[GitHub] shababqcd opened a new issue #12586: Memory issue with Module forward function

[GitBox <gi...@apache.org>]

shababqcd opened a new issue #12586: Memory issue with Module forward function
URL: https://github.com/apache/incubator-mxnet/issues/12586
 
 
   
   ## 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.
   

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