File size: 7,639 Bytes
b4b2877 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 | #!/usr/bin/env python3
"""
Combine sensor-only NN predictions with transition matrix at inference time.
P(y|x,prev) ∝ P_nn(y|x)^α × P_trans(y|prev)^β
Tune α,β on validation set.
"""
import os
import sys
import json
import re
import numpy as np
import torch
import torch.nn as nn
from collections import Counter
from sklearn.metrics import accuracy_score, f1_score
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from data.dataset import DATASET_DIR, TRAIN_VOLS, VAL_VOLS, TEST_VOLS
from tasks.train_pred_cls import (
ActionPredDataset, TransformerClassifier,
ACTION_CLASSES_COARSE, init_classes
)
# Initialize global classes
init_classes(coarse=True)
COARSE_CLASSES = ACTION_CLASSES_COARSE
ANNOTATION_DIR = "${PULSE_ROOT}"
def get_predictions(model, dataset, device):
"""Get softmax predictions from model."""
model.eval()
loader = torch.utils.data.DataLoader(dataset, batch_size=64, shuffle=False)
all_probs = []
all_labels = []
all_prev = []
with torch.no_grad():
for batch in loader:
features = batch['features'].to(device)
mask = batch['mask'].to(device)
logits = model(features, mask) # no prev_action
probs = torch.softmax(logits, dim=1).cpu().numpy()
all_probs.append(probs)
all_labels.extend(batch['label'])
all_prev.extend(batch['prev_label'])
return np.concatenate(all_probs), np.array(all_labels), np.array(all_prev)
def compute_transition_matrix(dataset, num_classes):
"""Compute P(current|prev) from dataset."""
counts = np.zeros((num_classes, num_classes))
for i in range(len(dataset)):
sample = dataset[i]
prev = sample['prev_label']
curr = sample['label']
counts[prev, curr] += 1
row_sums = counts.sum(axis=1, keepdims=True)
row_sums[row_sums == 0] = 1
return counts / row_sums
def combined_predict(nn_probs, trans_matrix, prev_labels, alpha, beta):
"""Combine NN and transition predictions."""
N, C = nn_probs.shape
combined = np.zeros_like(nn_probs)
for i in range(N):
trans_prob = trans_matrix[prev_labels[i]]
# Multiplicative combination with temperature
p = (nn_probs[i] ** alpha) * (trans_prob ** beta)
p_sum = p.sum()
if p_sum > 0:
combined[i] = p / p_sum
else:
combined[i] = trans_prob
return np.argmax(combined, axis=1)
def main():
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Models to evaluate (sensor-only, no prev_action)
models_info = [
# (results_dir, modalities, description)
('recog2a', 'imu', 'Recog: IMU'),
('recog2a', 'mocap,emg,eyetrack', 'Recog: MEE'),
('recog2a', 'mocap,emg,imu', 'Recog: MEI'),
('recog_coarse', 'imu', 'Recog10s: IMU'),
('recog_coarse', 'mocap,emg,imu', 'Recog10s: MEI'),
]
base_dir = '${PULSE_ROOT}/results'
for results_dir, modalities, desc in models_info:
mod_str = modalities.replace(',', '-')
# Find the model directory
result_base = os.path.join(base_dir, results_dir)
# Pattern: recog_cls_coarse_{mod_str}
model_dir = os.path.join(result_base, f'recog_cls_coarse_{mod_str}')
if not os.path.exists(model_dir):
print(f" Skip {desc}: {model_dir} not found")
continue
results_file = os.path.join(model_dir, 'results.json')
if not os.path.exists(results_file):
continue
r = json.load(open(results_file))
args_dict = r['args']
# Recreate datasets
mods = modalities.split(',')
window_sec = args_dict['window_sec']
downsample = args_dict['downsample']
train_ds = ActionPredDataset(
TRAIN_VOLS, mods, window_sec=window_sec,
downsample=downsample, coarse=True, mode='recognition')
stats = train_ds.get_stats()
val_ds = ActionPredDataset(
VAL_VOLS, mods, window_sec=window_sec,
downsample=downsample, stats=stats, coarse=True, mode='recognition')
test_ds = ActionPredDataset(
TEST_VOLS, mods, window_sec=window_sec,
downsample=downsample, stats=stats, coarse=True, mode='recognition')
num_classes = len(COARSE_CLASSES)
# Build and load model (without prev_action)
model = TransformerClassifier(
train_ds.feat_dim, num_classes,
d_model=args_dict['hidden_dim'], nhead=4, num_layers=2,
dropout=args_dict['dropout'], use_prev_action=False
).to(device)
ckpt = torch.load(os.path.join(model_dir, 'model_best.pt'),
map_location=device, weights_only=True)
model.load_state_dict(ckpt)
# Get predictions
val_probs, val_labels, val_prev = get_predictions(model, val_ds, device)
test_probs, test_labels, test_prev = get_predictions(model, test_ds, device)
# Compute transition matrix from train
trans_matrix = compute_transition_matrix(train_ds, num_classes)
# Baseline: NN only
nn_preds = np.argmax(test_probs, axis=1)
nn_f1w = f1_score(test_labels, nn_preds, average='weighted', zero_division=0)
# Baseline: Transition only
trans_preds = np.array([np.argmax(trans_matrix[p]) for p in test_prev])
trans_f1w = f1_score(test_labels, trans_preds, average='weighted', zero_division=0)
# Grid search α, β on validation
best_val_f1 = -1
best_params = (1.0, 1.0)
for alpha in [0.0, 0.3, 0.5, 0.7, 1.0, 1.5, 2.0]:
for beta in [0.0, 0.3, 0.5, 0.7, 1.0, 1.5, 2.0]:
if alpha == 0 and beta == 0:
continue
preds = combined_predict(val_probs, trans_matrix, val_prev, alpha, beta)
f1w = f1_score(val_labels, preds, average='weighted', zero_division=0)
if f1w > best_val_f1:
best_val_f1 = f1w
best_params = (alpha, beta)
# Evaluate on test with best params
alpha, beta = best_params
combined_preds = combined_predict(test_probs, trans_matrix, test_prev, alpha, beta)
comb_f1w = f1_score(test_labels, combined_preds, average='weighted', zero_division=0)
comb_acc = accuracy_score(test_labels, combined_preds)
# Also try simple additive combination
best_val_f1_add = -1
best_w = 0.5
for w in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]:
preds_add = []
for i in range(len(val_probs)):
p = w * val_probs[i] + (1 - w) * trans_matrix[val_prev[i]]
preds_add.append(np.argmax(p))
f1w = f1_score(val_labels, preds_add, average='weighted', zero_division=0)
if f1w > best_val_f1_add:
best_val_f1_add = f1w
best_w = w
# Test with best w
preds_add = []
for i in range(len(test_probs)):
p = best_w * test_probs[i] + (1 - best_w) * trans_matrix[test_prev[i]]
preds_add.append(np.argmax(p))
add_f1w = f1_score(test_labels, preds_add, average='weighted', zero_division=0)
print(f"\n{desc} ({mod_str}):")
print(f" NN only: F1w={nn_f1w:.3f}")
print(f" Trans only: F1w={trans_f1w:.3f}")
print(f" Multiplicative (α={alpha:.1f}, β={beta:.1f}): F1w={comb_f1w:.3f}")
print(f" Additive (w={best_w:.1f}): F1w={add_f1w:.3f}")
if __name__ == '__main__':
main()
|