example/image-classification/fine-tune.py

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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
import numpy as np


def get_fine_tune_model(symbol, arg_params, num_classes, layer_name, dtype='float32'):
    """
    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 = symbol.get_internals()
    net = all_layers[layer_name+'_output']
    net = mx.symbol.FullyConnected(data=net, num_hidden=num_classes, name='fc')
    if dtype == 'float16':
        net = mx.sym.Cast(data=net, dtype=np.float32)
    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)

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. can be prefix of local model files prefix \
                        or a model name from common/modelzoo')
    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. by default here it uses no augmentations

    # use a small learning rate and less regularizations
    parser.set_defaults(image_shape='3,224,224',
                        num_epochs=30,
                        lr=.01,
                        lr_step_epochs='20',
                        wd=0,
                        mom=0)
    args = parser.parse_args()


    # load pretrained model and params
    dir_path = os.path.dirname(os.path.realpath(__file__))
    (prefix, epoch) = modelzoo.download_model(
        args.pretrained_model, os.path.join(dir_path, 'model'))
    if prefix is None:
        (prefix, epoch) = (args.pretrained_model, args.load_epoch)
    sym, arg_params, aux_params = mx.model.load_checkpoint(prefix, epoch)

    if args.dtype != 'float32':
        # load symbol of trained network, so we can cast it to support other dtype
        # fine tuning a network in a datatype which was not used for training originally,
        # requires access to the code used to generate the symbol used to train that model.
        # we then need to modify the symbol to add a layer at the beginning
        # to cast data to that dtype. We also need to cast output of layers before softmax
        # to float32 so that softmax can still be in float32.
        # if the network chosen from symols/ folder doesn't have cast for the new datatype,
        # it will still train in fp32
        if args.network not in ['inception-v3',\
                                 'inception-v4', 'resnet-v1', 'resnet', 'resnext', 'vgg']:
            raise ValueError('Given network does not have support for dtypes other than float32.\
                Please add a cast layer at the beginning to train in that mode.')
        from importlib import import_module
        net = import_module('symbols.'+args.network)
        sym = net.get_symbol(**vars(args))

    # remove the last fullc layer and add a new softmax layer
    (new_sym, new_args) = get_fine_tune_model(sym, arg_params, args.num_classes,
                                              args.layer_before_fullc, args.dtype)
    # train
    fit.fit(args        = args,
            network     = new_sym,
            data_loader = data.get_rec_iter,
            arg_params  = new_args,
            aux_params  = aux_params)