TGS Salt Identification Challenge

Last update: Nov 04, 2022

Overview

TGS Salt Identification Challenge

This is an open solution to the TGS Salt Identification Challenge.

Note

Unfortunately, we can no longer provide support for this repo. Hopefully, it should still work, but if it doesn't, we cannot really help.

More competitions 🎇

Check collection of public projects 🎁 , where you can find multiple Kaggle competitions with code, experiments and outputs.

Our goals

We are building entirely open solution to this competition. Specifically:

Learning from the process - updates about new ideas, code and experiments is the best way to learn data science. Our activity is especially useful for people who wants to enter the competition, but lack appropriate experience.
Encourage more Kagglers to start working on this competition.
Deliver open source solution with no strings attached. Code is available on our GitHub repository 💻 . This solution should establish solid benchmark, as well as provide good base for your custom ideas and experiments. We care about clean code 😃
We are opening our experiments as well: everybody can have live preview on our experiments, parameters, code, etc. Check: TGS Salt Identification Challenge 📈 or screen below.

Train and validation monitor 📊

Disclaimer

In this open source solution you will find references to the neptune.ai. It is free platform for community Users, which we use daily to keep track of our experiments. Please note that using neptune.ai is not necessary to proceed with this solution. You may run it as plain Python script 🐍 .

How to start?

Learn about our solutions

Check Kaggle forum and participate in the discussions.
See solutions below:

Link to Experiments	CV	LB	Open
solution 1	0.413	0.745	True
solution 2	0.794	0.798	True
solution 3	0.807	0.801	True
solution 4	0.802	0.809	True
solution 5	0.804	0.813	True
solution 6	0.819	0.824	True
solution 7	0.829	0.837	True
solution 8	0.830	0.845	True
solution 9	0.853	0.849	True

Start experimenting with ready-to-use code

You can jump start your participation in the competition by using our starter pack. Installation instruction below will guide you through the setup.

Installation

Clone repository

git clone https://github.com/minerva-ml/open-solution-salt-identification.git

Set-up environment

You can setup the project with default env variables and open NEPTUNE_API_TOKEN by running:

source Makefile

I suggest at least reading the step-by-step instructions to know what is happening.

Install conda environment salt

conda env create -f environment.yml

After it is installed you can activate/deactivate it by running:

conda activate salt

conda deactivate

Register to the neptune.ai (if you wish to use it) even if you don't register you can still see your experiment in Neptune. Just go to shared/showroom project and find it.

Set environment variables NEPTUNE_API_TOKEN and CONFIG_PATH.

If you are using the default neptune.yaml config then run:

export export CONFIG_PATH=neptune.yaml

otherwise you can change to your config.

Registered in Neptune:

Set NEPTUNE_API_TOKEN variable with your personal token:

export NEPTUNE_API_TOKEN=your_account_token

Create new project in Neptune and go to your config file (neptune.yaml) and change project name:

project: USER_NAME/PROJECT_NAME

Not registered in Neptune:

open token

export NEPTUNE_API_TOKEN=eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vdWkubmVwdHVuZS5tbCIsImFwaV9rZXkiOiJiNzA2YmM4Zi03NmY5LTRjMmUtOTM5ZC00YmEwMzZmOTMyZTQifQ==

Create data folder structure and set data paths in your config file (`neptune.yaml`)

Suggested directory structure:

project
|--   README.md
|-- ...
|-- data
    |-- raw
         |-- train 
            |-- images 
            |-- masks
         |-- test 
            |-- images
         |-- train.csv
         |-- sample_submission.csv
    |-- meta
        │-- depths.csv
        │-- metadata.csv # this is generated
        │-- auxiliary_metadata.csv # this is generated
    |-- stacking_data
        |-- out_of_folds_predictions # put oof predictions for multiple models/pipelines here
    |-- experiments
        |-- baseline # this is where your experiment files will be dumped
            |-- checkpoints # neural network checkpoints
            |-- transformers # serialized transformers after fitting
            |-- outputs # outputs of transformers if you specified save_output=True anywhere
            |-- out_of_fold_train_predictions.pkl # oof predictions on train
            |-- out_of_fold_test_predictions.pkl # oof predictions on test
            |-- submission.csv
        |-- empty_non_empty 
        |-- new_idea_exp

in neptune.yaml config file change data paths if you decide on a different structure:

  # Data Paths
  train_images_dir: data/raw/train
  test_images_dir: data/raw/test
  metadata_filepath: data/meta/metadata.csv
  depths_filepath: data/meta/depths.csv
  auxiliary_metadata_filepath: data/meta/auxiliary_metadata.csv
  stacking_data_dir: data/stacking_data

Run experiment based on U-Net:

Prepare metadata:

python prepare_metadata.py

Training and inference. Everything happens in main.py. Whenever you try new idea make sure to change the name of the experiment:

EXPERIMENT_NAME = 'baseline'

to a new name.

python main.py

You can always change the pipeline you want ot run in the main. For example, if I want to run just training and evaluation I can change `main.py':

if __name__ == '__main__':
    train_evaluate_cv()

References

1.Lovash Loss

@InProceedings{Berman_2018_CVPR,
author = {Berman, Maxim and Rannen Triki, Amal and Blaschko, Matthew B.},
title = {The Lovász-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}

Get involved

You are welcome to contribute your code and ideas to this open solution. To get started:

Check competition project on GitHub to see what we are working on right now.
Express your interest in paticular task by writing comment in this task, or by creating new one with your fresh idea.
We will get back to you quickly in order to start working together.
Check CONTRIBUTING for some more information.

User support

There are several ways to seek help:

Kaggle discussion is our primary way of communication.
Submit an issue directly in this repo.

Comments

Conflicts between open-solution-salt-identification and ipython

Hi, users are unable to run open-solution-salt-identification due to dependency conflict with ipython package. As shown in the following full dependency graph of open-solution-salt-identification, open-solution-salt-identification requires ipython==6.3.1, while steppy==0.1.6 requires _ipython >= 6.4.0. According to pip’s “first found wins” installation strategy, ipython 6.3.1 is the actually installed version. However, ipython 6.3.1 does not satisfy >=6.4.0.

Dependency tree------

open-solution-salt-identification-master<version range:>
| +- attrdict<version range:==2.0.0>
| | +- six<version range:>
| +- cffi<version range:==1.11.5>
| +- click<version range:==6.7>
| +- cython<version range:==0.28.2>
| +- imageio<version range:==2.2.0>
| | +- numpy<version range:>
| | +- pillow<version range:>
| +- imgaug<version range:==0.2.5>
| | +- numpy<version range:>=1.7.0>
| | +- scikit-image<version range:>=0.11.0>
| | +- scipy<version range:>
| | +- six<version range:>
| +- ipython<version range:==6.3.1>
| +- kaggle<version range:==1.3.11.1>
| | +- certifi<version range:>
| | +- python-dateutil<version range:>
| | +- requests<version range:>
| | +- six<version range:>=1.10>
| | +- tqdm<version range:>
| | +- urllib3<version range:>=1.15,<1.23.0>
| +- neptune-cli<version range:>
| | +- argcomplete<version range:>=1.9.3>
| | +- enum34<version range:>=1.1.6>
| | +- flask<version range:==0.12.0>
| | +- future<version range:>=0.16.0>
| | +- gitpython<version range:>=2.0.8>
| | +- humanize<version range:>=0.5.1>
| | +- kitchen<version range:>=1.2.4>
| | +- more-itertools<version range:>=4.1.0>
| | +- nvidia-ml-py3<version range:>=7.352.0>
| | +- pathlib2<version range:==2.3.0>
| | +- pillow<version range:>=1.7.6>
| | +- psutil<version range:>=5.4.3>
| | +- pyjwt<version range:>=1.5.2>
| | +- pykwalify<version range:<1.6.0,>=1.5.2>
| | | +- docopt<version range:>=0.6.2>
| | | +- python-dateutil<version range:>=2.4.2>
| | | +- pyyaml<version range:>=3.11>
| | +- raven<version range:>=6.1.0>
| | +- requests<version range:>=2.11.1>
| | +- requests-oauthlib<version range:>=0.8.0>
| | +- terminaltables<version range:>=2.1.0,<3.0.0>
| | +- tqdm<version range:>=4.11.2>
| | +- voluptuous<version range:>=0.9.3>
| | +- websocket-client<version range:>=0.35.0>
| +- numpy<version range:==1.14.0>
| +- opencv-python<version range:==3.4.0.12>
| +- pandas<version range:==0.22.0>
| +- pillow<version range:==5.1.0>
| +- pretrainedmodels<version range:==0.7.0>
| | +- munch<version range:>
| | | +- six<version range:>
| | +- torch<version range:>
| | +- torchvision<version range:>
| +- pycocotools<version range:==2.0.0>
| +- pydot-ng<version range:==1.0.0>
| | +- pyparsing<version range:>=2.0.1>
| +- pyyaml<version range:>=4.2b1>
| +- scikit-image<version range:==0.13.1>
| +- scikit-learn<version range:==0.19.2>
| +- scipy<version range:==1.0.0>
| +- steppy<version range:==0.1.6>
| | +- ipython<version range:>=6.4.0>
| | +- numpy<version range:>=1.14.0>
| | +- pydot-ng<version range:>=1.0.0>
| | | +- pyparsing<version range:>=2.0.1>
| | +- pytest<version range:>=3.6.0>
| | +- scikit-learn<version range:>=0.19.0>
| | +- scipy<version range:>=1.0.0>
| | +- setuptools<version range:>=39.2.0>
| | +- typing<version range:>=3.6.4>
| +- steppy-toolkit<version range:==0.1.5>
| | +- attrdict<version range:>=2.0.0>
| | | +- six<version range:>
| | +- neptune-cli<version range:>=2.8.5>
| | | +- argcomplete<version range:>=1.9.3>
| | | +- enum34<version range:>=1.1.6>
| | | +- flask<version range:==0.12.0>
| | | +- future<version range:>=0.16.0>
| | | +- gitpython<version range:>=2.0.8>
| | | +- humanize<version range:>=0.5.1>
| | | +- kitchen<version range:>=1.2.4>
| | | +- more-itertools<version range:>=4.1.0>
| | | +- nvidia-ml-py3<version range:>=7.352.0>
| | | +- pathlib2<version range:==2.3.0>
| | | +- pillow<version range:>=1.7.6>
| | | +- psutil<version range:>=5.4.3>
| | | +- pyjwt<version range:>=1.5.2>
| | | +- pykwalify<version range:<1.6.0,>=1.5.2>
| | | | +- docopt<version range:>=0.6.2>
| | | | +- python-dateutil<version range:>=2.4.2>
| | | | +- pyyaml<version range:>=3.11>
| | | +- raven<version range:>=6.1.0>
| | | +- requests<version range:>=2.11.1>
| | | +- requests-oauthlib<version range:>=0.8.0>
| | | +- terminaltables<version range:>=2.1.0,<3.0.0>
| | | +- tqdm<version range:>=4.11.2>
| | | +- voluptuous<version range:>=0.9.3>
| | | +- websocket-client<version range:>=0.35.0>
| | +- numpy<version range:>=1.14.3>
| | +- pandas<version range:>=0.23.0>
| | +- pytest<version range:>=3.6.0>
| | +- setuptools<version range:>=39.2.0>
| | +- steppy<version range:>=0.1.4>
| | | +- ipython<version range:>=6.4.0>
| | | +- numpy<version range:>=1.14.0>
| | | +- pydot-ng<version range:>=1.0.0>
| | | | +- pyparsing<version range:>=2.0.1>
| | | +- pytest<version range:>=3.6.0>
| | | +- scikit-learn<version range:>=0.19.0>
| | | +- scipy<version range:>=1.0.0>
| | | +- setuptools<version range:>=39.2.0>
| | | +- typing<version range:>=3.6.4>
| +- torch<version range:==0.3.1>
| +- torchvision<version range:==0.2.0>
| +- tqdm<version range:==4.23.0>

Solution

Could you please loose your version of ipython (change ipython==6.3.1 to >=6.3.1)? Thanks for your help. Best, Neolith

opened by NeolithEra 5

Problem with submission.csv

There is problem with submission.csv file as kaggle is not accepting the file and showing error

Evaluation Exception: Index was outside the bounds of the array.

There might be some problem with rle encoding

opened by ymittal23 5

Installation fails due to another conflicting pandas version

Hi, another dependency hell issue in open-solution-salt-identification. Users are unable to run open-solution-salt-identification due to dependency conflict with pandas package. As shown in the following full dependency graph of open-solution-salt-identification, this project directly requires pandas==0.22.0，while steppy-toolkit==0.1.5 requires _pandas>=0.23.0. According to pip’s “first found wins” installation strategy, pandas==0.22.0 is the actually installed version. However, pandas==0.22.0 does not satisfy >=0.23.0.

Dependency tree-----

open-solution-salt-identification-master<version range:>
| +- attrdict<version range:==2.0.0>
| | +- six<version range:>
| +- cffi<version range:==1.11.5>
| +- click<version range:==6.7>
| +- cython<version range:==0.28.2>
| +- imageio<version range:==2.2.0>
| | +- numpy<version range:>
| | +- pillow<version range:>
| +- imgaug<version range:==0.2.5>
| | +- numpy<version range:>=1.7.0>
| | +- scikit-image<version range:>=0.11.0>
| | +- scipy<version range:>
| | +- six<version range:>
| +- ipython<version range:==6.3.1>
| +- kaggle<version range:==1.3.11.1>
| | +- certifi<version range:>
| | +- python-dateutil<version range:>
| | +- requests<version range:>
| | +- six<version range:>=1.10>
| | +- tqdm<version range:>
| | +- urllib3<version range:>=1.15,<1.23.0>
| +- neptune-cli<version range:>
| | +- argcomplete<version range:>=1.9.3>
| | +- enum34<version range:>=1.1.6>
| | +- flask<version range:==0.12.0>
| | +- future<version range:>=0.16.0>
| | +- gitpython<version range:>=2.0.8>
| | +- humanize<version range:>=0.5.1>
| | +- kitchen<version range:>=1.2.4>
| | +- more-itertools<version range:>=4.1.0>
| | +- nvidia-ml-py3<version range:>=7.352.0>
| | +- pathlib2<version range:==2.3.0>
| | +- pillow<version range:>=1.7.6>
| | +- psutil<version range:>=5.4.3>
| | +- pyjwt<version range:>=1.5.2>
| | +- pykwalify<version range:<1.6.0,>=1.5.2>
| | | +- docopt<version range:>=0.6.2>
| | | +- python-dateutil<version range:>=2.4.2>
| | | +- pyyaml<version range:>=3.11>
| | +- raven<version range:>=6.1.0>
| | +- requests<version range:>=2.11.1>
| | +- requests-oauthlib<version range:>=0.8.0>
| | +- terminaltables<version range:>=2.1.0,<3.0.0>
| | +- tqdm<version range:>=4.11.2>
| | +- voluptuous<version range:>=0.9.3>
| | +- websocket-client<version range:>=0.35.0>
| +- numpy<version range:==1.14.0>
| +- opencv-python<version range:==3.4.0.12>
| +- pandas<version range:==0.22.0>
| +- pillow<version range:==5.1.0>
| +- pretrainedmodels<version range:==0.7.0>
| | +- munch<version range:>
| | | +- six<version range:>
| | +- torch<version range:>
| | +- torchvision<version range:>
| +- pycocotools<version range:==2.0.0>
| +- pydot-ng<version range:==1.0.0>
| | +- pyparsing<version range:>=2.0.1>
| +- pyyaml<version range:>=4.2b1>
| +- scikit-image<version range:==0.13.1>
| +- scikit-learn<version range:==0.19.2>
| +- scipy<version range:==1.0.0>
| +- steppy<version range:==0.1.6>
| | +- ipython<version range:>=6.4.0>
| | +- numpy<version range:>=1.14.0>
| | +- pydot-ng<version range:>=1.0.0>
| | | +- pyparsing<version range:>=2.0.1>
| | +- pytest<version range:>=3.6.0>
| | +- scikit-learn<version range:>=0.19.0>
| | +- scipy<version range:>=1.0.0>
| | +- setuptools<version range:>=39.2.0>
| | +- typing<version range:>=3.6.4>
| +- steppy-toolkit<version range:==0.1.5>
| | +- attrdict<version range:>=2.0.0>
| | | +- six<version range:>
| | +- neptune-cli<version range:>=2.8.5>
| | | +- argcomplete<version range:>=1.9.3>
| | | +- enum34<version range:>=1.1.6>
| | | +- flask<version range:==0.12.0>
| | | +- future<version range:>=0.16.0>
| | | +- gitpython<version range:>=2.0.8>
| | | +- humanize<version range:>=0.5.1>
| | | +- kitchen<version range:>=1.2.4>
| | | +- more-itertools<version range:>=4.1.0>
| | | +- nvidia-ml-py3<version range:>=7.352.0>
| | | +- pathlib2<version range:==2.3.0>
| | | +- pillow<version range:>=1.7.6>
| | | +- psutil<version range:>=5.4.3>
| | | +- pyjwt<version range:>=1.5.2>
| | | +- pykwalify<version range:<1.6.0,>=1.5.2>
| | | | +- docopt<version range:>=0.6.2>
| | | | +- python-dateutil<version range:>=2.4.2>
| | | | +- pyyaml<version range:>=3.11>
| | | +- raven<version range:>=6.1.0>
| | | +- requests<version range:>=2.11.1>
| | | +- requests-oauthlib<version range:>=0.8.0>
| | | +- terminaltables<version range:>=2.1.0,<3.0.0>
| | | +- tqdm<version range:>=4.11.2>
| | | +- voluptuous<version range:>=0.9.3>
| | | +- websocket-client<version range:>=0.35.0>
| | +- numpy<version range:>=1.14.3>
| | +- pandas<version range:>=0.23.0>
| | +- pytest<version range:>=3.6.0>
| | +- setuptools<version range:>=39.2.0>
| | +- steppy<version range:>=0.1.4>
| | | +- ipython<version range:>=6.4.0>
| | | +- numpy<version range:>=1.14.0>
| | | +- pydot-ng<version range:>=1.0.0>
| | | | +- pyparsing<version range:>=2.0.1>
| | | +- pytest<version range:>=3.6.0>
| | | +- scikit-learn<version range:>=0.19.0>
| | | +- scipy<version range:>=1.0.0>
| | | +- setuptools<version range:>=39.2.0>
| | | +- typing<version range:>=3.6.4>
| +- torch<version range:==0.3.1>
| +- torchvision<version range:==0.2.0>
| +- tqdm<version range:==4.23.0>

Solution

Relax the version range of pandas (change pandas==0.22.0 to >=0.22.0).

Thanks for your help. Best, Neolith

opened by NeolithEra 3

No module named 'neptune'

Firstly I run "pip install -r requirements.txt". Then I try to run "python main.py", but got a error "No module named 'neptune'". Did I miss something important ?

opened by qinhui99 2

TypeError: can't multiply sequence by non-int of type 'float' while training

I'm trying to run locally solution-3

Full stack of the error:

TypeError                                 Traceback (most recent call last)
<ipython-input-5-2da0ffaf5447> in <module>()
----> 1 train()

<ipython-input-3-0a8b44c1d62a> in train()
    314     pipeline_network = unet(config=CONFIG, train_mode=True)
    315     pipeline_network.clean_cache()
--> 316     pipeline_network.fit_transform(data)
    317     pipeline_network.clean_cache()
    318 

~/anaconda3/lib/python3.6/site-packages/steppy/base.py in fit_transform(self, data)
    321             else:
    322                 step_inputs = self._unpack(step_inputs)
--> 323             step_output_data = self._cached_fit_transform(step_inputs)
    324         return step_output_data
    325 

~/anaconda3/lib/python3.6/site-packages/steppy/base.py in _cached_fit_transform(self, step_inputs)
    441             else:
    442                 logger.info('Step {}, fitting and transforming...'.format(self.name))
--> 443                 step_output_data = self.transformer.fit_transform(**step_inputs)
    444                 logger.info('Step {}, persisting transformer to the {}'
    445                             .format(self.name, self.exp_dir_transformers_step))

~/anaconda3/lib/python3.6/site-packages/steppy/base.py in fit_transform(self, *args, **kwargs)
    603             dict: outputs
    604         """
--> 605         self.fit(*args, **kwargs)
    606         return self.transform(*args, **kwargs)
    607 

~/Desktop/ml/salt/open-solution-salt-identification-master/common_blocks/models.py in fit(self, datagen, validation_datagen, meta_valid)
     61             self.model = self.model.cuda()
     62 
---> 63         self.callbacks.set_params(self, validation_datagen=validation_datagen, meta_valid=meta_valid)
     64         self.callbacks.on_train_begin()
     65 

~/Desktop/ml/salt/open-solution-salt-identification-master/common_blocks/callbacks.py in set_params(self, *args, **kwargs)
     89     def set_params(self, *args, **kwargs):
     90         for callback in self.callbacks:
---> 91             callback.set_params(*args, **kwargs)
     92 
     93     def on_train_begin(self, *args, **kwargs):

~/Desktop/ml/salt/open-solution-salt-identification-master/common_blocks/callbacks.py in set_params(self, transformer, validation_datagen, *args, **kwargs)
    235         self.optimizer = transformer.optimizer
    236         self.loss_function = transformer.loss_function
--> 237         self.lr_scheduler = ExponentialLR(self.optimizer, self.gamma, last_epoch=-1)
    238 
    239     def on_train_begin(self, *args, **kwargs):

~/anaconda3/lib/python3.6/site-packages/torch/optim/lr_scheduler.py in __init__(self, optimizer, gamma, last_epoch)
    155     def __init__(self, optimizer, gamma, last_epoch=-1):
    156         self.gamma = gamma
--> 157         super(ExponentialLR, self).__init__(optimizer, last_epoch)
    158 
    159     def get_lr(self):

~/anaconda3/lib/python3.6/site-packages/torch/optim/lr_scheduler.py in __init__(self, optimizer, last_epoch)
     19                                    "in param_groups[{}] when resuming an optimizer".format(i))
     20         self.base_lrs = list(map(lambda group: group['initial_lr'], optimizer.param_groups))
---> 21         self.step(last_epoch + 1)
     22         self.last_epoch = last_epoch
     23 

~/anaconda3/lib/python3.6/site-packages/torch/optim/lr_scheduler.py in step(self, epoch)
     29             epoch = self.last_epoch + 1
     30         self.last_epoch = epoch
---> 31         for param_group, lr in zip(self.optimizer.param_groups, self.get_lr()):
     32             param_group['lr'] = lr
     33 

~/anaconda3/lib/python3.6/site-packages/torch/optim/lr_scheduler.py in get_lr(self)
    159     def get_lr(self):
    160         return [base_lr * self.gamma ** self.last_epoch
--> 161                 for base_lr in self.base_lrs]
    162 
    163 

~/anaconda3/lib/python3.6/site-packages/torch/optim/lr_scheduler.py in <listcomp>(.0)
    159     def get_lr(self):
    160         return [base_lr * self.gamma ** self.last_epoch
--> 161                 for base_lr in self.base_lrs]
    162 
    163 

TypeError: can't multiply sequence by non-int of type 'float'

SO suggest to convert list to numpy this way np.asarray(coff) * C

but actually I'm kind confused where to apply it

opened by Diyago 2

Can not find the 'metadata.csv'

When I run the command 'python main.py -- train--pipeline_name unet', I got the error "Can not find the 'metadata.csv'" . I checked all the files of salt-detection and still can not find it. Did I miss something important?

opened by qinhui99 2
Lovasz loss

Hi,

Thank you for the code. I am attempting to run your code with the lovasz hinge loss, and run into the following error -- please let me know if there is a quick way to fix this.

File "..../lovash_losses.py", line 105, in lovasz_hinge_flat errors = (1. - logits * Variable(signs)) RuntimeError: Variable data has to be a tensor, but got Variable

Apologies if this is a simple question -- recently converted from Keras, so not that familiar with Torch.

-Qing

opened by yuanqing811 1
OSError: [Errno 101] Network is unreachable

COULD YOU TELL ME HOW TO FIX IT, THANKS

Downloading: "https://download.pytorch.org/models/resnet152-b121ed2d.pth" to /ddn/home/.torch/models/resnet152-b121ed2d.pth Traceback (most recent call last): File "/ddn/data/enteESC[Denter/lib/python3.6/urllib/request.py", line 1318, in do_open encode_chunked=req.has_header('Transfer-encoding')) File "/ddn/data/enteESC[Denter/lib/python3.6/http/client.py", line 1239, in request self._send_request(method, url, body, headers, encode_chunked) File "/ddn/data/enteESC[Denter/lib/python3.6/http/client.py", line 1285, in _send_request self.endheaders(body, encode_chunked=encode_chunked) File "/ddn/data/enteESC[Denter/lib/python3.6/http/client.py", line 1234, in endheaders self._send_output(message_body, encode_chunked=encode_chunked) File "/ddn/data/enteESC[Denter/lib/python3.6/http/client.py", line 1026, in _send_output self.send(msg) File "/ddn/data/enteESC[Denter/lib/python3.6/http/client.py", line 964, in send self.connect() File "/ddn/data/lxdc43/enteESC[Denter/lib/python3.6/http/client.py", line 1392, in connect super().connect() File "/ddn/data/enteESC[Denter/lib/python3.6/http/client.py", line 936, in connect (self.host,self.port), self.timeout, self.source_address) File "/ddn/data/enteESC[Denter/lib/python3.6/socket.py", line 724, in create_connection raise err File "/ddn/data/enteESC[Denter/lib/python3.6/socket.py", line 713, in create_connection sock.connect(sa) OSError: [Errno 101] Network is unreachable

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "/ddn/home/lxdc43/kaggle/salt_identification/main.py", line 771, in train_evaluate_predict_cv() File "/ddn/home/kaggle/salt_identification/main.py", line 504, in train_evaluate_predict_cv fold_id) File "/ddn/home/kaggle/salt_identification/main.py", line 600, in fold_fit_evaluate_predict_loop iou, iout, predicted_masks_valid = fold_fit_evaluate_loop(train_data_split, valid_data_split, fold_id) File "/ddn/home/kaggle/salt_identification/main.py", line 635, in fold_fit_evaluate_loop pipeline_network = unet(config=config, suffix='fold{}'.format(fold_id), train_mode=True) File "/ddn/home/kaggle/salt_identification/main.py", line 268, in unet transformer=models.PyTorchUNet(**config.model['unet']), File "/ddn/home/kaggle/salt_identification/common_blocks/models.py", line 50, in init self.set_model() File "/ddn/home/kaggle/salt_identification/common_blocks/models.py", line 160, in set_model **config['model_config']) File "/ddn/home/lxdc43/kaggle/salt_identification/common_blocks/unet_models.py", line 114, in init self.encoder = torchvision.models.resnet152(pretrained=pretrained) File "/ddn/data/lxdc43/enteESC[Denter/lib/python3.6/site-packages/torchvision/models/resnet.py", line 212, in resnet152 model.load_state_dict(model_zoo.load_url(model_urls['resnet152'])) File "/ddn/data/lxdc43/enteESC[Denter/lib/python3.6/site-packages/torch/utils/model_zoo.py", line 64, in load_url _download_url_to_file(url, cached_file, hash_prefix) File "/ddn/data/lxdc43/enteESC[Denter/lib/python3.6/site-packages/torch/utils/model_zoo.py", line 69, in _download_url_to_file u = urlopen(url) File "/ddn/data/lxdc43/enteESC[Denter/lib/python3.6/urllib/request.py", line 223, in urlopen return opener.open(url, data, timeout) File "/ddn/data/lxdc43/enteESC[Denter/lib/python3.6/urllib/request.py", line 526, in open response = self._open(req, data) File "/ddn/data/lxdc43/enteESC[Denter/lib/python3.6/urllib/request.py", line 544, in _open '_open', req) File "/ddn/data/lxdc43/enteESC[Denter/lib/python3.6/urllib/request.py", line 504, in _call_chain result = func(*args) File "/ddn/data/lxdc43/enteESC[Denter/lib/python3.6/urllib/request.py", line 1361, in https_open context=self._context, check_hostname=self._check_hostname) File "/ddn/data/lxdc43/enteESC[Denter/lib/python3.6/urllib/request.py", line 1320, in do_open raise URLError(err) urllib.error.URLError: <urlopen error [Errno 101] Network is unreachable>

opened by OsloAI 1
IndexError: too many indices for array

Could you tell me how to fix blew issues? Thank you very much.

Traceback (most recent call last): File "/home/work/tsg/main.py", line 771, in train_evaluate_predict_cv() File "/home/work/tsg/main.py", line 504, in train_evaluate_predict_cv fold_id) File "/home/work/tsg/main.py", line 600, in fold_fit_evaluate_predict_loop iou, iout, predicted_masks_valid = fold_fit_evaluate_loop(train_data_split, valid_data_split, fold_id) File "/home/work/tsg/main.py", line 637, in fold_fit_evaluate_loop pipeline_network.fit_transform(train_pipe_input) File "/home/anaconda3/lib/python3.6/site-packages/steppy/base.py", line 317, in fit_transform step_inputs[input_step.name] = input_step.fit_transform(data) File "/home/anaconda3/lib/python3.6/site-packages/steppy/base.py", line 323, in fit_transform step_output_data = self._cached_fit_transform(step_inputs) File "/home/work/tsg/common_blocks/utils.py", line 453, in _cached_fit_transform step_output_data = self.transformer.fit_transform(**step_inputs) File "/home/anaconda3/lib/python3.6/site-packages/steppy/base.py", line 605, in fit_transform self.fit(*args, **kwargs) File "/home/work/tsg/common_blocks/models.py", line 75, in fit self.callbacks.on_batch_end(metrics=metrics) File "/home/work/tsg/common_blocks/callbacks.py", line 120, in on_batch_end callback.on_batch_end(*args, **kwargs) File "/home/work/tsg/common_blocks/callbacks.py", line 151, in on_batch_end loss = loss.data.cpu().numpy()[0] IndexError: too many indices for array

opened by OsloAI 1
KFoldBySortedValue issue
Hello neptune-team, I'm a fellow Kaggler. I find your contribution very valuable and I'm exploring your code.

I stumbled on KFoldBySortedValue while doing static analysis.

Shouldn't

https://github.com/neptune-ml/open-solution-salt-detection/blob/239016c742d913a6822d33edb6f3beaac959cc9b/src/utils.py#L416

be

def get_n_splits(self, X=None, y=None, groups=None): return self.n_splits

Btw... did you get any improvement in sorting by depth the dataset?

Thank you
opened by thundo 1
index 0 is out of bounds for axis 0 with size 0

Could you tell me how to fix this problem? thanks

if: Expression Syntax. then: Command not found. 2018-10-01 08-21-52 salt-detection >>> creating metadata neptune: Executing in Offline Mode.

0%| | 0/2 [00:00<?, ?it/s] Traceback (most recent call last): File "/ddn/home/kaggle/salt_identification/main.py", line 770, in prepare_metadata() File "/ddn/home/kaggle/salt_identification/main.py", line 351, in prepare_metadata depths_filepath=PARAMS.depths_filepath File "/ddn/home/kaggle/salt_identification/common_blocks/utils.py", line 134, in generate_metadata depth = depths[depths['id'] == image_id]['z'].values[0] IndexError: index 0 is out of bounds for axis 0 with size 0

opened by OsloAI 1
Memory Allocation of Solution 4

Hi~ Thanks for your team's great work. I implemented the solution 4 in a local machine. However, I found something quite annoying in the loop of cross-validation. Here are 2 pictures about the memory problem: The first one is the statistics of memory using during the fold_0. The second one is "fold_1". It is obvious that it will cost 3GB RAM after each training processing: I started this training process last night and went to bed. I saw the error of "cannot allocate memory" this morning. Can u give me some tip so how to solve this problem?

opened by xhh232018 1

Confusion Matrix

As per the discussions on Kaggle, yours implementation is the only implementation that is fully correct for the given metric but there is one thing that I couldn't understand as per your code. Here are these three functions:

def compute_ious(gt, predictions):
    gt_ = get_segmentations(gt)
    predictions_ = get_segmentations(predictions)

    if len(gt_) == 0 and len(predictions_) == 0:
        return np.ones((1, 1))
    elif len(gt_) != 0 and len(predictions_) == 0:
        return np.zeros((1, 1))
    else:
        iscrowd = [0 for _ in predictions_]
        ious = cocomask.iou(gt_, predictions_, iscrowd)
        if not np.array(ious).size:
            ious = np.zeros((1, 1))
        return ious


def compute_precision_at(ious, threshold):
    mx1 = np.max(ious, axis=0)
    mx2 = np.max(ious, axis=1)
    tp = np.sum(mx2 >= threshold)
    fp = np.sum(mx2 < threshold)
    fn = np.sum(mx1 < threshold)
    return float(tp) / (tp + fp + fn)

def compute_eval_metric(gt, predictions):
    thresholds = [0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95]
    ious = compute_ious(gt, predictions)
    precisions = [compute_precision_at(ious, th) for th in thresholds]
    return sum(precisions) / len(precisions)

Now, given the fact that compute_ious function works on a single prediction and it's corresponding groundtruth, ious will be a singleton array. Then, how are you calculating TP/FP from that? Am I missing something here?

opened by AakashKumarNain 1

Cannot find the 'metadata.csv'

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.

opened by dremofly 6
GPU utilization is 0 most of the time

nvidia-smi gives 0% percentage GPU-Util most of the time(in offline mode). Am I doing something wrong? Using the same config file as in [solution-3]. CPU usage is high most of the time.

opened by SergazyK 1

Releases(solution-6)

solution-6(Sep 25, 2018)
Improved augmentation pipe

moved from reflect padding to edge replication padding

Source code(tar.gz)
Source code(zip)
solution-5(Sep 17, 2018)
refactored models

added lovash loss

added fine tuning

added resize+pad option

Source code(tar.gz)
Source code(zip)
solution-4(Sep 10, 2018)

added k-fold validation added averaging over k folds decoupled network train/valid from postprocessing added saving model predictions oof train and test fixed pytorch memory issues
Source code(tar.gz)
Source code(zip)
solution-3-simplified(Sep 3, 2018)

Source code(tar.gz)
Source code(zip)
cloud(Sep 3, 2018)
introduced option to run training on GCP using neptune.

Source code(tar.gz)
Source code(zip)
solution-3(Aug 3, 2018)
updated hyperparams

made smarter augmentations

Source code(tar.gz)
Source code(zip)
chestnut(Jul 25, 2018)
simple end to end pipeline

Source code(tar.gz)
Source code(zip)

Owner

neptune.ai

Metadata store for MLOps

GitHub Repository https://www.kaggle.com/c/tgs-salt-identification-challenge

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TGS Salt Identification Challenge

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Overview

TGS Salt Identification Challenge

Note

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Our goals

Disclaimer

How to start?

Learn about our solutions

Start experimenting with ready-to-use code

Installation

Clone repository

Set-up environment

Create data folder structure and set data paths in your config file (neptune.yaml)

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Dependency tree------

Solution

Dependency tree-----

Solution

Releases(solution-6)

solution-6(Sep 25, 2018)

solution-5(Sep 17, 2018)

solution-4(Sep 10, 2018)

solution-3-simplified(Sep 3, 2018)

cloud(Sep 3, 2018)

solution-3(Aug 3, 2018)

chestnut(Jul 25, 2018)

Owner

neptune.ai

Spectrum is an AI that uses machine learning to generate Rap song lyrics

Applying PVT to Semantic Segmentation

[NeurIPS2021] Code Release of Learning Transferable Perturbations

Hyperopt for solving CIFAR-100 with a convolutional neural network (CNN) built with Keras and TensorFlow, GPU backend

A Research-oriented Federated Learning Library and Benchmark Platform for Graph Neural Networks. Accepted to ICLR'2021 - DPML and MLSys'21 - GNNSys workshops.

ICLR21 Tent: Fully Test-Time Adaptation by Entropy Minimization

Kaggleship: Kaggle Notebooks

High dimensional black-box optimizer using Latent Action Monte Carlo Tree Search algorithm

torchlm is aims to build a high level pipeline for face landmarks detection, it supports training, evaluating, exporting, inference(Python/C++) and 100+ data augmentations

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DivNoising is an unsupervised denoising method to generate diverse denoised samples for any noisy input image. This repository contains the code to reproduce the results reported in the paper https://openreview.net/pdf?id=agHLCOBM5jP

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Implementation of experiments in the paper Clockwork Variational Autoencoders (project website) using JAX and Flax

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Create data folder structure and set data paths in your config file (`neptune.yaml`)