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optimisation

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Jordon Brooks 2023-07-25 01:42:16 +01:00
parent d0f0b21cb5
commit b97293d7ca
3 changed files with 112 additions and 97 deletions
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19
video_compression_model.py
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@ -3,30 +3,37 @@ import tensorflow as tf
PRESET_SPEED_CATEGORIES = ["ultrafast", "superfast", "veryfast", "faster", "fast", "medium", "slow", "slower", "veryslow"]
NUM_PRESET_SPEEDS = len(PRESET_SPEED_CATEGORIES)
NUM_FRAMES = 5 # Number of consecutive frames in a sequence
NUM_CHANNELS = 3 # Number of color channels in the video frames (RGB images have 3 channels)
#policy = tf.keras.mixed_precision.Policy('mixed_float16')
#tf.keras.mixed_precision.set_global_policy(policy)
class VideoCompressionModel(tf.keras.Model):
def __init__(self, NUM_CHANNELS=3, NUM_FRAMES=5):
def __init__(self, NUM_CHANNELS=3, NUM_FRAMES=5, regularization_factor=1e-4):
super(VideoCompressionModel, self).__init__()
self.NUM_CHANNELS = NUM_CHANNELS
self.NUM_FRAMES = NUM_FRAMES
# Regularization
self.regularizer = tf.keras.regularizers.l2(regularization_factor)
# Embedding layer for preset_speed
self.preset_embedding = tf.keras.layers.Embedding(NUM_PRESET_SPEEDS, 16)
self.preset_embedding = tf.keras.layers.Embedding(NUM_PRESET_SPEEDS, 16, embeddings_regularizer=self.regularizer)
# Encoder layers
self.encoder = tf.keras.Sequential([
tf.keras.layers.Conv3D(32, (3, 3, 3), activation='relu', padding='same', input_shape=(None, None, None, NUM_CHANNELS + 1 + 16)), # Notice the adjusted channel number
tf.keras.layers.Conv3D(32, (3, 3, 3), activation='relu', padding='same', input_shape=(None, None, None, NUM_CHANNELS + 1 + 16), kernel_regularizer=self.regularizer),
tf.keras.layers.MaxPooling3D((2, 2, 2)),
# Add more encoder layers as needed
])
# Decoder layers
self.decoder = tf.keras.Sequential([
tf.keras.layers.Conv3DTranspose(32, (3, 3, 3), activation='relu', padding='same'),
tf.keras.layers.Conv3DTranspose(32, (3, 3, 3), activation='relu', padding='same', kernel_regularizer=self.regularizer),
tf.keras.layers.UpSampling3D((2, 2, 2)),
# Add more decoder layers as needed
tf.keras.layers.Conv3D(NUM_CHANNELS, (3, 3, 3), activation='sigmoid', padding='same') # Output layer for video frames
tf.keras.layers.Conv3D(NUM_CHANNELS, (3, 3, 3), activation='sigmoid', padding='same', kernel_regularizer=self.regularizer) # Output layer for video frames
])
def call(self, inputs):