## Description
MXNET taking more and more memory after every forward propagation of a
Generator Network
## Environment info (Required)
CUDA 8.0 on ubuntu 16.4 and mxnet 1.3.0
Package used (Python/R/Scala/Julia):
(I'm using Python 2.7)
## Minimum reproducible example
```
import mxnet as mx
class RandIter(mx.io.DataIter):
def __init__(self, batch_size, ndim):
self.batch_size = batch_size
self.ndim = ndim
self.provide_data = [('rand', (batch_size, ndim, 1, 1))]
self.provide_label = []
def iter_next(self):
return True
def getdata(self):
#Returns random numbers from a gaussian (normal) distribution
#with mean=0 and standard deviation = 1
return [mx.random.normal(0, 1.0, shape=(self.batch_size, self.ndim, 1,
1))]
batch_size=16
Z = 100
rand_iter = RandIter(batch_size, Z)
no_bias = True
fix_gamma = True
epsilon = 1e-5 + 1e-12
rand = mx.sym.Variable('rand')
g1 = mx.sym.Deconvolution(rand, name='g1', kernel=(4,4), num_filter=1024,
no_bias=no_bias)
gbn1 = mx.sym.BatchNorm(g1, name='gbn1', fix_gamma=fix_gamma, eps=epsilon)
gact1 = mx.sym.Activation(gbn1, name='gact1', act_type='relu')
g2 = mx.sym.Deconvolution(gact1, name='g2', kernel=(4,4), stride=(2,2),
pad=(1,1), num_filter=512, no_bias=no_bias)
gbn2 = mx.sym.BatchNorm(g2, name='gbn2', fix_gamma=fix_gamma, eps=epsilon)
gact2 = mx.sym.Activation(gbn2, name='gact2', act_type='relu')
g3 = mx.sym.Deconvolution(gact2, name='g3', kernel=(4,4), stride=(2,2),
pad=(1,1), num_filter=256, no_bias=no_bias)
gbn3 = mx.sym.BatchNorm(g3, name='gbn3', fix_gamma=fix_gamma, eps=epsilon)
gact3 = mx.sym.Activation(gbn3, name='gact3', act_type='relu')
g4 = mx.sym.Deconvolution(gact3, name='g4', kernel=(4,4), stride=(2,2),
pad=(1,1), num_filter=128, no_bias=no_bias)
gbn4 = mx.sym.BatchNorm(g4, name='gbn4', fix_gamma=fix_gamma, eps=epsilon)
gact4 = mx.sym.Activation(gbn4, name='gact4', act_type='relu')
g5 = mx.sym.Deconvolution(gact4, name='g5', kernel=(4,4), stride=(2,2),
pad=(1,1), num_filter=3, no_bias=no_bias)
generatorSymbol = mx.sym.Activation(g5, name='gact5', act_type='tanh')
#Hyper-parameters
sigma = 0.02
lr = 0.0002
beta1 = 0.5
# Define the compute context, use GPU if available
ctx = mx.gpu() if mx.test_utils.list_gpus() else mx.cpu()
#=============Generator Module=============
generator = mx.mod.Module(symbol=generatorSymbol, data_names=('rand',),
label_names=None, context=ctx)
generator.bind(data_shapes=rand_iter.provide_data)
generator.init_params(initializer=mx.init.Normal(sigma))
print('Training...')
for i in range(10000000):
rbatch = rand_iter.next()
generator.forward(rbatch, is_train=True)
```
## Steps to reproduce
1. Here we just do the forward propagation, 10000000 times. If running this
code, you can see that python is taking more and more of your RAM while the
code is running till it fills the RAM.
## What have you tried to solve it?
1.
2.
[ Full content available at:
https://github.com/apache/incubator-mxnet/issues/12586 ]
This message was relayed via gitbox.apache.org for [email protected]
