gyglim

"""Simple example on how to log scalars and images to tensorboard without tensor ops.

License: BSD License 2.0
"""
__author__ = "Michael Gygli"

import tensorflow as tf
from StringIO import StringIO
import matplotlib.pyplot as plt
import numpy as np

gyglim

def rank_loss(prediction, margin=1):
    '''
    Implementation of a pairwise rank loss, based on https://github.com/Lasagne/Lasagne/issues/168#issuecomment-81134242
    :param prediction:
    :param target_var:
    :return:
    '''
    score_pos = prediction[0::2]
    score_neg = prediction[1::2]

gyglim

"""Function to build timeout dectorators for specific times.

Example usage:

    @timeout(1)
    def x(): time.sleep(2)
    x()

will raise a TimeoutException.

gyglim

from moviepy.editor import VideoFileClip
import cv2
size= (128, 128)


video_file='/home/gyglim/scratch_gygli/gifscom/shot_detection/debug_videos/UEoUURJ1Dck.mp4'
v1 = VideoFileClip(video_file, target_resolution=size, resize_algorithm='bilinear').subclip(0, 60)
def read_resized():
    for f in v1.iter_frames():
        pass

gyglim

"""Wrapper around subprocess that logs the calls to Popen and tracks how many pipes are open"""
import inspect
import os
import subprocess as sp
import logging
import sys
logger = logging.getLogger('subprocess')
logger.setLevel(logging.DEBUG)

gyglim

{
    "test": [
        "horse018.jpg",
        "horse087.jpg",
        "horse193.jpg",
        "horse179.jpg",
        "horse285.jpg",
        "horse017.jpg",
        "horse059.jpg",
        "horse088.jpg",

gyglim

# TODO: Get weizmann_32_32_trainval.mat from https://www.cs.toronto.edu/~yujiali/papers/chopps.zip
# TODO: Get horse.mat                   from https://www.cs.toronto.edu/~yujiali/papers/cvpr13_data.zip
# TODO: Update the paths below
split_path='YOUR_PATH_TO/weizmann_32_32_trainval.mat'
data_path='YOUR_PATH_TO/horse.mat'

# Imports
import matplotlib.pyplot as plt
import numpy as np
import scipy.io

gyglim

{
  "citation": "\n@inproceedings{coates2011stl10,\n  title={{An Analysis of Single Layer Networks in Unsupervised Feature Learning}},\n  author={Coates, Adam and Ng, Andrew and Lee, Honglak},\n  booktitle={AISTATS},\n  year={2011},\n  note = {\\url{https://cs.stanford.edu/~acoates/papers/coatesleeng_aistats_2011.pdf}},\n}\n",
  "description": "The STL-10 dataset is an image recognition dataset for developing unsupervised\nfeature learning, deep learning, self-taught learning algorithms. It is inspired\nby the CIFAR-10 dataset but with some modifications. In particular, each class\nhas fewer labeled training examples than in CIFAR-10, but a very large set of \nunlabeled examples is provided to learn image models prior to supervised\ntraining. The primary challenge is to make use of the unlabeled data (which\ncomes from a similar but different distribution from the labeled data) to build\na useful prior. All images were acquired from labeled examples on ImageNet.\n",
  "downloadSize": "2640397119",
  "location

gyglim

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