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7111e1a | 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 | import torch
import sys
sys.path.append('.')
from crnn_model import get_crnn_model
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def test_model(path, label):
c = torch.load(path, map_location=device, weights_only=False)
config = c.get('config', {})
model = get_crnn_model(
model_type = config.get('model_type', 'standard'),
img_height = config.get('img_height', 64),
num_chars = c['model_state_dict']['fc.weight'].shape[0],
hidden_size = config.get('hidden_size', 128),
num_lstm_layers = config.get('num_lstm_layers', 1),
).to(device)
model.load_state_dict(c['model_state_dict'], strict=False)
epoch = c.get('epoch', 'N/A')
val_loss = c.get('val_loss', None) # fine-tuned checkpoints (EMNIST, IAM)
val_cer = c.get('val_cer', None) # synthetic baseline checkpoint
if val_loss is not None:
metric_str = f"val_loss={val_loss:.4f}"
elif val_cer is not None:
metric_str = f"val_cer={val_cer:.4f}%"
else:
metric_str = "no metric saved"
print(f"{label}: epoch={epoch} {metric_str}")
print("=" * 55)
test_model('checkpoints/best_model.pth', 'Synthetic ')
test_model('checkpoints/best_model_emnist.pth', 'EMNIST ')
test_model('checkpoints/best_model_iam.pth', 'IAM ')
print("=" * 55) |