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Jordon Brooks 2023-08-23 21:01:01 +01:00
parent db43239b3d
commit 98df94b180
3 changed files with 29 additions and 11 deletions
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21
video_compression_model.py
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@ -7,7 +7,7 @@ import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
from featureExtraction import preprocess_frame, scale_crf, scale_speed_preset
from globalVars import HEIGHT, LOGGER, NUM_COLOUR_CHANNELS, NUM_PRESET_SPEEDS, PRESET_SPEED_CATEGORIES, WIDTH
from globalVars import LOGGER, NUM_COLOUR_CHANNELS, PRESET_SPEED_CATEGORIES
#from tensorflow.keras.mixed_precision import Policy
@ -107,13 +107,20 @@ class VideoCompressionModel(tf.keras.Model):
layers.Conv2DTranspose(64, (3, 3), dilation_rate=2, padding='same'), # Using Dilated Convolution
#layers.BatchNormalization(),
layers.LeakyReLU(),
# Use Sub-Pixel Convolutional Layer
layers.Conv2DTranspose(NUM_COLOUR_CHANNELS * 16, (3, 3), padding='same'), # 16 times the number of color channels
layers.Lambda(lambda x: tf.nn.depth_to_space(x, block_size=4)) # Sub-Pixel Convolutional Layer with block_size=4
# First Sub-Pixel Convolutional Layer
layers.Conv2DTranspose(NUM_COLOUR_CHANNELS * 4, (3, 3), padding='same'), # 4 times the number of color channels for first upscaling by 2
layers.Lambda(lambda x: tf.nn.depth_to_space(x, block_size=2)), # Sub-Pixel Convolutional Layer with block_size=2
# Second Sub-Pixel Convolutional Layer
layers.Conv2DTranspose(NUM_COLOUR_CHANNELS * 4, (3, 3), padding='same'), # 4 times the number of color channels for second upscaling by 2
layers.Lambda(lambda x: tf.nn.depth_to_space(x, block_size=2)), # Sub-Pixel Convolutional Layer with block_size=2
layers.Activation('sigmoid')
])
def call(self, inputs):
#print(f"Input: {inputs.shape}")
encoded = self.encoder(inputs)
return self.decoder(encoded)
#print(f"encoded: {encoded.shape}")
decoded = self.decoder(encoded)
#print(f"decoded: {decoded.shape}")
return decoded