tmyapple opened a new issue #19768:
URL: https://github.com/apache/incubator-mxnet/issues/19768
## Description
MaskRCNN second stage has a deconv layer - implemented as
**Conv2DTranspose** (in GLUON-CV).
By examination of this layer you will see that it contains a bias, but does
not really use it.
It is weird, because the bias is actually exist but is not really needed. So
although, in the layer kwargs the default of **no_bias=False**, and the deconv
layer in the mask-rcnn has the default value:
``` class mxnet.gluon.nn.Conv2D(channels, kernel_size, strides=(1, 1),
padding=(0, 0), dilation=(1, 1), groups=1, layout='NCHW', activation=None,
use_bias=True, weight_initializer=None, bias_initializer='zeros',
in_channels=0, **kwargs)[source]
```
In inference time the bias is not used.
### Error Message
No error message, the problem is with the Conv2DTranspose Operation
## To Reproduce
```python
import gluoncv
name = "mask_rcnn_resnet18_v1b_coco"
mask_rcnn = gluoncv.model_zoo.get_model(name, pretrained=True, ctx=mx.cpu(0))
mask_rcnn.mask.deconv._kwargs
```
This will give you the deconv layer args where you will find no_bias=False
Further inspection will show the existence of a bias:
```python
mask_rcnn.mask.deconv.weight.data().shape
```
Now in order to see that it doesn't use the bias the fastest approach is to
re-initialize the bias and examine the results. If it is the same as the
original results, than the bias didn't influence at all and therefore is not
used:
```python
import gluoncv
from gluoncv import model_zoo, data, utils
rom matplotlib import pyplot as plt
import mxnet as mx
import numpy as np
name = "mask_rcnn_resnet18_v1b_coco"
mask_rcnn = gluoncv.model_zoo.get_model(name, pretrained=True, ctx=mx.cpu(0))
mask_rcnn.mask.deconv.bias.initialize(init.Constant(mx.nd.zeros(256)),
force_reinit=True)
x, orig_img = data.transforms.presets.rcnn.load_test("biking-600.jpg") #
Replace biking-600.jpg with a real image path that you have
ids, scores, bboxes, masks = [xx[0].asnumpy() for xx in mask_rcnn(x)]
# paint segmentation mask on images directly
width, height = orig_img.shape[1], orig_img.shape[0]
masks, _ = utils.viz.expand_mask(masks, bboxes, (width, height), scores)
orig_img = utils.viz.plot_mask(orig_img, masks)
# identical to Faster RCNN object detection
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(1, 1, 1)
ax = utils.viz.plot_bbox(orig_img, bboxes, scores, ids,
class_names=mask_rcnn.classes, ax=ax)
plt.show()
print(np.sum(masks)) # This value stays the same whether you reinitialize
the bias or not - which means it is not used
print(np.sum(scores))
print(np.sum(bboxes))
```
## Environment
<details>
<summary>Environment Information</summary>
```
----------Python Info----------
Version : 3.6.11
Compiler : GCC 5.4.0 20160609
Build : ('default', 'Jun 29 2020 05:15:03')
Arch : ('64bit', 'ELF')
------------Pip Info-----------
Version : 20.2.4
Directory : /home/tamirt/venv3.6/lib/python3.6/site-packages/pip
----------MXNet Info-----------
Version : 1.7.0
Directory : /home/tamirt/venv3.6/lib/python3.6/site-packages/mxnet
Commit Hash : 64f737cdd59fe88d2c5b479f25d011c5156b6a8a
64f737cdd59fe88d2c5b479f25d011c5156b6a8a
64f737cdd59fe88d2c5b479f25d011c5156b6a8a
64f737cdd59fe88d2c5b479f25d011c5156b6a8a
64f737cdd59fe88d2c5b479f25d011c5156b6a8a
64f737cdd59fe88d2c5b479f25d011c5156b6a8a
64f737cdd59fe88d2c5b479f25d011c5156b6a8a
64f737cdd59fe88d2c5b479f25d011c5156b6a8a
64f737cdd59fe88d2c5b479f25d011c5156b6a8a
64f737cdd59fe88d2c5b479f25d011c5156b6a8a
Library :
['/home/tamirt/venv3.6/lib/python3.6/site-packages/mxnet/libmxnet.so']
Build features:
✖ CUDA
✖ CUDNN
✖ NCCL
✖ CUDA_RTC
✖ TENSORRT
✔ CPU_SSE
✔ CPU_SSE2
✔ CPU_SSE3
✔ CPU_SSE4_1
✔ CPU_SSE4_2
✖ CPU_SSE4A
✔ CPU_AVX
✖ CPU_AVX2
✔ OPENMP
✖ SSE
✔ F16C
✖ JEMALLOC
✔ BLAS_OPEN
✖ BLAS_ATLAS
✖ BLAS_MKL
✖ BLAS_APPLE
✔ LAPACK
✔ MKLDNN
✔ OPENCV
✖ CAFFE
✖ PROFILER
✔ DIST_KVSTORE
✖ CXX14
✖ INT64_TENSOR_SIZE
✔ SIGNAL_HANDLER
✖ DEBUG
✖ TVM_OP
----------System Info----------
Platform : Linux-4.15.0-129-generic-x86_64-with-Ubuntu-16.04-xenial
system : Linux
node : hai-211-lap.qb.hailotech
release : 4.15.0-129-generic
version : #132~16.04.1-Ubuntu SMP Wed Dec 16 06:46:04 UTC 2020
----------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: 142
Model name: Intel(R) Core(TM) i7-8665U CPU @ 1.90GHz
Stepping: 12
CPU MHz: 2794.722
CPU max MHz: 4800.0000
CPU min MHz: 400.0000
BogoMIPS: 4199.88
Virtualization: VT-x
L1d cache: 32K
L1i cache: 32K
L2 cache: 256K
L3 cache: 8192K
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 dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx
pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl
xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx
smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe
popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch
cpuid_fault epb invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow
vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid
mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm
ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp md_clear flush_l1d
arch_capabilities
----------Network Test----------
Setting timeout: 10
Timing for MXNet: https://github.com/apache/incubator-mxnet, DNS: 0.0932
sec, LOAD: 0.6922 sec.
Timing for Gluon Tutorial(en): http://gluon.mxnet.io, DNS: 0.0122 sec, LOAD:
0.0945 sec.
Error open Gluon Tutorial(cn): https://zh.gluon.ai, <urlopen error [SSL:
CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:852)>, DNS
finished in 0.016017675399780273 sec.
Timing for FashionMNIST:
https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz,
DNS: 0.0934 sec, LOAD: 0.8560 sec.
Timing for PYPI: https://pypi.python.org/pypi/pip, DNS: 0.0251 sec, LOAD:
1.3593 sec.
Error open Conda: https://repo.continuum.io/pkgs/free/, HTTP Error 403:
Forbidden, DNS finished in 0.014355659484863281 sec.
----------Environment----------
KMP_DUPLICATE_LIB_OK="True"
KMP_INIT_AT_FORK="FALSE"
```
</details>
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