146 lines
4.1 KiB
Python
146 lines
4.1 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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#
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################################################################################
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"""
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Low Shot SVM training.
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Relevant transfer tasks: Low-shot Image Classification VOC07 and Places205 low
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shot samples.
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"""
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from __future__ import division
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from __future__ import absolute_import
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from __future__ import unicode_literals
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from __future__ import print_function
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import multiprocessing as mp
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import tqdm
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import argparse
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import logging
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import numpy as np
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import os
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import pickle
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import sys
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from sklearn.svm import LinearSVC
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import svm_helper
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import pdb
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def task(cls, cost, opts, features, targets):
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suffix = '_'.join(
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opts.targets_data_file.split('/')[-1].split('.')[0].split('_')[-2:])
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out_file = svm_helper.get_low_shot_output_file(opts, cls, cost, suffix)
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if not os.path.exists(out_file):
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clf = LinearSVC(
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C=cost,
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class_weight={
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1: 2,
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-1: 1
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},
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intercept_scaling=1.0,
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verbose=0,
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penalty='l2',
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loss='squared_hinge',
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tol=0.0001,
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dual=True,
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max_iter=2000,
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)
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train_feats, train_cls_labels = svm_helper.get_cls_feats_labels(
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cls, features, targets, opts.dataset)
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clf.fit(train_feats, train_cls_labels)
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#cls_labels = targets[:, cls].astype(dtype=np.int32, copy=True)
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#cls_labels[np.where(cls_labels == 0)] = -1
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#clf.fit(features, cls_labels)
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with open(out_file, 'wb') as fwrite:
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pickle.dump(clf, fwrite)
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return 0
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def mp_helper(args):
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return task(*args)
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def train_svm_low_shot(opts):
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assert os.path.exists(opts.data_file), "Data file not found. Abort!"
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if not os.path.exists(opts.output_path):
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os.makedirs(opts.output_path)
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features, targets = svm_helper.load_input_data(opts.data_file,
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opts.targets_data_file)
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# normalize the features: N x 9216 (example shape)
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features = svm_helper.normalize_features(features)
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# parse the cost values for training the SVM on
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costs_list = svm_helper.parse_cost_list(opts.costs_list)
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# classes for which SVM testing should be done
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num_classes, cls_list = svm_helper.get_low_shot_svm_classes(
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targets, opts.dataset)
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num_task = len(cls_list) * len(costs_list)
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args_cls = []
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args_cost = []
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for cls in cls_list:
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for cost in costs_list:
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args_cls.append(cls)
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args_cost.append(cost)
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args_opts = [opts] * num_task
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args_features = [features] * num_task
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args_targets = [targets] * num_task
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pool = mp.Pool(mp.cpu_count())
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for _ in tqdm.tqdm(
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pool.imap_unordered(
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mp_helper,
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zip(args_cls, args_cost, args_opts, args_features,
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args_targets)),
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total=num_task):
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pass
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def main():
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parser = argparse.ArgumentParser(description='Low-shot SVM model training')
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parser.add_argument(
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'--data_file',
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type=str,
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default=None,
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help="Numpy file containing image features")
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parser.add_argument(
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'--targets_data_file',
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type=str,
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default=None,
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help="Numpy file containing image labels")
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parser.add_argument(
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'--costs_list',
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type=str,
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default="0.01,0.1",
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help="comma separated string containing list of costs")
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parser.add_argument(
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'--output_path',
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type=str,
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default=None,
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help="path where to save the trained SVM models")
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parser.add_argument(
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'--random_seed',
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type=int,
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default=100,
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help="random seed for SVM classifier training")
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parser.add_argument(
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'--dataset', type=str, default="voc", help='voc | places')
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if len(sys.argv) == 1:
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parser.print_help()
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sys.exit(1)
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opts = parser.parse_args()
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train_svm_low_shot(opts)
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if __name__ == '__main__':
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main()
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