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')