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66 lines
2.2 KiB
66 lines
2.2 KiB
"""Convert a Caffe model file to TensorFlow checkpoint format. |
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Assume that the network built is a equivalent (or a sub-) to the Caffe |
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definition. |
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""" |
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import tensorflow as tf |
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from nets import caffe_scope |
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from nets import nets_factory |
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slim = tf.contrib.slim |
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# =========================================================================== # |
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# Main flags. |
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# =========================================================================== # |
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tf.app.flags.DEFINE_string( |
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'model_name', 'ssd_300_vgg', 'Name of the model to convert.') |
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tf.app.flags.DEFINE_string( |
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'num_classes', 21, 'Number of classes in the dataset.') |
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tf.app.flags.DEFINE_string( |
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'caffemodel_path', None, |
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'The path to the Caffe model file to convert.') |
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FLAGS = tf.app.flags.FLAGS |
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# =========================================================================== # |
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# Main converting routine. |
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# =========================================================================== # |
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def main(_): |
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# Caffe scope... |
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caffemodel = caffe_scope.CaffeScope() |
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caffemodel.load(FLAGS.caffemodel_path) |
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tf.logging.set_verbosity(tf.logging.INFO) |
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with tf.Graph().as_default(): |
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global_step = slim.create_global_step() |
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num_classes = int(FLAGS.num_classes) |
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# Select the network. |
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ssd_class = nets_factory.get_network(FLAGS.model_name) |
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ssd_params = ssd_class.default_params._replace(num_classes=num_classes) |
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ssd_net = ssd_class(ssd_params) |
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ssd_shape = ssd_net.params.img_shape |
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# Image placeholder and model. |
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shape = (1, ssd_shape[0], ssd_shape[1], 3) |
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img_input = tf.placeholder(shape=shape, dtype=tf.float32) |
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# Create model. |
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with slim.arg_scope(ssd_net.arg_scope_caffe(caffemodel)): |
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ssd_net.net(img_input, is_training=False) |
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init_op = tf.global_variables_initializer() |
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with tf.Session() as session: |
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# Run the init operation. |
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session.run(init_op) |
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# Save model in checkpoint. |
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saver = tf.train.Saver() |
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ckpt_path = FLAGS.caffemodel_path.replace('.caffemodel', '.ckpt') |
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saver.save(session, ckpt_path, write_meta_graph=False) |
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if __name__ == '__main__': |
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tf.app.run() |
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