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