def setup_kp_network(network_str):
fn = KP_NETWORK_OPTIONS[network_str]['url']
file_url = join('https://lev.cs.rpi.edu/public/models/', fn)
network_params_path = ut.grab_file_url(file_url, appname='ibeis')
network_params = ut.load_cPkl(network_params_path)
# network_params also includes normalization constants needed for the dataset, and is assumed to be a dictionary
# with keys mean, std, and params
network_exp = KP_NETWORK_OPTIONS[network_str]['exp']()
ll.set_all_param_values(network_exp, network_params['params'])
X = T.tensor4()
network_fn = tfn([X], ll.get_output(network_exp, X, deterministic=True))
return {'mean': network_params['mean'], 'std': network_params['std'], 'networkfn': network_fn,
'input_size': KP_NETWORK_OPTIONS[network_str]['size']}
def setup_kp_network(network_str):
fn = KP_NETWORK_OPTIONS[network_str]['url']
file_url = join('https://lev.cs.rpi.edu/public/models/', fn)
network_params_path = ut.grab_file_url(file_url, appname='ibeis')
network_params = ut.load_cPkl(network_params_path)
# network_params also includes normalization constants needed for the dataset, and is assumed to be a dictionary
# with keys mean, std, and params
network_exp = KP_NETWORK_OPTIONS[network_str]['exp']()
ll.set_all_param_values(network_exp, network_params['params'])
X = T.tensor4()
network_fn = tfn([X], ll.get_output(network_exp, X, deterministic=True))
return {
'mean': network_params['mean'],
'std': network_params['std'],
'networkfn': network_fn,
'input_size': KP_NETWORK_OPTIONS[network_str]['size']
}
def setup_te_network(network_str):
fn = TE_NETWORK_OPTIONS[network_str]['url']
file_url = join('https://lev.cs.rpi.edu/public/models/', fn)
network_params_path = ut.grab_file_url(file_url, appname='ibeis')
network_params = ut.load_cPkl(network_params_path)
# network_params also includes normalization constants needed for the dataset, and is assumed to be a dictionary
# with keys mean, std, and params
network_exp = TE_NETWORK_OPTIONS[network_str]['exp']()
ll.set_all_param_values(network_exp, network_params['params'])
X = T.tensor4()
network_fn = tfn([X], ll.get_output(
network_exp[-1], X, deterministic=True))
retdict = {'mean': network_params['mean'], 'std': network_params[
'std'], 'networkfn': network_fn}
if any([i in network_str for i in ('upsample', 'jet')]):
retdict['mod_acc'] = 8
return retdict
def setup_te_network(network_str):
fn = TE_NETWORK_OPTIONS[network_str]['url']
file_url = join('https://lev.cs.rpi.edu/public/models/', fn)
network_params_path = ut.grab_file_url(file_url, appname='ibeis')
network_params = ut.load_cPkl(network_params_path)
# network_params also includes normalization constants needed for the dataset, and is assumed to be a dictionary
# with keys mean, std, and params
network_exp = TE_NETWORK_OPTIONS[network_str]['exp']()
ll.set_all_param_values(network_exp, network_params['params'])
X = T.tensor4()
network_fn = tfn([X], ll.get_output(network_exp[-1], X,
deterministic=True))
retdict = {
'mean': network_params['mean'],
'std': network_params['std'],
'networkfn': network_fn
}
if any([i in network_str for i in ('upsample', 'jet')]):
retdict['mod_acc'] = 8
return retdict
def updateVersion(nextVersion):
f = open('networkVersion.txt', 'w')
f.write(str(nextVersion))
f.close()
if __name__ == '__main__':
nextVersion = getVersion()
with open(join(dataset_loc, "Flukes/patches/annot_full_64_100r_zs/vgg16_c43_10ep_adam_l21e-3.pkl"), 'r') as f:
model = pickle.load(f)
test_dset = load_dataset(join(dataset_loc, "Flukes/patches/TESTannot_full_64_100r_zs"))
segmenter = build_segmenter_vgg()
ll.set_all_param_values(segmenter, model)
X = T.tensor4()
segmenter_out = ll.get_output(segmenter, X, deterministic=True)
segmenter_fn = tfn([X], segmenter_out)
dset_for_model = {section:preproc_dataset(test_dset[section]) for section in ['train', 'valid', 'test']}
segmentation_outputs = segmenter_fn(dset_for_model['train']['X'])
segmentation_outputs_valid = segmenter_fn(dset_for_model['valid']['X'])
usedGids = set()
#open MongoDB
c = MongoClient()
db = c['annotationInfo']
collection = db['networkResults']
cursor = collection.find({'version':nextVersion})
values = cursor[:]
for value in values:
usedGids.add(value['gid'])
ibs = ibeis.opendb(dbdir='/home/zach/data/IBEIS/humpbacks')
