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a10ba7f | 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 | import torch
import numpy as np
import h5py
import os
import sys
from pathlib import Path
from tqdm import tqdm
# Add project root to path
sys.path.append(str(Path(__file__).parent.parent))
from src.models.student import LIPEV2Student, LIPEV2StudentGold, LIPEV2StudentBaseline
def evaluate_on_gaze360(model_path, h5_path, invert_yaw=False, invert_pitch=False):
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f"Evaluating model: {model_path}")
print(f"On dataset: {h5_path}")
print(f"Correction: Invert Yaw={invert_yaw}, Invert Pitch={invert_pitch}")
print(f"Device: {device}")
# Detect Architecture
is_gold = 'gold' in model_path.lower() or 'id_5' in model_path.lower() or 'id_2' in model_path.lower() or 'id_6' in model_path.lower() or 'id_8' in model_path.lower()
is_baseline = 'baseline' in model_path.lower()
if is_gold:
print("Architecture: V5-GOLD (DualPool / ID 5,6,8)")
model = LIPEV2StudentGold().to(device)
elif is_baseline:
print("Architecture: Baseline (Addition)")
model = LIPEV2StudentBaseline().to(device)
else:
print("Architecture: Standard (Concatenation / ID 1,3,4,7)")
model = LIPEV2Student().to(device)
state_dict = torch.load(model_path, map_location=device)
# Handle SWA weights if necessary
if 'n_averaged' in state_dict:
# It's an AveragedModel from SWA
new_state_dict = {}
for k, v in state_dict.items():
if k.startswith('module.'):
new_state_dict[k[7:]] = v
else:
new_state_dict[k] = v
state_dict = new_state_dict
model.load_state_dict(state_dict, strict=False)
model.eval()
results = {
'all': {'error': 0.0, 'count': 0},
'frontal_45': {'error': 0.0, 'count': 0}
}
with h5py.File(h5_path, 'r') as f:
left_patches = f['left_patches'][:]
right_patches = f['right_patches'][:]
landmarks = f['landmarks'][:]
gaze_gt = f['gaze'][:] # (pitch, yaw) in radians
num_samples = left_patches.shape[0]
with torch.no_grad():
for i in tqdm(range(num_samples), desc="Testing"):
# Prepare inputs
lp = torch.from_numpy(left_patches[i]).float().unsqueeze(0).to(device) / 255.0
rp = torch.from_numpy(right_patches[i]).float().unsqueeze(0).to(device) / 255.0
lm = torch.from_numpy(landmarks[i]).float().view(1, -1).to(device)
gt = torch.from_numpy(gaze_gt[i]).float().to(device)
# Predict
if is_gold:
out = model(lp, lm)
else:
out = model(lp, lm, state='A')
p_l, y_l = out[0], out[1]
if is_gold:
out_r = model(rp, lm)
else:
out_r = model(rp, lm, state='A')
p_r, y_r = out_r[0], out_r[1]
# Convert Logits to Degrees
def logits_to_deg(p_logits, y_logits):
idx = torch.arange(90).float().to(device)
p_prob = torch.softmax(p_logits, dim=1)
y_prob = torch.softmax(y_logits, dim=1)
p_deg = (torch.sum(p_prob * idx, dim=1) * 2 - 90)
y_deg = (torch.sum(y_prob * idx, dim=1) * 2 - 90)
return p_deg, y_deg
p_deg_l, y_deg_l = logits_to_deg(p_l, y_l)
p_deg_r, y_deg_r = logits_to_deg(p_r, y_r)
p_final = (p_deg_l + p_deg_r) / 2
y_final = (y_deg_l + y_deg_r) / 2
# Apply Coordinate Correction (if needed)
if invert_pitch: p_final = -p_final
if invert_yaw: y_final = -y_final
if args.swap_axes:
p_final, y_final = y_final, p_final
# Ground Truth to Degrees (robust_v16: 0=Pitch, 1=Yaw)
gt_deg = gt * (180.0 / np.pi)
pitch_gt = gt_deg[0]
yaw_gt = gt_deg[1]
# --- NEW: Standard 3D Angular Error Calculation ---
def angles_to_unit_vector(pitch_deg, yaw_deg):
p = np.radians(pitch_deg)
y = np.radians(yaw_deg)
# Standard mapping: x=cos(p)sin(y), y=sin(p), z=cos(p)cos(y)
# Note: Coordinate system depends on dataset conventions,
# but for angular distance, consistency is key.
vx = np.cos(p) * np.sin(y)
vy = np.sin(p)
vz = np.cos(p) * np.cos(y)
return np.array([vx, vy, vz])
v_pred = angles_to_unit_vector(p_final.item(), y_final.item())
v_gt = angles_to_unit_vector(pitch_gt.item(), yaw_gt.item())
# Dot product for cosine similarity
cos_sim = np.clip(np.dot(v_pred, v_gt), -1.0, 1.0)
angular_error = np.degrees(np.arccos(cos_sim))
# Update "All Cases"
results['all']['error'] += angular_error
results['all']['count'] += 1
# Update "Frontal 45"
if abs(yaw_gt.item()) <= 45.0:
results['frontal_45']['error'] += angular_error
results['frontal_45']['count'] += 1
print(f"\n" + "="*45)
print(f"{'SUBSET':<20} | {'SAMPLES':<10} | {'Ang Error (deg)':<10}")
print(f"-"*45)
for key, data in results.items():
if data['count'] > 0:
mae = data['error'] / data['count']
name = "All Cases" if key == 'all' else "Frontal +/- 45"
print(f"{name:<20} | {data['count']:<10} | {mae:.4f}")
print(f"="*45)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str, default='checkpoints/baseline_v16/best_student_p11.pt')
parser.add_argument('--h5', type=str, default='data/processed/gaze360_robust_v16_test_B.h5')
parser.add_argument('--invert_yaw', action='store_true', default=False)
parser.add_argument('--invert_pitch', action='store_true', default=False)
parser.add_argument('--swap_axes', action='store_true', default=False)
args = parser.parse_args()
evaluate_on_gaze360(
model_path=args.model,
h5_path=args.h5,
invert_yaw=args.invert_yaw,
invert_pitch=args.invert_pitch
)
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