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4 changed files with 126 additions and 67 deletions
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@ -7,28 +7,23 @@ import numpy as np
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import tensorflow as tf
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from tensorflow.keras import layers
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from featureExtraction import preprocess_frame, scale_crf, scale_speed_preset
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from globalVars import LOGGER, NUM_COLOUR_CHANNELS, PRESET_SPEED_CATEGORIES
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from globalVars import DATATYPE, LOGGER, NUM_COLOUR_CHANNELS, PRESET_SPEED_CATEGORIES
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if DATATYPE == tf.float16:
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from tensorflow.keras.mixed_precision import Policy
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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 combine_batch(frame, crf, speed, include_controls=True, resize=True):
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processed_frame = preprocess_frame(frame, resize)
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height, width, _ = processed_frame.shape
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policy = Policy('mixed_float16')
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tf.keras.mixed_precision.set_global_policy(policy)
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combined = [processed_frame]
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if include_controls:
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crf_array = np.full((height, width, 1), crf)
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speed_array = np.full((height, width, 1), speed)
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combined.extend([crf_array, speed_array])
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return np.concatenate(combined, axis=-1)
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def is_black(frame, threshold=10):
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"""Check if a frame is mostly black."""
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return np.mean(frame) < threshold
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def combine_batch(frame, resize=True):
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return preprocess_frame(frame, resize)
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def frame_generator(videos, max_frames=None):
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base_dir = "test_data/validation/"
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@ -44,13 +39,17 @@ def frame_generator(videos, max_frames=None):
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if not ret_compressed or not ret_uncompressed:
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break
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# Skip black frames
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if is_black(compressed_frame) or is_black(uncompressed_frame):
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continue
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CRF = scale_crf(video["crf"])
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SPEED = scale_speed_preset(PRESET_SPEED_CATEGORIES.index(video["preset_speed"]))
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validation = combine_batch(compressed_frame, CRF, SPEED, include_controls=False)
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training = combine_batch(uncompressed_frame, 10, scale_speed_preset(PRESET_SPEED_CATEGORIES.index("veryslow")))
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validation_image = combine_batch(compressed_frame)
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training_image = combine_batch(uncompressed_frame)
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yield training, validation
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yield ({'image': training_image, 'CRF': CRF, 'Speed': SPEED}, validation_image)
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frame_count += 1
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if max_frames is not None and frame_count >= max_frames:
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@ -60,62 +59,120 @@ def frame_generator(videos, max_frames=None):
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cap_uncompressed.release()
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def create_dataset(videos, batch_size, max_frames=None):
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# Determine the output signature by processing a single video to obtain its shape
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video_generator_instance = frame_generator(videos, max_frames)
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sample_uncompressed, sample_compressed = next(video_generator_instance)
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output_signature = (
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tf.TensorSpec(shape=tf.shape(sample_uncompressed), dtype=tf.float32),
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tf.TensorSpec(shape=tf.shape(sample_compressed), dtype=tf.float32)
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{
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'image': tf.TensorSpec(shape=tf.shape(sample_uncompressed['image']), dtype=DATATYPE),
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'CRF': tf.TensorSpec(shape=(), dtype=DATATYPE),
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'Speed': tf.TensorSpec(shape=(), dtype=DATATYPE),
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},
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tf.TensorSpec(shape=tf.shape(sample_compressed), dtype=DATATYPE)
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)
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dataset = tf.data.Dataset.from_generator(
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lambda: frame_generator(videos, max_frames), # Include max_frames argument through lambda
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lambda: frame_generator(videos, max_frames),
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output_signature=output_signature
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)
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dataset = dataset.batch(batch_size).shuffle(20).prefetch(1) #.prefetch(tf.data.experimental.AUTOTUNE)
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dataset = dataset.batch(batch_size).shuffle(20).prefetch(1)
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return dataset
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class SeparableTranspose2D(layers.Layer):
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def __init__(self, filters, kernel_size, strides=(1, 1), padding='same', **kwargs):
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super(SeparableTranspose2D, self).__init__(**kwargs)
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self.filters = filters
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self.kernel_size = kernel_size
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self.strides = strides
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self.padding = padding
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# Use UpSampling2D for resizing
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self.upsample = layers.UpSampling2D(size=strides)
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# Depthwise convolution
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self.depthwise_conv = layers.DepthwiseConv2D(kernel_size=kernel_size, padding=padding)
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# Pointwise convolution
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self.pointwise_conv = layers.Conv2D(filters, kernel_size=(1, 1), padding=padding)
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def call(self, inputs):
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x = self.upsample(inputs)
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x = self.depthwise_conv(x)
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x = self.pointwise_conv(x)
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return x
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class VideoCompressionModel(tf.keras.Model):
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def __init__(self):
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super(VideoCompressionModel, self).__init__()
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input_shape = (None, None, NUM_COLOUR_CHANNELS + 2)
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input_shape = (None, None, NUM_COLOUR_CHANNELS)
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# Encoder part of the model
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self.encoder = tf.keras.Sequential([
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layers.InputLayer(input_shape=input_shape),
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layers.Conv2D(32, (3, 3), padding='same'),
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layers.SeparableConv2D(64, (3, 3), padding='same'),
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layers.LeakyReLU(),
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layers.MaxPooling2D((2, 2), padding='same'),
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layers.Dropout(0.4),
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layers.SeparableConv2D(16, (3, 3), padding='same'),
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layers.SeparableConv2D(128, (3, 3), padding='same'),
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layers.LeakyReLU(),
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layers.MaxPooling2D((2, 2), padding='same'),
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layers.Dropout(0.4),
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])
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# Decoder part of the model using Transposed Convolutions for upsampling
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# Fully connected layers for processing CRF and Speed
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self.dense_crf_speed = tf.keras.Sequential([
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layers.Dense(64, activation='relu'),
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layers.Dense(128, activation='relu'),
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])
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# Decoder part of the model
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self.decoder = tf.keras.Sequential([
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layers.Conv2DTranspose(16, (3, 3), padding='same'),
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SeparableTranspose2D(128, (3, 3), padding='same'),
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layers.LeakyReLU(),
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layers.Dropout(0.4),
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layers.Conv2DTranspose(32, (3, 3), strides=(2, 2), padding='same'),
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SeparableTranspose2D(64, (3, 3), padding='same'),
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layers.LeakyReLU(),
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layers.Dropout(0.4),
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layers.UpSampling2D((2, 2)),
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layers.UpSampling2D((2, 2)),
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layers.Conv2D(NUM_COLOUR_CHANNELS, (3, 3), padding='same', activation='sigmoid')
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])
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def call(self, inputs):
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#print(f"Input: {inputs.shape}")
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encoded = self.encoder(inputs)
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#print(f"encoded: {encoded.shape}")
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decoded = self.decoder(encoded)
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#print(f"decoded: {decoded.shape}")
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# Extract the image, CRF, and Speed values from the inputs dictionary
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image = inputs['image']
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crf = inputs['CRF']
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speed = inputs['Speed']
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# CRF and Speed are 1D tensors with shape [batch_size]
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# Concatenate them to create a [batch_size, 2] tensor
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crf_speed_vector = tf.concat([tf.expand_dims(crf, -1), tf.expand_dims(speed, -1)], axis=-1)
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# Process the combined crf_speed_vector through your dense layers
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# This will produce a tensor with shape [batch_size, 128]
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crf_speed_features = self.dense_crf_speed(crf_speed_vector)
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# Reshape the tensor to match spatial dimensions
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# New shape: [batch_size, 1, 1, 128]
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crf_speed_features = tf.reshape(crf_speed_features, [-1, 1, 1, 128])
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# Tile the tensor to match spatial dimensions of encoded tensor
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# Tiled shape: [batch_size, 90, 160, 128]
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crf_speed_features = tf.tile(crf_speed_features, [1, 90, 160, 1])
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# Pass the image through the encoder
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encoded = self.encoder(image)
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# Concatenate the encoded tensor with the crf_speed_features tensor
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combined_features = tf.concat([encoded, crf_speed_features], axis=-1)
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# Pass the combined features through the decoder
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decoded = self.decoder(combined_features)
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return decoded
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