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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/06/08 16:23:02 UTC
[GitHub] thomelane opened a new issue #11203: ConvTranspose2d giving
incorrect output
thomelane opened a new issue #11203: ConvTranspose2d giving incorrect output
URL: https://github.com/apache/incubator-mxnet/issues/11203
When applying ConvTranspose2d with stride and dilation, the output is incorrect.
Working through a simple example:
* Input Shape: 1x3x3
* Kernel size: 3x3
* Padding: Same (i.e.2x2 in this case)
* Stride: 2x2
* Dilation: 2x2
# Manual Calculation
# MXNet Output
We get a different output from MXNet, which I believe is incorrect. Output is offset at top and left sides, and then clusters on bottom and right sides.
```
...
conv = mx.gluon.nn.Conv2DTranspose(in_channels=1, channels=1,
kernel_size=(3,3), padding=(2,2),
strides=(2,2), dilation=(2,2))
....
```
```
[[[[ 0. 0. 0. 0. 0.]
[ 0. 1. 0. 2. 3.]
[ 0. 0. 0. 0. 0.]
[ 0. 4. 0. 5. 6.]
[ 0. 7. 0. 8. 9.]]]]
<NDArray 1x1x5x5 @cpu(0)>
```
# PyTorch
Also, PyTorch returns an output that matches the manual calculations above. Code for this can be found below too.
```
...
conv = torch.nn.ConvTranspose2d(in_channels=1, out_channels=1,
kernel_size=(3,3), padding=(2,2),
stride=(2,2), dilation=(2,2))
...
```
```
tensor([[[[ 1., 0., 2., 0., 3.],
[ 0., 0., 0., 0., 0.],
[ 4., 0., 5., 0., 6.],
[ 0., 0., 0., 0., 0.],
[ 7., 0., 8., 0., 9.]]]])
```
## Environment info (Required)
```
(mxnet_p36) ubuntu@ip-172-31-68-231:~$ python diagnose.py
----------Python Info----------
Version : 3.6.4
Compiler : GCC 7.2.0
Build : ('default', 'Jan 16 2018 18:10:19')
Arch : ('64bit', '')
------------Pip Info-----------
Version : 10.0.1
Directory : /home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/pip
----------MXNet Info-----------
Version : 1.2.0
Directory : /home/ubuntu/anaconda3/envs/mxnet_p36/lib/python3.6/site-packages/mxnet
Commit Hash : 297c64fd2ee404612aa3ecc880b940fb2538039c
----------System Info----------
Platform : Linux-4.4.0-1057-aws-x86_64-with-debian-stretch-sid
system : Linux
node : ip-172-31-68-231
release : 4.4.0-1057-aws
version : #66-Ubuntu SMP Thu May 3 12:49:47 UTC 2018
----------Hardware Info----------
machine : x86_64
processor : x86_64
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 8
On-line CPU(s) list: 0-7
Thread(s) per core: 2
Core(s) per socket: 4
Socket(s): 1
NUMA node(s): 1
Vendor ID: GenuineIntel
CPU family: 6
Model: 79
Model name: Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz
Stepping: 1
CPU MHz: 2699.984
CPU max MHz: 3000.0000
CPU min MHz: 1200.0000
BogoMIPS: 4600.08
Hypervisor vendor: Xen
Virtualization type: full
L1d cache: 32K
L1i cache: 32K
L2 cache: 256K
L3 cache: 46080K
NUMA node0 CPU(s): 0-7
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single retpoline kaiser fsgsbase bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx xsaveopt
----------Network Test----------
Setting timeout: 10
Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0040 sec, LOAD: 0.3429 sec.
Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.0385 sec, LOAD: 0.3615 sec.
Timing for Gluon Tutorial(cn): https://zh.gluon.ai, DNS: 0.4978 sec, LOAD: 0.3804 sec.
Timing for FashionMNIST: https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz, DNS: 0.0207 sec, LOAD: 0.1522 sec.
Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0027 sec, LOAD: 0.0859 sec.
Timing for Conda: https://repo.continuum.io/pkgs/free/, DNS: 0.0284 sec, LOAD: 0.0201 sec.
```
Package used (Python/R/Scala/Julia):
I'm using Python
## Minimum reproducible example
MXNet (giving incorrect output):
```
import mxnet as mx
data = mx.nd.array(((0,0,0),
(0,1,0),
(0,0,0)))
kernel = mx.nd.array(((1,2,3),
(4,5,6),
(7,8,9)))
data_batch = data.expand_dims(0).expand_dims(0)
weight = kernel.expand_dims(0).expand_dims(0)
# initialize and set weight
conv = mx.gluon.nn.Conv2DTranspose(in_channels=1, channels=1,
kernel_size=(3,3), padding=(2,2),
strides=(2,2), dilation=(2,2))
conv.initialize()
conv.weight.set_data(weight)
print(conv(data_batch))
```
```
[[[[ 0. 0. 0. 0. 0.]
[ 0. 1. 0. 2. 3.]
[ 0. 0. 0. 0. 0.]
[ 0. 4. 0. 5. 6.]
[ 0. 7. 0. 8. 9.]]]]
<NDArray 1x1x5x5 @cpu(0)>
```
PyTorch (giving correct output):
```
import torch
data = torch.tensor(((0,0,0),
(0,1,0),
(0,0,0)), dtype=torch.float32)
kernel = torch.tensor(((1,2,3),
(4,5,6),
(7,8,9)), dtype=torch.float32)
data_batch = data.expand(1,1,-1,-1)
weight = kernel.expand(1,1,-1,-1)
conv = torch.nn.ConvTranspose2d(in_channels=1, out_channels=1,
kernel_size=(3,3), padding=(2,2),
stride=(2,2), dilation=(2,2))
conv.weight.data = weight
print(conv(data_batch).round())
```
```
tensor([[[[ 1., 0., 2., 0., 3.],
[ 0., 0., 0., 0., 0.],
[ 4., 0., 5., 0., 6.],
[ 0., 0., 0., 0., 0.],
[ 7., 0., 8., 0., 9.]]]])
```
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