wangg12

It's not immediately obvious how to pull down the code for a PR and test it locally. But it's pretty easy. (This assumes you have a remote for the main repo named upstream.)

Getting the PR code

1. Make note of the PR number. For example, Rod's latest is PR #37: Psiphon-Labs/psiphon-tunnel-core#37

2. Fetch the PR's pseudo-branch (or bookmark or rev pointer whatever the word is), and give it a local branch name. Here we'll name it pr37:

$ git fetch upstream pull/37/head:pr37

wangg12

from tensorflow.python.layers import base
import tensorflow as tf

class AddCoords(base.Layer):
    
    """Add coords to a tensor"""
    def __init__(self, x_dim=64, y_dim=64, with_r=False):
        super(AddCoords, self).__init__()
        self.x_dim = x_dim
        self.y_dim = y_dim

wangg12

import keras.backend as K
import tensorflow as tf
from keras.layers import Layer

"""Not tested, I'll play around with GANs soon with it."""


class CoordConv2D(Layer):
    def __init__(self, channel, kernel, padding='valid', **kwargs):
        self.layer = Conv2D(channel, kernel, padding=padding)

wangg12

import tensorflow as tf
from tensorflow.python.framework import ops
import numpy as np

# Define custom py_func which takes also a grad op as argument:
def py_func(func, inp, Tout, stateful=True, name=None, grad=None):
    
    # Need to generate a unique name to avoid duplicates:
    rnd_name = 'PyFuncGrad' + str(np.random.randint(0, 1E+8))
    

wangg12

import os
import fnmatch

def recursive_glob(rootdir='.', pattern='*'):
	"""Search recursively for files matching a specified pattern.
	
	Adapted from http://stackoverflow.com/questions/2186525/use-a-glob-to-find-files-recursively-in-python
	"""

	matches = []

wangg12

from __future__ import print_function

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

import tensorflow as tf

def weight_variable(shape):
  initial = tf.truncated_normal(shape, stddev=0.1)
  return tf.Variable(initial)

wangg12

import torch
import torch.nn as nn
from torch.nn import Parameter
from torch.autograd import Variable, Function
from collections import defaultdict
import graphviz

"""
This is a rather distorted implementation of graph visualization in PyTorch.

wangg12

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


class Bottleneck(nn.Module):
    cardinality = 32  # the size of the set of transformations

    def __init__(self, nb_channels_in, nb_channels, nb_channels_out, stride=1):
        super().__init__()

wangg12

#
#  mnist_cnn_bn.py   date. 5/21/2016
#

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import numpy as np

wangg12

"""
Custom datasets from both in-memory and out-of-memory data
"""

import torch.utils.data as data

from PIL import Image
import os
import os.path