207 lines
7.6 KiB
Python
207 lines
7.6 KiB
Python
# train_model.py
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import os
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
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import json
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import numpy as np
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import cv2
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import argparse
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import tensorflow as tf
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from video_compression_model import NUM_CHANNELS, VideoCompressionModel, PRESET_SPEED_CATEGORIES, VideoDataGenerator
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from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
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from global_train import LOGGER
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# Constants
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BATCH_SIZE = 4
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EPOCHS = 100
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LEARNING_RATE = 0.000001
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TRAIN_SAMPLES = 100
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MODEL_SAVE_FILE = "models/model.tf"
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MODEL_CHECKPOINT_DIR = "checkpoints"
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EARLY_STOP = 10
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WIDTH = 638
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HEIGHT = 360
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def load_video_metadata(list_path):
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LOGGER.trace(f"Entering: load_video_metadata({list_path})")
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try:
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with open(list_path, "r") as json_file:
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file = json.load(json_file)
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LOGGER.trace(f"load_video_metadata returning: {file}")
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return file
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except FileNotFoundError:
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LOGGER.error(f"Metadata file {list_path} not found.")
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raise
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except json.JSONDecodeError:
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LOGGER.error(f"Error decoding JSON from {list_path}.")
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raise
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def load_video_samples(list_path, samples=TRAIN_SAMPLES):
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"""
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Load video samples from the metadata list.
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Args:
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- list_path (str): Path to the metadata JSON file.
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- samples (int): Number of total samples to be extracted.
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Returns:
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- list: Extracted video samples.
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"""
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LOGGER.trace(f"Entering: load_video_samples({list_path}, {samples})" )
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details_list = load_video_metadata(list_path)
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all_samples = []
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num_videos = len(details_list)
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frames_per_video = int(samples / num_videos)
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LOGGER.info(f"Loading {frames_per_video} frames from {num_videos} videos")
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for video_details in details_list:
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video_file = video_details["video_file"]
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uncompressed_video_file = video_details["uncompressed_video_file"]
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crf = video_details['crf'] / 63.0
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preset_speed = PRESET_SPEED_CATEGORIES.index(video_details['preset_speed'])
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video_details['preset_speed'] = preset_speed
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compressed_frames, uncompressed_frames = [], []
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try:
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cap = cv2.VideoCapture(os.path.join(os.path.dirname(list_path), video_file))
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cap_uncompressed = cv2.VideoCapture(os.path.join(os.path.dirname(list_path), uncompressed_video_file))
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if not cap.isOpened() or not cap_uncompressed.isOpened():
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raise RuntimeError(f"Could not open video files {video_file} or {uncompressed_video_file}, searched under: {os.path.dirname(list_path)}")
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for _ in range(frames_per_video):
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ret, frame_compressed = cap.read()
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ret_uncompressed, frame = cap_uncompressed.read()
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if not ret or not ret_uncompressed:
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continue
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# Check frame dimensions and resize if necessary
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if frame.shape[:2] != (WIDTH, HEIGHT):
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LOGGER.warn(f"Resizing video: {video_file}")
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frame = cv2.resize(frame, (WIDTH, HEIGHT), interpolation=cv2.INTER_AREA)
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if frame_compressed.shape[:2] != (WIDTH, HEIGHT):
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LOGGER.warn(f"Resizing video: {uncompressed_video_file}")
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frame_compressed = cv2.resize(frame_compressed, (WIDTH, HEIGHT), interpolation=cv2.INTER_AREA)
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frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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frame_compressed = cv2.cvtColor(frame_compressed, cv2.COLOR_BGR2RGB)
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uncompressed_frames.append(normalize(frame))
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compressed_frames.append(normalize(frame_compressed))
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all_samples.extend({
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"frame": frame,
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"compressed_frame": frame_compressed,
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"crf": crf,
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"preset_speed": preset_speed,
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"video_file": video_file
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} for frame, frame_compressed in zip(uncompressed_frames, compressed_frames))
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except Exception as e:
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LOGGER.error(f"Error during video sample loading: {e}")
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raise
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finally:
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cap.release()
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cap_uncompressed.release()
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return all_samples
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def normalize(frame):
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"""
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Normalize pixel values of the frame to range [0, 1].
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Args:
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- frame (ndarray): Image frame.
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Returns:
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- ndarray: Normalized frame.
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"""
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LOGGER.trace(f"Normalizing frame")
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return frame / 255.0
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def save_model(model):
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try:
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LOGGER.debug("Attempting to save the model.")
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os.makedirs("models", exist_ok=True)
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model.save(MODEL_SAVE_FILE, save_format='tf')
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LOGGER.info("Model saved successfully!")
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except Exception as e:
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LOGGER.error(f"Error saving the model: {e}")
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raise
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def main():
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# Argument parsing
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parser = argparse.ArgumentParser(description="Train the video compression model.")
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parser.add_argument('-b', '--batch_size', type=int, default=BATCH_SIZE, help='Batch size for training.')
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parser.add_argument('-e', '--epochs', type=int, default=EPOCHS, help='Number of epochs for training.')
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parser.add_argument('-s', '--training_samples', type=int, default=TRAIN_SAMPLES, help='Number of training samples.')
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parser.add_argument('-l', '--learning_rate', type=float, default=LEARNING_RATE, help='Learning rate for training.')
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parser.add_argument('-c', '--continue_training', type=str, nargs='?', const=MODEL_SAVE_FILE, default=None, help='Path to the saved model to continue training. If used without a value, defaults to the MODEL_SAVE_FILE.')
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args = parser.parse_args()
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# Display training configuration
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LOGGER.info("Starting the training with the given configuration.")
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LOGGER.info("Training configuration:")
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LOGGER.info(f"Batch size: {args.batch_size}")
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LOGGER.info(f"Epochs: {args.epochs}")
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LOGGER.info(f"Training samples: {args.training_samples}")
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LOGGER.info(f"Learning rate: {args.learning_rate}")
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LOGGER.info(f"Continue training from: {args.continue_training}")
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# Load training and validation samples
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LOGGER.debug("Loading training and validation samples.")
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training_samples = load_video_samples("test_data/training/training.json")
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validation_samples = load_video_samples("test_data/validation/validation.json", args.training_samples // 2)
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train_generator = VideoDataGenerator(training_samples, args.batch_size)
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val_generator = VideoDataGenerator(validation_samples, args.batch_size)
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# Load or initialize model
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if args.continue_training:
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model = tf.keras.models.load_model(args.continue_training)
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else:
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model = VideoCompressionModel()
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# Set optimizer and compile the model
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optimizer = tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
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model.compile(loss='mean_squared_error', optimizer=optimizer)
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# Define checkpoints and early stopping
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checkpoint_callback = ModelCheckpoint(
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filepath=os.path.join(MODEL_CHECKPOINT_DIR, "epoch-{epoch:02d}.tf"),
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save_weights_only=False,
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save_best_only=False,
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verbose=1,
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save_format="tf"
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)
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early_stop = EarlyStopping(monitor='val_loss', patience=EARLY_STOP, verbose=1, restore_best_weights=True)
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# Train the model
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LOGGER.info("Starting model training.")
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model.fit(
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train_generator,
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steps_per_epoch=len(train_generator),
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epochs=args.epochs,
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validation_data=val_generator,
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validation_steps=len(val_generator),
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callbacks=[early_stop, checkpoint_callback]
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)
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LOGGER.info("Model training completed.")
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save_model(model)
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if __name__ == "__main__":
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try:
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main()
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except Exception as e:
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LOGGER.error(f"Unexpected error during training: {e}")
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raise
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