tomrunia

def decode(serialized_example, sess):
    '''
    Given a serialized example in which the frames are stored as
    compressed JPG images 'frames/0001', 'frames/0002' etc., this
    function samples SEQ_NUM_FRAMES from the frame list, decodes them from
    JPG into a tensor and packs them to obtain a tensor of shape (N,H,W,3).
    Returns the the tuple (frames, class_label (tf.int64)

    :param serialized_example: serialized example from tf.data.TFRecordDataset
    :return: tuple: (frames (tf.uint8), class_label (tf.int64)

tomrunia

import numpy as np
from tensorflow.python.summary.event_accumulator import EventAccumulator

import matplotlib as mpl
import matplotlib.pyplot as plt

def plot_tensorflow_log(path):

    # Loading too much data is slow...
    tf_size_guidance = {

tomrunia

# Initialize placeholders for feeding in to the queue
pl_queue_screens = tf.placeholder(tf.float32, shape=[config.seq_length, config.image_size, config.image_size, config.input_channels], name="queue_inputs")
pl_queue_targets = tf.placeholder(tf.uint8, shape=[config.seq_length], name="queue_targets_cnt")

#  ...

capacity = config.min_after_dequeue + 10 * (config.num_gpus*config.batch_size)
q = tf.RandomShuffleQueue(

tomrunia

# MIT License
#
# Copyright (c) 2017 Tom Runia
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to conditions.

tomrunia

#!/usr/bin/python
import os

# YouTube video searching API
from apiclient.discovery import build
from apiclient.errors import HttpError
from oauth2client.tools import argparser

# Downloading YouTube videos
import pafy

tomrunia

    # Initialize the camera
    camera = bpy.data.cameras.new('Camera')
    camera_obj = bpy.data.objects.new('Camera', camera)
    scene.objects.link(camera_obj)

    scene.objects.active = camera_obj
    scene.camera = camera_obj
    camera_obj.select = True

    camera_obj.location = (0, 0, 0)

tomrunia

Block	Elements	Kernel Size	Filter depth	Output depth	Stride	Misc. Info
	Convolution	[5, 5]	3	64	[1, 1]
conv1	ReLU	-	-	-	-
	Max-pool	[3, 3]	-	-	[2, 2]
	Convolution	[5, 5]	64	64	[1, 1]
conv2	ReLU	-	-	-	-
	Max-pool	[3, 3]	-	-	[2, 2]

tomrunia

# Initialize placeholders for feeding in to the queue
self.queue_inputs       = tf.placeholder(tf.float32, shape=[None, self.config.seq_length, self.config.image_size, self.config.image_size], name="queue_inputs")
self.queue_targets      = tf.placeholder(tf.uint8, shape=[None, self.config.seq_length], name="queue_targets")

min_after_dequeue = 10000
capacity = min_after_dequeue + 3 * self.config.batch_size

q = tf.FIFOQueue(

tomrunia

def load_and_enqueue(input_dir, sess, coord, enqueue_op, queue_inputs, queue_targets, num_examples, examples_per_file=100, rewrite_targets=True):

    # Check if we have a sufficient number of HDF5 files to load all the samples
    filenames_queue = glob.glob(os.path.join(input_dir, "train/*.h5"))
    filenames_queue.sort()
    assert len(filenames_queue) > 0

    examples_available = len(filenames_queue)*examples_per_file
    num_examples = min(examples_available, num_examples)

tomrunia

require 'image'
require 'lfs'
require 'cunn'
require 'nngraph'

function segment(model, flow_mag_ang_file, minmax_file, output_file)

    local file = io.open(minmax_file)
    local minmaxes = {}
    local ind = 1;