def view_publish_dir():
r"""
CommandLine:
python -m wbia.algo.hots.distinctiveness_normalizer --test-view_publish_dir
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.hots.distinctiveness_normalizer import * # NOQA
>>> view_publish_dir()
"""
ut.vd(PUBLISH_DIR)
def view_publish_dir():
r"""
CommandLine:
python -m ibeis.algo.hots.distinctiveness_normalizer --test-view_publish_dir
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.distinctiveness_normalizer import * # NOQA
>>> view_publish_dir()
"""
ut.vd(PUBLISH_DIR)
def view_distinctiveness_model_dir():
r"""
CommandLine:
python -m wbia.algo.hots.distinctiveness_normalizer --test-view_distinctiveness_model_dir
Example:
>>> # DISABLE_DOCTEST
>>> from wbia.algo.hots.distinctiveness_normalizer import * # NOQA
>>> view_distinctiveness_model_dir()
"""
global_distinctdir = sysres.get_global_distinctiveness_modeldir()
ut.vd(global_distinctdir)
def view_training_directories():
r"""
CommandLine:
python -m ibeis_cnn.ingest_data --test-view_training_directories
Example:
>>> # UTILITY_SCRIPT
>>> from ibeis_cnn.ingest_data import * # NOQA
>>> result = view_training_directories()
>>> print(result)
"""
ut.vd(ingest_ibeis.get_juction_dpath())
def view_distinctiveness_model_dir():
r"""
CommandLine:
python -m ibeis.algo.hots.distinctiveness_normalizer --test-view_distinctiveness_model_dir
Example:
>>> # DISABLE_DOCTEST
>>> from ibeis.algo.hots.distinctiveness_normalizer import * # NOQA
>>> view_distinctiveness_model_dir()
"""
global_distinctdir = sysres.get_global_distinctiveness_modeldir()
ut.vd(global_distinctdir)
def test_siamese_performance(model, data, labels, flat_metadata, dataname=''):
r"""
CommandLine:
utprof.py -m ibeis_cnn --tf pz_patchmatch --db liberty --test --weights=liberty:current --arch=siaml2_128 --test
python -m ibeis_cnn --tf netrun --db liberty --arch=siaml2_128 --test --ensure
python -m ibeis_cnn --tf netrun --db liberty --arch=siaml2_128 --test --ensure --weights=new
python -m ibeis_cnn --tf netrun --db liberty --arch=siaml2_128 --train --weights=new
python -m ibeis_cnn --tf netrun --db pzmtest --weights=liberty:current --arch=siaml2_128 --test # NOQA
python -m ibeis_cnn --tf netrun --db pzmtest --weights=liberty:current --arch=siaml2_128
"""
import vtool as vt
import plottool as pt
# TODO: save in model.trainind_dpath/diagnostics/figures
ut.colorprint('\n[siam_perf] Testing Siamese Performance', 'white')
#epoch_dpath = model.get_epoch_diagnostic_dpath()
epoch_dpath = model.arch_dpath
ut.vd(epoch_dpath)
dataname += ' ' + model.get_history_hashid() + '\n'
history_text = ut.list_str(model.era_history, newlines=True)
ut.write_to(ut.unixjoin(epoch_dpath, 'era_history.txt'), history_text)
#if True:
# import matplotlib as mpl
# mpl.rcParams['agg.path.chunksize'] = 100000
#data = data[::50]
#labels = labels[::50]
#from ibeis_cnn import utils
#data, labels = utils.random_xy_sample(data, labels, 10000, model.data_per_label_input)
FULL = not ut.get_argflag('--quick')
fnum_gen = pt.make_fnum_nextgen()
ut.colorprint('[siam_perf] Show era history', 'white')
fig = model.show_era_loss(fnum=fnum_gen())
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180)
# hack
ut.colorprint('[siam_perf] Show weights image', 'white')
fig = model.show_weights_image(fnum=fnum_gen())
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180)
#model.draw_all_conv_layer_weights(fnum=fnum_gen())
#model.imwrite_weights(1)
#model.imwrite_weights(2)
# Compute each type of score
ut.colorprint('[siam_perf] Building Scores', 'white')
test_outputs = model.predict2(model, data)
network_output = test_outputs['network_output_determ']
# hack converting network output to distances for non-descriptor networks
if len(network_output.shape) == 2 and network_output.shape[1] == 1:
cnn_scores = network_output.T[0]
elif len(network_output.shape) == 1:
cnn_scores = network_output
elif len(network_output.shape) == 2 and network_output.shape[1] > 1:
assert model.data_per_label_output == 2
vecs1 = network_output[0::2]
vecs2 = network_output[1::2]
cnn_scores = vt.L2(vecs1, vecs2)
else:
assert False
cnn_scores = cnn_scores.astype(np.float64)
# Segfaults with the data passed in is large (AND MEMMAPPED apparently)
# Fixed in hesaff implementation
SIFT = FULL
if SIFT:
sift_scores, sift_list = test_sift_patchmatch_scores(data, labels)
sift_scores = sift_scores.astype(np.float64)
ut.colorprint('[siam_perf] Learning Encoders', 'white')
# Learn encoders
encoder_kw = {
#'monotonize': False,
'monotonize': True,
}
cnn_encoder = vt.ScoreNormalizer(**encoder_kw)
cnn_encoder.fit(cnn_scores, labels)
if SIFT:
sift_encoder = vt.ScoreNormalizer(**encoder_kw)
sift_encoder.fit(sift_scores, labels)
# Visualize
ut.colorprint('[siam_perf] Visualize Encoders', 'white')
viz_kw = dict(
with_scores=False,
with_postbayes=False,
with_prebayes=False,
target_tpr=.95,
)
inter_cnn = cnn_encoder.visualize(
figtitle=dataname + ' CNN scores. #data=' + str(len(data)),
fnum=fnum_gen(), **viz_kw)
if SIFT:
inter_sift = sift_encoder.visualize(
figtitle=dataname + ' SIFT scores. #data=' + str(len(data)),
fnum=fnum_gen(), **viz_kw)
# Save
pt.save_figure(fig=inter_cnn.fig, dpath=epoch_dpath)
if SIFT:
pt.save_figure(fig=inter_sift.fig, dpath=epoch_dpath)
# Save out examples of hard errors
#cnn_fp_label_indicies, cnn_fn_label_indicies =
#cnn_encoder.get_error_indicies(cnn_scores, labels)
#sift_fp_label_indicies, sift_fn_label_indicies =
#sift_encoder.get_error_indicies(sift_scores, labels)
with_patch_examples = FULL
if with_patch_examples:
ut.colorprint('[siam_perf] Visualize Confusion Examples', 'white')
cnn_indicies = cnn_encoder.get_confusion_indicies(cnn_scores, labels)
if SIFT:
sift_indicies = sift_encoder.get_confusion_indicies(sift_scores, labels)
warped_patch1_list, warped_patch2_list = list(zip(*ut.ichunks(data, 2)))
samp_args = (warped_patch1_list, warped_patch2_list, labels)
_sample = functools.partial(draw_results.get_patch_sample_img, *samp_args)
cnn_fp_img = _sample({'fs': cnn_scores}, cnn_indicies.fp)[0]
cnn_fn_img = _sample({'fs': cnn_scores}, cnn_indicies.fn)[0]
cnn_tp_img = _sample({'fs': cnn_scores}, cnn_indicies.tp)[0]
cnn_tn_img = _sample({'fs': cnn_scores}, cnn_indicies.tn)[0]
if SIFT:
sift_fp_img = _sample({'fs': sift_scores}, sift_indicies.fp)[0]
sift_fn_img = _sample({'fs': sift_scores}, sift_indicies.fn)[0]
sift_tp_img = _sample({'fs': sift_scores}, sift_indicies.tp)[0]
sift_tn_img = _sample({'fs': sift_scores}, sift_indicies.tn)[0]
#if ut.show_was_requested():
#def rectify(arr):
# return np.flipud(arr)
SINGLE_FIG = False
if SINGLE_FIG:
def dump_img(img_, lbl, fnum):
fig, ax = pt.imshow(img_, figtitle=dataname + ' ' + lbl, fnum=fnum)
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180)
dump_img(cnn_fp_img, 'cnn_fp_img', fnum_gen())
dump_img(cnn_fn_img, 'cnn_fn_img', fnum_gen())
dump_img(cnn_tp_img, 'cnn_tp_img', fnum_gen())
dump_img(cnn_tn_img, 'cnn_tn_img', fnum_gen())
dump_img(sift_fp_img, 'sift_fp_img', fnum_gen())
dump_img(sift_fn_img, 'sift_fn_img', fnum_gen())
dump_img(sift_tp_img, 'sift_tp_img', fnum_gen())
dump_img(sift_tn_img, 'sift_tn_img', fnum_gen())
#vt.imwrite(dataname + '_' + 'cnn_fp_img.png', (cnn_fp_img))
#vt.imwrite(dataname + '_' + 'cnn_fn_img.png', (cnn_fn_img))
#vt.imwrite(dataname + '_' + 'sift_fp_img.png', (sift_fp_img))
#vt.imwrite(dataname + '_' + 'sift_fn_img.png', (sift_fn_img))
else:
print('Drawing TP FP TN FN')
fnum = fnum_gen()
pnum_gen = pt.make_pnum_nextgen(4, 2)
fig = pt.figure(fnum)
pt.imshow(cnn_fp_img, title='CNN FP', fnum=fnum, pnum=pnum_gen())
pt.imshow(sift_fp_img, title='SIFT FP', fnum=fnum, pnum=pnum_gen())
pt.imshow(cnn_fn_img, title='CNN FN', fnum=fnum, pnum=pnum_gen())
pt.imshow(sift_fn_img, title='SIFT FN', fnum=fnum, pnum=pnum_gen())
pt.imshow(cnn_tp_img, title='CNN TP', fnum=fnum, pnum=pnum_gen())
pt.imshow(sift_tp_img, title='SIFT TP', fnum=fnum, pnum=pnum_gen())
pt.imshow(cnn_tn_img, title='CNN TN', fnum=fnum, pnum=pnum_gen())
pt.imshow(sift_tn_img, title='SIFT TN', fnum=fnum, pnum=pnum_gen())
pt.set_figtitle(dataname + ' confusions')
pt.adjust_subplots(left=0, right=1.0, bottom=0., wspace=.01, hspace=.05)
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180, figsize=(9, 18))
with_patch_desc = FULL
if with_patch_desc:
ut.colorprint('[siam_perf] Visualize Patch Descriptors', 'white')
fnum = fnum_gen()
fig = pt.figure(fnum=fnum, pnum=(1, 1, 1))
num_rows = 7
pnum_gen = pt.make_pnum_nextgen(num_rows, 3)
# Compare actual output descriptors
for index in ut.random_indexes(len(sift_list), num_rows):
vec_sift = sift_list[index]
vec_cnn = network_output[index]
patch = data[index]
pt.imshow(patch, fnum=fnum, pnum=pnum_gen())
pt.plot_descriptor_signature(vec_cnn, 'cnn vec', fnum=fnum, pnum=pnum_gen())
pt.plot_sift_signature(vec_sift, 'sift vec', fnum=fnum, pnum=pnum_gen())
pt.set_figtitle('Patch Descriptors')
pt.adjust_subplots(left=0, right=0.95, bottom=0., wspace=.1, hspace=.15)
pt.save_figure(fig=fig, dpath=epoch_dpath, dpi=180, figsize=(9, 18))
def grab_liberty_siam_dataset(pairs=250000):
"""
References:
http://www.cs.ubc.ca/~mbrown/patchdata/patchdata.html
https://github.com/osdf/datasets/blob/master/patchdata/dataset.py
Notes:
"info.txt" contains the match information Each row of info.txt
corresponds corresponds to a separate patch, with the patches ordered
from left to right and top to bottom in each bitmap image.
3 types of metadata files
info.txt - contains patch ids that correspond with the order of patches
in the bmp images
In the format:
pointid, unused
interest.txt -
interest points corresponding to patches with patchids
has same number of rows as info.txt
In the format:
reference image id, x, y, orientation, scale (in log2 units)
m50_<d>_<d>_0.txt -
matches files
patchID1 3DpointID1 unused1 patchID2 3DpointID2 unused2
CommandLine:
python -m ibeis_cnn.ingest_data --test-grab_liberty_siam_dataset --show
Example:
>>> # ENABLE_DOCTEST
>>> from ibeis_cnn.ingest_data import * # NOQA
>>> pairs = 500
>>> dataset = grab_liberty_siam_dataset(pairs)
>>> ut.quit_if_noshow()
>>> from ibeis_cnn import draw_results
>>> #ibsplugin.rrr()
>>> flat_metadata = {}
>>> data, labels = dataset.subset('full')
>>> ut.quit_if_noshow()
>>> warped_patch1_list = data[::2]
>>> warped_patch2_list = data[1::2]
>>> dataset.interact()
>>> ut.show_if_requested()
"""
datakw = {
'detector': 'dog',
'pairs': pairs,
}
assert datakw['detector'] in ['dog', 'harris']
assert pairs in [500, 50000, 100000, 250000]
liberty_urls = {
'dog': 'http://www.cs.ubc.ca/~mbrown/patchdata/liberty.zip',
'harris': 'http://www.cs.ubc.ca/~mbrown/patchdata/liberty_harris.zip',
}
url = liberty_urls[datakw['detector']]
ds_path = ut.grab_zipped_url(url)
ds_name = splitext(basename(ds_path))[0]
alias_key = 'liberty;' + ut.dict_str(datakw, nl=False, explicit=True)
cfgstr = ','.join([str(val) for key, val in ut.iteritems_sorted(datakw)])
# TODO: allow a move of the base data prefix
training_dpath = ut.ensure_app_resource_dir('ibeis_cnn', 'training',
ds_name)
if ut.get_argflag('--vtd'):
ut.vd(training_dpath)
ut.ensuredir(training_dpath)
data_fpath = join(training_dpath, 'liberty_data_' + cfgstr + '.pkl')
labels_fpath = join(training_dpath, 'liberty_labels_' + cfgstr + '.pkl')
if not ut.checkpath(data_fpath, verbose=True):
data, labels = ingest_helpers.extract_liberty_style_patches(
ds_path, pairs)
ut.save_data(data_fpath, data)
ut.save_data(labels_fpath, labels)
# 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=None,
training_dpath=training_dpath,
data_shape=(64, 64, 1),
data_per_label=2,
output_dims=1,
num_labels=num_labels,
)
return dataset
def vd(ChapX):
"""
CommandLine:
python -m wbia Chap3.vd
"""
ut.vd(ChapX.base_dpath)