def aoi2_train(ibs,
species_list=None,
train_gid_list=None,
purge=True,
cache=False):
from wbia_cnn.ingest_wbia import get_aoi2_training_data
from wbia_cnn.process import numpy_processed_directory5
from wbia_cnn.models.aoi2 import train_aoi2
from wbia_cnn.utils import save_model
data_path = join(ibs.get_cachedir(), 'extracted')
extracted_path = get_aoi2_training_data(
ibs,
dest_path=data_path,
target_species_list=species_list,
train_gid_list=train_gid_list,
purge=purge,
cache=cache,
)
id_file, X_file, y_file = numpy_processed_directory5(extracted_path)
output_path = join(ibs.get_cachedir(), 'training', 'aoi2')
model_path = train_aoi2(output_path, X_file, y_file)
model_state = ut.load_cPkl(model_path)
assert 'species_list' not in model_state
model_state['species_list'] = species_list
save_model(model_state, model_path)
return model_path
def labeler_train_wbia_cnn(ibs,
species_list=None,
species_mapping=None,
viewpoint_mapping=None,
**kwargs):
from wbia_cnn.ingest_wbia import get_cnn_labeler_training_images
from wbia_cnn.process import numpy_processed_directory2
from wbia_cnn.models.labeler import train_labeler
from wbia_cnn.utils import save_model
data_path = join(ibs.get_cachedir(), 'extracted')
extracted_path = get_cnn_labeler_training_images(
ibs,
data_path,
category_list=species_list,
category_mapping=species_mapping,
viewpoint_mapping=viewpoint_mapping,
**kwargs,
)
id_file, X_file, y_file = numpy_processed_directory2(extracted_path)
output_path = join(ibs.get_cachedir(), 'training', 'labeler')
model_path = train_labeler(output_path, X_file, y_file)
# Add the species_list to the model
model_state = ut.load_cPkl(model_path)
assert 'category_list' not in model_state
model_state['category_list'] = species_list
assert 'viewpoint_mapping' not in model_state
model_state['viewpoint_mapping'] = viewpoint_mapping
save_model(model_state, model_path)
return model_path
def classifier2_train(ibs,
species_list=None,
species_mapping={},
train_gid_set=None,
**kwargs):
from wbia_cnn.ingest_wbia import get_cnn_classifier2_training_images
from wbia_cnn.process import numpy_processed_directory3
from wbia_cnn.models.classifier2 import train_classifier2
from wbia_cnn.utils import save_model
if species_list is not None:
species_list = sorted(species_list)
data_path = join(ibs.get_cachedir(), 'extracted')
values = get_cnn_classifier2_training_images(
ibs,
species_list,
category_mapping=species_mapping,
train_gid_set=train_gid_set,
dest_path=data_path,
**kwargs,
)
extracted_path, category_list = values
id_file, X_file, y_file = numpy_processed_directory3(extracted_path)
output_path = join(ibs.get_cachedir(), 'training', 'classifier2')
model_path = train_classifier2(output_path, X_file, y_file, purge=True)
# Add the species_list to the model
model_state = ut.load_cPkl(model_path)
assert 'category_list' not in model_state
model_state['category_list'] = category_list
save_model(model_state, model_path)
# Return model path
return model_path
def aoi_train(ibs, species_list=None):
from wbia_cnn.ingest_wbia import get_aoi_training_data
from wbia_cnn.process import numpy_processed_directory4
from wbia_cnn.models.aoi import train_aoi
from wbia_cnn.utils import save_model
data_path = join(ibs.get_cachedir(), 'extracted')
extracted_path = get_aoi_training_data(ibs,
data_path,
target_species_list=species_list)
id_file, X_file, y_file = numpy_processed_directory4(extracted_path)
output_path = join(ibs.get_cachedir(), 'training', 'aoi')
model_path = train_aoi(output_path, X_file, y_file)
model_state = ut.load_cPkl(model_path)
assert 'species_list' not in model_state
model_state['species_list'] = species_list
save_model(model_state, model_path)
return model_path
def classifier_binary_train(ibs, species_list, **kwargs):
from wbia_cnn.ingest_wbia import get_cnn_classifier_binary_training_images
from wbia_cnn.process import numpy_processed_directory2
from wbia_cnn.models.classifier import train_classifier
from wbia_cnn.utils import save_model
data_path = join(ibs.get_cachedir(), 'extracted')
extracted_path = get_cnn_classifier_binary_training_images(
ibs, species_list, dest_path=data_path, **kwargs)
id_file, X_file, y_file = numpy_processed_directory2(extracted_path)
output_path = join(ibs.get_cachedir(), 'training', 'classifier-binary')
model_path = train_classifier(output_path, X_file, y_file)
# Add the species_list to the model
model_state = ut.load_cPkl(model_path)
assert 'species_list' not in model_state
model_state['species_list'] = species_list
save_model(model_state, model_path)
# Return model path
return model_path
def background_train(ibs,
species,
train_gid_set=None,
global_limit=500000,
**kwargs):
"""
Example:
>>> values = output_path, X_file, y_file
>>> print(values)
>>> output_path, X_file, y_file = values
>>> from ibeis_cnn.models.background import train_background
>>> values = (
>>> '/data/ibeis/IMS_Master/_ibsdb/_ibeis_cache/training/background',
>>> '/data/ibeis/IMS_Master/_ibsdb/_ibeis_cache/extracted/background/raw/X.npy',
>>> '/data/ibeis/IMS_Master/_ibsdb/_ibeis_cache/extracted/background/labels/y.npy'
>>> )
>>> output_path, X_file, y_file = values
"""
from wbia_cnn.ingest_wbia import get_background_training_patches2
from wbia_cnn.process import numpy_processed_directory2
from wbia_cnn.models.background import train_background
from wbia_cnn.utils import save_model
data_path = join(ibs.get_cachedir(), 'extracted')
extracted_path = get_background_training_patches2(
ibs,
species,
data_path,
patch_size=50,
train_gid_set=train_gid_set,
global_limit=global_limit,
**kwargs,
)
id_file, X_file, y_file = numpy_processed_directory2(extracted_path)
output_path = join(ibs.get_cachedir(), 'training', 'background')
model_path = train_background(output_path, X_file, y_file)
model_state = ut.load_cPkl(model_path)
assert 'species' not in model_state
model_state['species'] = species
save_model(model_state, model_path)
return model_path