairface/activations_by_ethnicity.pkl'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[22], line 23\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[38;5;66;03m#let's save the activations by ethnicity\u001b[39;00m\n\u001b[1;32m 22\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01mpickle\u001b[39;00m\n\u001b[0;32m---> 23\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/home/ubuntu/T2I_Interp_toolkit/data/fairface/activations_by_ethnicity.pkl\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mwb\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m 24\u001b[0m pickle\u001b[38;5;241m.\u001b[39mdump(activations_by_ethnicity, f)\n", + "File \u001b[0;32m~/.venv/lib/python3.10/site-packages/IPython/core/interactiveshell.py:324\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m 317\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m {\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m}:\n\u001b[1;32m 318\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 319\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIPython won\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt let you open fd=\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m by default \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 320\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 321\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou can use builtins\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m open.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 322\u001b[0m )\n\u001b[0;32m--> 324\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mio_open\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/home/ubuntu/T2I_Interp_toolkit/data/fairface/activations_by_ethnicity.pkl'" + ] + } + ], + "source": [ + "# Train classifier on FLATTENED spatial representations\n", + "# [B, S, C] → [B, S*C] where each image is one sample with S*C features\n", + "\n", + "activations_by_ethnicity = batch_cache_image_activations_by_class(\n", + " model=model,\n", + " images_dict=data, # <-- Using REAL IMAGES, not prompts\n", + " layer_name=\"unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\",\n", + " timestep=500, # Noise level for UNet forward pass\n", + " batch_size=64,\n", + " encode_batch_size=64,\n", + " aggregate_spatial='flatten', # Flatten ALL spatial: [B, S, C] → [B, S*C]\n", + " empty_text_embed=True, # Unconditional (no text)\n", + " max_images_per_class=2000 # Start with 20 images per ethnicity\n", + ")\n", + "\n", + "print(\"\\n✓ Training on flattened spatial representations\")\n", + "print(f\"✓ Each image is one sample with S*C features\")\n", + "for k, v in activations_by_ethnicity.items():\n", + " print(f\" {k}: {v.shape}\")\n", + "\n", + "#let's save the activations by ethnicity\n", + "'''\n", + "import pickle\n", + "with open(\"/home/ubuntu/T2I_Interp_toolkit/data/fairface/activations_by_ethnicity.pkl\", \"wb\") as f:\n", + " pickle.dump(activations_by_ethnicity, f)\n", + "'''\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "2828004a", + "metadata": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "import pickle\n", + "\n", + "p = Path(\"/home/ubuntu/T2I_Interp_toolkit/data/fairface/activations_by_ethnicity.pkl\")\n", + "p.parent.mkdir(parents=True, exist_ok=True)\n", + "if not p.exists():\n", + " with open(p, \"wb\") as f: pickle.dump(activations_by_ethnicity, f)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "d3e31813", + "metadata": {}, + "outputs": [], + "source": [ + "#Let's load the dataset\n", + "import pickle\n", + "with open(\"/home/ubuntu/T2I_Interp_toolkit/data/fairface/activations_by_ethnicity.pkl\", \"rb\") as f:\n", + " activations_by_ethnicity = pickle.load(f)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "edec83f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input dimension: 1310720\n", + "Number of classes: 7\n", + "Classes: ['Black', 'East_Asian', 'Indian', 'Latino_Hispanic', 'Middle_Eastern', 'Southeast_Asian', 'White']\n" + ] + }, + { + "ename": "OutOfMemoryError", + "evalue": "CUDA out of memory. Tried to allocate 9.77 GiB. GPU 0 has a total capacity of 94.50 GiB of which 8.31 GiB is free. Including non-PyTorch memory, this process has 86.18 GiB memory in use. Of the allocated memory 84.25 GiB is allocated by PyTorch, and 1.24 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mOutOfMemoryError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[26], line 21\u001b[0m\n\u001b[1;32m 12\u001b[0m classifier \u001b[38;5;241m=\u001b[39m K_Steering(\n\u001b[1;32m 13\u001b[0m input_dim\u001b[38;5;241m=\u001b[39minput_dim,\n\u001b[1;32m 14\u001b[0m num_classes\u001b[38;5;241m=\u001b[39mnum_classes,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 17\u001b[0m device\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcuda\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 18\u001b[0m )\n\u001b[1;32m 20\u001b[0m \u001b[38;5;66;03m# Train on the cached activations\u001b[39;00m\n\u001b[0;32m---> 21\u001b[0m \u001b[43mclassifier\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 22\u001b[0m \u001b[43m \u001b[49m\u001b[43mactivations_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mactivations_by_ethnicity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 23\u001b[0m \u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 24\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m4\u001b[39;49m\n\u001b[1;32m 25\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m 27\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mClassifier trained! You can now use it for debiasing.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "Cell \u001b[0;32mIn[4], line 50\u001b[0m, in \u001b[0;36mK_Steering.fit\u001b[0;34m(self, activations_dict, epochs, batch_size, class_weights, val_split)\u001b[0m\n\u001b[1;32m 47\u001b[0m y_list \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 49\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, class_name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclass_names):\n\u001b[0;32m---> 50\u001b[0m X_class \u001b[38;5;241m=\u001b[39m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtensor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mactivations_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[43mclass_name\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat32\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 51\u001b[0m y_class \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mfull((X_class\u001b[38;5;241m.\u001b[39msize(\u001b[38;5;241m0\u001b[39m),), i, dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mlong, device\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[1;32m 52\u001b[0m X_list\u001b[38;5;241m.\u001b[39mappend(X_class)\n", + "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 9.77 GiB. GPU 0 has a total capacity of 94.50 GiB of which 8.31 GiB is free. Including non-PyTorch memory, this process has 86.18 GiB memory in use. Of the allocated memory 84.25 GiB is allocated by PyTorch, and 1.24 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)" + ] + } + ], + "source": [ + "# Train classifier on cached activations\n", + "\n", + "# Now use the cached activations to train the K_Steering classifier\n", + "input_dim = activations_by_ethnicity['Black'].shape[-1]\n", + "num_classes = len(activations_by_ethnicity)\n", + "\n", + "print(f\"Input dimension: {input_dim}\")\n", + "print(f\"Number of classes: {num_classes}\")\n", + "print(f\"Classes: {list(activations_by_ethnicity.keys())}\")\n", + "\n", + "# Initialize and train the classifier\n", + "classifier = K_Steering(\n", + " input_dim=input_dim,\n", + " num_classes=num_classes,\n", + " hidden_dim=256,\n", + " lr=1e-3,\n", + " device='cuda'\n", + ")\n", + "\n", + "# Train on the cached activations\n", + "classifier.fit(\n", + " activations_dict=activations_by_ethnicity,\n", + " epochs=100,\n", + " batch_size=4\n", + ")\n", + "\n", + "print(\"\\nClassifier trained! You can now use it for debiasing.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "92118a1d", + "metadata": {}, + "source": [ + "## Here is the markdown for the flow of the gradient editin process:\n", + "- Each denoising timestep: The UNet runs to predict noise\n", + "During UNet forward pass: Your hook at attn1.to_out[0] is triggered\n", + "In the hook:\n", + "- Extract activations from that layer\n", + "- Pass through your ethnicity classifier\n", + "- Compute gradient: ∂(classifier_loss)/∂(activations)\n", + "- Steer activations using gradient direction\n", + "Continue UNet execution with steered activations" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "3299a74b", + "metadata": {}, + "outputs": [], + "source": [ + "# CORRECTED debias_hook - processes each spatial position independently\n", + "\n", + "def debias_hook(module, input, output, alpha=1.0):\n", + " \"\"\"\n", + " Hook that evens out ethnicity probability distribution during forward pass.\n", + " \n", + " Processes whole images (flattened spatial):\n", + " 1. Flatten spatial: [B, S, C] -> [B, S*C] (each image = one sample)\n", + " 2. Pass through classifier trained on flattened features\n", + " 3. Compute gradients per image\n", + " 4. Reshape back and apply steering\n", + " \"\"\"\n", + " # Get activation shape\n", + " act = output\n", + " original_shape = act.shape\n", + " \n", + " # Classifier was trained on flattened spatial representations: [B, S*C]\n", + " # During steering: flatten each image, get gradients, reshape back\n", + " # Process one image at a time in the batch dimension\n", + " \n", + " if act.ndim == 3:\n", + " # Attention output: [B, S, C]\n", + " B, S, C = act.shape\n", + " # Flatten spatial and channel dimensions per image: [B, S, C] -> [B, S*C]\n", + " act_flattened = act.reshape(B, S * C)\n", + " \n", + " elif act.ndim == 4:\n", + " # Conv output: [B, C, H, W]\n", + " B, C, H, W = act.shape\n", + " # Flatten spatial and channel: [B, C, H, W] -> [B, C*H*W]\n", + " act_flattened = act.reshape(B, C * H * W)\n", + " else:\n", + " # 2D: already [B, features]\n", + " B = act.shape[0]\n", + " act_flattened = act.clone()\n", + " if act.ndim == 3:\n", + " S, C = act.shape[1], act.shape[2]\n", + " elif act.ndim == 4:\n", + " C, H, W = act.shape[1], act.shape[2], act.shape[3]\n", + " \n", + " # CRITICAL: Create completely independent tensor for classifier\n", + " # Step 1: Detach from UNet computation graph\n", + " act_detached = act_flattened.detach()\n", + " \n", + " # Step 2: Convert to numpy (breaks all PyTorch graph connections)\n", + " act_np = act_detached.cpu().numpy()\n", + " \n", + " # Step 3: Create NEW tensor from numpy copy (completely independent)\n", + " # This tensor has NO connection to UNet's autograd graph\n", + " act_tensor = torch.from_numpy(act_np.copy()).to(\n", + " device=classifier.device,\n", + " dtype=torch.float32\n", + " )\n", + " \n", + " # Step 4: EXPLICITLY enable gradients for this computation\n", + " # The UNet forward pass may be in torch.no_grad() context, so we override it\n", + " with torch.enable_grad():\n", + " # Create tensor with gradients enabled\n", + " act_tensor.requires_grad_(True)\n", + " \n", + " # Verify independence\n", + " assert act_tensor.grad_fn is None, \"Tensor should not have grad_fn from UNet\"\n", + " assert not act_flattened.requires_grad, \"Original activations should not require grad\"\n", + " \n", + " # Ensure classifier is in train mode for proper gradient flow\n", + " classifier.classifier.eval()\n", + " \n", + " # Forward pass through classifier - each image gets prediction\n", + " logits = classifier.classifier(act_tensor) # [B, num_classes]\n", + " \n", + " # Target: uniform distribution - match K_Steering.steer_attributes_unsupervised approach\n", + " # Separate logits into over-represented (avoid) and under-represented (target)\n", + " \n", + " # Get mean logit per image across all classes\n", + " mean_logits = logits.mean(dim=1, keepdim=True) # [B, 1]\n", + " \n", + " # Identify which classes are above/below mean for each image\n", + " logits_deviation = logits - mean_logits # [B, num_classes]\n", + " \n", + " # Classes above mean = over-represented (need to decrease)\n", + " # Classes below mean = under-represented (need to increase)\n", + " avoid_mask = logits_deviation > 0 # Over-represented\n", + " target_mask = logits_deviation < 0 # Under-represented\n", + " \n", + " # Build loss to minimize over-represented and maximize under-represented\n", + " loss = 0.0\n", + " \n", + " if avoid_mask.any():\n", + " avoid_logits = logits * avoid_mask.float()\n", + " loss = loss + avoid_logits.sum() / (avoid_mask.sum() + 1e-8) # Minimize these\n", + " \n", + " if target_mask.any():\n", + " target_logits = logits * target_mask.float()\n", + " loss = loss - target_logits.sum() / (target_mask.sum() + 1e-8) # Maximize these (negative)\n", + " \n", + " # This loss encourages all classes to move toward the mean, achieving uniformity\n", + " \n", + " # Compute gradients for each image (ONLY through classifier, not UNet)\n", + " grad = torch.autograd.grad(\n", + " outputs=loss,\n", + " inputs=act_tensor,\n", + " retain_graph=False,\n", + " create_graph=False,\n", + " only_inputs=True,\n", + " )[0] # [B, S*C]\n", + " \n", + " # Steering direction (negative gradient to minimize bias)\n", + " steering_direction = -grad\n", + " \n", + " # Restore classifier to eval mode\n", + " #classifier.classifier.eval()\n", + " \n", + " # Step 5: Move steering back via numpy (complete isolation from both graphs)\n", + " steering_np = steering_direction.detach().cpu().numpy()\n", + " \n", + " # Step 6: Create NEW tensor on original device (no graph connections)\n", + " steering_tensor = torch.from_numpy(steering_np.copy()).to(\n", + " device=act_flattened.device,\n", + " dtype=act_flattened.dtype\n", + " )\n", + " \n", + " # Step 7: Apply steering - simple addition, no gradients\n", + " steered_flat = act_flattened.detach() + alpha * steering_tensor\n", + " \n", + "\n", + " # Reshape back to original shape\n", + " if len(original_shape) == 3:\n", + " # [B, S*C] -> [B, S, C]\n", + " B, S, C = original_shape\n", + " steered_output = steered_flat.reshape(B, S, C)\n", + " elif len(original_shape) == 4:\n", + " # [B, C*H*W] -> [B, C, H, W]\n", + " B, C, H, W = original_shape\n", + " steered_output = steered_flat.reshape(B, C, H, W)\n", + " else:\n", + " steered_output = steered_flat\n", + " \n", + " return steered_output\n", + "\n", + "\n", + "def debias_tester_hook(module, input, output, alpha=1.0):\n", + " \"\"\" Hook that just steers towards one ethnic group - let's say white\"\"\"\n", + " act = output\n", + " original_shape = act.shape\n", + " \n", + " # Classifier was trained on flattened spatial representations: [B, S*C]\n", + " # During steering: flatten each image, get gradients, reshape back\n", + " # Process one image at a time in the batch dimension\n", + " print(\"Here is the shape of the activations: \", act.shape)\n", + " if act.ndim == 3:\n", + " # Attention output: [B, S, C]\n", + " B, S, C = act.shape\n", + " # Flatten spatial and channel dimensions per image: [B, S, C] -> [B, S*C]\n", + " act_flattened = act.reshape(B, S * C)\n", + " \n", + " elif act.ndim == 4:\n", + " # Conv output: [B, C, H, W]\n", + " B, C, H, W = act.shape\n", + " # Flatten spatial and channel: [B, C, H, W] -> [B, C*H*W]\n", + " act_flattened = act.reshape(B, C * H * W)\n", + " else:\n", + " # 2D: already [B, features]\n", + " B = act.shape[0]\n", + " act_flattened = act.clone()\n", + " if act.ndim == 3:\n", + " S, C = act.shape[1], act.shape[2]\n", + " elif act.ndim == 4:\n", + " C, H, W = act.shape[1], act.shape[2], act.shape[3]\n", + " \n", + " # CRITICAL: Create completely independent tensor for classifier\n", + " # Step 1: Detach from UNet computation graph\n", + " act_detached = act_flattened.detach()\n", + " \n", + " # Step 2: Convert to numpy (breaks all PyTorch graph connections)\n", + " act_np = act_detached.cpu().numpy()\n", + " \n", + " # Step 3: Create NEW tensor from numpy copy (completely independent)\n", + " # This tensor has NO connection to UNet's autograd graph\n", + " act_tensor = torch.from_numpy(act_np.copy()).to(\n", + " device=classifier.device,\n", + " dtype=torch.float32\n", + " )\n", + " \n", + "\n", + " steered_flat = classifier.steer_attributes(act_tensor, target_class=\"white\", alpha=alpha)\n", + " # Step 4: EXPLICITLY enable gradients for this computation\n", + " # The UNet forward pass may be in torch.no_grad() context, so we override it\n", + "\n", + " # Step 7: Apply steering - simple addition, no gradients\n", + " #steered_flat = act_flattened.detach() + alpha * steering_tensor\n", + " \n", + "\n", + " # Reshape back to original shape\n", + " if len(original_shape) == 3:\n", + " # [B, S*C] -> [B, S, C]\n", + " B, S, C = original_shape\n", + " steered_output = steered_flat.reshape(B, S, C)\n", + " elif len(original_shape) == 4:\n", + " # [B, C*H*W] -> [B, C, H, W]\n", + " B, C, H, W = original_shape\n", + " steered_output = steered_flat.reshape(B, C, H, W)\n", + " else:\n", + " steered_output = steered_flat\n", + " \n", + " return steered_output" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "165cae83", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/.venv/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", + " warnings.warn(\n", + "/home/ubuntu/.venv/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Total classes: 7\n", + " Black: 200 images\n", + " East_Asian: 200 images\n", + " Indian: 200 images\n", + " Latino_Hispanic: 200 images\n", + " Middle_Eastern: 200 images\n", + " Southeast_Asian: 200 images\n", + " White: 200 images\n", + "\n", + "Training on 7 classes: ['Black', 'East_Asian', 'Indian', 'Latino_Hispanic', 'Middle_Eastern', 'Southeast_Asian', 'White']\n", + "Total samples: 1400\n", + "Train: 1120, Val: 280\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 1/100: 100%|██████████| 35/35 [00:00<00:00, 47.03it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1: Loss=1.8479, Train=25.8%, Val=39.3%\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch 2/100: 100%|██████████| 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"stdout", + "output_type": "stream", + "text": [ + "Epoch 92: Loss=0.8838, Train=68.2%, Val=42.5%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Processing batches: 100%|██████████| 4/4 [00:01<00:00, 2.28it/s]\n" + "Epoch 93/100: 100%|██████████| 35/35 [00:00<00:00, 56.29it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Final activation shape: (200, 1310720)\n", - "\n", - "============================================================\n", - "Processing class: East_Asian (200 images)\n", - "============================================================\n", - "Encoding 200 images to latent space...\n", - "Extracting activations from layer: unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\n" + "Epoch 93: Loss=0.1851, Train=95.5%, Val=43.6%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Processing batches: 100%|██████████| 4/4 [00:01<00:00, 2.36it/s]\n" + "Epoch 94/100: 100%|██████████| 35/35 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"============================================================\n", - "Encoding 200 images to latent space...\n", - "Extracting activations from layer: unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\n" + "Epoch 95: Loss=0.0083, Train=100.0%, Val=47.9%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Processing batches: 100%|██████████| 4/4 [00:01<00:00, 2.37it/s]\n" + "Epoch 96/100: 100%|██████████| 35/35 [00:00<00:00, 55.12it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Final activation shape: (200, 1310720)\n", - "\n", - "============================================================\n", - "Processing class: Middle_Eastern (200 images)\n", - "============================================================\n", - "Encoding 200 images to latent space...\n", - "Extracting activations from layer: unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\n" + "Epoch 96: Loss=0.0057, Train=100.0%, Val=48.6%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Processing batches: 100%|██████████| 4/4 [00:01<00:00, 2.39it/s]\n" + "Epoch 97/100: 100%|██████████| 35/35 [00:00<00:00, 54.20it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Final activation shape: (200, 1310720)\n", - "\n", - "============================================================\n", - "Processing class: Southeast_Asian (200 images)\n", - "============================================================\n", - "Encoding 200 images to latent space...\n", - "Extracting activations from layer: unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\n" + "Epoch 97: Loss=0.0045, Train=100.0%, Val=48.9%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Processing batches: 100%|██████████| 4/4 [00:01<00:00, 2.37it/s]\n" + "Epoch 98/100: 100%|██████████| 35/35 [00:00<00:00, 54.26it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Final activation shape: (200, 1310720)\n", - "\n", - "============================================================\n", - "Processing class: White (200 images)\n", - "============================================================\n", - "Encoding 200 images to latent space...\n", - "Extracting activations from layer: unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\n" + "Epoch 98: Loss=0.0038, Train=100.0%, Val=47.9%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "Processing batches: 100%|██████████| 4/4 [00:01<00:00, 2.34it/s]" + "Epoch 99/100: 100%|██████████| 35/35 [00:00<00:00, 53.70it/s]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Final activation shape: (200, 1310720)\n", - "\n", - "✓ Training on flattened spatial representations\n", - "✓ Each image is one sample with S*C features\n", - " Black: (200, 1310720)\n", - " East_Asian: (200, 1310720)\n", - " Indian: (200, 1310720)\n", - " Latino_Hispanic: (200, 1310720)\n", - " Middle_Eastern: (200, 1310720)\n", - " Southeast_Asian: (200, 1310720)\n", - " White: (200, 1310720)\n" + "Epoch 99: Loss=0.0042, Train=100.0%, Val=48.9%\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "\n" + "Epoch 100/100: 100%|██████████| 35/35 [00:00<00:00, 54.15it/s]\n" ] - } - ], - "source": [ - "# Train classifier on FLATTENED spatial representations\n", - "# [B, S, C] → [B, S*C] where each image is one sample with S*C features\n", - "\n", - "activations_by_ethnicity = batch_cache_image_activations_by_class(\n", - " model=model,\n", - " images_dict=data, # <-- Using REAL IMAGES, not prompts\n", - " layer_name=\"unet.down_blocks[0].attentions[0].transformer_blocks[0].attn1.to_out[0].output\",\n", - " timestep=500, # Noise level for UNet forward pass\n", - " batch_size=64,\n", - " encode_batch_size=8,\n", - " aggregate_spatial='flatten', # Flatten ALL spatial: [B, S, C] → [B, S*C]\n", - " empty_text_embed=True, # Unconditional (no text)\n", - " max_images_per_class=200 # Start with 20 images per ethnicity\n", - ")\n", - "\n", - "print(\"\\n✓ Training on flattened spatial representations\")\n", - "print(f\"✓ Each image is one sample with S*C features\")\n", - "for k, v in activations_by_ethnicity.items():\n", - " print(f\" {k}: {v.shape}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "edec83f9", - "metadata": {}, - "outputs": [ + }, { "name": "stdout", "output_type": "stream", "text": [ - "Input dimension: 1310720\n", - "Number of classes: 7\n", - "Classes: ['Black', 'East_Asian', 'Indian', 'Latino_Hispanic', 'Middle_Eastern', 'Southeast_Asian', 'White']\n", - "Epoch 0: loss = 35.9847, accuracy = 15.93%\n", - "Epoch 1: loss = 3.5234, accuracy = 26.86%\n", - "Epoch 2: loss = 1.9024, accuracy = 36.29%\n", - "Epoch 3: loss = 1.4164, accuracy = 47.57%\n", - "Epoch 4: loss = 1.3428, accuracy = 52.93%\n", - "Epoch 5: loss = 1.4316, accuracy = 48.14%\n", - "Epoch 6: loss = 1.0734, accuracy = 61.21%\n", - "Epoch 7: loss = 0.8734, accuracy = 69.29%\n", - "Epoch 8: loss = 0.8614, accuracy = 71.14%\n", - "Epoch 9: loss = 0.7260, accuracy = 75.50%\n", - "Epoch 10: loss = 0.7223, accuracy = 75.29%\n", - "Epoch 11: loss = 0.6580, accuracy = 77.14%\n", - "Epoch 12: loss = 0.8270, accuracy = 70.57%\n", - "Epoch 13: loss = 0.4778, accuracy = 83.93%\n", - "Epoch 14: loss = 0.5676, accuracy = 81.57%\n", - "Epoch 15: loss = 0.6290, accuracy = 79.57%\n", - "Epoch 16: loss = 0.7361, accuracy = 75.07%\n", - "Epoch 17: loss = 0.3840, accuracy = 87.21%\n", - "Epoch 18: loss = 0.2924, accuracy = 89.71%\n", - "Epoch 19: loss = 0.2201, accuracy = 92.21%\n", - "Epoch 20: loss = 0.1707, accuracy = 94.57%\n", - "Epoch 21: loss = 0.2443, accuracy = 91.57%\n", - "Epoch 22: loss = 0.1884, accuracy = 93.64%\n", - "Epoch 23: loss = 0.1852, accuracy = 93.57%\n", - "Epoch 24: loss = 0.1250, accuracy = 95.50%\n", - "Epoch 25: loss = 0.1615, accuracy = 94.71%\n", - "Epoch 26: loss = 0.2085, accuracy = 93.14%\n", - "Epoch 27: loss = 0.3009, accuracy = 89.57%\n", - "Epoch 28: loss = 0.0943, accuracy = 97.07%\n", - "Epoch 29: loss = 0.0373, accuracy = 99.00%\n", - "Epoch 30: loss = 0.1081, accuracy = 96.00%\n", - "Epoch 31: loss = 0.6014, accuracy = 84.29%\n", - "Epoch 32: loss = 1.2810, accuracy = 64.57%\n", - "Epoch 33: loss = 0.8461, accuracy = 71.79%\n", - "Epoch 34: loss = 0.5150, accuracy = 81.29%\n", - "Epoch 35: loss = 0.5879, accuracy = 81.14%\n", - "Epoch 36: loss = 0.3178, accuracy = 89.93%\n", - "Epoch 37: loss = 0.3939, accuracy = 86.07%\n", - "Epoch 38: loss = 0.2478, accuracy = 91.86%\n", - "Epoch 39: loss = 0.1039, accuracy = 96.71%\n", - "Epoch 40: loss = 0.0237, accuracy = 99.43%\n", - "Epoch 41: loss = 0.0071, accuracy = 100.00%\n", - "Epoch 42: loss = 0.0041, accuracy = 100.00%\n", - "Epoch 43: loss = 0.0034, accuracy = 100.00%\n", - "Epoch 44: loss = 0.0029, accuracy = 100.00%\n", - "Epoch 45: loss = 0.0024, accuracy = 100.00%\n", - "Epoch 46: loss = 0.0021, accuracy = 100.00%\n", - "Epoch 47: loss = 0.0019, accuracy = 100.00%\n", - "Epoch 48: loss = 0.0016, accuracy = 100.00%\n", - "Epoch 49: loss = 0.0015, accuracy = 100.00%\n", - "Epoch 50: loss = 0.0014, accuracy = 100.00%\n", - "Epoch 51: loss = 0.0013, accuracy = 100.00%\n", - "Epoch 52: loss = 0.0012, accuracy = 100.00%\n", - "Epoch 53: loss = 0.0011, accuracy = 100.00%\n", - "Epoch 54: loss = 0.0010, accuracy = 100.00%\n", - "Epoch 55: loss = 0.0010, accuracy = 100.00%\n", - "Epoch 56: loss = 0.0009, accuracy = 100.00%\n", - "Epoch 57: loss = 0.0008, accuracy = 100.00%\n", - "Epoch 58: loss = 0.0008, accuracy = 100.00%\n", - "Epoch 59: loss = 0.0008, accuracy = 100.00%\n", - "Epoch 60: loss = 0.0007, accuracy = 100.00%\n", - "Epoch 61: loss = 0.0007, accuracy = 100.00%\n", - "Epoch 62: loss = 0.0006, accuracy = 100.00%\n", - "Epoch 63: loss = 0.0006, accuracy = 100.00%\n", - "Epoch 64: loss = 0.0006, accuracy = 100.00%\n", - "Epoch 65: loss = 0.0005, accuracy = 100.00%\n", - "Epoch 66: loss = 0.0005, accuracy = 100.00%\n", - "Epoch 67: loss = 0.0005, accuracy = 100.00%\n", - "Epoch 68: loss = 0.0005, accuracy = 100.00%\n", - "Epoch 69: loss = 0.0004, accuracy = 100.00%\n", - "Epoch 70: loss = 0.0004, accuracy = 100.00%\n", - "Epoch 71: loss = 0.0004, accuracy = 100.00%\n", - "Epoch 72: loss = 0.0004, accuracy = 100.00%\n", - "Epoch 73: loss = 0.0004, accuracy = 100.00%\n", - "Epoch 74: loss = 0.0003, accuracy = 100.00%\n", - "Epoch 75: loss = 0.0003, accuracy = 100.00%\n", - "Epoch 76: loss = 0.0003, accuracy = 100.00%\n", - "Epoch 77: loss = 0.0003, accuracy = 100.00%\n", - "Epoch 78: loss = 0.0003, accuracy = 100.00%\n", - "Epoch 79: loss = 0.0003, accuracy = 100.00%\n", - "Epoch 80: loss = 0.0003, accuracy = 100.00%\n", - "Epoch 81: loss = 0.0003, accuracy = 100.00%\n", - "Epoch 82: loss = 0.0002, accuracy = 100.00%\n", - "Epoch 83: loss = 0.0002, accuracy = 100.00%\n", - "Epoch 84: loss = 0.0002, accuracy = 100.00%\n", - "Epoch 85: loss = 0.0002, accuracy = 100.00%\n", - "Epoch 86: loss = 0.0002, accuracy = 100.00%\n", - "Epoch 87: loss = 0.0002, accuracy = 100.00%\n", - "Epoch 88: loss = 0.0002, accuracy = 100.00%\n", - "Epoch 89: loss = 0.0002, accuracy = 100.00%\n", - "Epoch 90: loss = 0.0002, accuracy = 100.00%\n", - "Epoch 91: loss = 0.0002, accuracy = 100.00%\n", - "Epoch 92: loss = 0.0002, accuracy = 100.00%\n", - "Epoch 93: loss = 0.0002, accuracy = 100.00%\n", - "Epoch 94: loss = 0.0002, accuracy = 100.00%\n", - "Epoch 95: loss = 0.0002, accuracy = 100.00%\n", - "Epoch 96: loss = 0.0001, accuracy = 100.00%\n", - "Epoch 97: loss = 0.0001, accuracy = 100.00%\n", - "Epoch 98: loss = 0.0001, accuracy = 100.00%\n", - "Epoch 99: loss = 0.0001, accuracy = 100.00%\n", - "Accuracy for Black: 100.00%\n", - "Accuracy for East_Asian: 100.00%\n", - "Accuracy for Indian: 100.00%\n", - "Accuracy for Latino_Hispanic: 100.00%\n", - "Accuracy for Middle_Eastern: 100.00%\n", - "Accuracy for Southeast_Asian: 100.00%\n", - "Accuracy for White: 100.00%\n", + "Epoch 100: Loss=0.0025, Train=100.0%, Val=48.6%\n", "\n", - "Classifier trained! You can now use it for debiasing.\n" + "✓ Best validation accuracy: 48.9%\n" + ] + }, + { + "ename": "RuntimeError", + "evalue": "Parent directory /home/ubuntu/T2I_Interp_toolkit/data/fairface does not exist.", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[11], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m#Let's train an output classifier on the fairface data.\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01meval\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m eval_classifier \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43meval\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_ethnicity_classifier\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[43mimages_dict\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m \u001b[49m\u001b[43msave_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/home/ubuntu/T2I_Interp_toolkit/data/fairface/ethnicity_classifier.pth\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m32\u001b[39;49m\n\u001b[1;32m 9\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m 10\u001b[0m \u001b[38;5;66;03m#let's give it one black image from fairface to predict\u001b[39;00m\n\u001b[1;32m 11\u001b[0m black_image \u001b[38;5;241m=\u001b[39m data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBlack\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;241m0\u001b[39m]\n", + "File \u001b[0;32m~/T2I_Interp_toolkit/eval.py:302\u001b[0m, in \u001b[0;36mtrain_ethnicity_classifier\u001b[0;34m(images_dict, save_path, epochs, batch_size)\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[38;5;66;03m# Save if path provided\u001b[39;00m\n\u001b[1;32m 301\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m save_path:\n\u001b[0;32m--> 302\u001b[0m \u001b[43mclassifier\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43msave_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 304\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m classifier\n", + "File \u001b[0;32m~/T2I_Interp_toolkit/eval.py:249\u001b[0m, in \u001b[0;36mEthnicityClassifier.save\u001b[0;34m(self, path)\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21msave\u001b[39m(\u001b[38;5;28mself\u001b[39m, path):\n\u001b[1;32m 248\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Save classifier.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 249\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\n\u001b[1;32m 250\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mstate_dict\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstate_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 251\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mclass_names\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclass_names\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 252\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnum_classes\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_classes\u001b[49m\n\u001b[1;32m 253\u001b[0m \u001b[43m \u001b[49m\u001b[43m}\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 254\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m✓ Saved to \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpath\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/.venv/lib/python3.10/site-packages/torch/serialization.py:849\u001b[0m, in \u001b[0;36msave\u001b[0;34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization, _disable_byteorder_record)\u001b[0m\n\u001b[1;32m 846\u001b[0m _check_save_filelike(f)\n\u001b[1;32m 848\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _use_new_zipfile_serialization:\n\u001b[0;32m--> 849\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43m_open_zipfile_writer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m opened_zipfile:\n\u001b[1;32m 850\u001b[0m _save(\n\u001b[1;32m 851\u001b[0m obj,\n\u001b[1;32m 852\u001b[0m opened_zipfile,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 855\u001b[0m _disable_byteorder_record,\n\u001b[1;32m 856\u001b[0m )\n\u001b[1;32m 857\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n", + "File \u001b[0;32m~/.venv/lib/python3.10/site-packages/torch/serialization.py:716\u001b[0m, in \u001b[0;36m_open_zipfile_writer\u001b[0;34m(name_or_buffer)\u001b[0m\n\u001b[1;32m 714\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 715\u001b[0m container \u001b[38;5;241m=\u001b[39m _open_zipfile_writer_buffer\n\u001b[0;32m--> 716\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcontainer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname_or_buffer\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.venv/lib/python3.10/site-packages/torch/serialization.py:687\u001b[0m, in \u001b[0;36m_open_zipfile_writer_file.__init__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 685\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(torch\u001b[38;5;241m.\u001b[39m_C\u001b[38;5;241m.\u001b[39mPyTorchFileWriter(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfile_stream))\n\u001b[1;32m 686\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 687\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_C\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mPyTorchFileWriter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m)\n", + "\u001b[0;31mRuntimeError\u001b[0m: Parent directory /home/ubuntu/T2I_Interp_toolkit/data/fairface does not exist." ] } ], "source": [ - "# Train classifier on cached activations\n", - "\n", - "# Now use the cached activations to train the K_Steering classifier\n", - "input_dim = activations_by_ethnicity['Black'].shape[-1]\n", - "num_classes = len(activations_by_ethnicity)\n", - "\n", - "print(f\"Input dimension: {input_dim}\")\n", - "print(f\"Number of classes: {num_classes}\")\n", - "print(f\"Classes: {list(activations_by_ethnicity.keys())}\")\n", - "\n", - "# Initialize and train the classifier\n", - "classifier = K_Steering(\n", - " input_dim=input_dim,\n", - " num_classes=num_classes,\n", - " hidden_dim=256,\n", - " lr=1e-3,\n", - " device='cuda'\n", - ")\n", + "#Let's train an output classifier on the fairface data.\n", + "import eval\n", "\n", - "# Train on the cached activations\n", - "classifier.fit(\n", - " activations_dict=activations_by_ethnicity,\n", + "eval_classifier = eval.train_ethnicity_classifier(\n", + " images_dict = data,\n", + " save_path=\"/home/ubuntu/T2I_Interp_toolkit/data/fairface/ethnicity_classifier.pth\",\n", " epochs=100,\n", " batch_size=32\n", ")\n", - "\n", - "print(\"\\nClassifier trained! You can now use it for debiasing.\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "92118a1d", - "metadata": {}, - "source": [ - "## Here is the markdown for the flow of the gradient editin process:\n", - "- Each denoising timestep: The UNet runs to predict noise\n", - "During UNet forward pass: Your hook at attn1.to_out[0] is triggered\n", - "In the hook:\n", - "- Extract activations from that layer\n", - "- Pass through your ethnicity classifier\n", - "- Compute gradient: ∂(classifier_loss)/∂(activations)\n", - "- Steer activations using gradient direction\n", - "Continue UNet execution with steered activations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3299a74b", - "metadata": {}, - "outputs": [], - "source": [ - "# CORRECTED debias_hook - processes each spatial position independently\n", - "\n", - "def debias_hook(module, input, output, alpha=1.0):\n", - " \"\"\"\n", - " Hook that evens out ethnicity probability distribution during forward pass.\n", - " \n", - " Processes whole images (flattened spatial):\n", - " 1. Flatten spatial: [B, S, C] -> [B, S*C] (each image = one sample)\n", - " 2. Pass through classifier trained on flattened features\n", - " 3. Compute gradients per image\n", - " 4. Reshape back and apply steering\n", - " \"\"\"\n", - " # Get activation shape\n", - " act = output\n", - " original_shape = act.shape\n", - " \n", - " # Classifier was trained on flattened spatial representations: [B, S*C]\n", - " # During steering: flatten each image, get gradients, reshape back\n", - " # Process one image at a time in the batch dimension\n", - " \n", - " if act.ndim == 3:\n", - " # Attention output: [B, S, C]\n", - " B, S, C = act.shape\n", - " # Flatten spatial and channel dimensions per image: [B, S, C] -> [B, S*C]\n", - " act_flattened = act.reshape(B, S * C)\n", - " \n", - " elif act.ndim == 4:\n", - " # Conv output: [B, C, H, W]\n", - " B, C, H, W = act.shape\n", - " # Flatten spatial and channel: [B, C, H, W] -> [B, C*H*W]\n", - " act_flattened = act.reshape(B, C * H * W)\n", - " else:\n", - " # 2D: already [B, features]\n", - " B = act.shape[0]\n", - " act_flattened = act.clone()\n", - " if act.ndim == 3:\n", - " S, C = act.shape[1], act.shape[2]\n", - " elif act.ndim == 4:\n", - " C, H, W = act.shape[1], act.shape[2], act.shape[3]\n", - " \n", - " # CRITICAL: Create completely independent tensor for classifier\n", - " # Step 1: Detach from UNet computation graph\n", - " act_detached = act_flattened.detach()\n", - " \n", - " # Step 2: Convert to numpy (breaks all PyTorch graph connections)\n", - " act_np = act_detached.cpu().numpy()\n", - " \n", - " # Step 3: Create NEW tensor from numpy copy (completely independent)\n", - " # This tensor has NO connection to UNet's autograd graph\n", - " act_tensor = torch.from_numpy(act_np.copy()).to(\n", - " device=classifier.device,\n", - " dtype=torch.float32\n", - " )\n", - " \n", - " # Step 4: EXPLICITLY enable gradients for this computation\n", - " # The UNet forward pass may be in torch.no_grad() context, so we override it\n", - " with torch.enable_grad():\n", - " # Create tensor with gradients enabled\n", - " act_tensor.requires_grad_(True)\n", - " \n", - " # Verify independence\n", - " assert act_tensor.grad_fn is None, \"Tensor should not have grad_fn from UNet\"\n", - " assert not act_flattened.requires_grad, \"Original activations should not require grad\"\n", - " \n", - " # Ensure classifier is in train mode for proper gradient flow\n", - " classifier.classifier.eval()\n", - " \n", - " # Forward pass through classifier - each image gets prediction\n", - " logits = classifier.classifier(act_tensor) # [B, num_classes]\n", - " \n", - " # Target: uniform distribution - match K_Steering.steer_attributes_unsupervised approach\n", - " # Separate logits into over-represented (avoid) and under-represented (target)\n", - " \n", - " # Get mean logit per image across all classes\n", - " mean_logits = logits.mean(dim=1, keepdim=True) # [B, 1]\n", - " \n", - " # Identify which classes are above/below mean for each image\n", - " logits_deviation = logits - mean_logits # [B, num_classes]\n", - " \n", - " # Classes above mean = over-represented (need to decrease)\n", - " # Classes below mean = under-represented (need to increase)\n", - " avoid_mask = logits_deviation > 0 # Over-represented\n", - " target_mask = logits_deviation < 0 # Under-represented\n", - " \n", - " # Build loss to minimize over-represented and maximize under-represented\n", - " loss = 0.0\n", - " \n", - " if avoid_mask.any():\n", - " avoid_logits = logits * avoid_mask.float()\n", - " loss = loss + avoid_logits.sum() / (avoid_mask.sum() + 1e-8) # Minimize these\n", - " \n", - " if target_mask.any():\n", - " target_logits = logits * target_mask.float()\n", - " loss = loss - target_logits.sum() / (target_mask.sum() + 1e-8) # Maximize these (negative)\n", - " \n", - " # This loss encourages all classes to move toward the mean, achieving uniformity\n", - " \n", - " # Compute gradients for each image (ONLY through classifier, not UNet)\n", - " grad = torch.autograd.grad(\n", - " outputs=loss,\n", - " inputs=act_tensor,\n", - " retain_graph=False,\n", - " create_graph=False,\n", - " only_inputs=True,\n", - " )[0] # [B, S*C]\n", - " \n", - " # Steering direction (negative gradient to minimize bias)\n", - " steering_direction = -grad\n", - " \n", - " # Restore classifier to eval mode\n", - " #classifier.classifier.eval()\n", - " \n", - " # Step 5: Move steering back via numpy (complete isolation from both graphs)\n", - " steering_np = steering_direction.detach().cpu().numpy()\n", - " \n", - " # Step 6: Create NEW tensor on original device (no graph connections)\n", - " steering_tensor = torch.from_numpy(steering_np.copy()).to(\n", - " device=act_flattened.device,\n", - " dtype=act_flattened.dtype\n", - " )\n", - " \n", - " # Step 7: Apply steering - simple addition, no gradients\n", - " steered_flat = act_flattened.detach() + alpha * steering_tensor\n", - " \n", - "\n", - " # Reshape back to original shape\n", - " if len(original_shape) == 3:\n", - " # [B, S*C] -> [B, S, C]\n", - " B, S, C = original_shape\n", - " steered_output = steered_flat.reshape(B, S, C)\n", - " elif len(original_shape) == 4:\n", - " # [B, C*H*W] -> [B, C, H, W]\n", - " B, C, H, W = original_shape\n", - " steered_output = steered_flat.reshape(B, C, H, W)\n", - " else:\n", - " steered_output = steered_flat\n", - " \n", - " return steered_output\n" + "#let's give it one black image from fairface to predict\n", + "black_image = data[\"Black\"][0]\n", + "output = eval_classifier.predict(black_image)\n", + "print(output)\n" ] }, { @@ -1386,7 +2972,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "6f05f8de", "metadata": {}, "outputs": [ @@ -2915,6 +4501,14 @@ " return images\n", "\n", "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "def class_distributions(images, batch_size=1, target_class=\"white\"):\n", + " \"This method just runs the model and gets images and uses an output classifier trained on images to get the class distribution\"\n", + " pass \n", "def analyze_ethnicity_distribution(images, batch_size=1):\n", " \"\"\"\n", " Analyze ethnicity distribution in generated images using the trained classifier.\n", diff --git a/test_steering_direction.py b/test_steering_direction.py deleted file mode 100644 index a316c27..0000000 --- a/test_steering_direction.py +++ /dev/null @@ -1,89 +0,0 @@ -# Test if steering direction is correct -# Run this in a notebook cell to verify the gradient logic - -import torch -import numpy as np - -print("="*80) -print("TESTING STEERING DIRECTION") -print("="*80) - -# Get classifier input dimension -expected_dim = list(classifier.classifier.children())[0].in_features -print(f"\nClassifier expects: {expected_dim}-dimensional input") - -# Create dummy activations -dummy_act = torch.randn(10, expected_dim, device=classifier.device, dtype=torch.float32) - -# Test WITHOUT steering (baseline) -print("\n1. BASELINE (no steering):") -classifier.classifier.eval() -with torch.no_grad(): - logits_before = classifier.classifier(dummy_act) - probs_before = torch.softmax(logits_before, dim=-1) - - # Check uniformity - mean_prob = probs_before.mean(dim=0) - target_uniform = 1.0 / classifier.num_classes - uniformity_before = torch.abs(mean_prob - target_uniform).sum().item() - - print(f" Mean probabilities across samples: {mean_prob.cpu().numpy()}") - print(f" Target uniform: {target_uniform:.4f}") - print(f" Uniformity score: {uniformity_before:.4f}") - -# Test WITH steering (using our hook logic) -print("\n2. AFTER STEERING (alpha=1.0):") -dummy_act_tensor = dummy_act.clone() - -with torch.enable_grad(): - dummy_act_tensor.requires_grad_(True) - classifier.classifier.train() - - logits = classifier.classifier(dummy_act_tensor) - - # OUR LOSS FORMULA - mean_logits = logits.mean(dim=1, keepdim=True) - deviation = logits - mean_logits - - avoid_mask = deviation > 0 - target_mask = deviation < 0 - - loss = 0.0 - if avoid_mask.any(): - loss += (logits * avoid_mask.float()).sum() / (avoid_mask.sum() + 1e-8) - if target_mask.any(): - loss -= (logits * target_mask.float()).sum() / (target_mask.sum() + 1e-8) - - print(f" Loss value: {loss.item():.4f}") - - grad = torch.autograd.grad(loss, dummy_act_tensor)[0] - steering = -grad - - classifier.classifier.eval() - -# Apply steering -dummy_act_steered = dummy_act.detach() + 1.0 * steering.detach() - -# Check uniformity after steering -with torch.no_grad(): - logits_after = classifier.classifier(dummy_act_steered) - probs_after = torch.softmax(logits_after, dim=-1) - - mean_prob_after = probs_after.mean(dim=0) - uniformity_after = torch.abs(mean_prob_after - target_uniform).sum().item() - - print(f" Mean probabilities after steering: {mean_prob_after.cpu().numpy()}") - print(f" Uniformity score: {uniformity_after:.4f}") - -# Compare -print("\n3. RESULT:") -if uniformity_after < uniformity_before: - improvement = ((uniformity_before - uniformity_after) / uniformity_before) * 100 - print(f" ✓ IMPROVED: {uniformity_before:.4f} → {uniformity_after:.4f} ({improvement:.1f}% better)") -else: - worsening = ((uniformity_after - uniformity_before) / uniformity_before) * 100 - print(f" ❌ WORSENED: {uniformity_before:.4f} → {uniformity_after:.4f} ({worsening:.1f}% worse)") - print(f" → BUG: Steering direction or loss formula is incorrect!") - -print("\n" + "="*80) -