bob issueshttps://gitlab.idiap.ch/groups/bob/-/issues2018-05-15T08:33:02Zhttps://gitlab.idiap.ch/bob/bob.ip.pytorch_extractor/-/issues/2PyTorch (0.3.0) has an unchecked dependency on pyyaml2018-05-15T08:33:02ZGuillaume HEUSCHPyTorch (0.3.0) has an unchecked dependency on pyyamlWhen running the CI locally on my machine for this package, I got the following error:
```bash
pkg_resources.DistributionNotFound: The 'pyyaml' distribution was not found and is required by torch
```
This is a known issue and it was fi...When running the CI locally on my machine for this package, I got the following error:
```bash
pkg_resources.DistributionNotFound: The 'pyyaml' distribution was not found and is required by torch
```
This is a known issue and it was fixed in the latest pyTorch version (0.4.0) (see https://github.com/pytorch/pytorch/commit/03f2ad9029165d20585d00b11f45fe50de6b687f). Unfortunately, this version is not available in conda (at least in our channels), so what is the procedure to adopt ? Add ```pyyaml``` in ```requirements.txt``` ?
Any piece of advice welcome @amohammadi @andre.anjos @tiago.pereira !
Thanks
https://gitlab.idiap.ch/bob/bob.fusion.base/-/issues/5This package only support bob.bio score format.....2018-06-24T19:49:37ZTiago de Freitas PereiraThis package only support bob.bio score format........ and would be useful to have the support to `bob.measure` score format... and would be useful to have the support to `bob.measure` score formathttps://gitlab.idiap.ch/bob/bob.db.maskattack/-/issues/2Implement rc configuration for global variables2018-07-13T14:26:28ZGuillaume HEUSCHImplement rc configuration for global variableshttps://gitlab.idiap.ch/bob/bob.pad.face/merge_requests/72
https://www.idiap.ch/software/bob/docs/bob/docs/master/bob.extension/doc/rc.html
https://gitlab.idiap.ch/bob/bob.bio.base/issues/107https://gitlab.idiap.ch/bob/bob.pad.face/merge_requests/72
https://www.idiap.ch/software/bob/docs/bob/docs/master/bob.extension/doc/rc.html
https://gitlab.idiap.ch/bob/bob.bio.base/issues/107Guillaume HEUSCHGuillaume HEUSCHhttps://gitlab.idiap.ch/bob/bob.db.cpqd/-/issues/2Make this as a conda package2020-02-12T17:01:39ZTiago de Freitas PereiraMake this as a conda package- [ ] Conda package
- [ ] Nightlies
- [ ] Publish
ping @zmostaani @andre.anjos- [ ] Conda package
- [ ] Nightlies
- [ ] Publish
ping @zmostaani @andre.anjosTiago de Freitas PereiraTiago de Freitas Pereirahttps://gitlab.idiap.ch/bob/bob.db.nivl/-/issues/2Link to the stable doc is incorrect2019-01-11T14:35:43ZTiago de Freitas PereiraLink to the stable doc is incorrectTiago de Freitas PereiraTiago de Freitas Pereirahttps://gitlab.idiap.ch/bob/bob.learn.boosting/-/issues/2Nightlies (MacOSX) are failing because of this one2019-08-07T13:40:26ZTiago de Freitas PereiraNightlies (MacOSX) are failing because of this oneSegmentation fault :-|
https://gitlab.idiap.ch/bob/bob.nightlies/-/jobs/151320
```
+ nosetests --with-coverage --cover-package=bob.learn.boosting -sv bob.learn.boosting
test01_stump_boosting (bob.learn.boosting.tests.test_boosting.Test...Segmentation fault :-|
https://gitlab.idiap.ch/bob/bob.nightlies/-/jobs/151320
```
+ nosetests --with-coverage --cover-package=bob.learn.boosting -sv bob.learn.boosting
test01_stump_boosting (bob.learn.boosting.tests.test_boosting.TestBoosting) ... Starting 1 rounds of boosting
Starting round 1
Finished round 1 / 1
/Users/gitlab/builds/b6d3167a/0/bob/bob.nightlies/miniconda/conda-bld/bob.learn.boosting_1540886930750/test_tmp/run_test.sh: line 8: 42994 Segmentation fault: 11 nosetests --with-coverage --cover-package=bob.learn.boosting -sv bob.learn.boosting
Tests failed for bob.learn.boosting-2.0.16b0-py36ha733eee_11.tar.bz2 - moving package to /Users/gitlab/builds/b6d3167a/0/bob/bob.nightlies/miniconda/conda-bld/broken
WARNING:conda_build.build:Tests failed for bob.learn.boosting-2.0.16b0-py36ha733eee_11.tar.bz2 - moving package to /Users/gitlab/builds/b6d3167a/0/bob/bob.nightlies/miniconda/conda-bld/broken
TESTS FAILED: bob.learn.boosting-2.0.16b0-py36ha733eee_11.tar.bz2
(09:11:43.089) Error: Command Failed "/Users/gitlab/builds/b6d3167a/0/bob/bob.nightlies/miniconda/bin/conda build --no-anaconda-upload --variant-config-files /Users/gitlab/builds/b6d3167a/0/bob/bob.nightlies/src/bob.learn.boosting/_ci/conda_build_config.yaml --python=3.6 conda"
(09:11:43.095) Error: Command Failed "./_ci/build.sh"
(09:11:43.102) Error: Package bob.learn.boosting rebuild FAILED - aborting...
```André AnjosAndré Anjoshttps://gitlab.idiap.ch/bob/bob.db.cuhk_cufsf/-/issues/2Some annotations are inverted2018-11-19T18:26:19ZTiago de Freitas PereiraSome annotations are invertedI would need to redo some experimentsI would need to redo some experimentshttps://gitlab.idiap.ch/bob/bob.devtools/-/issues/2Garbled output2019-04-19T19:10:12ZAndré AnjosGarbled outputWith the new build system, the output of commands and the CI infrastructure can sometimes get garbled:
https://gitlab.idiap.ch/bob/bob.devtools/-/jobs/154891
It would be nice to fix this.With the new build system, the output of commands and the CI infrastructure can sometimes get garbled:
https://gitlab.idiap.ch/bob/bob.devtools/-/jobs/154891
It would be nice to fix this.André AnjosAndré Anjoshttps://gitlab.idiap.ch/bob/bob.db.casiasurf/-/issues/2bob.db.casiasurf is not in bob.nightlies2019-09-09T06:27:41ZAmir MOHAMMADIbob.db.casiasurf is not in bob.nightliesThis package is used at test time of bob.pad.face and bob.pad.face is in nightlies
Please add bob.db.casiasurf to bob.nightlies as well.This package is used at test time of bob.pad.face and bob.pad.face is in nightlies
Please add bob.db.casiasurf to bob.nightlies as well.Guillaume HEUSCHGuillaume HEUSCHhttps://gitlab.idiap.ch/bob/bob.learn.activation/-/issues/2Maybe load function must be a class method2019-08-13T05:09:31ZTiago de Freitas PereiraMaybe load function must be a class methodI was observing how load works and for me makes more sense this method to be a class method.
https://www.idiap.ch/software/bob/docs/bob/bob.learn.activation/master/py_api.html#bob.learn.activation.Activation.load
Worth changing this fo...I was observing how load works and for me makes more sense this method to be a class method.
https://www.idiap.ch/software/bob/docs/bob/bob.learn.activation/master/py_api.html#bob.learn.activation.Activation.load
Worth changing this for bob.bio.base#106 ?
ping @andre.anjoshttps://gitlab.idiap.ch/bob/bob.paper.fargo_tbiom_2019/-/issues/2mac OSx build ?2019-08-19T09:24:11ZGuillaume HEUSCHmac OSx build ?Hey @tiago.pereira
Sorry to bother you (again), but I don't understand why the mac OSX build is failing. Indeed, it should not be built in the first place, since the ``conda/meta.yml`` specifies:
```
build:
skip: true # [not linux]
...Hey @tiago.pereira
Sorry to bother you (again), but I don't understand why the mac OSX build is failing. Indeed, it should not be built in the first place, since the ``conda/meta.yml`` specifies:
```
build:
skip: true # [not linux]
```
To add some weirdness, the following pipeline https://gitlab.idiap.ch/bob/bob.paper.fargo_tbiom_2019/-/jobs/170682 succeeded 2 hours ago, and I did not change much in the ``conda/meta.yml`` file since.
Any idea ?
Thankshttps://gitlab.idiap.ch/bob/bob.paper.xcsmad_facepad/-/issues/2Issues in building this package2019-10-16T14:27:09ZTiago de Freitas PereiraIssues in building this packageHi @kkotwal,
The issues we were having with the builds are solved.
I just triggered your pipeline.
Check it out here
https://gitlab.idiap.ch/bob/bob.paper.xcsmad_facepad/pipelines/34411
cheersHi @kkotwal,
The issues we were having with the builds are solved.
I just triggered your pipeline.
Check it out here
https://gitlab.idiap.ch/bob/bob.paper.xcsmad_facepad/pipelines/34411
cheershttps://gitlab.idiap.ch/bob/bob.pipelines/-/issues/2Using scikit learn pipelines with Dask2020-03-02T14:00:48ZTiago de Freitas PereiraUsing scikit learn pipelines with DaskOpening this issue just as a note for posterity.
Today I've done an exercise using scikit-learn pipelines and dask https://ml.dask.org/compose.html
We could leverage from the scikit-api and benefit from its caching mechanism too.
You c...Opening this issue just as a note for posterity.
Today I've done an exercise using scikit-learn pipelines and dask https://ml.dask.org/compose.html
We could leverage from the scikit-api and benefit from its caching mechanism too.
You can check the small snipped below how I use it (I made an adaptor to transform our algorithms in scikit-estimators).
Two things to be observed. I couldn't use the `cache` since most of our stuff is C++ based (not picklable).
And since things are not picklable, I made a very shitty job with the adaptor in order to integrate it with Dask as you can see in code.
In order to have the instance creation of Bob objects in the Worker (like @andre.anjos is doing with the SampleLoader (probably for the same reason (there's another reason for that too))), the method `fit` creates the Bob object and returns itself.
I think the current design is cleaner. I will give up this one.
ping @amohammadi
```python
from sklearn.pipeline import Pipeline
# Local client
import dask.bag
from dask.distributed import Client, LocalCluster
import bob.bio.base
import bob.bio.face
import numpy
cache_dir = "./cache"
from sklearn.base import BaseEstimator
class Scikit2BobEstimator(BaseEstimator):
"""
Base class to adapt from bob algorithms to scikit estimators
Check here for more info:
https://scikit-learn.org/stable/modules/generated/sklearn.base.BaseEstimator.html
"""
def __init__(self, bob_object):
self.bob_class = bob_object
def fit(self, X, y, **kwargs):
self.bob_object = self.bob_class(**kwargs)
return self
def transform(self, X, **kwargs):
"""
Here `X` can be our samples where the annotations can be shipped.
"""
annotations = {"leye": (10, 10), "reye": (20, 10)}
return [self.bob_object(x, annotations=annotations) for x in X]
### Starting the client
cluster = LocalCluster(nanny=False, processes=False, n_workers=1, threads_per_worker=1)
cluster.scale_up(1)
client = Client(cluster)
####### PREPROCESSOR #########
# Using face crop
CROPPED_IMAGE_HEIGHT = 80
CROPPED_IMAGE_WIDTH = CROPPED_IMAGE_HEIGHT * 4 // 5
## eye positions for frontal images
RIGHT_EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 4 - 1)
LEFT_EYE_POS = (CROPPED_IMAGE_HEIGHT // 5, CROPPED_IMAGE_WIDTH // 4 * 3)
import functools
preprocessor = Scikit2BobEstimator(
functools.partial(
bob.bio.face.preprocessor.FaceCrop,
cropped_image_size=(CROPPED_IMAGE_HEIGHT, CROPPED_IMAGE_WIDTH),
cropped_positions={"leye": LEFT_EYE_POS, "reye": RIGHT_EYE_POS},
)
)
### EXTRACTOR #######
extractor = Scikit2BobEstimator(bob.bio.base.extractor.Linearize)
estimators = [("preprocess", preprocessor), ("extractor", extractor)]
#### HERE I COULD CACHE IT #####
# pipeline = Pipeline(estimators, memory=cache_dir)
pipeline = Pipeline(estimators)
X = [numpy.random.rand(3, 100, 100) for _ in range(100)]
db = dask.bag.from_sequence(X)
db = db.map_partitions(pipeline.fit_transform)
print(db.compute(scheduler=client))
client.shutdown()
```https://gitlab.idiap.ch/bob/bob.db.swan/-/issues/2Add this package to Bob meta packages2020-02-14T14:58:21ZAmir MOHAMMADIAdd this package to Bob meta packagesbob/bob bob/docs bob/bob.nightliesbob/bob bob/docs bob/bob.nightliesAmir MOHAMMADIAmir MOHAMMADIhttps://gitlab.idiap.ch/bob/bob.db.ldhf/-/issues/2Add this to bob and docs repositories2020-02-14T14:56:59ZAmir MOHAMMADIAdd this to bob and docs repositoriesAmir MOHAMMADIAmir MOHAMMADIhttps://gitlab.idiap.ch/bob/bob.paper.pad_mccnns_swirdiff/-/issues/2Branches2020-03-09T08:20:30ZAnjith GEORGEanjith.george@idiap.chBranchesNow that the revision is submitted, we can use `bob.db.hqwmca` for all further experiments is it?.
And can you merge the branch to master if the configs are final.Now that the revision is submitted, we can use `bob.db.hqwmca` for all further experiments is it?.
And can you merge the branch to master if the configs are final.Guillaume HEUSCHGuillaume HEUSCHhttps://gitlab.idiap.ch/bob/bob.db.meds/-/issues/2Create protocols2020-04-07T12:14:19ZTiago de Freitas PereiraCreate protocolsHey @ydayer,
As discussed previously, we need some verification protocols for this dataset.
Looking at the meds README (/i***p/r******e/database/MEDS/), there are two things that we can analyse in this dataset, the ethnicity aspect (ca...Hey @ydayer,
As discussed previously, we need some verification protocols for this dataset.
Looking at the meds README (/i***p/r******e/database/MEDS/), there are two things that we can analyse in this dataset, the ethnicity aspect (caucasian/black) and age.
Unfortunatelly, we don't have enough data for gender.
Would be nice to have 3 fold verification protocol containing **only men** AND **only black/caucasian** such that:
- [x] the `world` has all the samples that has only one image per identiy
- [x] the `dev` set has 50% of the images with more than one image per identiy
- [x] the `eval` set has 50% of the images with more than one image per identiy
- [x] for each fold you randomize the identities in the dev/eval set
Is it possible to carry this on?
Thanks
Cheershttps://gitlab.idiap.ch/bob/bob.db.livdet2013/-/issues/2DEPRECATION. Question2020-04-24T08:18:51ZTiago de Freitas PereiraDEPRECATION. QuestionHi @bob, sorry for the spam, but this is needed.
With our new efforts to put this new pipeline mechanism in place, shall we deprecate this package?
Who is against it?
ThanksHi @bob, sorry for the spam, but this is needed.
With our new efforts to put this new pipeline mechanism in place, shall we deprecate this package?
Who is against it?
Thankshttps://gitlab.idiap.ch/bob/bob.db.verafinger/-/issues/2DEPRECATION. Question2022-05-16T14:42:09ZTiago de Freitas PereiraDEPRECATION. QuestionHi @bob, sorry for the spam, but this is needed.
With our new efforts to put this new pipeline mechanism in place, shall we deprecate this package?
Who is against it?
ThanksHi @bob, sorry for the spam, but this is needed.
With our new efforts to put this new pipeline mechanism in place, shall we deprecate this package?
Who is against it?
ThanksBob 9.0.0https://gitlab.idiap.ch/bob/bob.db.morph/-/issues/2This dataset has wrong annotations2020-12-22T08:28:41ZTiago de Freitas PereiraThis dataset has wrong annotationsHey @ydayer,
I'm rearranging world, dev, and eval in this dataset.
Have you noticed that the metadata is inconsistent?
For instance, if you do:
```python
>>> dataframe[dataframe.id_num==286810]
id_num picture_num dob ...Hey @ydayer,
I'm rearranging world, dev, and eval in this dataset.
Have you noticed that the metadata is inconsistent?
For instance, if you do:
```python
>>> dataframe[dataframe.id_num==286810]
id_num picture_num dob doa race gender age photo
37850 286810 1 04/04/1986 05/11/2006 B M 20 Album2/286810_01M20.JPG
37851 286810 2 04/04/1986 08/16/2006 A M 20 Album2/286810_02M20.JPG
37849 286810 0 04/04/1986 01/24/2006 H M 19 Album2/286810_00M19.JPG
```
```python
>>> dataframe[dataframe.id_num==295087]
id_num picture_num dob doa race gender age photo
39551 295087 0 05/18/1960 10/23/2006 A M 46 Album2/295087_00M46.JPG
39552 295087 1 05/18/1960 10/25/2006 H M 46 Album2/295087_01M46.JPG
````
```python
dataframe[dataframe.id_num==328749]
id_num picture_num dob doa race gender age photo
50810 328749 0 07/28/1971 05/12/2006 W M 34 Album2/328749_00M34.JPG
50811 328749 1 07/28/1971 05/19/2007 A M 35 Album2/328749_01M35.JPG
```
There are several more examples