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Cannot find the 'metadata.csv' #24

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dremofly opened this issue Aug 10, 2018 · 6 comments
Open

Cannot find the 'metadata.csv' #24

dremofly opened this issue Aug 10, 2018 · 6 comments

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@dremofly
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After run the code, I'm sorry to find 'metadata.csv' does not exist. And I could not find it in the homepage of TGS. I was wondering how to solve this problem.

@jakubczakon
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You need to generate it first by running

neptune run --config configs/neptune.yaml main.py prepare_metadata

remember to specify the paths in the neptune.yaml first.

Thanks for spotting this. The Readme.md was missing this part of the instruction.

@saurabh502
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How to generate metadata.csv using python only?

when I am running below command:
python main.py prepare_metadata

I am getting error:

neptune: Executing in Offline Mode.
neptune: Error: Invalid parameter 'prepare_metadata'. Parameter names must begin with double dash.

@dremofly
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@saurabh502
Right command is
python main.py -- prepare_metadata

@dremofly
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@jakubczakon
Thanks for your replying. I have solved this problem by myself. And I found that there was an error in README. The command of training the model missing a space bettween train and the later --.

@LisburnLad
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LisburnLad commented Aug 29, 2018

@jakubczakon

Hi - could you clarify where the metadata.csv file should be?

I eventually got the prepare_metadata step running in the cloud, which has put the metadata.csv file into SAL-10\output\metadata.csv

I then changed the neptune.yaml file back to "metadata_filepath: /input/metadata.csv" as per the instructions and ran the line to train the network, but this comes up with an error:

"You cannot use input that is not in Neptune storage."

Does the command line to train the network need to include "SAL-10" somewhere? Plus the metadata.csv file is sitting in the output directory of SAL-10 - does it need to move to an input directory?

Thanks

@jakubczakon
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jakubczakon commented Aug 30, 2018

Hi @LisburnLad

There was a typo-like mistake in the command. It should indeed, contain the reference to SAL-10

neptune send 
--worker m-p100 \
--environment pytorch-0.3.1-gpu-py3 \
--config configs/neptune.yaml \
--input /SAL-10/output/metadata.csv \
main.py train --pipeline_name unet

Should work in your case.

jakubczakon added a commit that referenced this issue Oct 13, 2018
* Hypercolumn (#16)

* fixed lovash loss, added helpers for loss weighing (#14)

* updated results exploration, added unet with hypercolumn

* updated with lighter hypercolumn setup

* Model average (#17)

* added prediction average notebook

* added simple average notebook

* added replication pad instead of zero pad (#18)

* changed to heng-like arch, added channel and spatial squeeze and excite, extended hypercolumn (#19)

* Update unet_models.py

typo in resnet unet fixed

* added resnet 18 an50 pretrained options, unified hyper and vanilla in one class (#20)

* Update models.py

Changed old class import and namings

* Loss design (#21)

* local

* initial

* formated results

* added focal, added border weighing, added size weighing added focus, added loss desing notebook

* fixed wrong focal definition, updated loss api

* exp with dropped borders

* set best params, not using weighing for now

* Dev depth experiments (#23)

* add depth layer in input

* reduce lr on plateau scheduler

* depth channels transformer

* fix reduce lr

* bugfix

* change default config

* added adaptive threshold in callbacks (#24)

* added adaptive threshold in callbacks

* fix

* added initial lr selector (#25)

* Initial lb selector (#26)

* added initial lr selector

* small refactor

* Auxiliary data small masks (#27)

* exping

* auxiliary data for border masks generated
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