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Michael Gygli gyglim

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gyglim / youtube_video_ids.txt
Last active July 29, 2020 15:55
Videos with few or no shot boundaries
LtPYB0gpVrM
7TLngTUG9BU
3YeFK_mBl48
GReJlr_Fr8M
hQLqAou-_hU
zecY4uuXwSI
EaleKN9GQ54
RK1pR1Edax0
MVoLvrS_fFI
PxlEbfLy6GI
@gyglim
gyglim / stl10_dataset_info.json
Created March 30, 2020 13:34
{
"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
gyglim / weizman_dataset.py
Last active December 7, 2018 13:31
Example for loading the splits used in "Exploring Compositional High Order Pattern Potentials for Structured Output Learning" from Li et al.
# 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
gyglim / weizman_splits.json
Created December 7, 2018 12:55
Splits used Deep Value Networks Learn to Evaluate and Iteratively Refine Structured Outputs, which follows: Exploring Compositional High Order Pattern Potentials for Structured Output Learning
{
"test": [
"horse018.jpg",
"horse087.jpg",
"horse193.jpg",
"horse179.jpg",
"horse285.jpg",
"horse017.jpg",
"horse059.jpg",
"horse088.jpg",
@gyglim
gyglim / logging_subprocess.py
Created March 17, 2017 09:16
Wrapper around subprocess to debug moviepy OSErrors
"""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
gyglim / resize_speed_test.py
Last active March 14, 2017 10:29
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
gyglim / timeout_decorator.py
Created March 8, 2017 14:36
Decorator to timeout function calls and correctly re-raise Exceptions
"""Function to build timeout dectorators for specific times.
Example usage:
@timeout(1)
def x(): time.sleep(2)
x()
will raise a TimeoutException.
@gyglim
gyglim / rank_loss.py
Last active September 21, 2017 15:23
Lasagne rank loss
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
gyglim / tensorboard_logging.py
Last active August 23, 2023 21:29
Logging to tensorboard without tensorflow operations. Uses manually generated summaries instead of summary ops
"""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