resnet/fmodel.py

import tensorflow as tf
import os
from foolbox.models import TensorFlowModel

from resnet18.resnet_model import Model


def create_model():
    graph = tf.Graph()
    with graph.as_default():
        images = tf.placeholder(tf.float32, (None, 64, 64, 3))

        # preprocessing
        _R_MEAN = 123.68
        _G_MEAN = 116.78
        _B_MEAN = 103.94
        _CHANNEL_MEANS = [_R_MEAN, _G_MEAN, _B_MEAN]
        features = images - tf.constant(_CHANNEL_MEANS)

        model = Model(
            resnet_size=18,
            bottleneck=False,
            num_classes=200,
            num_filters=64,
            kernel_size=3,
            conv_stride=1,
            first_pool_size=0,
            first_pool_stride=2,
            second_pool_size=7,
            second_pool_stride=1,
            block_sizes=[2, 2, 2, 2],
            block_strides=[1, 2, 2, 2],
            final_size=512,
            version=2,
            data_format=None,
        )

        logits = model(features, False)

        with tf.variable_scope("utilities"):
            saver = tf.train.Saver()

    return graph, saver, images, logits


def create_fmodel():
    graph, saver, images, logits = create_model()
    sess = tf.Session(graph=graph)
    path = os.path.dirname(os.path.abspath(__file__))
    path = os.path.join(path, "resnet18", "checkpoints", "models_repo")
    saver.restore(sess, tf.train.latest_checkpoint(path))

    with sess.as_default():
        fmodel = TensorFlowModel(images, logits, bounds=(0, 255))
    return fmodel


if __name__ == "__main__":
    # executable for debuggin and testing
    print(create_fmodel())