def train_P_net(image_set, root_path, dataset_path, prefix, end_epoch,
frequent, lr):
imdb = IMDB(data_name, image_set, root_path, dataset_path)
gt_imdb = imdb.gt_imdb()
gt_imdb = imdb.append_flipped_images(gt_imdb)
sym = P_Net
train_net(sym, prefix, end_epoch, gt_imdb, 12, frequent, lr)
def train_O_net(image_set, root_path, dataset_path, prefix, ctx, pretrained,
epoch, begin_epoch, end_epoch, frequent, lr, resume):
imdb = IMDB("mtcnn", image_set, root_path, dataset_path)
gt_imdb = imdb.gt_imdb()
gt_imdb = imdb.append_flipped_images(gt_imdb)
sym = O_Net()
train_net(sym, prefix, ctx, pretrained, epoch, begin_epoch, end_epoch,
gt_imdb, 48, frequent, not resume, lr)
def train_O_net(image_set, root_path, dataset_path, prefix, ctx,
pretrained, epoch, begin_epoch,
end_epoch, frequent, lr, resume):
imdb = IMDB("mtcnn", image_set, root_path, dataset_path)
gt_imdb = imdb.gt_imdb()
gt_imdb = imdb.append_flipped_images(gt_imdb)
sym = O_Net()
train_net(sym, prefix, ctx, pretrained, epoch, begin_epoch, end_epoch, gt_imdb,
48, frequent, not resume, lr)
def train_R_net(image_set, root_path, dataset_path, prefix, ctx,
pretrained, epoch, begin_epoch, end_epoch, batch_size, thread_num,
frequent, lr, lr_epoch, resume):
imdb = IMDB("mtcnn", image_set, root_path, dataset_path)
gt_imdb = imdb.gt_imdb()
gt_imdb = imdb.append_flipped_images(gt_imdb)
sym = R_Net()
train_net(sym, prefix, ctx, pretrained, epoch, begin_epoch, end_epoch, gt_imdb, batch_size, thread_num,
24, frequent, not resume, lr, lr_epoch)
def train_GA_net(mode, image_set, root_path, dataset_path, prefix, ctx,
pretrained, epoch, begin_epoch, end_epoch, batch_size,
thread_num, frequent, lr, lr_epoch, resume):
imdb = IMDB("GA", 112, image_set, root_path, dataset_path)
gt_imdb = imdb.gt_imdb()
gt_imdb = imdb.append_flipped_images(gt_imdb)
sym = GA_Net112(mode, batch_size)
train_net(mode, sym, prefix, ctx, pretrained, epoch, begin_epoch,
end_epoch, gt_imdb, batch_size, thread_num, 112, 112, frequent,
not resume, lr, lr_epoch)