vmarkovtsev

from PIL import Image

def create_motion_blur_func(dim: int, angle: float):
    kernel = create_motion_blur_kernel(dim, angle)

    def motion_blur_func(images):
        return tf.nn.depthwise_conv2d(images, kernel, strides=[1] * 4, padding="SAME")
    
    return motion_blur_func

vmarkovtsev

import logging
from pathlib import Path


def create_motion_blur_func(images_shape: Tuple[int], dim: int, angle: float):
    kernel = create_motion_blur_kernel(dim, angle)

    # This is new: input signature definition
    @tf.function(input_signature=[tf.TensorSpec(images_shape, tf.float32)])
    def motion_blur_func(images):

vmarkovtsev

# Remember to remove the old model!
# rm *.tflite

import subprocess
from tensorflow.lite.python.interpreter import load_delegate


def create_motion_blur_func_edgetpu(images_shape, dim, angle, edgetpu=True):
    name = "motion_blur_%s_%d_%.2f" % ("_".join(map(str, images_shape)), dim, angle)
    ctor = lambda: create_motion_blur_func(images_shape, dim, angle)

vmarkovtsev

# The kernel size is 15 instead of 5 for greater detail.
dim = 15
kernel = np.squeeze(create_lanczos_resize_kernel(dim).numpy()[:, :, 0, 0])
plt.contourf(np.linspace(-dim, dim, dim * 2 + 1), np.linspace(-dim, dim, dim * 2 + 1), kernel)
plt.colorbar()
plt.show()

vmarkovtsev

# Remember to remove the old model!
# rm *.tflite

from typing import Optional


def create_lanczos_resize_func_edgetpu(images_shape: Tuple[int], dim: int, factor: int, edgetpu=True):
    name = "lanczos_resize_%s_%d_%d" % ("_".join(map(str, images_shape)), dim, factor)
    ctor = lambda: create_lanczos_resize_func(images_shape, dim, factor)
    

vmarkovtsev

def save_image(tensor: Union[np.ndarray, tf.Tensor], path: str):
    if hasattr(tensor, "numpy"):
        tensor = tensor.numpy()
    tensor = np.clip(tensor, 0, 255)   # <<< this is new
    Image.fromarray(np.squeeze(tensor).astype(np.uint8), "RGB").save(path)

vmarkovtsev

def create_lanczos_resize_func_edgetpu(images_shape: Tuple[int], dim: int, factor: int, edgetpu=True):
    name = "lanczos_resize_%s_%d_%d" % ("_".join(map(str, images_shape)), dim, factor)
    ctor = lambda: create_lanczos_resize_func(images_shape, dim, factor)
    
    return create_func_edgetpu(images_shape, ctor, name, edgetpu)


def create_lanczos_resize_func(images_shape: Tuple[int], dim: int, factor: int):
    kernel = create_lanczos_resize_kernel(dim)
    

vmarkovtsev

#!/bin/bash
# License: Public Domain.

echo "export PYSPARK_PYTHON=python3" | tee -a  /etc/profile.d/spark_config.sh  /etc/*bashrc /usr/lib/spark/conf/spark-env.sh
echo "export PYTHONHASHSEED=0" | tee -a /etc/profile.d/spark_config.sh /etc/*bashrc /usr/lib/spark/conf/spark-env.sh
echo "spark.executorEnv.PYTHONHASHSEED=0" >> /etc/spark/conf/spark-defaults.conf

# Only run on the master node
ROLE=$(/usr/share/google/get_metadata_value attributes/dataproc-role)
if [[ "${ROLE}" == 'Master' ]]; then

vmarkovtsev

Open ~/.bashrc. Append export WINDOWID=... in the header as follows:

# ~/.bashrc: executed by bash(1) for non-login shells.
# see /usr/share/doc/bash/examples/startup-files (in the package bash-doc)
# for examples

# If not running interactively, don't do anything
case $- in
 *i*) ;;

vmarkovtsev

# Remember to remove the old model!
# rm *.tflite

import json
import urllib.request


def generate_edgetpu_model(log: logging.Logger, images_shape: Tuple[int], func: callable, name: str):
    """Convert tf.function to Edge TPU model."""