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import traceback
from pathlib import Path
log_path = Path('load_predict_log.txt')
with log_path.open('w', encoding='utf-8') as f:
def log(*args, **kwargs):
print(*args, file=f, **kwargs)
f.flush()
try:
log('Starting model load & predict test')
import tensorflow as tf
import numpy as np
from PIL import Image
import os
img_path = Path('data/UTKFace/53_1_1_20170110122449716.jpg.chip.jpg')
log('Image path:', str(img_path))
# Try HDF5 / .h5 first
h5_path = Path('final_model.h5')
keras_path = Path('saved_model_age_regressor.keras')
saved_model_dir = Path('saved_model_age_regressor')
if h5_path.exists():
try:
log('Attempting to load HDF5 model:', str(h5_path))
m = tf.keras.models.load_model(str(h5_path), compile=False)
log('Loaded HDF5 model:', type(m))
img = Image.open(img_path).convert('RGB').resize((224,224))
x = np.expand_dims(np.array(img, dtype=np.float32)/255.0, 0)
log('Running predict on HDF5 model...')
pred = m.predict(x)
log('Prediction result (HDF5):', pred.tolist())
except Exception:
log('Exception while loading/predicting from HDF5:')
traceback.print_exc(file=f)
elif keras_path.exists():
try:
log('Attempting to load Keras native file:', str(keras_path))
m = tf.keras.models.load_model(str(keras_path), compile=False)
log('Loaded Keras native model:', type(m))
img = Image.open(img_path).convert('RGB').resize((224,224))
x = np.expand_dims(np.array(img, dtype=np.float32)/255.0, 0)
pred = m.predict(x)
log('Prediction result (KERAS):', pred.tolist())
except Exception:
log('Exception while loading/predicting from Keras file:')
traceback.print_exc(file=f)
elif saved_model_dir.exists():
try:
log('HDF5/.keras not found; attempting to wrap TF SavedModel using TFSMLayer...')
try:
from keras.layers import TFSMLayer
except Exception as e:
log('TFSMLayer import failed:', e)
raise
# Build wrapper model
inputs = tf.keras.Input(shape=(224,224,3))
tfsml = TFSMLayer(str(saved_model_dir), call_endpoint='serving_default')
outputs = tfsml(inputs)
wrapper = tf.keras.Model(inputs, outputs)
log('Wrapper model created; running predict...')
img = Image.open(img_path).convert('RGB').resize((224,224))
x = np.expand_dims(np.array(img, dtype=np.float32)/255.0, 0)
pred = wrapper.predict(x)
# The SavedModel serving signature can return a dict mapping names->arrays
if isinstance(pred, dict):
log('Prediction returned a dict with keys:', list(pred.keys()))
import numpy as _np
for k, v in pred.items():
try:
arr = _np.array(v)
log(f"Output '{k}': shape={arr.shape} values={arr.flatten()[:10].tolist()}")
except Exception as _e:
log(f"Could not convert output '{k}' to numpy array:", _e)
else:
try:
log('Prediction result (wrapped SavedModel):', pred.tolist())
except Exception:
log('Prediction result (wrapped SavedModel) type:', type(pred))
except Exception:
log('Exception while wrapping/using SavedModel:')
traceback.print_exc(file=f)
else:
log('No model file found: looked for final_model.h5, saved_model_age_regressor.keras, or saved_model_age_regressor/')
log('Finished load & predict test')
except Exception:
traceback.print_exc(file=f)
log('Top-level exception')
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