def testdata_showchip():
import ibeis
ibs = ibeis.opendb(defaultdb='PZ_MTEST')
aid_list = ut.get_argval(('--aids', '--aid'), type_=list, default=None)
if aid_list is None:
aid_list = ibs.get_valid_aids()[0:4]
weight_label = ut.get_argval('--weight_label',
type_=str,
default='fg_weights')
annote = not ut.get_argflag('--no-annote')
kwargs = dict(ori=ut.get_argflag('--ori'),
weight_label=weight_label,
annote=annote)
kwargs['notitle'] = ut.get_argflag('--notitle')
kwargs['pts'] = ut.get_argflag('--drawpts')
kwargs['ell'] = ut.get_argflag('--drawell')
kwargs['ell_alpha'] = ut.get_argval('--ellalpha', default=.4)
kwargs['ell_linewidth'] = ut.get_argval('--ell_linewidth', default=2)
ut.print_dict(kwargs)
default_config = dict(
ibeis.algo.Config.FeatureWeightConfig().parse_items())
cfgdict = ut.argparse_dict(default_config)
print('[viz_chip.testdata] cfgdict = %r' % (cfgdict, ))
config2_ = ibs.new_query_params(cfgdict=cfgdict)
print('query_cfgstr = ' + config2_.query_cfgstr)
print('feat_cfgstr = ' + config2_.feat_cfgstr)
print('[viz_chip.testdata] aid_list = %r' % (aid_list, ))
return ibs, aid_list, kwargs, config2_
def testdata_showchip():
import wbia
ibs = wbia.opendb(defaultdb='PZ_MTEST')
aid_list = ut.get_argval(('--aids', '--aid'), type_=list, default=None)
if aid_list is None:
aid_list = ibs.get_valid_aids()[0:4]
weight_label = ut.get_argval('--weight_label',
type_=str,
default='fg_weights')
annote = not ut.get_argflag('--no-annote')
kwargs = dict(ori=ut.get_argflag('--ori'),
weight_label=weight_label,
annote=annote)
kwargs['notitle'] = ut.get_argflag('--notitle')
kwargs['pts'] = ut.get_argflag('--drawpts')
kwargs['ell'] = True or ut.get_argflag('--drawell')
kwargs['ell_alpha'] = ut.get_argval('--ellalpha', default=0.4)
kwargs['ell_linewidth'] = ut.get_argval('--ell_linewidth', default=2)
kwargs['draw_lbls'] = ut.get_argval('--draw_lbls', default=True)
logger.info('kwargs = ' + ut.repr4(kwargs, nl=True))
default_config = dict(wbia.algo.Config.FeatureWeightConfig().parse_items())
cfgdict = ut.argparse_dict(default_config)
logger.info('[viz_chip.testdata] cfgdict = %r' % (cfgdict, ))
config2_ = cfgdict
logger.info('[viz_chip.testdata] aid_list = %r' % (aid_list, ))
return ibs, aid_list, kwargs, config2_
def detect_feats_main():
import pyhesaff
from pyhesaff._pyhesaff import grab_test_imgpath
from pyhesaff._pyhesaff import argparse_hesaff_params
import cv2
import ubelt as ub
img_fpath = grab_test_imgpath(ub.argval('--fname', default='astro.png'))
kwargs = argparse_hesaff_params()
print('kwargs = %r' % (kwargs, ))
(kpts, vecs) = pyhesaff.detect_feats(img_fpath, **kwargs)
if ub.argflag('--show'):
# Show keypoints
imgBGR = cv2.imread(img_fpath)
default_showkw = dict(ori=False,
ell=True,
ell_linewidth=2,
ell_alpha=.4,
ell_color='distinct')
print('default_showkw = %r' % (default_showkw, ))
import utool as ut
showkw = ut.argparse_dict(default_showkw)
import plottool as pt
pt.interact_keypoints.ishow_keypoints(imgBGR, kpts, vecs, **showkw)
pt.show_if_requested()
def argparse_hesaff_params():
alias_dict = {'affine_invariance': 'ai'}
alias_dict = {'rotation_invariance': 'ri'}
default_dict_ = get_hesaff_default_params()
try:
import utool as ut
hesskw = ut.argparse_dict(default_dict_, alias_dict=alias_dict)
except Exception as ex:
print('ex = {!r}'.format(ex))
return default_dict_
return hesskw
def testdata_matching_affine_inliers():
import vtool.tests.dummy as dummy
import vtool as vt
scale_thresh = 2.0
xy_thresh = ut.get_argval('--xy-thresh', type_=float, default=.01)
dlen_sqrd2 = 447271.015
ori_thresh = 1.57
xy_thresh_sqrd = dlen_sqrd2 * xy_thresh
featkw = ut.argparse_dict(vt.get_extract_features_default_params())
fname1 = ut.get_argval('--fname1', type_=str, default='easy1.png')
fname2 = ut.get_argval('--fname2', type_=str, default='easy2.png')
(kpts1, kpts2, fm, fs, rchip1, rchip2) = dummy.testdata_ratio_matches(fname1, fname2, **featkw)
aff_inliers, aff_errors, Aff = get_best_affine_inliers_(
kpts1, kpts2, fm, fs, xy_thresh_sqrd, scale_thresh, ori_thresh)
return kpts1, kpts2, fm, aff_inliers, rchip1, rchip2, xy_thresh_sqrd
def testdata_matching_affine_inliers():
import vtool.tests.dummy as dummy
import vtool as vt
scale_thresh = 2.0
xy_thresh = ut.get_argval('--xy-thresh', type_=float, default=.01)
dlen_sqrd2 = 447271.015
ori_thresh = 1.57
xy_thresh_sqrd = dlen_sqrd2 * xy_thresh
featkw = ut.argparse_dict(vt.get_extract_features_default_params())
fname1 = ut.get_argval('--fname1', type_=str, default='easy1.png')
fname2 = ut.get_argval('--fname2', type_=str, default='easy2.png')
(kpts1, kpts2, fm, fs, rchip1,
rchip2) = dummy.testdata_ratio_matches(fname1, fname2, **featkw)
aff_inliers, aff_errors, Aff = get_best_affine_inliers_(
kpts1, kpts2, fm, fs, xy_thresh_sqrd, scale_thresh, ori_thresh)
return kpts1, kpts2, fm, aff_inliers, rchip1, rchip2, xy_thresh_sqrd
def testdata_showchip():
import ibeis
ibs = ibeis.opendb(defaultdb='PZ_MTEST')
aid_list = ut.get_argval(('--aids', '--aid'), type_=list, default=None)
if aid_list is None:
aid_list = ibs.get_valid_aids()[0:4]
weight_label = ut.get_argval('--weight_label', type_=str, default='fg_weights')
annote = not ut.get_argflag('--no-annote')
kwargs = dict(ori=ut.get_argflag('--ori'), weight_label=weight_label, annote=annote)
kwargs['notitle'] = ut.get_argflag('--notitle')
kwargs['pts'] = ut.get_argflag('--drawpts')
kwargs['ell'] = ut.get_argflag('--drawell')
kwargs['ell_alpha'] = ut.get_argval('--ellalpha', default=.4)
kwargs['ell_linewidth'] = ut.get_argval('--ell_linewidth', default=2)
ut.print_dict(kwargs)
default_config = dict(ibeis.algo.Config.FeatureWeightConfig().parse_items())
cfgdict = ut.argparse_dict(default_config)
print('[viz_chip.testdata] cfgdict = %r' % (cfgdict,))
config2_ = ibs.new_query_params(cfgdict=cfgdict)
print('query_cfgstr = ' + config2_.query_cfgstr)
print('feat_cfgstr = ' + config2_.feat_cfgstr)
print('[viz_chip.testdata] aid_list = %r' % (aid_list,))
return ibs, aid_list, kwargs, config2_
def print_acfg_list(acfg_list,
expanded_aids_list=None,
ibs=None,
combined=False,
**kwargs):
r"""
Args:
acfg_list (list):
expanded_aids_list (list): (default = None)
ibs (IBEISController): ibeis controller object(default = None)
combined (bool): (default = False)
CommandLine:
python -m ibeis.expt.annotation_configs --exec-print_acfg_list --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.expt.annotation_configs import * # NOQA
>>> import ibeis
>>> acfg_list = '?'
>>> expanded_aids_list = None
>>> ibs = None
>>> combined = False
>>> result = print_acfg_list(acfg_list, expanded_aids_list, ibs, combined)
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
_tup = compress_acfg_list_for_printing(acfg_list)
nonvaried_compressed_dict, varied_compressed_dict_list = _tup
ut.colorprint('+=== <Info acfg_list> ===', 'white')
#print('Printing acfg_list info. len(acfg_list) = %r' % (len(acfg_list),))
print('non-varied aidcfg = ' + ut.dict_str(nonvaried_compressed_dict))
seen_ = ut.ddict(list)
# get default kwkeys for annot info
if ibs is not None:
annotstats_kw = kwargs.copy()
kwkeys = ut.parse_func_kwarg_keys(ibs.get_annot_stats_dict)
annotstats_kw.update(
ut.argparse_dict(dict(zip(kwkeys, [None] * len(kwkeys))),
only_specified=True))
hashid_list = []
for acfgx in range(len(acfg_list)):
acfg = acfg_list[acfgx]
title = ('q_cfgname=' + acfg['qcfg']['_cfgname'] + ' d_cfgname=' +
acfg['dcfg']['_cfgname'])
ut.colorprint(
'+--- acfg %d / %d -- %s ---- ' %
(acfgx + 1, len(acfg_list), title), 'lightgray')
print('acfg = ' +
ut.dict_str(varied_compressed_dict_list[acfgx], strvals=True))
if expanded_aids_list is not None:
qaids, daids = expanded_aids_list[acfgx]
key = (ut.hashstr_arr27(qaids,
'qaids'), ut.hashstr_arr27(daids, 'daids'))
if key not in seen_:
if ibs is not None:
seen_[key].append(acfgx)
stats_, locals_ = ibs.get_annotconfig_stats(
qaids,
daids,
verbose=False,
combined=combined,
**annotstats_kw)
hashids = (stats_['qaid_stats']['qhashid'],
stats_['daid_stats']['dhashid'])
hashid_list.append(hashids)
stats_str2 = ut.dict_str(stats_,
strvals=True,
newlines=True,
explicit=False,
nobraces=False)
print('annot_config_stats = ' + stats_str2)
else:
dupindex = seen_[key]
print('DUPLICATE of index %r' % (dupindex, ))
dupdict = varied_compressed_dict_list[dupindex[0]]
print('DUP OF acfg = ' + ut.dict_str(dupdict, strvals=True))
print('hashid summary = ' + ut.list_str(hashid_list, nl=1))
ut.colorprint('L___ </Info acfg_list> ___', 'white')
def get_ibeis_part_siam_dataset(**kwargs):
"""
PARTS based network data
CommandLine:
python -m ibeis_cnn.ingest_data --test-get_ibeis_part_siam_dataset --show
python -m ibeis_cnn.ingest_data --test-get_ibeis_part_siam_dataset --show --db PZ_Master1 --acfg_name timectrl
python -m ibeis_cnn.ingest_data --test-get_ibeis_part_siam_dataset --show --db PZ_MTEST --acfg_name unctrl --dryrun
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis_cnn.ingest_data import * # NOQA
>>> from ibeis_cnn import draw_results
>>> import ibeis
>>> kwargs = {} # ut.argparse_dict({'max_examples': None, 'num_top': 3})
>>> dataset = get_ibeis_part_siam_dataset(**kwargs)
>>> ut.quit_if_noshow()
>>> dataset.interact(ibs=dataset.getprop('ibs'))
>>> ut.show_if_requested()
"""
import ibeis
datakw = ut.argparse_dict(
{
'colorspace': 'gray',
'acfg_name': 'ctrl',
#'db': None,
'db': 'PZ_MTEST',
},
alias_dict={'acfg_name': ['acfg']},
verbose=True)
datakw.update(kwargs)
print('\n\n[get_ibeis_part_siam_dataset] START')
alias_key = ut.dict_str(datakw, nl=False, explicit=True)
dbname = datakw.pop('db')
try:
if NOCACHE_DATASET:
raise Exception('forced cache off')
# Try and short circut cached loading
dataset = DataSet.from_alias_key(alias_key)
dataset.setprop('ibs', lambda: ibeis.opendb(db=dbname))
return dataset
except Exception as ex:
ut.printex(ex,
'alias definitions have changed. alias_key=%r' %
(alias_key, ),
iswarning=True)
with ut.Indenter('[LOAD IBEIS DB]'):
ibs = ibeis.opendb(db=dbname)
# Nets dir is the root dir for all training on this data
training_dpath = ibs.get_neuralnet_dir()
ut.ensuredir(training_dpath)
with ut.Indenter('[BuildDS]'):
# Get training data pairs
colorspace = datakw.pop('colorspace')
(aid_pairs, label_list,
flat_metadata) = ingest_ibeis.get_aidpairs_partmatch(ibs, **datakw)
# Extract and cache the data, labels, and metadata
if ut.get_argflag('--dryrun'):
print('exiting due to dry run')
import sys
sys.exit(0)
tup = ingest_ibeis.cached_part_match_training_data_fpaths(
ibs, aid_pairs, label_list, flat_metadata, colorspace=colorspace)
data_fpath, labels_fpath, metadata_fpath, training_dpath, data_shape = tup
print('\n[get_ibeis_part_siam_dataset] FINISH\n\n')
# hack for caching num_labels
labels = ut.load_data(labels_fpath)
num_labels = len(labels)
dataset = DataSet.new_training_set(
alias_key=alias_key,
data_fpath=data_fpath,
labels_fpath=labels_fpath,
metadata_fpath=metadata_fpath,
training_dpath=training_dpath,
data_shape=data_shape,
data_per_label=2,
output_dims=1,
num_labels=num_labels,
)
dataset.setprop('ibs', ibs)
return dataset
def get_ibeis_patch_siam_dataset(**kwargs):
"""
CommandLine:
python -m ibeis_cnn.ingest_data --test-get_ibeis_patch_siam_dataset --show
python -m ibeis_cnn.ingest_data --test-get_ibeis_patch_siam_dataset --show --db PZ_Master1 --acfg_name default
python -m ibeis_cnn.ingest_data --test-get_ibeis_patch_siam_dataset --show --db PZ_Master1 --acfg_name timectrl
python -m ibeis_cnn.ingest_data --test-get_ibeis_patch_siam_dataset --show --db PZ_MTEST --acfg_name unctrl --dryrun
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis_cnn.ingest_data import * # NOQA
>>> from ibeis_cnn import draw_results
>>> import ibeis
>>> kwargs = {} # ut.argparse_dict({'max_examples': None, 'num_top': 3})
>>> dataset = get_ibeis_patch_siam_dataset(**kwargs)
>>> ut.quit_if_noshow()
>>> dataset.interact()
>>> ut.show_if_requested()
"""
datakw = ut.argparse_dict(
{
#'db': 'PZ_MTEST',
'max_examples': None,
#'num_top': 3,
'num_top': None,
'min_featweight': .8 if not ut.WIN32 else None,
'controlled': True,
'colorspace': 'gray',
'acfg_name': None,
},
alias_dict={'acfg_name': ['acfg', 'a']},
verbose=True)
datakw.update(kwargs)
#ut.get_func_kwargs(ingest_ibeis.get_aidpairs_and_matches)
if datakw['acfg_name'] is not None:
del datakw['controlled']
if datakw['max_examples'] is None:
del datakw['max_examples']
if datakw['num_top'] is None:
del datakw['num_top']
with ut.Indenter('[LOAD IBEIS DB]'):
import ibeis
dbname = ut.get_argval('--db', default='PZ_MTEST')
ibs = ibeis.opendb(dbname=dbname, defaultdb='PZ_MTEST')
# Nets dir is the root dir for all training on this data
training_dpath = ibs.get_neuralnet_dir()
ut.ensuredir(training_dpath)
print('\n\n[get_ibeis_patch_siam_dataset] START')
#log_dir = join(training_dpath, 'logs')
#ut.start_logging(log_dir=log_dir)
alias_key = ibs.get_dbname() + ';' + ut.dict_str(
datakw, nl=False, explicit=True)
try:
if NOCACHE_DATASET:
raise Exception('forced cache off')
# Try and short circut cached loading
dataset = DataSet.from_alias_key(alias_key)
dataset.setprop('ibs', lambda: ibeis.opendb(db=dbname))
return dataset
except Exception as ex:
ut.printex(ex,
'alias definitions have changed. alias_key=%r' %
(alias_key, ),
iswarning=True)
with ut.Indenter('[BuildDS]'):
# Get training data pairs
colorspace = datakw.pop('colorspace')
patchmatch_tup = ingest_ibeis.get_aidpairs_and_matches(ibs, **datakw)
aid1_list, aid2_list, kpts1_m_list, kpts2_m_list, fm_list, metadata_lists = patchmatch_tup
# Extract and cache the data
# TODO: metadata
if ut.get_argflag('--dryrun'):
print('exiting due to dry run')
import sys
sys.exit(0)
tup = ingest_ibeis.cached_patchmetric_training_data_fpaths(
ibs,
aid1_list,
aid2_list,
kpts1_m_list,
kpts2_m_list,
fm_list,
metadata_lists,
colorspace=colorspace)
data_fpath, labels_fpath, metadata_fpath, training_dpath, data_shape = tup
print('\n[get_ibeis_patch_siam_dataset] FINISH\n\n')
# hack for caching num_labels
labels = ut.load_data(labels_fpath)
num_labels = len(labels)
dataset = DataSet.new_training_set(
alias_key=alias_key,
data_fpath=data_fpath,
labels_fpath=labels_fpath,
metadata_fpath=metadata_fpath,
training_dpath=training_dpath,
data_shape=data_shape,
data_per_label=2,
output_dims=1,
num_labels=num_labels,
)
dataset.setprop('ibs', ibs)
return dataset
def train_aoi(output_path, data_fpath, labels_fpath):
r"""
CommandLine:
python -m ibeis_cnn.train --test-train_aoi
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis_cnn.train import * # NOQA
>>> result = train_aoi()
>>> print(result)
"""
era_size = 256
batch_size = 16
max_epochs = era_size * 16
hyperparams = ut.argparse_dict({
'era_size': era_size,
'learning_rate': .01,
'rate_schedule': 0.75,
'momentum': .9,
'weight_decay': 0.0001,
'augment_on': True,
'augment_on_validate': True,
'whiten_on': False,
'max_epochs': max_epochs,
'stopping_patience': max_epochs,
'class_weight': None,
})
ut.colorprint('[netrun] Ensuring Dataset', 'yellow')
dataset = ingest_data.get_numpy_dataset2('aoi', data_fpath, labels_fpath,
output_path)
X_train, y_train = dataset.subset('train')
X_valid, y_valid = dataset.subset('valid')
print('dataset.training_dpath = %r' % (dataset.training_dpath, ))
input_shape = (
batch_size,
dataset.data_shape[0] + 4,
)
ut.colorprint('[netrun] Architecture Specification', 'yellow')
model = AoIModel(input_shape=input_shape,
training_dpath=dataset.training_dpath,
**hyperparams)
ut.colorprint('[netrun] Initialize architecture', 'yellow')
model.output_dims = 1
model.input_shape = (
None,
dataset.data_shape[0] + 4,
)
model.batch_size = batch_size
model.init_arch()
ut.colorprint('[netrun] * Initializing new weights', 'lightgray')
if model.has_saved_state():
model.load_model_state()
ut.colorprint('[netrun] Training Requested', 'yellow')
# parse training arguments
config = ut.argparse_dict(
dict(
era_size=era_size,
max_epochs=max_epochs,
show_confusion=False,
))
model.monitor_config.update(**config)
print('\n[netrun] Model Info')
model.print_layer_info()
ut.colorprint('[netrun] Begin training', 'yellow')
model.fit(X_train, y_train, X_valid=X_valid, y_valid=y_valid)
model_path = model.save_model_state()
return model_path
def demo_refresh():
r"""
CommandLine:
python -m ibeis.algo.graph.refresh demo_refresh \
--num_pccs=40 --size=2 --show
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis.algo.graph.refresh import * # NOQA
>>> demo_refresh()
>>> ut.show_if_requested()
"""
from ibeis.algo.graph import demo
demokw = ut.argparse_dict({'num_pccs': 50, 'size': 4})
refreshkw = ut.argparse_funckw(RefreshCriteria)
# make an inference object
infr = demo.demodata_infr(size_std=0, **demokw)
edges = list(infr.dummy_verif.find_candidate_edges(K=100))
scores = np.array(infr.dummy_verif.predict_edges(edges))
sortx = scores.argsort()[::-1]
edges = ut.take(edges, sortx)
scores = scores[sortx]
ys = infr.match_state_df(edges)[POSTV].values
y_remainsum = ys[::-1].cumsum()[::-1]
# Do oracle reviews and wait to converge
refresh = RefreshCriteria(**refreshkw)
xdata = []
pprob_any = []
rfrac_any = []
for count, (edge, y) in enumerate(zip(edges, ys)):
refresh.add(y, user_id='user:oracle')
rfrac_any.append(y_remainsum[count] / y_remainsum[0])
pprob_any.append(refresh.prob_any_remain())
xdata.append(count + 1)
if refresh.check():
break
xdata = xdata
ydatas = ut.odict([
('Est. probability any remain', pprob_any),
('Fraction remaining', rfrac_any),
])
ut.quit_if_noshow()
import plottool_ibeis as pt
pt.qtensure()
from ibeis.scripts.thesis import TMP_RC
import matplotlib as mpl
mpl.rcParams.update(TMP_RC)
pt.multi_plot(
xdata, ydatas, xlabel='# manual reviews', rcParams=TMP_RC, marker='',
ylim=(0, 1), use_legend=False,
)
demokw = ut.map_keys({'num_pccs': '#PCC', 'size': 'PCC size'},
demokw)
thresh = refreshkw.pop('thresh')
refreshkw['span'] = refreshkw.pop('window')
pt.relative_text((.02, .58 + .0), ut.get_cfg_lbl(demokw, sep=' ')[1:],
valign='bottom')
pt.relative_text((.02, .68 + .0), ut.get_cfg_lbl(refreshkw, sep=' ')[1:],
valign='bottom')
legend = pt.gca().legend()
legend.get_frame().set_alpha(1.0)
pt.plt.plot([xdata[0], xdata[-1]], [thresh, thresh], 'g--', label='thresh')
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 parse_args():
ds_default = None
arch_default = 'siaml2_128'
weights_tag_default = None
# Test values
if False:
ds_default = 'liberty'
weights_tag_default = 'current'
assert ut.inIPython()
# Parse commandline args
ds_tag = ut.get_argval(('--dataset', '--ds'), type_=str,
default=ds_default)
datatype = ut.get_argval(('--datatype', '--dt'), type_=str,
default='siam-patch')
arch_tag = ut.get_argval(('--arch', '-a'), default=arch_default)
weights_tag = ut.get_argval(('--weights', '+w'), type_=str,
default=weights_tag_default)
# Incorporate new config stuff?
#NEW = False
#if NEW:
# default_dstag_cfg = {
# 'ds': 'PZ_MTEST',
# 'mode': 'patches',
# 'arch': arch_default
# }
# named_defaults_dict = {
# '': default_dstag_cfg
# }
# ut.parse_argv_cfg('dstag', named_defaults_dict=named_defaults_dict)
hyperparams = ut.argparse_dict(
{
#'batch_size': 128,
'batch_size': 256,
#'learning_rate': .0005,
'learning_rate': .1,
'momentum': .9,
#'weight_decay': 0.0005,
'weight_decay': 0.0001,
},
alias_dict={
'weight_decay': ['decay'],
'learning_rate': ['learn_rate'],
}
)
requests = ut.argparse_dict(
{
'train': False,
'test': False,
'testall': False,
'publish': False,
'ensuredata': False,
}
)
requests['test'] = requests['test'] or requests['testall']
# breakup weights tag into extern_ds and checkpoint
if weights_tag is not None and ':' in weights_tag:
extern_ds_tag, checkpoint_tag = weights_tag.split(':')
else:
extern_ds_tag = None
checkpoint_tag = weights_tag
# resolve aliases
ds_tag = DS_TAG_ALIAS2.get(ds_tag, ds_tag)
extern_ds_tag = DS_TAG_ALIAS2.get(extern_ds_tag, extern_ds_tag)
checkpoint_tag = CHECKPOINT_TAG_ALIAS.get(checkpoint_tag, checkpoint_tag)
tags = {
'ds_tag': ds_tag,
'extern_ds_tag': extern_ds_tag,
'checkpoint_tag': checkpoint_tag,
'arch_tag': arch_tag,
'datatype': datatype,
}
ut.colorprint('[netrun] * ds_tag=%r' % (ds_tag,), 'lightgray')
ut.colorprint('[netrun] * arch_tag=%r' % (arch_tag,), 'lightgray')
ut.colorprint('[netrun] * extern_ds_tag=%r' % (extern_ds_tag,), 'lightgray')
ut.colorprint('[netrun] * checkpoint_tag=%r' % (checkpoint_tag,), 'lightgray')
return requests, hyperparams, tags
def argparse_hesaff_params():
alias_dict = {'affine_invariance': 'ai'}
alias_dict = {'rotation_invariance': 'ri'}
hesskw = ut.argparse_dict(get_hesaff_default_params(),
alias_dict=alias_dict)
return hesskw
def train_classifier(output_path, data_fpath, labels_fpath):
r"""
CommandLine:
python -m ibeis_cnn.train --test-train_classifier
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis_cnn.train import * # NOQA
>>> result = train_classifier()
>>> print(result)
"""
era_size = 16
max_epochs = 256
hyperparams = ut.argparse_dict(
{
'era_size' : era_size,
'batch_size' : 128,
'learning_rate' : .01,
'rate_schedule' : 0.75,
'momentum' : .9,
'weight_decay' : 0.0001,
'augment_on' : True,
'whiten_on' : True,
'max_epochs' : max_epochs,
}
)
ut.colorprint('[netrun] Ensuring Dataset', 'yellow')
dataset = ingest_data.get_numpy_dataset2('classifier', data_fpath, labels_fpath, output_path)
X_train, y_train = dataset.subset('train')
X_valid, y_valid = dataset.subset('valid')
print('dataset.training_dpath = %r' % (dataset.training_dpath,))
ut.colorprint('[netrun] Architecture Specification', 'yellow')
model = ClassifierModel(
data_shape=dataset.data_shape,
training_dpath=dataset.training_dpath,
**hyperparams)
ut.colorprint('[netrun] Init encoder and convert labels', 'yellow')
if hasattr(model, 'init_encoder'):
model.init_encoder(y_train)
ut.colorprint('[netrun] Initialize archchitecture', 'yellow')
model.init_arch()
ut.colorprint('[netrun] * Initializing new weights', 'lightgray')
if model.has_saved_state():
model.load_model_state()
# else:
# model.reinit_weights()
# ut.colorprint('[netrun] Need to initialize training state', 'yellow')
# X_train, y_train = dataset.subset('train')
# model.ensure_data_params(X_train, y_train)
ut.colorprint('[netrun] Training Requested', 'yellow')
# parse training arguments
config = ut.argparse_dict(dict(
monitor=True,
monitor_updates=True,
show_confusion=True,
era_size=era_size,
max_epochs=max_epochs,
))
model.monitor_config.update(**config)
if getattr(model, 'encoder', None) is not None:
class_list = list(model.encoder.classes_)
y_train = np.array([class_list.index(_) for _ in y_train ])
y_valid = np.array([class_list.index(_) for _ in y_valid ])
print('\n[netrun] Model Info')
model.print_layer_info()
ut.colorprint('[netrun] Begin training', 'yellow')
model.fit(X_train, y_train, X_valid=X_valid, y_valid=y_valid)
model_path = model.save_model_state()
return model_path
def argparse_hesaff_params():
alias_dict = {'affine_invariance': 'ai'}
alias_dict = {'rotation_invariance': 'ri'}
default_dict_ = get_hesaff_default_params()
hesskw = ut.argparse_dict(default_dict_, alias_dict=alias_dict)
return hesskw
def print_acfg_list(acfg_list, expanded_aids_list=None, ibs=None,
combined=False, **kwargs):
r"""
Args:
acfg_list (list):
expanded_aids_list (list): (default = None)
ibs (IBEISController): ibeis controller object(default = None)
combined (bool): (default = False)
CommandLine:
python -m ibeis.expt.annotation_configs --exec-print_acfg_list --show
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.expt.annotation_configs import * # NOQA
>>> import ibeis
>>> acfg_list = '?'
>>> expanded_aids_list = None
>>> ibs = None
>>> combined = False
>>> result = print_acfg_list(acfg_list, expanded_aids_list, ibs, combined)
>>> print(result)
>>> ut.quit_if_noshow()
>>> import plottool as pt
>>> ut.show_if_requested()
"""
_tup = compress_acfg_list_for_printing(acfg_list)
nonvaried_compressed_dict, varied_compressed_dict_list = _tup
ut.colorprint('+=== <Info acfg_list> ===', 'white')
#print('Printing acfg_list info. len(acfg_list) = %r' % (len(acfg_list),))
print('non-varied aidcfg = ' + ut.dict_str(nonvaried_compressed_dict))
seen_ = ut.ddict(list)
# get default kwkeys for annot info
if ibs is not None:
annotstats_kw = kwargs.copy()
kwkeys = ut.parse_func_kwarg_keys(ibs.get_annot_stats_dict)
annotstats_kw.update(ut.argparse_dict(
dict(zip(kwkeys, [None] * len(kwkeys))), only_specified=True))
for acfgx in range(len(acfg_list)):
acfg = acfg_list[acfgx]
title = ('q_cfgname=' + acfg['qcfg']['_cfgname'] +
' d_cfgname=' + acfg['dcfg']['_cfgname'])
ut.colorprint('+--- acfg %d / %d -- %s ---- ' %
(acfgx + 1, len(acfg_list), title), 'lightgray')
print('acfg = ' + ut.dict_str(varied_compressed_dict_list[acfgx],
strvals=True))
if expanded_aids_list is not None:
qaids, daids = expanded_aids_list[acfgx]
key = (ut.hashstr_arr27(qaids, 'qaids'),
ut.hashstr_arr27(daids, 'daids'))
if key not in seen_:
if ibs is not None:
seen_[key].append(acfgx)
annotconfig_stats_strs, _ = ibs.get_annotconfig_stats(
qaids, daids, verbose=True, combined=combined,
**annotstats_kw)
else:
dupindex = seen_[key]
print('DUPLICATE of index %r' % (dupindex,))
dupdict = varied_compressed_dict_list[dupindex[0]]
print('DUP OF acfg = ' + ut.dict_str(dupdict, strvals=True))
ut.colorprint('L___ </Info acfg_list> ___', 'white')
def print_acfg_list(
acfg_list,
expanded_aids_list=None,
ibs=None,
combined=False,
only_summary=False,
**kwargs
):
r"""
Args:
acfg_list (list):
expanded_aids_list (list): (default = None)
ibs (IBEISController): wbia controller object(default = None)
combined (bool): (default = False)
CommandLine:
python -m wbia.expt.annotation_configs --exec-print_acfg_list
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.expt.annotation_configs import * # NOQA
>>> import wbia
>>> ibs = wbia.opendb('testdb1')
>>> a = ['default']
>>> acfg_list, expanded_aids_list = wbia.expt.experiment_helpers.get_annotcfg_list(
>>> ibs, acfg_name_list=a, verbose=0)
>>> combined = False
>>> result = print_acfg_list(acfg_list, expanded_aids_list, ibs, combined)
>>> print(result)
"""
_tup = compress_acfg_list_for_printing(acfg_list)
nonvaried_compressed_dict, varied_compressed_dict_list = _tup
ut.colorprint('+=== <Info acfg_list> ===', 'white')
# logger.info('Printing acfg_list info. len(acfg_list) = %r' % (len(acfg_list),))
logger.info('non-varied aidcfg = ' + ut.repr2(nonvaried_compressed_dict))
seen_ = ut.ddict(list)
# get default kwkeys for annot info
if ibs is not None:
annotstats_kw = kwargs.copy()
kwkeys = ut.parse_func_kwarg_keys(ibs.get_annot_stats_dict)
annotstats_kw.update(
ut.argparse_dict(dict(zip(kwkeys, [None] * len(kwkeys))), only_specified=True)
)
hashid_list = []
for acfgx in range(len(acfg_list)):
acfg = acfg_list[acfgx]
title = (
'q_cfgname='
+ acfg['qcfg']['_cfgname']
+ ' d_cfgname='
+ acfg['dcfg']['_cfgname']
)
if not only_summary:
ut.colorprint(
'+--- acfg %d / %d -- %s ---- ' % (acfgx + 1, len(acfg_list), title),
'gray',
)
logger.info('acfg = ' + ut.repr2(varied_compressed_dict_list[acfgx], si=True))
if expanded_aids_list is not None:
qaids, daids = expanded_aids_list[acfgx]
key = (ut.hashstr_arr27(qaids, 'qaids'), ut.hashstr_arr27(daids, 'daids'))
if key not in seen_:
if ibs is not None:
seen_[key].append(acfgx)
stats_ = ibs.get_annotconfig_stats(
qaids, daids, verbose=False, combined=combined, **annotstats_kw
)
hashids = (
stats_['qaid_stats']['qhashid'],
stats_['daid_stats']['dhashid'],
)
hashid_list.append(hashids)
stats_str2 = ut.repr2(
stats_, si=True, nl=True, explicit=False, nobraces=False
)
if not only_summary:
logger.info('annot_config_stats = ' + stats_str2)
else:
dupindex = seen_[key]
dupdict = varied_compressed_dict_list[dupindex[0]]
if not only_summary:
logger.info('DUPLICATE of index %r' % (dupindex,))
logger.info('DUP OF acfg = ' + ut.repr2(dupdict, si=True))
logger.info('hashid summary = ' + ut.repr2(hashid_list, nl=1))
ut.colorprint('L___ </Info acfg_list> ___', 'white')
