Apfelin

import discord
import asyncio
import speech_recognition as sr
from threading import Thread

# bot token and wit.ai api key
TOKEN = ""
WIT_AI_KEY = ""

# we need a sink for the listen function, so we just define our own

BIGBALLON

#!/bin/bash
#
# script to extract ImageNet dataset
# ILSVRC2012_img_train.tar (about 138 GB)
# ILSVRC2012_img_val.tar (about 6.3 GB)
# make sure ILSVRC2012_img_train.tar & ILSVRC2012_img_val.tar in your current directory
#
#  https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md
# 
#  train/

movefast

from torch.utils.data.sampler import Sampler
from torch.utils.data.sampler import RandomSampler

class WeightedLossSampler(Sampler, Callback):
    
    def __init__(self, data_source, replacement=True, bs=64, init_fac=1, ls_init_fac=1e-2):
        self.num_samples = len(data_source)
        self.weights = to_gpu(torch.ones(self.num_samples)*init_fac)
        self.replacement = replacement
        self.i = 0

bkj

#!/usr/bin/env python

"""
    pytorch_lifted_loss.py
"""

import torch
import torch.nn as nn
from torch.autograd import Variable

PolarNick239

#include <queue>
#include <boost/thread/mutex.hpp>
#include <boost/thread/condition_variable.hpp>

// Based on https://www.justsoftwaresolutions.co.uk/threading/implementing-a-thread-safe-queue-using-condition-variables.html
template<typename Data>
class BlockingQueue {
private:
    std::queue<Data>            queue;
    mutable boost::mutex        queue_mutex;

normalize_image.ipynb

ndronen

#!/usr/bin/env python
"""
A quick, partial implementation of ENet (https://arxiv.org/abs/1606.02147) using PyTorch.
The original Torch ENet implementation can process a 480x360 image in ~12 ms (on a P2 AWS
instance).  TensorFlow takes ~35 ms.  The PyTorch implementation takes ~25 ms, an improvement
over TensorFlow, but worse than the original Torch.
"""

from __future__ import absolute_import

fasiha

Experimenting with pitch detection and spectral estimators

See the question and discussion on StackOverflow: How to get the fundamental frequency using Harmonic Product Spectrum?.

We’re trying to estimate the fundamental frequency of a voiced A4 note (440 Hz). (See the question for link to audio clip.)

Harmonic product spectrum

Result: full data, 0 to 2 KHz

vsubhashini

##Sequence to Sequence -- Video to Text

Paper : ICCV 2015 PDF

Download Model: S2VT_VGG_RGB_MODEL (333MB)

Project Page

Description

mooware

# colored stream handler for python logging framework (use the ColorStreamHandler class).
#
# based on:
# http://stackoverflow.com/questions/384076/how-can-i-color-python-logging-output/1336640#1336640

# how to use:
# i used a dict-based logging configuration, not sure what else would work.
#
# import logging, logging.config, colorstreamhandler
#