updated
This commit is contained in:
parent
93ccce5ec1
commit
ed5eb91578
6 changed files with 181 additions and 171 deletions
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@ -1,5 +1,9 @@
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# DeepEncode.py
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import os
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
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import tensorflow as tf
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import numpy as np
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import cv2
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@ -33,12 +37,16 @@ def predict_frame(uncompressed_frame, model, crf_value, preset_speed_value):
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crf_array = np.array([crf_value])
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preset_speed_array = np.array([preset_speed_value])
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crf_array = np.expand_dims(np.array([crf_value]), axis=-1) # Shape: (1, 1)
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preset_speed_array = np.expand_dims(np.array([preset_speed_value]), axis=-1) # Shape: (1, 1)
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# Expand dimensions to include batch size
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uncompressed_frame = np.expand_dims(uncompressed_frame, 0)
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#display_frame = np.clip(cv2.cvtColor(uncompressed_frame[0], cv2.COLOR_BGR2RGB) * 255.0, 0, 255).astype(np.uint8)
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#cv2.imshow("uncomp", display_frame)
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#cv2.waitKey(10)
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#cv2.waitKey(0)
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compressed_frame = model.predict({
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"compressed_frame": uncompressed_frame,
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@ -1,3 +1,3 @@
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import log
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LOGGER = log.Logger(level="INFO", logfile="training.log", reset_logfile=True)
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LOGGER = log.Logger(level="DEBUG", logfile="training.log", reset_logfile=True)
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@ -1,73 +1,73 @@
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[
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{
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"video_file": "x264_crf-51_preset-ultrafast.mkv",
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"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"compressed_video_file": "x264_crf-51_preset-ultrafast.mkv",
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"original_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"crf": 51,
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"preset_speed": "ultrafast"
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},
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{
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"video_file": "x264_crf-16_preset-veryslow.mkv",
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"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"compressed_video_file": "x264_crf-16_preset-veryslow.mkv",
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"original_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"crf": 16,
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"preset_speed": "veryslow"
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},
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{
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"video_file": "x264_crf-18_preset-ultrafast.mkv",
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"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"compressed_video_file": "x264_crf-18_preset-ultrafast.mkv",
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"original_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"crf": 18,
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"preset_speed": "ultrafast"
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},
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{
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"video_file": "x264_crf-18_preset-veryslow.mkv",
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"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"compressed_video_file": "x264_crf-18_preset-veryslow.mkv",
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"original_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"crf": 18,
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"preset_speed": "veryslow"
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},
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{
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"video_file": "x264_crf-50_preset-veryslow.mkv",
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"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"compressed_video_file": "x264_crf-50_preset-veryslow.mkv",
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"original_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"crf": 50,
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"preset_speed": "veryslow"
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},
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{
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"video_file": "x264_crf-51_preset-fast.mkv",
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"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"compressed_video_file": "x264_crf-51_preset-fast.mkv",
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"original_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"crf": 51,
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"preset_speed": "fast"
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},
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{
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"video_file": "x264_crf-51_preset-faster.mkv",
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"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"compressed_video_file": "x264_crf-51_preset-faster.mkv",
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"original_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"crf": 51,
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"preset_speed": "faster"
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},
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{
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"video_file": "x264_crf-51_preset-medium.mkv",
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"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"compressed_video_file": "x264_crf-51_preset-medium.mkv",
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"original_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"crf": 51,
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"preset_speed": "medium"
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},
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{
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"video_file": "x264_crf-51_preset-slow.mkv",
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"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"compressed_video_file": "x264_crf-51_preset-slow.mkv",
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"original_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"crf": 51,
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"preset_speed": "slow"
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},
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{
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"video_file": "x264_crf-51_preset-slower.mkv",
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"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"compressed_video_file": "x264_crf-51_preset-slower.mkv",
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"original_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"crf": 51,
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"preset_speed": "slower"
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},
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{
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"video_file": "x264_crf-51_preset-superfast.mkv",
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"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"compressed_video_file": "x264_crf-51_preset-superfast.mkv",
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"original_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"crf": 51,
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"preset_speed": "superfast"
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},
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{
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"video_file": "x264_crf-51_preset-veryfast.mkv",
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"uncompressed_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"compressed_video_file": "x264_crf-51_preset-veryfast.mkv",
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"original_video_file": "../x264_crf-5_preset-veryslow.mkv",
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"crf": 51,
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"preset_speed": "veryfast"
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}
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@ -1,8 +1,7 @@
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[
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{
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"video_file": "Scene2_x264_crf-51_preset-veryslow.mkv",
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"uncompressed_video_file": "Scene2_x264_crf-5_preset-veryslow.mkv",
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"compressed_video_file": "Scene2_x264_crf-51_preset-veryslow.mkv",
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"original_video_file": "Scene2_x264_crf-5_preset-veryslow.mkv",
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"crf": 51,
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"preset_speed": "veryslow"
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}
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122
train_model.py
122
train_model.py
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@ -1,15 +1,14 @@
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# train_model.py
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import math
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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 video_compression_model import WIDTH, HEIGHT, 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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@ -17,13 +16,12 @@ 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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LEARNING_RATE = 0.01
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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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@ -40,92 +38,30 @@ def load_video_metadata(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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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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frames_per_video = math.ceil(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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compressed_video_file = video_details["compressed_video_file"]
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original_video_file = video_details["original_video_file"]
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crf = video_details['crf'] / 51
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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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# Store video details without loading frames
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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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"frames_per_video": frames_per_video,
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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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"compressed_video_file": os.path.join(os.path.dirname(list_path), compressed_video_file),
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"original_video_file": os.path.join(os.path.dirname(list_path), original_video_file)
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} for _ in range(frames_per_video))
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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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@ -138,6 +74,7 @@ def save_model(model):
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raise
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def main():
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global BATCH_SIZE, EPOCHS, TRAIN_SAMPLES, LEARNING_RATE, MODEL_SAVE_FILE
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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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@ -148,22 +85,29 @@ def main():
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args = parser.parse_args()
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BATCH_SIZE = args.batch_size
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EPOCHS = args.epochs
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TRAIN_SAMPLES = args.training_samples
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LEARNING_RATE = args.learning_rate
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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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LOGGER.info(f"Batch size: {BATCH_SIZE}")
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LOGGER.info(f"Epochs: {EPOCHS}")
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LOGGER.info(f"Training samples: {TRAIN_SAMPLES}")
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LOGGER.info(f"Learning rate: {LEARNING_RATE}")
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LOGGER.info(f"Continue training from: {MODEL_SAVE_FILE}")
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LOGGER.debug(f"Max video resolution: {WIDTH}x{HEIGHT}")
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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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training_samples = load_video_samples("test_data/training/training.json", TRAIN_SAMPLES)
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validation_samples = load_video_samples("test_data/validation/validation.json", math.ceil(TRAIN_SAMPLES / 10))
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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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train_generator = VideoDataGenerator(training_samples, BATCH_SIZE)
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val_generator = VideoDataGenerator(validation_samples, BATCH_SIZE)
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# Load or initialize model
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if args.continue_training:
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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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optimizer = tf.keras.optimizers.Adam(learning_rate=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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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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epochs=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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# video_compression_model.py
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import cv2
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import numpy as np
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import tensorflow as tf
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@ -8,6 +9,28 @@ from global_train import LOGGER
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PRESET_SPEED_CATEGORIES = ["ultrafast", "superfast", "veryfast", "faster", "fast", "medium", "slow", "slower", "veryslow"]
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NUM_PRESET_SPEEDS = len(PRESET_SPEED_CATEGORIES)
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NUM_CHANNELS = 3
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WIDTH = 638
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HEIGHT = 360
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#from tensorflow.keras.mixed_precision import Policy
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#policy = Policy('mixed_float16')
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#tf.keras.mixed_precision.set_global_policy(policy)
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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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class VideoDataGenerator(tf.keras.utils.Sequence):
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def __init__(self, video_details_list, batch_size):
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@ -19,28 +42,59 @@ class VideoDataGenerator(tf.keras.utils.Sequence):
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return int(np.ceil(len(self.video_details_list) / float(self.batch_size)))
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def __getitem__(self, idx):
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try:
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start_idx = idx * self.batch_size
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end_idx = (idx + 1) * self.batch_size
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batch_data = self.video_details_list[start_idx:end_idx]
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x1 = np.array([item["frame"] for item in batch_data])
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x2 = np.array([item["compressed_frame"] for item in batch_data])
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x3 = np.array([item["crf"] for item in batch_data])
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x4 = np.array([item["preset_speed"] for item in batch_data])
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# Determine the number of videos and frames per video
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num_videos = len(batch_data)
|
||||
frames_per_video = batch_data[0]['frames_per_video'] # Assuming all videos have the same number of frames
|
||||
|
||||
# Pre-allocate arrays for the batch data
|
||||
x1 = np.empty((num_videos * frames_per_video, HEIGHT, WIDTH, NUM_CHANNELS))
|
||||
x2 = np.empty_like(x1)
|
||||
x3 = np.empty((num_videos * frames_per_video, 1))
|
||||
x4 = np.empty_like(x3)
|
||||
|
||||
# Iterate over the videos and frames, filling the pre-allocated arrays
|
||||
for i, item in enumerate(batch_data):
|
||||
compressed_video_file = item["compressed_video_file"]
|
||||
original_video_file = item["original_video_file"]
|
||||
crf = item["crf"]
|
||||
preset_speed = item["preset_speed"]
|
||||
|
||||
cap_compressed = cv2.VideoCapture(compressed_video_file)
|
||||
cap_original = cv2.VideoCapture(original_video_file)
|
||||
for j in range(frames_per_video):
|
||||
compressed_ret, compressed_frame = cap_compressed.read()
|
||||
original_ret, original_frame = cap_original.read()
|
||||
if not compressed_ret or not original_ret:
|
||||
continue
|
||||
|
||||
# Check frame dimensions and resize if necessary
|
||||
if original_frame.shape[:2] != (WIDTH, HEIGHT):
|
||||
LOGGER.info(f"Resizing video: {original_video_file}")
|
||||
original_frame = cv2.resize(original_frame, (WIDTH, HEIGHT), interpolation=cv2.INTER_AREA)
|
||||
if compressed_frame.shape[:2] != (WIDTH, HEIGHT):
|
||||
LOGGER.info(f"Resizing video: {compressed_video_file}")
|
||||
compressed_frame = cv2.resize(compressed_frame, (WIDTH, HEIGHT), interpolation=cv2.INTER_AREA)
|
||||
|
||||
original_frame = cv2.cvtColor(original_frame, cv2.COLOR_BGR2RGB)
|
||||
compressed_frame = cv2.cvtColor(compressed_frame, cv2.COLOR_BGR2RGB)
|
||||
|
||||
# Store the processed frames and metadata directly in the pre-allocated arrays
|
||||
x1[i * frames_per_video + j] = normalize(original_frame)
|
||||
x2[i * frames_per_video + j] = normalize(compressed_frame)
|
||||
x3[i * frames_per_video + j] = crf
|
||||
x4[i * frames_per_video + j] = preset_speed
|
||||
|
||||
cap_original.release()
|
||||
cap_compressed.release()
|
||||
|
||||
y = x2
|
||||
|
||||
inputs = {"uncompressed_frame": x1, "compressed_frame": x2, "crf": x3, "preset_speed": x4}
|
||||
return inputs, y
|
||||
|
||||
except IndexError:
|
||||
LOGGER.error(f"Index {idx} out of bounds in VideoDataGenerator.")
|
||||
raise
|
||||
except Exception as e:
|
||||
LOGGER.error(f"Unexpected error in VideoDataGenerator: {e}")
|
||||
raise
|
||||
|
||||
|
||||
class VideoCompressionModel(tf.keras.Model):
|
||||
|
|
@ -79,6 +133,42 @@ class VideoCompressionModel(tf.keras.Model):
|
|||
tf.keras.layers.Conv2D(NUM_CHANNELS, (3, 3), activation='sigmoid', padding='same') # Output layer for video frames
|
||||
])
|
||||
|
||||
def call(self, inputs):
|
||||
LOGGER.trace("Calling VideoCompressionModel.")
|
||||
uncompressed_frame, compressed_frame, crf, preset_speed = inputs['uncompressed_frame'], inputs['compressed_frame'], inputs['crf'], inputs['preset_speed']
|
||||
|
||||
# Convert frames to float32
|
||||
uncompressed_frame = tf.cast(uncompressed_frame, tf.float16)
|
||||
compressed_frame = tf.cast(compressed_frame, tf.float16)
|
||||
|
||||
# Embedding for preset speed
|
||||
preset_speed_embedded = self.embedding(preset_speed)
|
||||
preset_speed_embedded = tf.keras.layers.Flatten()(preset_speed_embedded)
|
||||
|
||||
# Reshaping CRF to match the shape of preset_speed_embedded
|
||||
crf_expanded = tf.keras.layers.Flatten()(tf.repeat(crf, 16, axis=-1))
|
||||
|
||||
|
||||
# Concatenating the CRF and preset speed information
|
||||
integrated_info = tf.keras.layers.Concatenate(axis=-1)([crf_expanded, preset_speed_embedded])
|
||||
integrated_info = self.fc(integrated_info)
|
||||
|
||||
# Integrate the CRF and preset speed information into the frames as additional channels (features)
|
||||
_, height, width, _ = uncompressed_frame.shape
|
||||
current_shape = tf.shape(inputs["uncompressed_frame"])
|
||||
|
||||
height = current_shape[1]
|
||||
width = current_shape[2]
|
||||
integrated_info_repeated = tf.tile(tf.reshape(integrated_info, [-1, 1, 1, 32]), [1, height, width, 1])
|
||||
|
||||
# Merge uncompressed and compressed frames
|
||||
frames_merged = tf.keras.layers.Concatenate(axis=-1)([uncompressed_frame, compressed_frame, integrated_info_repeated])
|
||||
|
||||
compressed_representation = self.encoder(frames_merged)
|
||||
reconstructed_frame = self.decoder(compressed_representation)
|
||||
|
||||
return reconstructed_frame
|
||||
|
||||
def model_summary(self):
|
||||
try:
|
||||
LOGGER.info("Generating model summary.")
|
||||
|
|
@ -90,34 +180,3 @@ class VideoCompressionModel(tf.keras.Model):
|
|||
except Exception as e:
|
||||
LOGGER.error(f"Unexpected error during model summary generation: {e}")
|
||||
raise
|
||||
|
||||
def call(self, inputs):
|
||||
LOGGER.trace("Calling VideoCompressionModel.")
|
||||
uncompressed_frame, compressed_frame, crf, preset_speed = inputs['uncompressed_frame'], inputs['compressed_frame'], inputs['crf'], inputs['preset_speed']
|
||||
|
||||
# Convert frames to float32
|
||||
uncompressed_frame = tf.cast(uncompressed_frame, tf.float32)
|
||||
compressed_frame = tf.cast(compressed_frame, tf.float32)
|
||||
|
||||
# Integrate CRF and preset speed into the network
|
||||
preset_speed_embedded = self.embedding(preset_speed)
|
||||
crf_expanded = tf.expand_dims(crf, -1)
|
||||
integrated_info = tf.keras.layers.Concatenate(axis=-1)([crf_expanded, tf.keras.layers.Flatten()(preset_speed_embedded)])
|
||||
integrated_info = self.fc(integrated_info)
|
||||
|
||||
# Integrate the CRF and preset speed information into the frames as additional channels (features)
|
||||
_, height, width, _ = uncompressed_frame.shape
|
||||
current_shape = tf.shape(inputs["uncompressed_frame"])
|
||||
|
||||
height = current_shape[1]
|
||||
width = current_shape[2]
|
||||
integrated_info_repeated = tf.tile(tf.reshape(integrated_info, [-1, 1, 1, 32]), [1, height, width, 1])
|
||||
|
||||
|
||||
# Merge uncompressed and compressed frames
|
||||
frames_merged = tf.keras.layers.Concatenate(axis=-1)([uncompressed_frame, compressed_frame, integrated_info_repeated])
|
||||
|
||||
compressed_representation = self.encoder(frames_merged)
|
||||
reconstructed_frame = self.decoder(compressed_representation)
|
||||
|
||||
return reconstructed_frame
|
||||
|
|
|
|||
Reference in a new issue