Michal Lukac Cospel

## normalized_model.py
# create the base model
base_model = tf.keras.applications.MobileNetV2(include_top=False, weights="imagenet", input_shape=shape)

# apply normalization on input
inputs = tf.keras.Input(shape=shape, name="input")
x = PreprocessTFLayer()(inputs)
x = base_model(x)
x = tf.keras.layers.GlobalAveragePooling2D()(x)
outputs = tf.keras.layers.Dense(2, activation="softmax", name="probs")(x)

## preprocess_layer.py
class PreprocessTFLayer(tf.keras.layers.Layer):
    def __init__(self, name="preprocess_tf", **kwargs):
        super(PreprocessTFLayer, self).__init__(name=name, **kwargs)
        self.preprocess = preprocess_tf

    def call(self, input):
        return self.preprocess(input)

    def get_config(self):
        config = super(PreprocessTFLayer, self).get_config()

## preprocess_tensorflow.py
@tf.function
def preprocess_tf(x):
    """
    Preprocessing for Keras (MobileNetV2, ResNetV2).
    :param x: np.asarray([image, image, ...], dtype="float32") in RGB
    :return: normalized image tf style (RGB)
    """
    batch, height, width, channels = x.shape
    x = tf.cast(x, tf.float32)

## binary-threshold
import cv2
import numpy as np

# load image in cv2
img = cv2.imread('test_image.jpg')

# the image should be converted to grayscale
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# apply threshod

## antiaaliasing-tf2-keras.py
import tensorflow as tf
import numpy as np


class BlurPool(tf.keras.layers.Layer):
    """
    https://arxiv.org/abs/1904.11486
    https://github.com/adobe/antialiased-cnns
    https://github.com/adobe/antialiased-cnns/issues/10
    """

## confusion.html
<!doctype html>
<html lang="en">
<head>
    <meta charset="utf-8">
    <title>D3 Confusion/Correlation Matrix</title>
    <meta name="description" content="D3 Heatmap Demo">
    <meta name="author" content="Marcos Iglesias">
    <script src="https://d3js.org/d3.v5.min.js"></script>
    <link rel="stylesheet" href="src/css/styles.css">
</head>

## redis-hash-ring-worker.py
from redis import Redis
from redis_hashring import RingNode
import gevent
import threading
import random
import signal
N_KEYS = 10

# we cannot use thread object
class Listener():#threading.Thread):

## tensorflow_rename_variables.py
import sys, getopt

import tensorflow as tf

usage_str = 'python tensorflow_rename_variables.py --checkpoint_dir=path/to/dir/ ' \
            '--replace_from=substr --replace_to=substr --add_prefix=abc --dry_run'


def rename(checkpoint_dir, replace_from, replace_to, add_prefix, dry_run):
    checkpoint = tf.train.get_checkpoint_state(checkpoint_dir)

## keybase.md

      
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                Cospel
                / keybase.md
            
            
              Created
              February 4, 2018 08:44
            
          
    Keybase proof

I hereby claim:

I am cospel on github.
I am michallukac (https://keybase.io/michallukac) on keybase.
I have a public key ASDoorxH9WsFryZFoIKVQYAm99NyZs1rGub_EI5tpHfEWQo

To claim this, I am signing this object:

  
## rbm_after_refactor.py
import tensorflow as tf
import numpy as np
import os

import zconfig
import utils


class RBM(object):
	# create the base model
	base_model = tf.keras.applications.MobileNetV2(include_top=False, weights="imagenet", input_shape=shape)

	# apply normalization on input
	inputs = tf.keras.Input(shape=shape, name="input")
	x = PreprocessTFLayer()(inputs)
	x = base_model(x)
	x = tf.keras.layers.GlobalAveragePooling2D()(x)
	outputs = tf.keras.layers.Dense(2, activation="softmax", name="probs")(x)
	class PreprocessTFLayer(tf.keras.layers.Layer):
	def __init__(self, name="preprocess_tf", **kwargs):
	super(PreprocessTFLayer, self).__init__(name=name, **kwargs)
	self.preprocess = preprocess_tf

	def call(self, input):
	return self.preprocess(input)

	def get_config(self):
	config = super(PreprocessTFLayer, self).get_config()
	@tf.function
	def preprocess_tf(x):
	"""
	Preprocessing for Keras (MobileNetV2, ResNetV2).
	:param x: np.asarray([image, image, ...], dtype="float32") in RGB
	:return: normalized image tf style (RGB)
	"""
	batch, height, width, channels = x.shape
	x = tf.cast(x, tf.float32)
	import cv2
	import numpy as np

	# load image in cv2
	img = cv2.imread('test_image.jpg')

	# the image should be converted to grayscale
	img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

	# apply threshod
	import tensorflow as tf
	import numpy as np


	class BlurPool(tf.keras.layers.Layer):
	"""
	https://arxiv.org/abs/1904.11486
	https://github.com/adobe/antialiased-cnns
	https://github.com/adobe/antialiased-cnns/issues/10
	"""
	<!doctype html>
	<html lang="en">
	<head>
	<meta charset="utf-8">
	<title>D3 Confusion/Correlation Matrix</title>
	<meta name="description" content="D3 Heatmap Demo">
	<meta name="author" content="Marcos Iglesias">
	<script src="https://d3js.org/d3.v5.min.js"></script>
	<link rel="stylesheet" href="src/css/styles.css">
	</head>
	from redis import Redis
	from redis_hashring import RingNode
	import gevent
	import threading
	import random
	import signal
	N_KEYS = 10

	# we cannot use thread object
	class Listener():#threading.Thread):
	import sys, getopt

	import tensorflow as tf

	usage_str = 'python tensorflow_rename_variables.py --checkpoint_dir=path/to/dir/ ' \
	'--replace_from=substr --replace_to=substr --add_prefix=abc --dry_run'


	def rename(checkpoint_dir, replace_from, replace_to, add_prefix, dry_run):
	checkpoint = tf.train.get_checkpoint_state(checkpoint_dir)
	import tensorflow as tf
	import numpy as np
	import os

	import zconfig
	import utils


	class RBM(object):