Source code for nnabla.models.imagenet.xception

# Copyright 2019,2020,2021 Sony Corporation.
# Copyright 2021 Sony Group Corporation.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
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from __future__ import absolute_import

from nnabla.utils.nnp_graph import NnpNetworkPass

from .base import ImageNetBase

[docs]class Xception(ImageNetBase): """ Xception model. The following is a list of string that can be specified to ``use_up_to`` option in ``__call__`` method; * ``'classifier'`` (default): The output of the final affine layer for classification. * ``'pool'``: The output of the final global average pooling. * ``'lastconv'``: The input of the final global average pooling without ReLU activation. * ``'lastconv+relu'``: Network up to ``'lastconv'`` followed by ReLU activation. References: * `Francois Chollet, Xception: Deep Learning with Depthwise Separable Convolutions. <>`_ """ _KEY_VARIABLE = { 'classifier': 'Unit/Affine', 'pool': 'Unit/AveragePooling', 'lastconv': 'Unit/SeparableConv_13/SepConvBN', 'lastconv+relu': 'Unit/ReLU_14', } def __init__(self): # Load nnp self._load_nnp('Xception.nnp', 'Xception/Xception.nnp') def _input_shape(self): return (3, 299, 299) def __call__(self, input_var=None, use_from=None, use_up_to='classifier', training=False, force_global_pooling=False, check_global_pooling=True, returns_net=False, verbose=0): assert use_from is None, 'This should not be set because it is for forward compatibility.' input_var = self.get_input_var(input_var) callback = NnpNetworkPass(verbose) callback.remove_and_rewire('ImageAugmentation') callback.set_variable('InputX', input_var) self.configure_global_average_pooling( callback, force_global_pooling, check_global_pooling, 'Unit/AveragePooling') callback.set_batch_normalization_batch_stat_all(training) self.use_up_to(use_up_to, callback) if not training: callback.remove_and_rewire('Unit/Dropout') callback.fix_parameters() batch_size = input_var.shape[0] net = self.nnp.get_network( 'Training', batch_size=batch_size, callback=callback) if returns_net: return net return list(net.outputs.values())[0]