sage.presets.configs

Filename = Foobar.py Description = Lorem ipsum dolor sit amet

Created on Sat Nov 27 17:09:58 2021

__author__ = nnarenraju __copyright__ = Copyright 2021, ProjectName __credits__ = nnarenraju __license__ = MIT Licence __version__ = 0.0.1 __maintainer__ = nnarenraju __email__ = nnarenraju@gmail.com __status__ = [‘inProgress’, ‘Archived’, ‘inUsage’, ‘Debugging’]

Github Repository: NULL

Documentation:

[1] Using OSnet

##Architecture model = SigmaModel

# Kernel sizes on modified OSnet (type 1) kernel_sizes = [] kernel_sizes.append([[16, 32, 64, 128, 256], [8, 16, 32, 64, 128]]) kernel_sizes.append([[8, 16, 32, 64, 128], [2, 4, 8, 16, 32]]) kernel_sizes.append([[2, 4, 8, 16, 32], [2, 4, 8, 16, 32]])

model_params = dict(

## OSnet + Resnet50 CBAM model_name=’sigmanet’, norm_layer = ‘instancenorm’, ## OSnet params # channels[0] is used when initial_dim_reduction == True channels=[16, 32, 64, 128], kernel_sizes=kernel_sizes, # strides[:2] is used when initial_dim_reduction == True strides=[2,2,8,4], stacking=False, initial_dim_reduction=False, # reduction value of 16 does not work with KaggleNet type kernels channel_gate_reduction=8, # ResNet CBAM params resnet_size = 50, # Common store_device = ‘cuda:2’,

)

Classes

SageNetOTF	Data storage
SageNetOTF_Aug27_Russet_diffseed_2	Data storage
SageNetOTF_Russet_BEST_HL	Data storage
SageNetOTF_Russet_BEST_HV	Data storage
SageNetOTF_Russet_BEST_LV	Data storage
SageNetOTF_Russet_HL_HardSampleMined	Data storage
Norland_D3_Odds_Ratio	On-the-fly training configuration for the Norland D3 odds-ratio run.

Module Contents

class SageNetOTF

Data storage

name = 'SageNet50_CBAM_OTF_Feb03_dummy'

export_dir

debug_dir = './DEBUG'

git_revparse

repo_abspath

rtune_optimise = False

rtune_params

Dataset

dataset

dataset_params

Architecture

model

model_params

Epochs and Batches

num_epochs = 500

batch_size = 64

validation_plot_freq = 1

Weight Types

weight_types = ['loss', 'accuracy', 'roc_auc', 'low_far_nsignals']

save_epoch_weight

save_best_option = 'loss'

save_checkpoint = True

checkpoint_freq = 1

resume_from_checkpoint = False

checkpoint_path = ''

pretrained = False

freeze_for_transfer = False

weights_path = 'weights_loss.pt'

Optimizer

optimiser

optimiser_params

Scheduler

scheduler

scheduler_params

Gradient Clipping

clip_norm = 10000

Automatic Mixed Precision

do_AMP = False

Storage Devices

store_device = 'cuda:0'

train_device = 'cuda:0'

Dataloader params

num_workers = 16

pin_memory = True

prefetch_factor = 4

persistent_workers = True

Loss Function

loss_function

network_snr_for_noise = False

ignore_dset_imbalance = False

subset_for_funsies = False

Generation

generation

Transforms

batchshuffle_noise = False

transforms

Optional things to do during training

epoch_testing = False

epoch_testing_dir = '/local/scratch/igr/nnarenraju/testing_64000_D4_seeded'

epoch_far_scaling_factor = 64000.0

Testing Phase

injection_file = 'injections.hdf'

evaluation_output = 'evaluation.hdf'

test_foreground_dataset = 'foreground.hdf'

test_foreground_output = 'testing_foutput.hdf'

test_background_dataset = 'background.hdf'

test_background_output = 'testing_boutput.hdf'

step_size = 0.1

trigger_threshold = 0.0

cluster_threshold = 0.0001

testing_device = 'cuda:1'

testing_dir = '/local/scratch/igr/nnarenraju/testing_month_D4_seeded'

far_scaling_factor = 2592000.0

debug = False

debug_size = 10000

verbose = True

class SageNetOTF_Aug27_Russet_diffseed_2

Bases: SageNetOTF

Data storage

name = 'SageNet50_halfnormSNR_Sept11_Russet_diffseed_another_dummy'

export_dir

debug_dir = './DEBUG'

git_revparse

repo_abspath

dataset

dataset_params

Architecture

save_epoch_weight

weights_path = 'weights_low_far_nsignals_39.pt'

Optimizer

seed_offset_train = 33554432

seed_offset_valid = 536870912

Generation

generation

Transforms

transforms

Architecture

model

model_params

Dataloader params

num_workers = 48

pin_memory = True

prefetch_factor = 4

persistent_workers = True

Storage Devices

store_device

train_device

Dataloader params

testing_device

testing_dir = '/home/nnarenraju/Research/ORChiD/test_data_d4'

test_foreground_output = 'testing_foutput_BEST_June_diff_seed_Sept11_2.hdf'

test_background_output = 'testing_boutput_BEST_June_diff_seed_Sept11_2.hdf'

class SageNetOTF_Russet_BEST_HL

Bases: SageNetOTF

Data storage

name = 'SageNet50_Russet_BEST_HL_dummy'

export_dir

debug_dir = './DEBUG'

git_revparse

repo_abspath

dataset

dataset_params

Architecture

seed_offset_train = 33554432

seed_offset_valid = 536870912

save_epoch_weight

Generation

generation

Transforms

transforms

Architecture

model

model_params

Dataloader params

num_workers = 32

pin_memory = True

prefetch_factor = 4

persistent_workers = True

Storage Devices

store_device

train_device

Dataloader params

testing_device

testing_dir = '/home/nnarenraju/Research/ORChiD/test_data_d4'

test_foreground_output = 'testing_foutput_HV.hdf'

test_background_output = 'testing_boutput_HV.hdf'

class SageNetOTF_Russet_BEST_HV

Bases: SageNetOTF

Data storage

name = 'SageNet50_Russet_BEST_HV'

export_dir

debug_dir = './DEBUG'

git_revparse

repo_abspath

dataset

dataset_params

Architecture

seed_offset_train = 33554432

seed_offset_valid = 536870912

save_epoch_weight

Generation

generation

Transforms

transforms

Architecture

model

model_params

Dataloader params

num_workers = 32

pin_memory = True

prefetch_factor = 4

persistent_workers = True

Storage Devices

store_device

train_device

Dataloader params

testing_device

testing_dir = '/home/nnarenraju/Research/ORChiD/test_data_d4'

test_foreground_output = 'testing_foutput_HV.hdf'

test_background_output = 'testing_boutput_HV.hdf'

class SageNetOTF_Russet_BEST_LV

Bases: SageNetOTF

Data storage

name = 'SageNet50_Russet_BEST_LV_continued'

export_dir

debug_dir = './DEBUG'

git_revparse

repo_abspath

dataset

dataset_params

Architecture

seed_offset_train = 33554432

seed_offset_valid = 536870912

save_epoch_weight

Generation

generation

Transforms

transforms

Architecture

model

model_params

Dataloader params

num_workers = 32

pin_memory = True

prefetch_factor = 4

persistent_workers = True

Storage Devices

store_device

train_device

Dataloader params

testing_device

testing_dir = '/home/nnarenraju/Research/ORChiD/test_data_d4'

test_foreground_output = 'testing_foutput_LV.hdf'

test_background_output = 'testing_boutput_LV.hdf'

class SageNetOTF_Russet_HL_HardSampleMined

Bases: SageNetOTF

Data storage

name = 'SageNet50_Russet_HL_HardSampleMined'

export_dir

debug_dir = './DEBUG'

git_revparse

repo_abspath

dataset

dataset_params

Architecture

seed_offset_train = 33554432

seed_offset_valid = 536870912

save_epoch_weight

Generation

generation

Transforms

transforms

Architecture

model

model_params

Dataloader params

num_workers = 32

pin_memory = True

prefetch_factor = 4

persistent_workers = True

Storage Devices

store_device

train_device

Dataloader params

testing_device

testing_dir = '/home/nnarenraju/Research/ORChiD/test_data_d4'

test_foreground_output = 'testing_foutput_HL_hardsampled.hdf'

test_background_output = 'testing_boutput_HL_hardsampled.hdf'

class Norland_D3_Odds_Ratio

Bases: SageNetOTF

On-the-fly training configuration for the Norland D3 odds-ratio run.

Inherits from SageNetOTF and overrides dataset generation, noise, transforms, and model settings for an experiment using the D3 template-placement metric. Coloured noise PSDs are drawn from the same limited_psds directory for both training and validation, so the PSD distribution matches exactly between the two splits.

Class Attributes

namestr

Human-readable run identifier used for export_dir construction.

export_dirpathlib.Path

Root directory where checkpoints and results are written.

datasettype

Dataset class (BBHDataset).

num_workersint

DataLoader worker count.

name = 'Norland_D3_Odds_Ratio_Apr29'

export_dir

debug_dir = './DEBUG'

git_revparse

repo_abspath

dataset

dataset_params

Architecture

num_workers = 32

pin_memory = True

prefetch_factor = 4

persistent_workers = True

Loss Function

seed_offset_train = 33554432

seed_offset_valid = 536870912

generation

Transforms

transforms

Optional things to do during training

model

model_params

Epochs and Batches

store_device

train_device

Dataloader params

testing_device

testing_dir = '/home/nnarenraju/Research/ORChiD/test_data_d3'

test_foreground_output = 'testing_foutput_D3_SageNet_odds_ratio.hdf'

test_background_output = 'testing_boutput_D3_SageNet_odds_ratio.hdf'