Multihuntr

import queue
import random

class RandomQueue(queue.Queue):
  def _put(self, item):
    n = len(self.queue)
    i = random.randint(0, n)
    self.queue.insert(i, item)

Multihuntr

# Simplest case (note: VideoDataset.__get__ puts frames on CPU)
import nvvl
d = nvvl.VideoDataset(['prepared.mp4'], 3)
fr = d[0]
print(type(fr))
print(fr.shape)

# Custom processing (note: VideoDataset.__get__ puts frames on CPU)
import nvvl
d = nvvl.VideoDataset(['prepared.mp4'], 3, processing={'a': nvvl.ProcessDesc()})

Multihuntr

import numpy as np
import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

def simple_model(input):
  # This ensures that the model will always be instantiated the same, for comparison.
  hidden_initializer = tf.constant_initializer(np.random.uniform(-0.025, 0.025, size=[784,100]))
  hidden = tf.layers.dense(input, 100, kernel_initializer=hidden_initializer)
  out_initializer = tf.constant_initializer(np.random.uniform(-0.025, 0.025, size=[100,10]))

Multihuntr

# Super simple 1D smoothing with just numpy. Just smooths a few sharp edges.

import math
import numpy as np

def gaussian_kernel(n=5):
  x = np.arange(n)/n
  g = math.e ** -(x**2/((n - 2)**2)) # drop the constant factor at the start
  return g/g.sum() # normalise so it sums to 1

Multihuntr

from PIL import Image


# Assumes tensor is shaped [c, h, w]
Image.fromarray(tensor.detach().cpu().numpy().transpose([1, 2, 0])).save('test.png')

# Assumes tensor is shaped [n, c, h, w]
Image.fromarray(tensor[0].detach().cpu().numpy().transpose([1, 2, 0])).save('test.png')

# Yeah, pretty simple, but annoying to remember... 

Multihuntr

def derive(_from, _coll='_derived', name=None):
    '''
    Creates a decorator that caches derived values.
    Utilises a property on the object to keep a collection of derived properties,
    but requires the calling obj to manage that collection (clearing, instantiation, etc).

    Args:
        _from (str): Property this property is derived from
        _coll (str, optional): Collection name of derived property names
        name (str, optional): Overwrite the property used to cache

Multihuntr

import bisect
import numpy as np
import collections.abc

class TwoLevelIndex(collections.abc.Sequence):
    def __init__(self, coll):
        counts = [len(thing) for thing in coll]
        self.cum_counts = np.cumsum(counts)
    def __getitem__(self, idx):
        seg_idx = bisect.bisect(self.cum_counts, idx)

Multihuntr

import collections

class ListLike(collections.abc.MutableSequence):
    '''
    A list-like interface wrapping dictionary-like objects.

    Uses integer keys, like you would for a list, has range checking,
    negative indexing and slicing working as you'd expect.
    i.e.
        with shelve.open('foo.dat') as sf:

Multihuntr

import numpy as np
import tensorflow as tf
import sys

from tensorflow.examples.tutorials.mnist import input_data

n_pseudo_batches =  int(sys.argv[1]) if len(sys.argv) > 1 else 128
actual_batch_size = int(sys.argv[2]) if len(sys.argv) > 2 else 32
iterations =        int(sys.argv[3]) if len(sys.argv) > 3 else 10

Multihuntr

# BIG NOTE: 
# I made this becuase I thought the method was interesting. It's not useful in it's current form. 
# If you can, use an index instead. https://stackoverflow.com/a/27895337

from sqlalchemy import Column, Integer, Float, ForeignKey, event, DDL
from sqlalchemy.schema import CreateSchema
from sqlalchemy.dialects.postgresql import DOUBLE_PRECISION
from sqlalchemy.ext.declarative import declarative_base, declared_attr

Base = declarative_base()