albanie

"""A minimal profiler to compare speed differences between reading a video with opencv
and DALI.

For both dataloaders, the goal is to read an mp4 video and then load batches of frames
onto the GPU.
"""
import os
import logging
import time
import subprocess

albanie

"""A small wrapper around hickle to allow the usage of python dictionaries with
slashes in key names.

Feature proposal: https://github.com/telegraphic/hickle/issues/124
Hickle license: https://github.com/telegraphic/hickle/blob/master/LICENSE
Author: Samuel Albanie
"""
# ---------------------------------------------
#                                  Monkey patch
# ---------------------------------------------

albanie

function dag = toDagNN(net, customObj)
%TODAGNN Converts a compiled AutoNN network to a DagNN object
%   DAG = toDagNN(NETOUTPUTS) converts a set of Autonn outputs
%   (i.e. a set of recursively nested layer objects), into a dagnn object, 
%   DAG.
%  TODO(samuel): clean up



  if nargin < 2

albanie

from __future__ import print_function
import matplotlib
matplotlib.use('Agg')

import colorsys
import caffe
import json
import pdb
import time
import sys

albanie

from __future__ import print_function
import matplotlib
matplotlib.use('Agg')

import colorsys
import caffe
import json
import pdb
import time
import sys

albanie

function issue_814
% demo for @segman

% fix random seed to get repeatable result
rng(1) ;

sample = rand(21, 21, 3, 'single') ;
label = [ 1 1 1 1 1 ;
         1 1 1 1 1 ;
         1 2 2 2 1 ;

albanie

import caffe
import pdb
import numpy as np

class PyBlankDataLayer(caffe.Layer):
    """
    A blank python data layer that initialises once with zeros 
    according during construction and does no further operations
    (useful for layer testing).    
    """

albanie

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np, math

"""Define the periodic function"""
def f(t):
    return math.cos(2 * math.pi * t) + 0.5 * math.cos( 4 * math.pi * t)
    
"""Define inputs, find outputs and plot graph"""
inputs = np.linspace(-2, 2, 100)