def FIRST_Fraction(CHANNELS=[['FIRST']],
PROJECT_DIR='Script_Experiments_Fraction_Learning',
TRIAL=0):
rgz_dir = 'rgz_rcnn_data'
cat = Catalog(rgz_dir=rgz_dir)
print('\nValidating sources...')
cat.collect_valid_sources()
for bin_opts, pink_opts, channels, learning in product(
BINARY_OPTS, PINK_OPTS, CHANNELS, LEARNING_MODES):
print(bin_opts, pink_opts)
# Make sure there are enough channels to do the mask with
if len(CHANNELS[0]) == 1 and bin_opts['convex']:
print(
f'Skipping this option set. CHANNELS is {CHANNELS[0]}, nothing to apply convex hull masking to. '
)
continue
chan_name = '_'.join(channels)
out_dir = f"{PROJECT_DIR}/{chan_name}_{bin_opts['project_dir']}_{pink_opts['som-width']}x{pink_opts['som-height']}_Trial{TRIAL}"
results = []
if not os.path.exists(f'{out_dir}/trained.pink'):
# This is painful, but since product() is returning a reference to a dict, we cant
# edit the project_dir in place to build up the folder name, as this gets carried
# through to later iterations. Hence, we can't **bin_opts below
bins = cat.dump_binary('source.binary',
channels=channels,
project_dir=out_dir,
norm=bin_opts['norm'],
sigma=bin_opts['sigma'],
log10=bin_opts['log10'],
convex=bin_opts['convex'],
fraction=bin_opts['fraction'])
train_bin, validate_bin = bins
pink = Pink(train_bin,
pink_args=pink_opts,
validate_binary=validate_bin)
pink.train(learning=learning)
else:
print('Loading in saved Pink instance')
pink = Pink.loader(f'{out_dir}/trained.pink')
for i, t in enumerate(pink.binary):
try:
pink.map(mode=i)
pink.map(mode='validate', SOM_mode=i)
pink.save('trained.pink')
pink.show_som(channel=0, mode=i)
pink.show_som(channel=0, mode=i, plt_mode='split')
pink.show_som(channel=0, mode=i, plt_mode='grid')
pink.show_som(channel=1, mode=i)
pink.show_som(channel=1, mode=i, plt_mode='split')
pink.show_som(channel=1, mode=i, plt_mode='grid')
except Exception as e:
print('Try caught something')
print(e)
import traceback
traceback.print_exc()
plt.close('all')
def main():
test_dir = 'Test_Experiment'
proj_dir = 'FIRST_Norm_Log_3'
pink_opts = {
'som-width': 10,
'som-height': 10,
'num-iter': 1,
'inter-store': 'keep'
}
rgz_dir = 'rgz_rcnn_data'
cat = Catalog(rgz_dir=rgz_dir)
print('\nValidating sources...')
cat.collect_valid_sources()
out_dir = f"{test_dir}/FIRST_{proj_dir}_{pink_opts['som-width']}x{pink_opts['som-height']}_10_iter"
if not os.path.exists(f'{out_dir}/trained.pink'):
bins = cat.dump_binary('source.binary',
channels=['FIRST'],
project_dir=out_dir,
norm=True,
sigma=3.,
log10=True,
fraction=0.75)
train_bin, validate_bin = bins
pink = Pink(train_bin,
pink_args=pink_opts,
validate_binary=validate_bin)
pink.train(learning=[('gaussian', '3.0',
'0.1'), ('gaussian', '1.4',
'0.05'), ('gaussian', '0.3', '0.01')])
pink.map()
pink.map(mode='validate', SOM_mode='train')
pink.save('trained.pink')
pink_opts = {
'som-width': 15,
'som-height': 15,
'num-iter': 1,
'inter-store': 'keep'
}
out_dir = f"{test_dir}/FIRST_{proj_dir}_{pink_opts['som-width']}x{pink_opts['som-height']}_20_iter"
if not os.path.exists(f'{out_dir}/trained.pink'):
bins = cat.dump_binary('source.binary',
channels=['FIRST'],
project_dir=out_dir,
norm=True,
sigma=2.,
log10=True,
fraction=0.75)
train_bin, validate_bin = bins
pink = Pink(train_bin,
pink_args=pink_opts,
validate_binary=validate_bin)
pink.train(learning=[('gaussian', '3.0',
'0.1'), ('gaussian', '1.4',
'0.05'), ('gaussian', '0.3', '0.01')])
pink.map()
pink.map(mode='validate', SOM_mode='train')
pink.save('trained.pink')
# pink_opts = {'som-width':10, 'som-height':10, 'num-iter':1, 'inter-store':'keep'}
# out_dir = f"{test_dir}/FIRST_{proj_dir}_{pink_opts['som-width']}x{pink_opts['som-height']}_30_iter"
# if not os.path.exists(f'{out_dir}/trained.pink'):
# bins = cat.dump_binary('source.binary', channels=['FIRST'], project_dir=out_dir,
# norm=True, sigma=2., log10=True, fraction=0.75)
# train_bin, validate_bin = bins
# pink = Pink(train_bin,
# pink_args=pink_opts,
# validate_binary=validate_bin)
# pink.train(learning=[('gaussian','3.0','0.1'),
# ('gaussian','1.4','0.05'),
# ('gaussian','0.3','0.01')])
# pink.map()
# pink.map(mode='validate', SOM_mode='train')
# pink.save('trained.pink')
# ---------------------------------------------------------
proj_dir = 'FIRST_WISE_Norm_Log_3'
pink_opts = {
'som-width': 10,
'som-height': 10,
'num-iter': 1,
'progress': 0.05,
'inter-store': 'keep'
}
out_dir = f"{test_dir}/{proj_dir}_{pink_opts['som-width']}x{pink_opts['som-height']}_10_iter"
if not os.path.exists(f'{out_dir}/trained.pink'):
bins = cat.dump_binary('source.binary',
channels=['FIRST', 'WISE_W1'],
project_dir=out_dir,
norm=True,
sigma=3.,
log10=True,
fraction=0.75)
train_bin, validate_bin = bins
pink = Pink(train_bin,
pink_args=pink_opts,
validate_binary=validate_bin)
pink.train(learning=[('gaussian', '3.0',
'0.1'), ('gaussian', '1.4',
'0.05'), ('gaussian', '0.3', '0.01')])
pink.map()
pink.map(mode='validate', SOM_mode='train')
pink.save('trained.pink')
pink_opts = {
'som-width': 15,
'som-height': 15,
'num-iter': 1,
'progress': 0.05,
'inter-store': 'keep'
}
out_dir = f"{test_dir}/{proj_dir}_{pink_opts['som-width']}x{pink_opts['som-height']}_20_iter"
if not os.path.exists(f'{out_dir}/trained.pink'):
bins = cat.dump_binary('source.binary',
channels=['FIRST', 'WISE_W1'],
project_dir=out_dir,
norm=True,
sigma=2.,
log10=True,
fraction=0.75)
train_bin, validate_bin = bins
pink = Pink(train_bin,
pink_args=pink_opts,
validate_binary=validate_bin)
pink.train(learning=[('gaussian', '3.0',
'0.1'), ('gaussian', '1.4',
'0.05'), ('gaussian', '0.3', '0.01')])
pink.map()
pink.map(mode='validate', SOM_mode='train')
pink.save('trained.pink')
