class CONFIDENCE(object):
UNKNOWN = None
GUESSING = 1
NOT_SURE = 2
PRETTY_SURE = 3
ABSOLUTELY_SURE = 4
INT_TO_CODE = ut.odict([
(ABSOLUTELY_SURE, 'absolutely_sure'),
(PRETTY_SURE, 'pretty_sure'),
(NOT_SURE, 'not_sure'),
(GUESSING, 'guessing'),
(UNKNOWN, 'unspecified'),
])
INT_TO_NICE = ut.odict([
(ABSOLUTELY_SURE, 'Doubtless'),
(PRETTY_SURE, 'Sure'),
(NOT_SURE, 'Unsure'),
(GUESSING, 'Guessing'),
(UNKNOWN, 'Unspecified'),
])
CODE_TO_NICE = ut.map_keys(INT_TO_CODE, INT_TO_NICE)
CODE_TO_INT = ut.invert_dict(INT_TO_CODE)
NICE_TO_CODE = ut.invert_dict(CODE_TO_NICE)
NICE_TO_INT = ut.invert_dict(INT_TO_NICE)
class QUAL(object):
EXCELLENT = 5
GOOD = 4
OK = 3
POOR = 2
JUNK = 1
UNKNOWN = None
INT_TO_CODE = ut.odict([
(EXCELLENT, 'excellent'),
(GOOD, 'good'),
(OK, 'ok'),
(POOR, 'poor'),
(JUNK, 'junk'),
(UNKNOWN, 'unspecified'),
])
INT_TO_NICE = ut.odict([
(EXCELLENT, 'Excellent'),
(GOOD, 'Good'),
(OK, 'OK'),
(POOR, 'Poor'),
(JUNK, 'Junk'),
(UNKNOWN, 'Unspecified'),
])
CODE_TO_NICE = ut.map_keys(INT_TO_CODE, INT_TO_NICE)
CODE_TO_INT = ut.invert_dict(INT_TO_CODE)
NICE_TO_CODE = ut.invert_dict(CODE_TO_NICE)
NICE_TO_INT = ut.invert_dict(INT_TO_NICE)
class META_DECISION(object): # NOQA
"""
Enumerated types of review codes and texts
Notes:
unreviewed: we dont have a meta decision
same: we know this is the same animal through non-visual means
diff: we know this is the different animal through non-visual means
Example:
>>> # ENABLE_DOCTEST
>>> from wbia.constants import * # NOQA
>>> assert hasattr(META_DECISION, 'CODE')
>>> assert hasattr(META_DECISION, 'NICE')
>>> code1 = META_DECISION.INT_TO_CODE[META_DECISION.NULL]
>>> code2 = META_DECISION.CODE.NULL
>>> assert code1 == code2
>>> nice1 = META_DECISION.INT_TO_NICE[META_DECISION.NULL]
>>> nice2 = META_DECISION.NICE.NULL
>>> assert nice1 == nice2
"""
NULL = None
DIFF = 0
SAME = 1
INT_TO_CODE = ut.odict([(NULL, 'null'), (DIFF, 'diff'), (SAME, 'same')])
INT_TO_NICE = ut.odict([(NULL, 'NULL'), (DIFF, 'Different'),
(SAME, 'Same')])
CODE_TO_NICE = ut.map_keys(INT_TO_CODE, INT_TO_NICE)
CODE_TO_INT = ut.invert_dict(INT_TO_CODE)
NICE_TO_CODE = ut.invert_dict(CODE_TO_NICE)
NICE_TO_INT = ut.invert_dict(INT_TO_NICE)
def get_oracle_name_decision(metatup, ibs, qaid, choicetup, oracle_method=1):
"""
Find what the correct decision should be ibs is the database we are working
with ibs_gt has pristine groundtruth
"""
if ut.VERBOSE:
print('Oracle is making decision using oracle_method=%r' %
oracle_method)
if metatup is None:
print('WARNING METATUP IS NONE')
return None
MAX_LOOK = 3 # the oracle should only see what the user sees
(sorted_nids, sorted_nscore, sorted_rawscore, sorted_aids,
sorted_ascores) = choicetup
(ibs_gt, aid1_to_aid2) = metatup
# Get the annotations that the user can see
aid_list2 = ut.get_list_column(sorted_aids, 0)
# Get the groundtruth name of the query
aid2_to_aid1 = ut.invert_dict(aid1_to_aid2)
qnid1 = ibs_gt.get_annot_name_rowids(aid2_to_aid1[qaid])
# Make an oracle decision by choosing a name (like a user would)
if oracle_method == 1:
chosen_names = oracle_method1(ibs_gt, ibs, qnid1, aid_list2,
aid2_to_aid1, sorted_nids, MAX_LOOK)
elif oracle_method == 2:
chosen_names = oracle_method2(ibs_gt, qnid1)
else:
raise AssertionError('unknown oracle method %r' % (oracle_method, ))
if ut.VERBOSE:
print('Oracle decision is chosen_names=%r' % (chosen_names, ))
return chosen_names
def get_oracle_name_decision(metatup, ibs, qaid, choicetup, oracle_method=1):
"""
Find what the correct decision should be ibs is the database we are working
with ibs_gt has pristine groundtruth
"""
if ut.VERBOSE:
print('Oracle is making decision using oracle_method=%r' % oracle_method)
if metatup is None:
print('WARNING METATUP IS NONE')
return None
MAX_LOOK = 3 # the oracle should only see what the user sees
(sorted_nids, sorted_nscore, sorted_rawscore, sorted_aids, sorted_ascores) = choicetup
(ibs_gt, aid1_to_aid2) = metatup
# Get the annotations that the user can see
aid_list2 = ut.get_list_column(sorted_aids, 0)
# Get the groundtruth name of the query
aid2_to_aid1 = ut.invert_dict(aid1_to_aid2)
qnid1 = ibs_gt.get_annot_name_rowids(aid2_to_aid1[qaid])
# Make an oracle decision by choosing a name (like a user would)
if oracle_method == 1:
chosen_names = oracle_method1(ibs_gt, ibs, qnid1, aid_list2, aid2_to_aid1, sorted_nids, MAX_LOOK)
elif oracle_method == 2:
chosen_names = oracle_method2(ibs_gt, qnid1)
else:
raise AssertionError('unknown oracle method %r' % (oracle_method,))
if ut.VERBOSE:
print('Oracle decision is chosen_names=%r' % (chosen_names,))
return chosen_names
def _update_state_gco(model,
weight_key='cut_weight',
name_label_key='name_label'):
import networkx as nx
# Get nx graph properties
external_nodes = sorted(list(model.graph.nodes()))
external_edges = list(model.graph.edges())
edge_to_weights = nx.get_edge_attributes(model.graph, weight_key)
node_to_labeling = nx.get_node_attributes(model.graph, name_label_key)
edge_weights = ut.dict_take(edge_to_weights, external_edges, 0)
external_labeling = [
node_to_labeling.get(node, -node) for node in external_nodes
]
# Map to internal ids for pygco
internal_nodes = ut.rebase_labels(external_nodes)
extern2_intern = dict(zip(external_nodes, internal_nodes))
internal_edges = ut.unflat_take(extern2_intern, external_edges)
internal_labeling = ut.rebase_labels(external_labeling)
internal_labeling = np.array(internal_labeling)
internal_edges = np.array(internal_edges)
n_nodes = len(internal_nodes)
# Model state
model.n_nodes = n_nodes
model.extern2_intern = extern2_intern
model.intern2_extern = ut.invert_dict(extern2_intern)
model.edges = internal_edges
model.edge_weights = edge_weights
# Model parameters
model.labeling = np.zeros(model.n_nodes, dtype=np.int32)
model._update_labels(labeling=internal_labeling)
model._update_weights()
def config_graph_subattrs(cfg, depc):
# TODO: if this hack is fully completed need a way of getting the
# full config belonging to both chip + feat
# cfg = request.config.feat_cfg
import networkx as netx
tablename = ut.invert_dict(depc.configclass_dict)[cfg.__class__]
#tablename = cfg.get_config_name()
ancestors = netx.dag.ancestors(depc.graph, tablename)
subconfigs_ = ut.dict_take(depc.configclass_dict, ancestors, None)
subconfigs = ut.filter_Nones(subconfigs_) # NOQA
def config_graph_subattrs(cfg, depc):
# TODO: if this hack is fully completed need a way of getting the
# full config belonging to both chip + feat
# cfg = request.config.feat_cfg
import networkx as netx
tablename = ut.invert_dict(depc.configclass_dict)[cfg.__class__]
#tablename = cfg.get_config_name()
ancestors = netx.dag.ancestors(depc.graph, tablename)
subconfigs_ = ut.dict_take(depc.configclass_dict, ancestors, None)
subconfigs = ut.filter_Nones(subconfigs_) # NOQA
class EVIDENCE_DECISION(object): # NOQA
"""
TODO: change to EVIDENCE_DECISION / VISUAL_DECISION
Enumerated types of review codes and texts
Notes:
Unreviewed: Not comparared yet.
nomatch: Visually comparable and the different
match: Visually comparable and the same
notcomp: Not comparable means it is actually impossible to determine.
unknown: means that it was reviewed, but we just can't figure it out.
"""
UNREVIEWED = None
NEGATIVE = 0
POSITIVE = 1
INCOMPARABLE = 2
UNKNOWN = 3
INT_TO_CODE = ut.odict([
(POSITIVE, 'match'),
(NEGATIVE, 'nomatch'),
(INCOMPARABLE, 'notcomp'),
(UNKNOWN, 'unknown'),
(UNREVIEWED, 'unreviewed'),
])
INT_TO_NICE = ut.odict([
(POSITIVE, 'Positive'),
(NEGATIVE, 'Negative'),
(INCOMPARABLE, 'Incomparable'),
(UNKNOWN, 'Unknown'),
(UNREVIEWED, 'Unreviewed'),
])
CODE_TO_NICE = ut.map_keys(INT_TO_CODE, INT_TO_NICE)
CODE_TO_INT = ut.invert_dict(INT_TO_CODE)
NICE_TO_CODE = ut.invert_dict(CODE_TO_NICE)
NICE_TO_INT = ut.invert_dict(INT_TO_NICE)
MATCH_CODE = CODE_TO_INT
def static_new(cls, depc, parent_rowids, cfgdict=None, tablename=None):
""" hack for autoreload """
request = cls()
if tablename is None:
try:
if hasattr(cls, '_tablename'):
tablename = cls._tablename
else:
tablename = ut.invert_dict(depc.requestclass_dict)[cls]
except Exception as ex:
ut.printex(ex, 'tablename must be given')
raise
request.tablename = tablename
request.parent_rowids = parent_rowids
request.depc = depc
if cfgdict is None:
cfgdict = {}
configclass = depc.configclass_dict[tablename]
config = configclass(**cfgdict)
request.config = config
# HACK FOR IBEIS
request.params = dict(config.parse_items())
# HACK-ier FOR BACKWARDS COMPATABILITY
if True:
# params.featweight_cfgstr = query_cfg._featweight_cfg.get_cfgstr()
# TODO: if this hack is fully completed need a way of getting the
# full config belonging to both chip + feat
try:
request.params['chip_cfgstr'] = config.chip_cfg.get_cfgstr()
request.params['chip_cfg_dict'] = config.chip_cfg.asdict()
request.params['feat_cfgstr'] = config.feat_cfg.get_cfgstr()
request.params[
'hesaff_params'] = config.feat_cfg.get_hesaff_params()
request.params[
'featweight_cfgstr'] = config.feat_weight_cfg.get_cfgstr()
except AttributeError:
pass
request.qparams = ut.DynStruct()
for key, val in request.params.items():
setattr(request.qparams, key, val)
return request
def static_new(cls, depc, parent_rowids, cfgdict=None, tablename=None):
""" hack for autoreload """
request = cls()
if tablename is None:
try:
if hasattr(cls, '_tablename'):
tablename = cls._tablename
else:
tablename = ut.invert_dict(depc.requestclass_dict)[cls]
except Exception as ex:
ut.printex(ex, 'tablename must be given')
raise
request.tablename = tablename
request.parent_rowids = parent_rowids
request.depc = depc
if cfgdict is None:
cfgdict = {}
configclass = depc.configclass_dict[tablename]
config = configclass(**cfgdict)
request.config = config
# HACK FOR IBEIS
request.params = dict(config.parse_items())
# HACK-ier FOR BACKWARDS COMPATABILITY
if True:
#params.featweight_cfgstr = query_cfg._featweight_cfg.get_cfgstr()
# TODO: if this hack is fully completed need a way of getting the
# full config belonging to both chip + feat
try:
request.params['chip_cfgstr'] = config.chip_cfg.get_cfgstr()
request.params['chip_cfg_dict'] = config.chip_cfg.asdict()
request.params['feat_cfgstr'] = config.feat_cfg.get_cfgstr()
request.params['hesaff_params'] = config.feat_cfg.get_hesaff_params()
request.params['featweight_cfgstr'] = config.feat_weight_cfg.get_cfgstr()
except AttributeError:
pass
request.qparams = ut.DynStruct()
for key, val in request.params.items():
setattr(request.qparams, key, val)
return request
def __init__(self,
infr,
selected_aids=[],
use_image=False,
temp_nids=None):
super(AnnotGraphInteraction, self).__init__()
self.infr = infr
self.selected_aids = selected_aids
self.node2_aid = nx.get_node_attributes(self.infr.graph, 'aid')
self.aid2_node = ut.invert_dict(self.node2_aid)
node2_label = {
#node: '%d:aid=%r' % (node, aid)
node: 'aid=%r' % (aid)
for node, aid in self.node2_aid.items()
}
#self.show_cuts = False
self.use_image = use_image
self.show_cuts = False
self.config = InferenceConfig()
nx.set_node_attributes(self.infr.graph,
name='label',
values=node2_label)
def __init__(verif, infr):
verif.rng = np.random.RandomState(4033913)
verif.dummy_params = {
NEGTV: {
'mean': 0.2,
'std': 0.25
},
POSTV: {
'mean': 0.85,
'std': 0.2
},
INCMP: {
'mean': 0.15,
'std': 0.1
},
}
verif.score_dist = randn
verif.infr = infr
verif.orig_nodes = set(infr.aids)
verif.orig_labels = infr.get_node_attrs('orig_name_label')
verif.orig_groups = ut.invert_dict(verif.orig_labels, False)
verif.orig_groups = ut.map_vals(set, verif.orig_groups)
def netrun():
r"""
CommandLine:
# --- UTILITY
python -m ibeis_cnn --tf get_juction_dpath --show
# --- DATASET BUILDING ---
# Build Dataset Aliases
python -m ibeis_cnn --tf netrun --db PZ_MTEST --acfg ctrl --ensuredata --show
python -m ibeis_cnn --tf netrun --db PZ_Master1 --acfg timectrl --ensuredata
python -m ibeis_cnn --tf netrun --db PZ_Master1 --acfg timectrl:pername=None --ensuredata
python -m ibeis_cnn --tf netrun --db PZ_Master1 --acfg timectrl:pername=None --ensuredata
python -m ibeis_cnn --tf netrun --db mnist --ensuredata --show
python -m ibeis_cnn --tf netrun --db mnist --ensuredata --show --datatype=category
python -m ibeis_cnn --tf netrun --db mnist --ensuredata --show --datatype=siam-patch
python -m ibeis_cnn --tf netrun --db PZ_Master1 --acfg ctrl:pername=None,excluderef=False,contributor_contains=FlankHack --ensuredata --show --datatype=siam-part
# Parts based datasets
python -m ibeis_cnn --tf netrun --db PZ_MTEST --acfg ctrl --datatype=siam-part --ensuredata --show
% Patch based dataset (big one)
python -m ibeis_cnn --tf netrun --db PZ_Master1 --acfg default:is_known=True,qmin_pername=2,view=primary,species=primary,minqual=ok --ensuredata --show --vtd
python -m ibeis_cnn --tf netrun --ds pzm4 --weights=new --arch=siaml2_128 --train --monitor
python -m ibeis_cnn --tf netrun --ds pzm4 --arch=siaml2_128 --test
python -m ibeis_cnn --tf netrun --ds pzm4 --arch=siaml2_128 --veryverbose --no-flask
# --- TRAINING ---
python -m ibeis_cnn --tf netrun --db PZ_Master1 --acfg default:is_known=True,qmin_pername=2,view=primary,species=primary,minqual=ok --weights=new --arch=siaml2_128 --train --monitor
python -m ibeis_cnn --tf netrun --ds timectrl_pzmaster1 --acfg ctrl:pername=None,excluderef=False,contributor_contains=FlankHack --train --weights=new --arch=siaml2_128 --monitor # NOQA
python -m ibeis_cnn --tf netrun --ds timectrl_pzmaster1 --acfg ctrl:pername=None,excluderef=False --train --weights=new --arch=siaml2_128 --monitor # NOQA
python -m ibeis_cnn --tf netrun --ds pzmtest --weights=new --arch=siaml2_128 --train --monitor --DEBUG_AUGMENTATION
python -m ibeis_cnn --tf netrun --ds pzmtest --weights=new --arch=siaml2_128 --train --monitor
python -m ibeis_cnn --tf netrun --ds flankhack --weights=new --arch=siaml2_partmatch --train --monitor --learning_rate=.00001
python -m ibeis_cnn --tf netrun --ds flankhack --weights=new --arch=siam_deepfaceish --train --monitor --learning_rate=.00001
# Different ways to train mnist
python -m ibeis_cnn --tf netrun --db mnist --weights=new --arch=mnist_siaml2 --train --monitor --datatype=siam-patch
python -m ibeis_cnn --tf netrun --db mnist --weights=new --arch=mnist-category --train --monitor --datatype=category
# --- INITIALIZED-TRAINING ---
python -m ibeis_cnn --tf netrun --ds pzmtest --arch=siaml2_128 --weights=gz-gray:current --train --monitor
# --- TESTING ---
python -m ibeis_cnn --tf netrun --db liberty --weights=liberty:current --arch=siaml2_128 --test
python -m ibeis_cnn --tf netrun --db PZ_Master0 --weights=combo:current --arch=siaml2_128 --testall
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis_cnn.netrun import * # NOQA
>>> netrun()
>>> ut.show_if_requested()
"""
ut.colorprint('[netrun] NET RUN', 'red')
requests, hyperparams, tags = parse_args()
ds_tag = tags['ds_tag']
datatype = tags['datatype']
extern_ds_tag = tags['extern_ds_tag']
arch_tag = tags['arch_tag']
checkpoint_tag = tags['checkpoint_tag']
# ----------------------------
# Choose the main dataset
ut.colorprint('[netrun] Ensuring Dataset', 'yellow')
dataset = ingest_data.grab_dataset(ds_tag, datatype)
if extern_ds_tag is not None:
extern_dpath = ingest_data.get_extern_training_dpath(extern_ds_tag)
else:
extern_dpath = None
print('dataset.training_dpath = %r' % (dataset.training_dpath,))
print('Dataset Alias Key: %r' % (dataset.alias_key,))
print('Current Dataset Tag: %r' % (
ut.invert_dict(DS_TAG_ALIAS2).get(dataset.alias_key, None),))
if requests['ensuredata']:
# Print alias key that maps to this particular dataset
if ut.show_was_requested():
interact_ = dataset.interact() # NOQA
return
print('...exiting')
sys.exit(1)
# ----------------------------
# Choose model architecture
# TODO: data will need to return info about number of labels in viewpoint models
# Specify model archichitecture
ut.colorprint('[netrun] Architecture Specification', 'yellow')
if arch_tag == 'siam2stream':
model = models.SiameseCenterSurroundModel(
data_shape=dataset.data_shape,
training_dpath=dataset.training_dpath, **hyperparams)
elif arch_tag.startswith('siam'):
model = models.SiameseL2(
data_shape=dataset.data_shape,
arch_tag=arch_tag,
training_dpath=dataset.training_dpath, **hyperparams)
elif arch_tag == 'mnist-category':
model = models.MNISTModel(
data_shape=dataset.data_shape,
output_dims=dataset.output_dims,
arch_tag=arch_tag,
training_dpath=dataset.training_dpath, **hyperparams)
pass
else:
raise ValueError('Unknown arch_tag=%r' % (arch_tag,))
ut.colorprint('[netrun] Initialize archchitecture', 'yellow')
model.init_arch()
# ----------------------------
# Choose weight initialization
ut.colorprint('[netrun] Setting weights', 'yellow')
if checkpoint_tag == 'new':
ut.colorprint('[netrun] * Initializing new weights', 'lightgray')
model.reinit_weights()
else:
checkpoint_tag = model.resolve_fuzzy_checkpoint_pattern(
checkpoint_tag, extern_dpath)
ut.colorprint('[netrun] * Resolving weights checkpoint_tag=%r' %
(checkpoint_tag,), 'lightgray')
if extern_dpath is not None:
model.load_extern_weights(dpath=extern_dpath,
checkpoint_tag=checkpoint_tag)
elif model.has_saved_state(checkpoint_tag=checkpoint_tag):
model.load_model_state(checkpoint_tag=checkpoint_tag)
else:
model_state_fpath = model.get_model_state_fpath(
checkpoint_tag=checkpoint_tag)
print('model_state_fpath = %r' % (model_state_fpath,))
ut.checkpath(model_state_fpath, verbose=True)
print('Known checkpoints are: ' + ut.repr3(model.list_saved_checkpoints()))
raise ValueError(('Unresolved weight init: '
'checkpoint_tag=%r, extern_ds_tag=%r') % (
checkpoint_tag, extern_ds_tag,))
#print('Model State:')
#print(model.get_state_str())
# ----------------------------
if not model.is_train_state_initialized():
ut.colorprint('[netrun] Need to initialize training state', 'yellow')
X_train, y_train = dataset.subset('train')
model.ensure_data_params(X_train, y_train)
# Run Actions
if requests['train']:
ut.colorprint('[netrun] Training Requested', 'yellow')
# parse training arguments
config = ut.argparse_dict(dict(
era_size=15,
max_epochs=1200,
rate_decay=.8,
))
model.monitor_config.update(**config)
X_train, y_train = dataset.subset('train')
X_valid, y_valid = dataset.subset('valid')
model.fit(X_train, y_train, X_valid=X_valid, y_valid=y_valid)
elif requests['test']:
#assert model.best_results['epoch'] is not None
ut.colorprint('[netrun] Test Requested', 'yellow')
if requests['testall']:
ut.colorprint('[netrun] * Testing on all data', 'lightgray')
X_test, y_test = dataset.subset('all')
flat_metadata = dataset.subset_metadata('all')
else:
ut.colorprint('[netrun] * Testing on test subset', 'lightgray')
X_test, y_test = dataset.subset('test')
flat_metadata = dataset.subset_metadata('test')
data, labels = X_test, y_test
dataname = dataset.alias_key
experiments.test_siamese_performance(model, data, labels,
flat_metadata, dataname)
else:
if not ut.get_argflag('--cmd'):
raise ValueError('nothing here. need to train or test')
if requests['publish']:
ut.colorprint('[netrun] Publish Requested', 'yellow')
publish_dpath = ut.truepath('~/Dropbox/IBEIS')
published_model_state = ut.unixjoin(
publish_dpath, model.arch_tag + '_model_state.pkl')
ut.copy(model.get_model_state_fpath(), published_model_state)
ut.view_directory(publish_dpath)
print('You need to get the dropbox link and '
'register it into the appropriate file')
# pip install dropbox
# https://www.dropbox.com/developers/core/start/python
# import dropbox # need oauth
#client.share('/myfile.txt', short_url=False)
# https://cthulhu.dyn.wildme.io/public/models/siaml2_128_model_state.pkl
if ut.get_argflag('--cmd'):
ut.embed()
def _inject_getter_attrs(metaself,
objname,
attrs,
configurable_attrs,
depc_name=None,
depcache_attrs=None,
settable_attrs=None,
aliased_attrs=None):
"""
Used by the metaclass to inject methods and properties into the class
inheriting from ObjectList1D
"""
if settable_attrs is None:
settable_attrs = []
settable_attrs = set(settable_attrs)
# Inform the class of which variables will be injected
metaself._settable_attrs = settable_attrs
metaself._attrs = attrs
metaself._configurable_attrs = configurable_attrs
if depcache_attrs is None:
metaself._depcache_attrs = []
else:
metaself._depcache_attrs = [
'%s_%s' % (tbl, col) for tbl, col in depcache_attrs
]
if aliased_attrs is not None:
metaself._attrs_aliases = aliased_attrs
else:
metaself._attrs_aliases = {}
# if not getattr(metaself, '__needs_inject__', True):
# return
attr_to_aliases = ut.invert_dict(metaself._attrs_aliases,
unique_vals=False)
# What is difference between configurable and depcache getters?
# Could depcache getters just be made configurable?
# I guess its just an efficincy thing. Actually its config2_-vs-config
# FIXME: rectify differences between normal / configurable / depcache
# getter
def _make_caching_setter(attrname, _rowid_setter):
def _setter(self, values, *args, **kwargs):
if self._ibs is None:
self._internal_attrs[attrname] = values
else:
if self._caching and attrname in self._internal_attrs:
self._internal_attrs[attrname] = values
_rowid_setter(self, self._rowids, values)
ut.set_funcname(_setter, '_set_' + attrname)
return _setter
def _make_caching_getter(attrname, _rowid_getter):
def _getter(self):
if self._ibs is None or (self._caching
and attrname in self._internal_attrs):
data = self._internal_attrs[attrname]
else:
data = _rowid_getter(self, self._rowids)
if self._caching:
self._internal_attrs[attrname] = data
return data
ut.set_funcname(_getter, '_get_' + attrname)
return _getter
# make default version use implicit rowids and another
# that takes explicit rowids.
def _make_setters(objname, attrname):
ibs_funcname = 'set_%s_%s' % (objname, attrname)
def _rowid_setter(self, rowids, values, *args, **kwargs):
ibs_callable = getattr(self._ibs, ibs_funcname)
ibs_callable(rowids, values, *args, **kwargs)
ut.set_funcname(_rowid_setter, '_rowid_set_' + attrname)
_setter = _make_caching_setter(attrname, _rowid_setter)
return _rowid_setter, _setter
# ---
def _make_getters(objname, attrname):
ibs_funcname = 'get_%s_%s' % (objname, attrname)
def _rowid_getter(self, rowids):
ibs_callable = getattr(self._ibs, ibs_funcname)
data = ibs_callable(rowids)
if self._asarray:
data = np.array(data)
return data
ut.set_funcname(_rowid_getter, '_rowid_get_' + attrname)
_getter = _make_caching_getter(attrname, _rowid_getter)
return _rowid_getter, _getter
def _make_cfg_getters(objname, attrname):
ibs_funcname = 'get_%s_%s' % (objname, attrname)
def _rowid_getter(self, rowids):
ibs_callable = getattr(self._ibs, ibs_funcname)
data = ibs_callable(rowids, config2_=self._config)
if self._asarray:
data = np.array(data)
return data
ut.set_funcname(_rowid_getter, '_rowid_get_' + attrname)
_getter = _make_caching_getter(attrname, _rowid_getter)
return _rowid_getter, _getter
def _make_depc_getters(depc_name, attrname, tbl, col):
def _rowid_getter(self, rowids):
depc = getattr(self._ibs, depc_name)
data = depc.get(tbl, rowids, col, config=self._config)
if self._asarray:
data = np.array(data)
return data
ut.set_funcname(_rowid_getter, '_rowid_get_' + attrname)
_getter = _make_caching_getter(attrname, _rowid_getter)
return _rowid_getter, _getter
# Collect setter / getter functions and properties
rowid_getters = []
getters = []
setters = []
properties = []
for attrname in attrs:
_rowid_getter, _getter = _make_getters(objname, attrname)
if attrname in settable_attrs:
_rowid_setter, _setter = _make_setters(objname, attrname)
setters.append(_setter)
else:
_setter = None
prop = property(fget=_getter, fset=_setter)
rowid_getters.append((attrname, _rowid_getter))
getters.append(_getter)
properties.append((attrname, prop))
for attrname in configurable_attrs:
_rowid_getter, _getter = _make_cfg_getters(objname, attrname)
prop = property(fget=_getter)
rowid_getters.append((attrname, _rowid_getter))
getters.append(_getter)
properties.append((attrname, prop))
if depcache_attrs is not None:
for tbl, col in depcache_attrs:
attrname = '%s_%s' % (tbl, col)
_rowid_getter, _getter = _make_depc_getters(
depc_name, attrname, tbl, col)
prop = property(fget=_getter, fset=None)
rowid_getters.append((attrname, _rowid_getter))
getters.append(_getter)
properties.append((attrname, prop))
aliases = []
# Inject all gathered information
for attrname, func in rowid_getters:
funcname = ut.get_funcname(func)
setattr(metaself, funcname, func)
# ensure aliases have rowid getters
for alias in attr_to_aliases.get(attrname, []):
alias_funcname = '_rowid_get_' + alias
setattr(metaself, alias_funcname, func)
for func in getters:
funcname = ut.get_funcname(func)
setattr(metaself, funcname, func)
for func in setters:
funcname = ut.get_funcname(func)
setattr(metaself, funcname, func)
for attrname, prop in properties:
setattr(metaself, attrname, prop)
for alias in attr_to_aliases.pop(attrname, []):
aliases.append((alias, attrname))
setattr(metaself, alias, prop)
if ut.get_argflag('--autogen-core'):
# TODO: turn on autogenertion given a flag
def expand_closure_source(funcname, func):
source = ut.get_func_sourcecode(func)
closure_vars = [
(k, v.cell_contents)
for k, v in zip(func.func_code.co_freevars, func.func_closure)
]
source = ut.unindent(source)
import re
for k, v in closure_vars:
source = re.sub('\\b' + k + '\\b', ut.repr2(v), source)
source = re.sub('def .*\(self', 'def ' + funcname + '(self',
source)
source = ut.indent(source.strip(), ' ') + '\n'
return source
explicit_lines = []
# build explicit version for jedi?
for funcname, func in getters:
source = expand_closure_source(funcname, func)
explicit_lines.append(source)
# build explicit version for jedi?
for funcname, func in setters:
source = expand_closure_source(funcname, func)
explicit_lines.append(source)
for attrname, prop in properties:
getter_name = None if prop.fget is None else ut.get_funcname(
prop.fget)
setter_name = None if prop.fset is None else ut.get_funcname(
prop.fset)
source = ' %s = property(%s, %s)' % (attrname, getter_name,
setter_name)
explicit_lines.append(source)
for alias, attrname in aliases:
source = ' %s = %s' % (alias, attrname)
explicit_lines.append(source)
explicit_source = '\n'.join([
'from ibeis import _ibeis_object',
'',
'',
'class _%s_base_class(_ibeis_object.ObjectList1D):',
' __needs_inject__ = False',
'',
]) % (objname, )
explicit_source += '\n'.join(explicit_lines)
explicit_fname = '_autogen_%s_base.py' % (objname, )
from os.path import dirname, join
ut.writeto(join(dirname(__file__), explicit_fname),
explicit_source + '\n')
if attr_to_aliases:
raise AssertionError('Unmapped aliases %r' % (attr_to_aliases, ))
class VIEW(object):
"""
categorical viewpoint using the faces of a Rhombicuboctahedron
References:
https://en.wikipedia.org/wiki/Rhombicuboctahedron
"""
UNKNOWN = None
R = 1
FR = 2
F = 3
FL = 4
L = 5
BL = 6
B = 7
BR = 8
U = 9
UF = 10
UB = 11
UL = 12
UR = 13
UFL = 14
UFR = 15
UBL = 16
UBR = 17
D = 18
DF = 19
DB = 20
DL = 21
DR = 22
DFL = 23
DFR = 24
DBL = 25
DBR = 26
INT_TO_CODE = ut.odict([
(UNKNOWN, 'unknown'),
(R, 'right'),
(FR, 'frontright'),
(F, 'front'),
(FL, 'frontleft'),
(L, 'left'),
(BL, 'backleft'),
(B, 'back'),
(BR, 'backright'),
(U, 'up'),
(UF, 'upfront'),
(UB, 'upback'),
(UL, 'upleft'),
(UR, 'upright'),
(UFL, 'upfrontleft'),
(UFR, 'upfrontright'),
(UBL, 'upbackleft'),
(UBR, 'upbackright'),
(D, 'down'),
(DF, 'downfront'),
(DB, 'downback'),
(DL, 'downleft'),
(DR, 'downright'),
(DFL, 'downfrontleft'),
(DFR, 'downfrontright'),
(DBL, 'downbackleft'),
(DBR, 'downbackright'),
])
INT_TO_NICE = ut.odict([
(UNKNOWN, 'Unknown'),
(R, 'Right'),
(FR, 'Front-Right'),
(F, 'Front'),
(FL, 'Front-Left'),
(L, 'Left'),
(BL, 'Back-Left'),
(B, 'Back'),
(BR, 'Back-Right'),
(U, 'Up'),
(UF, 'Up-Front'),
(UB, 'Up-Back'),
(UL, 'Up-Left'),
(UR, 'Up-Right'),
(UFL, 'Up-Front-Left'),
(UFR, 'Up-Front-Right'),
(UBL, 'Up-Back-Left'),
(UBR, 'Up-Back-Right'),
(D, 'Down'),
(DF, 'Down-Front'),
(DB, 'Down-Back'),
(DL, 'Down-Left'),
(DR, 'Down-Right'),
(DFL, 'Down-Front-Left'),
(DFR, 'Down-Front-Right'),
(DBL, 'Down-Back-Left'),
(DBR, 'Down-Back-Right'),
])
CODE_TO_NICE = ut.map_keys(INT_TO_CODE, INT_TO_NICE)
CODE_TO_INT = ut.invert_dict(INT_TO_CODE)
NICE_TO_CODE = ut.invert_dict(CODE_TO_NICE)
NICE_TO_INT = ut.invert_dict(INT_TO_NICE)
DIST = {
# DIST 0 PAIRS
(B, B): 0,
(BL, BL): 0,
(BR, BR): 0,
(D, D): 0,
(DB, DB): 0,
(DBL, DBL): 0,
(DBR, DBR): 0,
(DF, DF): 0,
(DFL, DFL): 0,
(DFR, DFR): 0,
(DL, DL): 0,
(DR, DR): 0,
(F, F): 0,
(FL, FL): 0,
(FR, FR): 0,
(L, L): 0,
(R, R): 0,
(U, U): 0,
(UB, UB): 0,
(UBL, UBL): 0,
(UBR, UBR): 0,
(UF, UF): 0,
(UFL, UFL): 0,
(UFR, UFR): 0,
(UL, UL): 0,
(UR, UR): 0,
# DIST 1 PAIRS
(B, BL): 1,
(B, BR): 1,
(B, DB): 1,
(B, DBL): 1,
(B, DBR): 1,
(B, UB): 1,
(B, UBL): 1,
(B, UBR): 1,
(BL, DBL): 1,
(BL, L): 1,
(BL, UBL): 1,
(BR, DBR): 1,
(BR, R): 1,
(BR, UBR): 1,
(D, DB): 1,
(D, DBL): 1,
(D, DBR): 1,
(D, DF): 1,
(D, DFL): 1,
(D, DFR): 1,
(D, DL): 1,
(D, DR): 1,
(DB, DBL): 1,
(DB, DBR): 1,
(DBL, DL): 1,
(DBL, L): 1,
(DBR, DR): 1,
(DBR, R): 1,
(DF, DFL): 1,
(DF, DFR): 1,
(DF, F): 1,
(DFL, DL): 1,
(DFL, F): 1,
(DFL, FL): 1,
(DFL, L): 1,
(DFR, DR): 1,
(DFR, F): 1,
(DFR, FR): 1,
(DFR, R): 1,
(DL, L): 1,
(DR, R): 1,
(F, FL): 1,
(F, FR): 1,
(F, UF): 1,
(F, UFL): 1,
(F, UFR): 1,
(FL, L): 1,
(FL, UFL): 1,
(FR, R): 1,
(FR, UFR): 1,
(L, UBL): 1,
(L, UFL): 1,
(L, UL): 1,
(R, UBR): 1,
(R, UFR): 1,
(R, UR): 1,
(U, UB): 1,
(U, UBL): 1,
(U, UBR): 1,
(U, UF): 1,
(U, UFL): 1,
(U, UFR): 1,
(U, UL): 1,
(U, UR): 1,
(UB, UBL): 1,
(UB, UBR): 1,
(UBL, UL): 1,
(UBR, UR): 1,
(UF, UFL): 1,
(UF, UFR): 1,
(UFL, UL): 1,
(UFR, UR): 1,
# DIST 2 PAIRS
(B, D): 2,
(B, DL): 2,
(B, DR): 2,
(B, L): 2,
(B, R): 2,
(B, U): 2,
(B, UL): 2,
(B, UR): 2,
(BL, BR): 2,
(BL, D): 2,
(BL, DB): 2,
(BL, DBR): 2,
(BL, DFL): 2,
(BL, DL): 2,
(BL, FL): 2,
(BL, U): 2,
(BL, UB): 2,
(BL, UBR): 2,
(BL, UFL): 2,
(BL, UL): 2,
(BR, D): 2,
(BR, DB): 2,
(BR, DBL): 2,
(BR, DFR): 2,
(BR, DR): 2,
(BR, FR): 2,
(BR, U): 2,
(BR, UB): 2,
(BR, UBL): 2,
(BR, UFR): 2,
(BR, UR): 2,
(D, F): 2,
(D, FL): 2,
(D, FR): 2,
(D, L): 2,
(D, R): 2,
(DB, DF): 2,
(DB, DFL): 2,
(DB, DFR): 2,
(DB, DL): 2,
(DB, DR): 2,
(DB, L): 2,
(DB, R): 2,
(DB, UB): 2,
(DB, UBL): 2,
(DB, UBR): 2,
(DBL, DBR): 2,
(DBL, DF): 2,
(DBL, DFL): 2,
(DBL, DFR): 2,
(DBL, DR): 2,
(DBL, FL): 2,
(DBL, UB): 2,
(DBL, UBL): 2,
(DBL, UBR): 2,
(DBL, UFL): 2,
(DBL, UL): 2,
(DBR, DF): 2,
(DBR, DFL): 2,
(DBR, DFR): 2,
(DBR, DL): 2,
(DBR, FR): 2,
(DBR, UB): 2,
(DBR, UBL): 2,
(DBR, UBR): 2,
(DBR, UFR): 2,
(DBR, UR): 2,
(DF, DL): 2,
(DF, DR): 2,
(DF, FL): 2,
(DF, FR): 2,
(DF, L): 2,
(DF, R): 2,
(DF, UF): 2,
(DF, UFL): 2,
(DF, UFR): 2,
(DFL, DFR): 2,
(DFL, DR): 2,
(DFL, FR): 2,
(DFL, UBL): 2,
(DFL, UF): 2,
(DFL, UFL): 2,
(DFL, UFR): 2,
(DFL, UL): 2,
(DFR, DL): 2,
(DFR, FL): 2,
(DFR, UBR): 2,
(DFR, UF): 2,
(DFR, UFL): 2,
(DFR, UFR): 2,
(DFR, UR): 2,
(DL, DR): 2,
(DL, F): 2,
(DL, FL): 2,
(DL, UBL): 2,
(DL, UFL): 2,
(DL, UL): 2,
(DR, F): 2,
(DR, FR): 2,
(DR, UBR): 2,
(DR, UFR): 2,
(DR, UR): 2,
(F, L): 2,
(F, R): 2,
(F, U): 2,
(F, UL): 2,
(F, UR): 2,
(FL, FR): 2,
(FL, U): 2,
(FL, UBL): 2,
(FL, UF): 2,
(FL, UFR): 2,
(FL, UL): 2,
(FR, U): 2,
(FR, UBR): 2,
(FR, UF): 2,
(FR, UFL): 2,
(FR, UR): 2,
(L, U): 2,
(L, UB): 2,
(L, UF): 2,
(R, U): 2,
(R, UB): 2,
(R, UF): 2,
(UB, UF): 2,
(UB, UFL): 2,
(UB, UFR): 2,
(UB, UL): 2,
(UB, UR): 2,
(UBL, UBR): 2,
(UBL, UF): 2,
(UBL, UFL): 2,
(UBL, UFR): 2,
(UBL, UR): 2,
(UBR, UF): 2,
(UBR, UFL): 2,
(UBR, UFR): 2,
(UBR, UL): 2,
(UF, UL): 2,
(UF, UR): 2,
(UFL, UFR): 2,
(UFL, UR): 2,
(UFR, UL): 2,
(UL, UR): 2,
# DIST 3 PAIRS
(B, DF): 3,
(B, DFL): 3,
(B, DFR): 3,
(B, FL): 3,
(B, FR): 3,
(B, UF): 3,
(B, UFL): 3,
(B, UFR): 3,
(BL, DF): 3,
(BL, DFR): 3,
(BL, DR): 3,
(BL, F): 3,
(BL, R): 3,
(BL, UF): 3,
(BL, UFR): 3,
(BL, UR): 3,
(BR, DF): 3,
(BR, DFL): 3,
(BR, DL): 3,
(BR, F): 3,
(BR, L): 3,
(BR, UF): 3,
(BR, UFL): 3,
(BR, UL): 3,
(D, UB): 3,
(D, UBL): 3,
(D, UBR): 3,
(D, UF): 3,
(D, UFL): 3,
(D, UFR): 3,
(D, UL): 3,
(D, UR): 3,
(DB, F): 3,
(DB, FL): 3,
(DB, FR): 3,
(DB, U): 3,
(DB, UFL): 3,
(DB, UFR): 3,
(DB, UL): 3,
(DB, UR): 3,
(DBL, F): 3,
(DBL, FR): 3,
(DBL, R): 3,
(DBL, U): 3,
(DBL, UF): 3,
(DBL, UR): 3,
(DBR, F): 3,
(DBR, FL): 3,
(DBR, L): 3,
(DBR, U): 3,
(DBR, UF): 3,
(DBR, UL): 3,
(DF, U): 3,
(DF, UBL): 3,
(DF, UBR): 3,
(DF, UL): 3,
(DF, UR): 3,
(DFL, R): 3,
(DFL, U): 3,
(DFL, UB): 3,
(DFL, UR): 3,
(DFR, L): 3,
(DFR, U): 3,
(DFR, UB): 3,
(DFR, UL): 3,
(DL, FR): 3,
(DL, R): 3,
(DL, U): 3,
(DL, UB): 3,
(DL, UBR): 3,
(DL, UF): 3,
(DL, UFR): 3,
(DR, FL): 3,
(DR, L): 3,
(DR, U): 3,
(DR, UB): 3,
(DR, UBL): 3,
(DR, UF): 3,
(DR, UFL): 3,
(F, UB): 3,
(F, UBL): 3,
(F, UBR): 3,
(FL, R): 3,
(FL, UB): 3,
(FL, UBR): 3,
(FL, UR): 3,
(FR, L): 3,
(FR, UB): 3,
(FR, UBL): 3,
(FR, UL): 3,
(L, UBR): 3,
(L, UFR): 3,
(L, UR): 3,
(R, UBL): 3,
(R, UFL): 3,
(R, UL): 3,
# DIST 4 PAIRS
(B, F): 4,
(BL, FR): 4,
(BR, FL): 4,
(D, U): 4,
(DB, UF): 4,
(DBL, UFR): 4,
(DBR, UFL): 4,
(DF, UB): 4,
(DFL, UBR): 4,
(DFR, UBL): 4,
(DL, UR): 4,
(DR, UL): 4,
(L, R): 4,
# UNDEFINED DIST PAIRS
(B, UNKNOWN): None,
(BL, UNKNOWN): None,
(BR, UNKNOWN): None,
(D, UNKNOWN): None,
(DB, UNKNOWN): None,
(DBL, UNKNOWN): None,
(DBR, UNKNOWN): None,
(DF, UNKNOWN): None,
(DFL, UNKNOWN): None,
(DFR, UNKNOWN): None,
(DL, UNKNOWN): None,
(DR, UNKNOWN): None,
(F, UNKNOWN): None,
(FL, UNKNOWN): None,
(FR, UNKNOWN): None,
(L, UNKNOWN): None,
(R, UNKNOWN): None,
(U, UNKNOWN): None,
(UB, UNKNOWN): None,
(UBL, UNKNOWN): None,
(UBR, UNKNOWN): None,
(UF, UNKNOWN): None,
(UFL, UNKNOWN): None,
(UFR, UNKNOWN): None,
(UL, UNKNOWN): None,
(UNKNOWN, B): None,
(UNKNOWN, BL): None,
(UNKNOWN, BR): None,
(UNKNOWN, D): None,
(UNKNOWN, DB): None,
(UNKNOWN, DBL): None,
(UNKNOWN, DBR): None,
(UNKNOWN, DF): None,
(UNKNOWN, DFL): None,
(UNKNOWN, DFR): None,
(UNKNOWN, DL): None,
(UNKNOWN, DR): None,
(UNKNOWN, F): None,
(UNKNOWN, FL): None,
(UNKNOWN, FR): None,
(UNKNOWN, L): None,
(UNKNOWN, R): None,
(UNKNOWN, U): None,
(UNKNOWN, UB): None,
(UNKNOWN, UBL): None,
(UNKNOWN, UBR): None,
(UNKNOWN, UF): None,
(UNKNOWN, UFL): None,
(UNKNOWN, UFR): None,
(UNKNOWN, UL): None,
(UNKNOWN, UR): None,
(UR, UNKNOWN): None,
(UNKNOWN, UNKNOWN): None,
}
# make distance symmetric
for (f1, f2), d in list(DIST.items()):
DIST[(f2, f1)] = d
QUAL_OK = 'ok'
QUAL_POOR = 'poor'
QUAL_JUNK = 'junk'
QUAL_UNKNOWN = 'UNKNOWN'
QUALITY_INT_TO_TEXT = OrderedDict([
(5, QUAL_EXCELLENT,),
(4, QUAL_GOOD,),
(3, QUAL_OK,),
(2, QUAL_POOR,),
# oops forgot 1. will be mapped to poor
(0, QUAL_JUNK,),
(-1, QUAL_UNKNOWN,),
])
QUALITY_TEXT_TO_INT = ut.invert_dict(QUALITY_INT_TO_TEXT)
QUALITY_INT_TO_TEXT[1] = QUAL_JUNK
#QUALITY_TEXT_TO_INTS = ut.invert_dict(QUALITY_INT_TO_TEXT)
QUALITY_TEXT_TO_INTS = ut.group_items(
list(QUALITY_INT_TO_TEXT.keys()),
list(QUALITY_INT_TO_TEXT.values()))
QUALITY_TEXT_TO_INTS[QUAL_UNKNOWN] = -1
QUALITY_INT_TO_TEXT[None] = QUALITY_INT_TO_TEXT[-1]
SEX_INT_TO_TEXT = {
None: 'UNKNOWN NAME',
-1 : 'UNKNOWN SEX',
0 : 'Female',
1 : 'Male',
}
def nx_transitive_reduction(G, mode=1):
"""
References:
https://en.wikipedia.org/wiki/Transitive_reduction#Computing_the_reduction_using_the_closure
http://dept-info.labri.fr/~thibault/tmp/0201008.pdf
http://stackoverflow.com/questions/17078696/im-trying-to-perform-the-transitive-reduction-of-directed-graph-in-python
CommandLine:
python -m utool.util_graph nx_transitive_reduction --show
Example:
>>> # DISABLE_DOCTEST
>>> from utool.util_graph import * # NOQA
>>> import utool as ut
>>> import networkx as nx
>>> G = nx.DiGraph([('a', 'b'), ('a', 'c'), ('a', 'e'),
>>> ('a', 'd'), ('b', 'd'), ('c', 'e'),
>>> ('d', 'e'), ('c', 'e'), ('c', 'd')])
>>> G = testdata_graph()[1]
>>> G_tr = nx_transitive_reduction(G, mode=1)
>>> G_tr2 = nx_transitive_reduction(G, mode=1)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> G_ = nx.dag.transitive_closure(G)
>>> pt.show_nx(G , pnum=(1, 5, 1), fnum=1)
>>> pt.show_nx(G_tr , pnum=(1, 5, 2), fnum=1)
>>> pt.show_nx(G_tr2 , pnum=(1, 5, 3), fnum=1)
>>> pt.show_nx(G_ , pnum=(1, 5, 4), fnum=1)
>>> pt.show_nx(nx.dag.transitive_closure(G_tr), pnum=(1, 5, 5), fnum=1)
>>> ut.show_if_requested()
"""
import utool as ut
import networkx as nx
has_cycles = not nx.is_directed_acyclic_graph(G)
if has_cycles:
# FIXME: this does not work for cycle graphs.
# Need to do algorithm on SCCs
G_orig = G
G = nx.condensation(G_orig)
nodes = list(G.nodes())
node2_idx = ut.make_index_lookup(nodes)
# For each node u, perform DFS consider its set of (non-self) children C.
# For each descendant v, of a node in C, remove any edge from u to v.
if mode == 1:
G_tr = G.copy()
for parent in G_tr.nodes():
# Remove self loops
if G_tr.has_edge(parent, parent):
G_tr.remove_edge(parent, parent)
# For each child of the parent
for child in list(G_tr.successors(parent)):
# Preorder nodes includes its argument (no added complexity)
for gchild in list(G_tr.successors(child)):
# Remove all edges from parent to non-child descendants
for descendant in nx.dfs_preorder_nodes(G_tr, gchild):
if G_tr.has_edge(parent, descendant):
G_tr.remove_edge(parent, descendant)
if has_cycles:
# Uncondense graph
uncondensed_G_tr = G.__class__()
mapping = G.graph['mapping']
uncondensed_G_tr.add_nodes_from(mapping.keys())
inv_mapping = ut.invert_dict(mapping, unique_vals=False)
for u, v in G_tr.edges():
u_ = inv_mapping[u][0]
v_ = inv_mapping[v][0]
uncondensed_G_tr.add_edge(u_, v_)
for key, path in inv_mapping.items():
if len(path) > 1:
directed_cycle = list(ut.itertwo(path, wrap=True))
uncondensed_G_tr.add_edges_from(directed_cycle)
G_tr = uncondensed_G_tr
else:
def make_adj_matrix(G):
edges = list(G.edges())
edge2_idx = ut.partial(ut.dict_take, node2_idx)
uv_list = ut.lmap(edge2_idx, edges)
A = np.zeros((len(nodes), len(nodes)))
A[tuple(np.array(uv_list).T)] = 1
return A
G_ = nx.dag.transitive_closure(G)
A = make_adj_matrix(G)
B = make_adj_matrix(G_)
#AB = A * B
#AB = A.T.dot(B)
AB = A.dot(B)
#AB = A.dot(B.T)
A_and_notAB = np.logical_and(A, np.logical_not(AB))
tr_uvs = np.where(A_and_notAB)
#nodes = G.nodes()
edges = list(zip(*ut.unflat_take(nodes, tr_uvs)))
G_tr = G.__class__()
G_tr.add_nodes_from(nodes)
G_tr.add_edges_from(edges)
if has_cycles:
# Uncondense graph
uncondensed_G_tr = G.__class__()
mapping = G.graph['mapping']
uncondensed_G_tr.add_nodes_from(mapping.keys())
inv_mapping = ut.invert_dict(mapping, unique_vals=False)
for u, v in G_tr.edges():
u_ = inv_mapping[u][0]
v_ = inv_mapping[v][0]
uncondensed_G_tr.add_edge(u_, v_)
for key, path in inv_mapping.items():
if len(path) > 1:
directed_cycle = list(ut.itertwo(path, wrap=True))
uncondensed_G_tr.add_edges_from(directed_cycle)
G_tr = uncondensed_G_tr
return G_tr
def test_vsone_errors(ibs, daids, qaid2_qres_vsmany, qaid2_qres_vsone, incinfo):
"""
ibs1 = ibs_gt
ibs2 = ibs (the current test database, sorry for the backwardness)
aid1_to_aid2 - maps annots from ibs1 to ibs2
"""
WASH = 'wash'
BOTH_FAIL = 'both_fail'
SINGLETON = 'singleton'
VSMANY_OUTPERFORMED = 'vsmany_outperformed'
VSMANY_DOMINATES = 'vsmany_dominates'
VSMANY_WINS = 'vsmany_wins'
VSONE_WINS = 'vsone_wins'
if 'testcases' not in incinfo:
testcases = {}
for case in [WASH, BOTH_FAIL, SINGLETON, VSMANY_OUTPERFORMED,
VSMANY_DOMINATES, VSMANY_WINS, VSONE_WINS]:
testcases[case] = []
incinfo['testcases'] = testcases
testcases = incinfo['testcases']
def append_case(case, testtup):
print('APPENDED NEW TESTCASE: case=%r' % (case,))
print('* testup = %r' % (testtup,))
print('* vuuid = %r' % (ibs_gt.get_annot_visual_uuids(testtup.qaid_t),))
if ut.get_argflag('--interupt-case') and case in [VSMANY_WINS, VSMANY_DOMINATES]:
incinfo['interactive'] = True
incinfo['use_oracle'] = False
incinfo['STOP'] = True
if ut.is_developer():
import plottool as pt # NOQA
IPYTHON_COMMANDS = """
>>> %pylab qt4
>>> from ibeis.viz.interact import interact_matches # NOQA
>>> #qres_vsmany = ut.search_stack_for_localvar('qres_vsmany')
>>> ibs = ut.search_stack_for_localvar('ibs')
>>> daids = ut.search_stack_for_localvar('daids')
>>> qnid_t = ut.search_stack_for_localvar('qnid_t')
>>> qres_vsone = ut.search_stack_for_localvar('qres_vsone')
>>> all_nids_t = ut.search_stack_for_localvar('all_nids_t')
>>> # Find index in daids of correct matches
>>> cm = qres_vsone
>>> correct_indices = np.where(np.array(all_nids_t) == qnid_t)[0]
>>> correct_aids2 = ut.take(daids, correct_indices)
>>> qaid = cm.qaid
>>> aid = correct_aids2[0]
>>> # Report visual uuid for inclusion or exclusion in script
>>> print(ibs.get_annot_visual_uuids([qaid, aid]))
>>> # Feature match things
>>> print('cm.filtkey_list = %r' % (cm.filtkey_list,))
>>> fm = cm.aid2_fm[aid]
>>> fs = cm.aid2_fs[aid]
>>> fsv = cm.aid2_fsv[aid]
>>> mx = 2
>>> qfx, dfx = fm[mx]
>>> fsv_single = fsv[mx]
>>> fs_single = fs[mx]
>>> # check featweights
>>> data_featweights = ibs.get_annot_fgweights([aid])[0]
>>> data_featweights[dfx]
>>> fnum = pt.next_fnum()
>>> bad_aid = cm.get_top_aids()[0]
>>> #match_interaction_good = interact_matches.MatchInteraction(ibs, cm, aid, annot_mode=1)
>>> #match_interaction_bad = interact_matches.MatchInteraction(ibs, cm, bad_aid)
>>> match_interaction_good = cm.ishow_matches(ibs, aid, annot_mode=1, fnum=1)
>>> match_interaction_bad = cm.ishow_matches(ibs, bad_aid, annot_mode=1, fnum=2)
>>> match_interaction = match_interaction_good
>>> self = match_interaction
>>> self.select_ith_match(mx)
>>> #impossible_to_match = len(correct_indices) > 0
"""
y = """
>>> from os.path import exists
>>> import vtool as vt
>>> import vtool.patch as vtpatch
>>> import vtool.image as vtimage # NOQA
>>> chip_list = ibs.get_annot_chips([aid])
>>> kpts_list = ibs.get_annot_kpts([aid])
>>> probchip_fpath_list = ibs.get_probchip_fpath(aid)
>>> probchip_list = [vt.imread(fpath, grayscale=True) if exists(fpath) else None for fpath in probchip_fpath_list]
>>> kpts = kpts_list[0]
>>> probchip = probchip_list[0]
>>> kp = kpts[dfx]
>>> patch = vt.get_warped_patch(probchip, kp)[0].astype(np.float32) / 255.0
>>> fnum2 = pt.next_fnum()
>>> pt.figure(fnum2, pnum=(1, 2, 1), doclf=True, docla=True)
>>> pt.imshow(probchip)
>>> pt.draw_kpts2([kp])
>>> pt.figure(fnum2, pnum=(1, 2, 2))
>>> pt.imshow(patch * 255)
>>> pt.update()
>>> vt.gaussian_average_patch(patch)
>>> cm.ishow_top(ibs, annot_mode=1)
"""
y
ut.set_clipboard(IPYTHON_COMMANDS)
#ut.spawn_delayed_ipython_paste()
ut.embed(remove_pyqt_hook=False)
IPYTHON_COMMANDS
testcases[case].append(testtup)
for qaid in six.iterkeys(qaid2_qres_vsmany):
qres_vsmany = qaid2_qres_vsmany[qaid]
qres_vsone = qaid2_qres_vsone[qaid]
nscoretup_vsone = qres_vsone.get_nscoretup()
nscoretup_vsmany = qres_vsmany.get_nscoretup()
metatup = incinfo['metatup']
ibs_gt, aid1_to_aid2 = metatup
aid2_to_aid1 = ut.invert_dict(aid1_to_aid2)
top_aids_vsone = ut.get_list_column(nscoretup_vsone.sorted_aids, 0)
top_aids_vsmany = ut.get_list_column(nscoretup_vsmany.sorted_aids, 0)
# tranform to groundtruth database coordinates
all_daids_t = ut.dict_take_list(aid2_to_aid1, daids)
top_aids_vsone_t = ut.dict_take_list(aid2_to_aid1, top_aids_vsone)
top_aids_vsmany_t = ut.dict_take_list(aid2_to_aid1, top_aids_vsmany)
qaid_t = aid2_to_aid1[qaid]
aids_tup = (all_daids_t, top_aids_vsone_t, top_aids_vsmany_t, (qaid_t,),)
nids_tup = ibs_gt.unflat_map(ibs_gt.get_annot_nids, aids_tup)
(all_nids_t, top_nids_vsone_t, top_nids_vsmany_t, (qnid_t,),) = nids_tup
vsmany_rank = ut.listfind(top_nids_vsmany_t, qnid_t)
vsone_rank = ut.listfind(top_nids_vsone_t, qnid_t)
impossible_to_match = ut.listfind(all_nids_t, qnid_t) is None
# Sort the test case into a category
testtup = TestTup(qaid_t, qaid, vsmany_rank, vsone_rank)
if vsmany_rank is None and vsone_rank is None and impossible_to_match:
append_case(SINGLETON, testtup)
elif vsmany_rank is not None and vsone_rank is None:
if vsmany_rank < 5:
append_case(VSMANY_DOMINATES, testtup)
else:
append_case(VSMANY_OUTPERFORMED, testtup)
elif vsmany_rank is None:
append_case(BOTH_FAIL, testtup)
elif vsone_rank > vsmany_rank:
append_case(VSMANY_WINS, testtup)
elif vsone_rank < vsmany_rank:
append_case(VSONE_WINS, testtup)
elif vsone_rank == vsmany_rank:
append_case(WASH, testtup)
else:
raise AssertionError('unenumerated case')
count_dict = ut.count_dict_vals(testcases)
print('+--')
#print(ut.dict_str(testcases))
print('---')
print(ut.dict_str(count_dict))
print('L__')
(
2,
QUAL_POOR,
),
# oops forgot 1. will be mapped to poor
(
0,
QUAL_JUNK,
),
(
-1,
QUAL_UNKNOWN,
),
])
QUALITY_TEXT_TO_INT = ut.invert_dict(QUALITY_INT_TO_TEXT)
QUALITY_INT_TO_TEXT[1] = QUAL_JUNK
# QUALITY_TEXT_TO_INTS = ut.invert_dict(QUALITY_INT_TO_TEXT)
QUALITY_TEXT_TO_INTS = ut.group_items(list(QUALITY_INT_TO_TEXT.keys()),
list(QUALITY_INT_TO_TEXT.values()))
QUALITY_TEXT_TO_INTS[QUAL_UNKNOWN] = -1
QUALITY_INT_TO_TEXT[None] = QUALITY_INT_TO_TEXT[-1]
SEX_INT_TO_TEXT = {
None: 'UNKNOWN NAME',
-1: 'UNKNOWN SEX',
0: 'Female',
1: 'Male',
2: 'INDETERMINATE SEX',
}
SEX_TEXT_TO_INT = ut.invert_dict(SEX_INT_TO_TEXT)
y
ut.set_clipboard(IPYTHON_COMMANDS)
#ut.spawn_delayed_ipython_paste()
ut.embed(remove_pyqt_hook=False)
IPYTHON_COMMANDS
testcases[case].append(testtup)
for qaid in six.iterkeys(qaid2_qres_vsmany):
qres_vsmany = qaid2_qres_vsmany[qaid]
qres_vsone = qaid2_qres_vsone[qaid]
nscoretup_vsone = qres_vsone.get_nscoretup()
nscoretup_vsmany = qres_vsmany.get_nscoretup()
metatup = incinfo['metatup']
ibs_gt, aid1_to_aid2 = metatup
aid2_to_aid1 = ut.invert_dict(aid1_to_aid2)
top_aids_vsone = ut.get_list_column(nscoretup_vsone.sorted_aids, 0)
top_aids_vsmany = ut.get_list_column(nscoretup_vsmany.sorted_aids, 0)
# tranform to groundtruth database coordinates
all_daids_t = ut.dict_take_list(aid2_to_aid1, daids)
top_aids_vsone_t = ut.dict_take_list(aid2_to_aid1, top_aids_vsone)
top_aids_vsmany_t = ut.dict_take_list(aid2_to_aid1, top_aids_vsmany)
qaid_t = aid2_to_aid1[qaid]
aids_tup = (
all_daids_t,
top_aids_vsone_t,
top_aids_vsmany_t,
(qaid_t, ),
)
def check_results(ibs_gt, ibs2, aid1_to_aid2, aids_list1_, incinfo):
"""
reports how well the incremental query ran when the oracle was calling the
shots.
"""
print('--------- CHECKING RESULTS ------------')
testcases = incinfo.get('testcases')
if testcases is not None:
count_dict = ut.count_dict_vals(testcases)
print('+--')
#print(ut.dict_str(testcases))
print('---')
print(ut.dict_str(count_dict))
print('L__')
# TODO: dont include initially added aids in the result reporting
aid_list1 = aids_list1_ # ibs_gt.get_valid_aids()
#aid_list1 = ibs_gt.get_aids_with_groundtruth()
aid_list2 = ibs2.get_valid_aids()
nid_list1 = ibs_gt.get_annot_nids(aid_list1)
nid_list2 = ibs2.get_annot_nids(aid_list2)
# Group annotations from test and gt database by their respective names
grouped_dict1 = ut.group_items(aid_list1, nid_list1)
grouped_dict2 = ut.group_items(aid_list2, nid_list2)
grouped_aids1 = list(six.itervalues(grouped_dict1))
grouped_aids2 = list(map(tuple, six.itervalues(grouped_dict2)))
#group_nids1 = list(six.iterkeys(grouped_dict1))
#group_nids2 = list(six.iterkeys(grouped_dict2))
# Transform annotation ids from database1 space to database2 space
grouped_aids1_t = [tuple(ut.dict_take_list(aid1_to_aid2, aids1)) for aids1 in grouped_aids1]
set_grouped_aids1_t = set(grouped_aids1_t)
set_grouped_aids2 = set(grouped_aids2)
# Find names we got right. (correct groupings of annotations)
# these are the annotation groups that are intersecting between
# the test database and groundtruth database
perfect_groups = set_grouped_aids2.intersection(set_grouped_aids1_t)
# Find names we got wrong. (incorrect groupings of annotations)
# The test database sets that were not perfect
nonperfect_groups = set_grouped_aids2.difference(perfect_groups)
# What we should have got
# The ground truth database sets that were not fully identified
missed_groups = set_grouped_aids1_t.difference(perfect_groups)
# Mark non perfect groups by their error type
false_negative_groups = [] # failed to link enough
false_positive_groups = [] # linked too much
for nonperfect_group in nonperfect_groups:
if ut.is_subset_of_any(nonperfect_group, missed_groups):
false_negative_groups.append(nonperfect_group)
else:
false_positive_groups.append(nonperfect_group)
# Get some more info on the nonperfect groups
# find which groups should have been linked
aid2_to_aid1 = ut.invert_dict(aid1_to_aid2)
false_negative_groups_t = [tuple(ut.dict_take_list(aid2_to_aid1, aids2)) for aids2 in false_negative_groups]
false_negative_group_nids_t = ibs_gt.unflat_map(ibs_gt.get_annot_nids, false_negative_groups_t)
assert all(map(ut.allsame, false_negative_group_nids_t)), 'inconsistent nids'
false_negative_group_nid_t = ut.get_list_column(false_negative_group_nids_t, 0)
# These are the links that should have been made
missed_links = ut.group_items(false_negative_groups, false_negative_group_nid_t)
print(ut.dict_str(missed_links))
print('# Name with failed links (FN) = %r' % len(false_negative_groups))
print('... should have reduced to %d names.' % (len(missed_links)))
print('# Name with wrong links (FP) = %r' % len(false_positive_groups))
print('# Name correct names (TP) = %r' % len(perfect_groups))
def initialize_graph_and_model(infr):
""" Unused in internal split stuff
pt.qt4ensure()
layout_info = pt.show_nx(graph, as_directed=False, fnum=1,
layoutkw=dict(prog='neato'), use_image=True,
verbose=0)
ax = pt.gca()
pt.zoom_factory()
pt.interactions.PanEvents()
"""
#import networkx as nx
#import itertools
cm_list = infr.cm_list
hack = True
hack = False
if hack:
cm_list = cm_list[:10]
qaid_list = [cm.qaid for cm in cm_list]
daids_list = [cm.daid_list for cm in cm_list]
unique_aids = sorted(ut.list_union(*daids_list + [qaid_list]))
if hack:
unique_aids = sorted(ut.isect(unique_aids, qaid_list))
aid2_aidx = ut.make_index_lookup(unique_aids)
# Construct K-broken graph
edges = []
edge_weights = []
#top = (infr.qreq_.qparams.K + 1) * 2
#top = (infr.qreq_.qparams.K) * 2
top = (infr.qreq_.qparams.K + 2)
for count, cm in enumerate(cm_list):
qidx = aid2_aidx[cm.qaid]
score_list = cm.annot_score_list
sortx = ut.argsort(score_list)[::-1]
score_list = ut.take(score_list, sortx)[:top]
daid_list = ut.take(cm.daid_list, sortx)[:top]
for score, daid in zip(score_list, daid_list):
if daid not in qaid_list:
continue
didx = aid2_aidx[daid]
edge_weights.append(score)
edges.append((qidx, didx))
# make symmetric
directed_edges = dict(zip(edges, edge_weights))
# Find edges that point in both directions
undirected_edges = {}
for (u, v), w in directed_edges.items():
if (v, u) in undirected_edges:
undirected_edges[(v, u)] += w
undirected_edges[(v, u)] /= 2
else:
undirected_edges[(u, v)] = w
edges = list(undirected_edges.keys())
edge_weights = list(undirected_edges.values())
nodes = list(range(len(unique_aids)))
nid_labeling = infr.qreq_.ibs.get_annot_nids(unique_aids)
labeling = ut.rebase_labels(nid_labeling)
import networkx as nx
from ibeis.viz import viz_graph
set_node_attrs = nx.set_node_attributes
set_edge_attrs = nx.set_edge_attributes
# Create match-based graph structure
graph = nx.DiGraph()
graph.add_nodes_from(nodes)
graph.add_edges_from(edges)
# Important properties
nid_list = infr.qreq_.ibs.get_annot_nids(unique_aids)
labeling = ut.rebase_labels(nid_list)
set_node_attrs(graph, 'name_label', dict(zip(nodes, labeling)))
set_edge_attrs(graph, 'weight', dict(zip(edges, edge_weights)))
# Visualization properties
import plottool as pt
ax2_aid = ut.invert_dict(aid2_aidx)
set_node_attrs(graph, 'aid', ax2_aid)
viz_graph.ensure_node_images(infr.qreq_.ibs, graph)
set_node_attrs(graph, 'framewidth', dict(zip(nodes, [3.0] * len(nodes))))
set_node_attrs(graph, 'framecolor', dict(zip(nodes, [pt.DARK_BLUE] * len(nodes))))
ut.color_nodes(graph, labelattr='name_label')
edge_colors = pt.scores_to_color(np.array(edge_weights), cmap_='viridis')
#import utool
#utool.embed()
#edge_colors = [pt.color_funcs.ensure_base255(color) for color in edge_colors]
#print('edge_colors = %r' % (edge_colors,))
set_edge_attrs(graph, 'color', dict(zip(edges, edge_colors)))
# Build inference model
from ibeis.algo.hots import graph_iden
#graph_iden.rrr()
model = graph_iden.InfrModel(graph)
#model = graph_iden.InfrModel(len(nodes), edges, edge_weights, labeling=labeling)
infr.model = model
def check_results(ibs_gt, ibs2, aid1_to_aid2, aids_list1_, incinfo):
"""
reports how well the incremental query ran when the oracle was calling the
shots.
"""
print('--------- CHECKING RESULTS ------------')
testcases = incinfo.get('testcases')
if testcases is not None:
count_dict = ut.count_dict_vals(testcases)
print('+--')
#print(ut.dict_str(testcases))
print('---')
print(ut.dict_str(count_dict))
print('L__')
# TODO: dont include initially added aids in the result reporting
aid_list1 = aids_list1_ # ibs_gt.get_valid_aids()
#aid_list1 = ibs_gt.get_aids_with_groundtruth()
aid_list2 = ibs2.get_valid_aids()
nid_list1 = ibs_gt.get_annot_nids(aid_list1)
nid_list2 = ibs2.get_annot_nids(aid_list2)
# Group annotations from test and gt database by their respective names
grouped_dict1 = ut.group_items(aid_list1, nid_list1)
grouped_dict2 = ut.group_items(aid_list2, nid_list2)
grouped_aids1 = list(six.itervalues(grouped_dict1))
grouped_aids2 = list(map(tuple, six.itervalues(grouped_dict2)))
#group_nids1 = list(six.iterkeys(grouped_dict1))
#group_nids2 = list(six.iterkeys(grouped_dict2))
# Transform annotation ids from database1 space to database2 space
grouped_aids1_t = [
tuple(ut.dict_take_list(aid1_to_aid2, aids1))
for aids1 in grouped_aids1
]
set_grouped_aids1_t = set(grouped_aids1_t)
set_grouped_aids2 = set(grouped_aids2)
# Find names we got right. (correct groupings of annotations)
# these are the annotation groups that are intersecting between
# the test database and groundtruth database
perfect_groups = set_grouped_aids2.intersection(set_grouped_aids1_t)
# Find names we got wrong. (incorrect groupings of annotations)
# The test database sets that were not perfect
nonperfect_groups = set_grouped_aids2.difference(perfect_groups)
# What we should have got
# The ground truth database sets that were not fully identified
missed_groups = set_grouped_aids1_t.difference(perfect_groups)
# Mark non perfect groups by their error type
false_negative_groups = [] # failed to link enough
false_positive_groups = [] # linked too much
for nonperfect_group in nonperfect_groups:
if ut.is_subset_of_any(nonperfect_group, missed_groups):
false_negative_groups.append(nonperfect_group)
else:
false_positive_groups.append(nonperfect_group)
# Get some more info on the nonperfect groups
# find which groups should have been linked
aid2_to_aid1 = ut.invert_dict(aid1_to_aid2)
false_negative_groups_t = [
tuple(ut.dict_take_list(aid2_to_aid1, aids2))
for aids2 in false_negative_groups
]
false_negative_group_nids_t = ibs_gt.unflat_map(ibs_gt.get_annot_nids,
false_negative_groups_t)
assert all(map(ut.allsame,
false_negative_group_nids_t)), 'inconsistent nids'
false_negative_group_nid_t = ut.get_list_column(
false_negative_group_nids_t, 0)
# These are the links that should have been made
missed_links = ut.group_items(false_negative_groups,
false_negative_group_nid_t)
print(ut.dict_str(missed_links))
print('# Name with failed links (FN) = %r' % len(false_negative_groups))
print('... should have reduced to %d names.' % (len(missed_links)))
print('# Name with wrong links (FP) = %r' % len(false_positive_groups))
print('# Name correct names (TP) = %r' % len(perfect_groups))
def initialize_graph_and_model(infr):
""" Unused in internal split stuff
pt.qt4ensure()
layout_info = pt.show_nx(graph, as_directed=False, fnum=1,
layoutkw=dict(prog='neato'), use_image=True,
verbose=0)
ax = pt.gca()
pt.zoom_factory()
pt.interactions.PanEvents()
"""
#import networkx as nx
#import itertools
cm_list = infr.cm_list
hack = True
hack = False
if hack:
cm_list = cm_list[:10]
qaid_list = [cm.qaid for cm in cm_list]
daids_list = [cm.daid_list for cm in cm_list]
unique_aids = sorted(ut.list_union(*daids_list + [qaid_list]))
if hack:
unique_aids = sorted(ut.isect(unique_aids, qaid_list))
aid2_aidx = ut.make_index_lookup(unique_aids)
# Construct K-broken graph
edges = []
edge_weights = []
#top = (infr.qreq_.qparams.K + 1) * 2
#top = (infr.qreq_.qparams.K) * 2
top = (infr.qreq_.qparams.K + 2)
for count, cm in enumerate(cm_list):
qidx = aid2_aidx[cm.qaid]
score_list = cm.annot_score_list
sortx = ut.argsort(score_list)[::-1]
score_list = ut.take(score_list, sortx)[:top]
daid_list = ut.take(cm.daid_list, sortx)[:top]
for score, daid in zip(score_list, daid_list):
if daid not in qaid_list:
continue
didx = aid2_aidx[daid]
edge_weights.append(score)
edges.append((qidx, didx))
# make symmetric
directed_edges = dict(zip(edges, edge_weights))
# Find edges that point in both directions
undirected_edges = {}
for (u, v), w in directed_edges.items():
if (v, u) in undirected_edges:
undirected_edges[(v, u)] += w
undirected_edges[(v, u)] /= 2
else:
undirected_edges[(u, v)] = w
edges = list(undirected_edges.keys())
edge_weights = list(undirected_edges.values())
nodes = list(range(len(unique_aids)))
nid_labeling = infr.qreq_.ibs.get_annot_nids(unique_aids)
labeling = ut.rebase_labels(nid_labeling)
import networkx as nx
from ibeis.viz import viz_graph
set_node_attrs = nx.set_node_attributes
set_edge_attrs = nx.set_edge_attributes
# Create match-based graph structure
graph = nx.DiGraph()
graph.add_nodes_from(nodes)
graph.add_edges_from(edges)
# Important properties
nid_list = infr.qreq_.ibs.get_annot_nids(unique_aids)
labeling = ut.rebase_labels(nid_list)
set_node_attrs(graph, 'name_label', dict(zip(nodes, labeling)))
set_edge_attrs(graph, 'weight', dict(zip(edges, edge_weights)))
# Visualization properties
import plottool as pt
ax2_aid = ut.invert_dict(aid2_aidx)
set_node_attrs(graph, 'aid', ax2_aid)
viz_graph.ensure_node_images(infr.qreq_.ibs, graph)
set_node_attrs(graph, 'framewidth', dict(zip(nodes,
[3.0] * len(nodes))))
set_node_attrs(graph, 'framecolor',
dict(zip(nodes, [pt.DARK_BLUE] * len(nodes))))
ut.color_nodes(graph, labelattr='name_label')
edge_colors = pt.scores_to_color(np.array(edge_weights),
cmap_='viridis')
#import utool
#utool.embed()
#edge_colors = [pt.color_funcs.ensure_base255(color) for color in edge_colors]
#print('edge_colors = %r' % (edge_colors,))
set_edge_attrs(graph, 'color', dict(zip(edges, edge_colors)))
# Build inference model
from ibeis.algo.hots import graph_iden
#graph_iden.rrr()
model = graph_iden.InfrModel(graph)
#model = graph_iden.InfrModel(len(nodes), edges, edge_weights, labeling=labeling)
infr.model = model
