fine-tune.py

import os
import argparse
import logging
logging.basicConfig(level=logging.DEBUG)
from common import find_mxnet
from common import data, fit, modelzoo
import mxnet as mx

def get_fine_tune_model(symbol, arg_params, num_classes, layer_name):
    """
    symbol: the pre-trained network symbol
    arg_params: the argument parameters of the pre-trained model
    num_classes: the number of classes for the fine-tune datasets
    layer_name: the layer name before the last fully-connected layer
    """
    all_layers = sym.get_internals()
    net = all_layers[layer_name+'_output']
    net = mx.symbol.FullyConnected(data=net, num_hidden=num_classes, name='fc')
    net = mx.symbol.SoftmaxOutput(data=net, name='softmax')
    new_args = dict({k:arg_params[k] for k in arg_params if 'fc' not in k})
    return (net, new_args)

def _save_model(args, rank=0):
    if args.model_prefix is None:
        return None
    dst_dir = os.path.dirname(args.model_prefix)
    if not os.path.isdir(dst_dir):
        os.mkdir(dst_dir)
    return mx.callback.do_checkpoint(args.model_prefix if rank == 0 else "%s-%d" % (
        args.model_prefix, rank))

def _get_lr_scheduler(args, kv):
    if 'lr_factor' not in args or args.lr_factor >= 1:
        return (args.lr, None)
    epoch_size = args.num_examples / args.batch_size
    if 'dist' in args.kv_store:
        epoch_size /= kv.num_workers
    begin_epoch = args.load_epoch if args.load_epoch else 0
    step_epochs = [int(l) for l in args.lr_step_epochs.split(',')]
    lr = args.lr
    for s in step_epochs:
        if begin_epoch >= s:
            lr *= args.lr_factor
    if lr != args.lr:
        logging.info('Adjust learning rate to %e for epoch %d' %(lr, begin_epoch))

    steps = [epoch_size * (x-begin_epoch) for x in step_epochs if x-begin_epoch > 0]
    return (lr, mx.lr_scheduler.MultiFactorScheduler(step=steps, factor=args.lr_factor))

if __name__ == "__main__":
    # parse args
    parser = argparse.ArgumentParser(description="fine-tune a dataset",
                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    train = fit.add_fit_args(parser)
    data.add_data_args(parser)
    aug = data.add_data_aug_args(parser)
    parser.add_argument('--pretrained-model', type=str,
                        help='the pre-trained model')
    parser.add_argument('--layer-before-fullc', type=str, default='flatten0',
                        help='the name of the layer before the last fullc layer')
    # use less augmentations for fine-tune
    data.set_data_aug_level(parser, 3)
    # use a small learning rate and less regularizations
  
    parser.set_defaults(
        # network
        network          = 'inception-bn',
        # data
        num_classes      = 1000,
        num_examples     = 1281167,
        image_shape      = '3,224,224',
        min_random_scale = 1, # if input image has min size k, suggest to use
                              # 256.0/x, e.g. 0.533 for 480
        # train
        num_epochs       = 60,
        lr_step_epochs   = '20,30,40,50',
        lr               = 0.01,
        batch_size       = 32,
        optimizer        = 'sgd',
        disp_batches     = 10,
        top_k            = 5,
        data_train       = '/data/imagenet1k/imagenet1k-train',
        data_val         = '/data/imagenet1k/imagenet1k-val'
    )
    args = parser.parse_args()

    #load pretrained model
    if args.pretrained_model:
        sym, args_params, aux_params = mx.model.load_checkpoint(args.pretrained_model, 126)#inception-bn

    # train
    fit.fit(args               = args,
            network            = sym,
            data_loader        = data.get_rec_iter,
            arg_params         = args_params,
            aux_params         = aux_params,    
    )