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import os
import argparse
import logging
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 = symbol.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)
if __name__ == "__main__":
# parse args
parser = argparse.ArgumentParser(description="fine-tune a dataset",
train = fit.add_fit_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, 1)
# 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
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)
# remove the last fullc layer
(new_sym, new_args) = get_fine_tune_model(
sym, arg_params, args.num_classes, args.layer_before_fullc)
# train = args,
network = new_sym,
data_loader = data.get_rec_iter,
arg_params = new_args,
aux_params = aux_params)