File size: 4,659 Bytes
78f2329 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | #!/usr/bin/env python3
"""
Quick test script to verify RBS debug logging works correctly.
This loads cached logits and runs a quick calibration test.
"""
import sys
import os
import logging
import numpy as np
import torch
import torch.nn.functional as F
# Add paths
sys.path.append('/hdd/haolan')
sys.path.append('/hdd/haolan/SMART')
from calibrator.Component.utils.utils import get_all_metrics
from calibrator.Component.model.temperature_scaling import TemperatureScalingCalibrator
# Set up logging to see debug messages
logging.basicConfig(level=logging.INFO, format='%(name)s - %(levelname)s - %(message)s')
print("="*80)
print("Testing RBS Debug Logging")
print("="*80)
# Load some cached logits for testing
cache_dir = "/hdd/haolan/SMART/cache/cifar10_resnet50_seed1"
test_logits = np.load(f"{cache_dir}/test_logits.npy")
test_labels = np.load(f"{cache_dir}/test_labels.npy")
val_logits = np.load(f"{cache_dir}/val_logits.npy")
val_labels = np.load(f"{cache_dir}/val_labels.npy")
# Convert to tensors
test_logits_tensor = torch.tensor(test_logits, dtype=torch.float32)
test_labels_tensor = torch.tensor(test_labels, dtype=torch.long)
val_logits_tensor = torch.tensor(val_logits, dtype=torch.float32)
val_labels_tensor = torch.tensor(val_labels, dtype=torch.long)
print(f"\nLoaded data:")
print(f" Test logits shape: {test_logits_tensor.shape}")
print(f" Test labels shape: {test_labels_tensor.shape}")
# Test 1: Uncalibrated
print("\n" + "="*80)
print("TEST 1: Uncalibrated Logits")
print("="*80)
uncal_metrics = get_all_metrics(
labels=test_labels_tensor,
logits=test_logits_tensor,
probs=None,
n_bins=15
)
print(f"Uncalibrated RBS: {uncal_metrics['rbs']:.6f}")
print(f"Uncalibrated NLL: {uncal_metrics['nll']:.6f}")
print(f"Uncalibrated ECE: {uncal_metrics['ece']:.6f}")
# Test 2: Temperature Scaling
print("\n" + "="*80)
print("TEST 2: Temperature Scaling")
print("="*80)
# Train temperature scaling
ts_calibrator = TemperatureScalingCalibrator(loss_type='CE')
ts_calibrator.fit(val_logits_tensor, val_labels_tensor)
optimal_temp = ts_calibrator.temperature.item()
print(f"Optimal temperature: {optimal_temp:.6f}")
# Get calibrated logits
calibrated_logits = ts_calibrator.calibrate(test_logits_tensor, return_logits=True)
print(f"Calibrated logits stats:")
print(f" Mean: {calibrated_logits.mean().item():.6f}")
print(f" Std: {calibrated_logits.std().item():.6f}")
print(f" Sample [0, :5]: {calibrated_logits[0, :5].tolist()}")
# Compute metrics with calibrated logits
cal_metrics = get_all_metrics(
labels=test_labels_tensor,
logits=calibrated_logits,
probs=None,
n_bins=15
)
print(f"\nTemperature Scaling RBS: {cal_metrics['rbs']:.6f}")
print(f"Temperature Scaling NLL: {cal_metrics['nll']:.6f}")
print(f"Temperature Scaling ECE: {cal_metrics['ece']:.6f}")
# Test 3: Using probabilities directly
print("\n" + "="*80)
print("TEST 3: Direct Probabilities (simulating SMART)")
print("="*80)
# Convert calibrated logits to probabilities
ts_probs = F.softmax(calibrated_logits, dim=1)
print(f"Probability stats:")
print(f" Mean: {ts_probs.mean().item():.6f}")
print(f" Std: {ts_probs.std().item():.6f}")
print(f" Sample [0, :5]: {ts_probs[0, :5].tolist()}")
print(f" Sum [0]: {ts_probs[0].sum().item():.6f}")
# Compute metrics with probabilities
probs_metrics = get_all_metrics(
labels=test_labels_tensor,
logits=None,
probs=ts_probs,
n_bins=15
)
print(f"\nDirect Probs RBS: {probs_metrics['rbs']:.6f}")
print(f"Direct Probs NLL: {probs_metrics['nll']:.6f}")
print(f"Direct Probs ECE: {probs_metrics['ece']:.6f}")
# Compare results
print("\n" + "="*80)
print("COMPARISON")
print("="*80)
print(f"Method RBS NLL ECE")
print(f"Uncalibrated: {uncal_metrics['rbs']:.6f} {uncal_metrics['nll']:.6f} {uncal_metrics['ece']:.6f}")
print(f"TS (logits): {cal_metrics['rbs']:.6f} {cal_metrics['nll']:.6f} {cal_metrics['ece']:.6f}")
print(f"TS (probs): {probs_metrics['rbs']:.6f} {probs_metrics['nll']:.6f} {probs_metrics['ece']:.6f}")
print(f"\nRBS Change (uncal→TS): {(cal_metrics['rbs'] - uncal_metrics['rbs']):.6f} ({100*(cal_metrics['rbs'] - uncal_metrics['rbs'])/uncal_metrics['rbs']:.2f}%)")
print(f"ECE Change (uncal→TS): {(cal_metrics['ece'] - uncal_metrics['ece']):.6f} ({100*(cal_metrics['ece'] - uncal_metrics['ece'])/uncal_metrics['ece']:.2f}%)")
print(f"\nNote: TS (logits) and TS (probs) should give identical results!")
print(f"Match: {abs(cal_metrics['rbs'] - probs_metrics['rbs']) < 1e-6}")
print("\n" + "="*80)
print("Test complete! Check the debug logs above.")
print("="*80)
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