Add : Project Added

Author: Shismohammad

Case Study/543470.jpg

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+License_Plate

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+
+import tensorflow as tf
+import core.common as common
+
+def darknet53(input_data):
+
+    input_data = common.convolutional(input_data, (3, 3,  3,  32))
+    input_data = common.convolutional(input_data, (3, 3, 32,  64), downsample=True)
+
+    for i in range(1):
+        input_data = common.residual_block(input_data,  64,  32, 64)
+
+    input_data = common.convolutional(input_data, (3, 3,  64, 128), downsample=True)
+
+    for i in range(2):
+        input_data = common.residual_block(input_data, 128,  64, 128)
+
+    input_data = common.convolutional(input_data, (3, 3, 128, 256), downsample=True)
+
+    for i in range(8):
+        input_data = common.residual_block(input_data, 256, 128, 256)
+
+    route_1 = input_data
+    input_data = common.convolutional(input_data, (3, 3, 256, 512), downsample=True)
+
+    for i in range(8):
+        input_data = common.residual_block(input_data, 512, 256, 512)
+
+    route_2 = input_data
+    input_data = common.convolutional(input_data, (3, 3, 512, 1024), downsample=True)
+
+    for i in range(4):
+        input_data = common.residual_block(input_data, 1024, 512, 1024)
+
+    return route_1, route_2, input_data
+
+def cspdarknet53(input_data):
+
+    input_data = common.convolutional(input_data, (3, 3,  3,  32), activate_type="mish")
+    input_data = common.convolutional(input_data, (3, 3, 32,  64), downsample=True, activate_type="mish")
+
+    route = input_data
+    route = common.convolutional(route, (1, 1, 64, 64), activate_type="mish")
+    input_data = common.convolutional(input_data, (1, 1, 64, 64), activate_type="mish")
+    for i in range(1):
+        input_data = common.residual_block(input_data,  64,  32, 64, activate_type="mish")
+    input_data = common.convolutional(input_data, (1, 1, 64, 64), activate_type="mish")
+
+    input_data = tf.concat([input_data, route], axis=-1)
+    input_data = common.convolutional(input_data, (1, 1, 128, 64), activate_type="mish")
+    input_data = common.convolutional(input_data, (3, 3, 64, 128), downsample=True, activate_type="mish")
+    route = input_data
+    route = common.convolutional(route, (1, 1, 128, 64), activate_type="mish")
+    input_data = common.convolutional(input_data, (1, 1, 128, 64), activate_type="mish")
+    for i in range(2):
+        input_data = common.residual_block(input_data, 64,  64, 64, activate_type="mish")
+    input_data = common.convolutional(input_data, (1, 1, 64, 64), activate_type="mish")
+    input_data = tf.concat([input_data, route], axis=-1)
+
+    input_data = common.convolutional(input_data, (1, 1, 128, 128), activate_type="mish")
+    input_data = common.convolutional(input_data, (3, 3, 128, 256), downsample=True, activate_type="mish")
+    route = input_data
+    route = common.convolutional(route, (1, 1, 256, 128), activate_type="mish")
+    input_data = common.convolutional(input_data, (1, 1, 256, 128), activate_type="mish")
+    for i in range(8):
+        input_data = common.residual_block(input_data, 128, 128, 128, activate_type="mish")
+    input_data = common.convolutional(input_data, (1, 1, 128, 128), activate_type="mish")
+    input_data = tf.concat([input_data, route], axis=-1)
+
+    input_data = common.convolutional(input_data, (1, 1, 256, 256), activate_type="mish")
+    route_1 = input_data
+    input_data = common.convolutional(input_data, (3, 3, 256, 512), downsample=True, activate_type="mish")
+    route = input_data
+    route = common.convolutional(route, (1, 1, 512, 256), activate_type="mish")
+    input_data = common.convolutional(input_data, (1, 1, 512, 256), activate_type="mish")
+    for i in range(8):
+        input_data = common.residual_block(input_data, 256, 256, 256, activate_type="mish")
+    input_data = common.convolutional(input_data, (1, 1, 256, 256), activate_type="mish")
+    input_data = tf.concat([input_data, route], axis=-1)
+
+    input_data = common.convolutional(input_data, (1, 1, 512, 512), activate_type="mish")
+    route_2 = input_data
+    input_data = common.convolutional(input_data, (3, 3, 512, 1024), downsample=True, activate_type="mish")
+    route = input_data
+    route = common.convolutional(route, (1, 1, 1024, 512), activate_type="mish")
+    input_data = common.convolutional(input_data, (1, 1, 1024, 512), activate_type="mish")
+    for i in range(4):
+        input_data = common.residual_block(input_data, 512, 512, 512, activate_type="mish")
+    input_data = common.convolutional(input_data, (1, 1, 512, 512), activate_type="mish")
+    input_data = tf.concat([input_data, route], axis=-1)
+
+    input_data = common.convolutional(input_data, (1, 1, 1024, 1024), activate_type="mish")
+    input_data = common.convolutional(input_data, (1, 1, 1024, 512))
+    input_data = common.convolutional(input_data, (3, 3, 512, 1024))
+    input_data = common.convolutional(input_data, (1, 1, 1024, 512))
+
+    input_data = tf.concat([tf.nn.max_pool(input_data, ksize=13, padding='SAME', strides=1), tf.nn.max_pool(input_data, ksize=9, padding='SAME', strides=1)
+                            , tf.nn.max_pool(input_data, ksize=5, padding='SAME', strides=1), input_data], axis=-1)
+    input_data = common.convolutional(input_data, (1, 1, 2048, 512))
+    input_data = common.convolutional(input_data, (3, 3, 512, 1024))
+    input_data = common.convolutional(input_data, (1, 1, 1024, 512))
+
+    return route_1, route_2, input_data
+
+def cspdarknet53_tiny(input_data):
+    input_data = common.convolutional(input_data, (3, 3, 3, 32), downsample=True)
+    input_data = common.convolutional(input_data, (3, 3, 32, 64), downsample=True)
+    input_data = common.convolutional(input_data, (3, 3, 64, 64))
+
+    route = input_data
+    input_data = common.route_group(input_data, 2, 1)
+    input_data = common.convolutional(input_data, (3, 3, 32, 32))
+    route_1 = input_data
+    input_data = common.convolutional(input_data, (3, 3, 32, 32))
+    input_data = tf.concat([input_data, route_1], axis=-1)
+    input_data = common.convolutional(input_data, (1, 1, 32, 64))
+    input_data = tf.concat([route, input_data], axis=-1)
+    input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)
+
+    input_data = common.convolutional(input_data, (3, 3, 64, 128))
+    route = input_data
+    input_data = common.route_group(input_data, 2, 1)
+    input_data = common.convolutional(input_data, (3, 3, 64, 64))
+    route_1 = input_data
+    input_data = common.convolutional(input_data, (3, 3, 64, 64))
+    input_data = tf.concat([input_data, route_1], axis=-1)
+    input_data = common.convolutional(input_data, (1, 1, 64, 128))
+    input_data = tf.concat([route, input_data], axis=-1)
+    input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)
+
+    input_data = common.convolutional(input_data, (3, 3, 128, 256))
+    route = input_data
+    input_data = common.route_group(input_data, 2, 1)
+    input_data = common.convolutional(input_data, (3, 3, 128, 128))
+    route_1 = input_data
+    input_data = common.convolutional(input_data, (3, 3, 128, 128))
+    input_data = tf.concat([input_data, route_1], axis=-1)
+    input_data = common.convolutional(input_data, (1, 1, 128, 256))
+    route_1 = input_data
+    input_data = tf.concat([route, input_data], axis=-1)
+    input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)
+
+    input_data = common.convolutional(input_data, (3, 3, 512, 512))
+
+    return route_1, input_data
+
+def darknet53_tiny(input_data):
+    input_data = common.convolutional(input_data, (3, 3, 3, 16))
+    input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)
+    input_data = common.convolutional(input_data, (3, 3, 16, 32))
+    input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)
+    input_data = common.convolutional(input_data, (3, 3, 32, 64))
+    input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)
+    input_data = common.convolutional(input_data, (3, 3, 64, 128))
+    input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)
+    input_data = common.convolutional(input_data, (3, 3, 128, 256))
+    route_1 = input_data
+    input_data = tf.keras.layers.MaxPool2D(2, 2, 'same')(input_data)
+    input_data = common.convolutional(input_data, (3, 3, 256, 512))
+    input_data = tf.keras.layers.MaxPool2D(2, 1, 'same')(input_data)
+    input_data = common.convolutional(input_data, (3, 3, 512, 1024))
+
+    return route_1, input_data
+
+

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+#! /usr/bin/env python
+# coding=utf-8
+
+import tensorflow as tf
+
+class BatchNormalization(tf.keras.layers.BatchNormalization):
+    """
+    "Frozen state" and "inference mode" are two separate concepts.
+    `layer.trainable = False` is to freeze the layer, so the layer will use
+    stored moving `var` and `mean` in the "inference mode", and both `gama`
+    and `beta` will not be updated !
+    """
+    def call(self, x, training=False):
+        if not training:
+            training = tf.constant(False)
+        training = tf.logical_and(training, self.trainable)
+        return super().call(x, training)
+
+def convolutional(input_layer, filters_shape, downsample=False, activate=True, bn=True, activate_type='leaky'):
+    if downsample:
+        input_layer = tf.keras.layers.ZeroPadding2D(((1, 0), (1, 0)))(input_layer)
+        padding = 'valid'
+        strides = 2
+    else:
+        strides = 1
+        padding = 'same'
+
+    conv = tf.keras.layers.Conv2D(filters=filters_shape[-1], kernel_size = filters_shape[0], strides=strides, padding=padding,
+                                  use_bias=not bn, kernel_regularizer=tf.keras.regularizers.l2(0.0005),
+                                  kernel_initializer=tf.random_normal_initializer(stddev=0.01),
+                                  bias_initializer=tf.constant_initializer(0.))(input_layer)
+
+    if bn: conv = BatchNormalization()(conv)
+    if activate == True:
+        if activate_type == "leaky":
+            conv = tf.nn.leaky_relu(conv, alpha=0.1)
+        elif activate_type == "mish":
+            conv = mish(conv)
+    return conv
+
+def mish(x):
+    return x * tf.math.tanh(tf.math.softplus(x))
+   
+def residual_block(input_layer, input_channel, filter_num1, filter_num2, activate_type='leaky'):
+    short_cut = input_layer
+    conv = convolutional(input_layer, filters_shape=(1, 1, input_channel, filter_num1), activate_type=activate_type)
+    conv = convolutional(conv       , filters_shape=(3, 3, filter_num1,   filter_num2), activate_type=activate_type)
+
+    residual_output = short_cut + conv
+    return residual_output
+
+
+
+def route_group(input_layer, groups, group_id):
+    convs = tf.split(input_layer, num_or_size_splits=groups, axis=-1)
+    return convs[group_id]
+
+def upsample(input_layer):
+    return tf.image.resize(input_layer, (input_layer.shape[1] * 2, input_layer.shape[2] * 2), method='bilinear')
+

Case Study/car.jpg

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+
+from easydict import EasyDict as edict
+
+
+__C                           = edict()
+
+
+cfg                           = __C
+
+# YOLO options
+__C.YOLO                      = edict()
+
+__C.YOLO.CLASSES              = "./classes/custom.names"
+__C.YOLO.ANCHORS              = [12,16, 19,36, 40,28, 36,75, 76,55, 72,146, 142,110, 192,243, 459,401]
+__C.YOLO.ANCHORS_V3           = [10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326]
+__C.YOLO.ANCHORS_TINY         = [23,27, 37,58, 81,82, 81,82, 135,169, 344,319]
+__C.YOLO.STRIDES              = [8, 16, 32]
+__C.YOLO.STRIDES_TINY         = [16, 32]
+__C.YOLO.XYSCALE              = [1.2, 1.1, 1.05]
+__C.YOLO.XYSCALE_TINY         = [1.05, 1.05]
+__C.YOLO.ANCHOR_PER_SCALE     = 3
+__C.YOLO.IOU_LOSS_THRESH      = 0.5
+
+
+