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178f61f | 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 | import torch
import sys
import os
from pathlib import Path
# Add project root to path
sys.path.append(str(Path(__file__).parent.parent.parent))
from src.models.teacher import load_teacher_model
def verify_teacher():
checkpoint_path = 'checkpoints/resnet50.pt'
if not os.path.exists(checkpoint_path):
print(f"Error: {checkpoint_path} not found.")
return False
print(f"Loading teacher model from {checkpoint_path}...")
try:
model = load_teacher_model(checkpoint_path, backbone='resnet50', device='cpu')
print("Teacher model loaded successfully!")
# Check parameter dtypes
for name, param in model.named_parameters():
if param.dtype != torch.float32:
print(f"Warning: Parameter {name} is {param.dtype}")
break # Just check the first one
# Test forward pass
dummy_input = torch.randn(1, 3, 224, 224)
with torch.no_grad():
p_logits, y_logits = model(dummy_input)
print(f"Output shapes: Pitch {p_logits.shape}, Yaw {y_logits.shape}")
p_deg, y_deg = model.get_angles(p_logits, y_logits)
print(f"Predicted angles (dummy): Pitch {p_deg.item():.2f}, Yaw {y_deg.item():.2f}")
return True
except Exception as e:
print(f"Error loading teacher: {e}")
# Print state dict keys to debug if loading failed due to mismatch
checkpoint = torch.load(checkpoint_path, map_location='cpu')
print(f"Keys in checkpoint: {list(checkpoint.keys())[:10]}...")
return False
if __name__ == '__main__':
verify_teacher()
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