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| import tensorflow as tf | |
| import tensorflow.keras.backend as K | |
| from tensorflow.keras.layers import Input, Conv2D, Add, ZeroPadding2D, UpSampling2D, Concatenate, MaxPooling2D | |
| from tensorflow.keras.layers import LeakyReLU, BatchNormalization | |
| from tensorflow.keras.models import Sequential, Model | |
| from tensorflow.keras.regularizers import l2 | |
| '''====================================================================================================''' | |
| '''BLOCKS''' | |
| '''Convolutional Block''' | |
| def yolo_ConvBlock (input_tensor, num_filters, filter_size, strides = (1,1) ): | |
| padding = 'valid' if strides == (2,2) else 'same' | |
| ### Layers | |
| x = Conv2D( num_filters, filter_size, strides, padding, use_bias=False, kernel_regularizer=l2(5e-4) ) (input_tensor) | |
| x = BatchNormalization() (x) | |
| x = LeakyReLU(alpha=0.1) (x) | |
| return x | |
| '''Residual Block''' | |
| def yolo_ResidualBlocks (input_tensor, num_filters, num_blocks ): | |
| ### Layers | |
| x = ZeroPadding2D( ((1,0),(1,0)) ) (input_tensor) # left & top padding | |
| x = yolo_ConvBlock ( x, num_filters, filter_size=(3,3), strides = (2,2) ) | |
| for _ in range( num_blocks ): | |
| y = yolo_ConvBlock ( x, num_filters//2, filter_size=(1,1), strides = (1,1) ) | |
| y = yolo_ConvBlock ( y, num_filters , filter_size=(3,3), strides = (1,1) ) | |
| x = Add() ([x, y]) | |
| return x | |
| '''Output Block''' | |
| def yolo_OutputBlock (x, num_filters, out_filters ): | |
| ### Layers | |
| x = yolo_ConvBlock ( x, 1*num_filters, filter_size=(1,1), strides = (1,1) ) | |
| x = yolo_ConvBlock ( x, 2*num_filters, filter_size=(3,3), strides = (1,1) ) | |
| x = yolo_ConvBlock ( x, 1*num_filters, filter_size=(1,1), strides = (1,1) ) | |
| x = yolo_ConvBlock ( x, 2*num_filters, filter_size=(3,3), strides = (1,1) ) | |
| x = yolo_ConvBlock ( x, 1*num_filters, filter_size=(1,1), strides = (1,1) ) | |
| y = yolo_ConvBlock ( x, 2*num_filters, filter_size=(3,3), strides = (1,1) ) | |
| y = Conv2D ( filters=out_filters, kernel_size=(1,1), strides=(1,1), | |
| padding='same', use_bias=True, kernel_regularizer=l2(5e-4) ) (y) | |
| return x, y | |
| '''====================================================================================================''' | |
| '''COMPLETE MODEL''' | |
| def yolo_body (input_tensor, num_out_filters): | |
| ''' | |
| Input: | |
| input_tensor = Input( shape=( *input_shape, 3 ) ) | |
| num_out_filter = ( num_anchors // 3 ) * ( 5 + num_classes ) | |
| Output: | |
| complete YOLO-v3 model | |
| ''' | |
| # 1st Conv block | |
| x = yolo_ConvBlock( input_tensor, num_filters=32, filter_size=(3,3), strides=(1,1) ) | |
| # 5 Resblocks | |
| x = yolo_ResidualBlocks ( x, num_filters= 64, num_blocks=1 ) | |
| x = yolo_ResidualBlocks ( x, num_filters= 128, num_blocks=2 ) | |
| x = yolo_ResidualBlocks ( x, num_filters= 256, num_blocks=8 ) | |
| x = yolo_ResidualBlocks ( x, num_filters= 512, num_blocks=8 ) | |
| x = yolo_ResidualBlocks ( x, num_filters=1024, num_blocks=4 ) | |
| darknet = Model( input_tensor, x ) # will use it just in a moment | |
| # 1st output block | |
| x, y1 = yolo_OutputBlock( x, num_filters= 512, out_filters=num_out_filters ) | |
| # 2nd output block | |
| x = yolo_ConvBlock( x, num_filters=256, filter_size=(1,1), strides=(1,1) ) | |
| x = UpSampling2D(2) (x) | |
| x = Concatenate() ( [x, darknet.layers[152].output] ) | |
| x, y2 = yolo_OutputBlock( x, num_filters= 256, out_filters=num_out_filters ) | |
| # 3rd output block | |
| x = yolo_ConvBlock( x, num_filters=128, filter_size=(1,1), strides=(1,1) ) | |
| x = UpSampling2D(2) (x) | |
| x = Concatenate() ( [x, darknet.layers[92].output] ) | |
| x, y3 = yolo_OutputBlock( x, num_filters= 128, out_filters=num_out_filters ) | |
| # Final model | |
| model = Model( input_tensor, [y1, y2, y3] ) | |
| return model | |