Source code for nnabla.models.imagenet.inception

# Copyright (c) 2017 Sony Corporation. All Rights Reserved.
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# 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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#     http://www.apache.org/licenses/LICENSE-2.0
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from __future__ import absolute_import
import nnabla as nn
from nnabla.utils.nnp_graph import NnpNetworkPass

from nnabla import logger

from .base import ImageNetBase


[docs]class InceptionV3(ImageNetBase): ''' InceptionV3 architecture. 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. * ``'prepool'``: The input of the final global average pooling, i.e. the output of the final inception block. References: * `Szegedy et al., Rethinking the Inception Architecture for Computer Vision. <https://arxiv.org/abs/1512.00567>`_ ''' _KEY_VARIABLE = { 'classifier': 'Affine', 'pool': 'AveragePooling_2', 'prepool': 'Inception_11/Concatenate', '_aux_classifier': 'Affine_2', '_include_no_aux': 'Conv_6/Convolution' } def __init__(self): self._load_nnp('Inception-v3.nnp', 'Inception-v3/Inception-v3.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, with_aux_tower=False): if not training: assert not with_aux_tower, "Aux Tower should be disabled when inference process." input_var = self.get_input_var(input_var) callback = NnpNetworkPass(verbose) callback.remove_and_rewire('ImageAugmentation') callback.set_variable('Iv3TrainInput', input_var) self.configure_global_average_pooling( callback, force_global_pooling, check_global_pooling, 'AveragePooling_2') callback.set_batch_normalization_batch_stat_all(training) if with_aux_tower: self.use_up_to('_aux_classifier', callback) funcs_to_drop = ("Affine_2", "SoftmaxCrossEntropy_2", "MulScalar_2") else: self.use_up_to('_include_no_aux', callback) funcs_to_drop = ("Conv_6/Convolution", "Conv_6/BatchNormalization", "Conv_6/ReLU", "AveragePooling", "Conv_7/Convolution", "Conv_7/BatchNormalization", "Conv_7/ReLU", "Affine_2", "SoftmaxCrossEntropy_2", "MulScalar_2") callback.drop_function(*funcs_to_drop) if not training: callback.remove_and_rewire('Dropout') callback.fix_parameters() self.use_up_to(use_up_to, callback) batch_size = input_var.shape[0] net = self.nnp.get_network( 'Train', batch_size=batch_size, callback=callback) if returns_net: return net elif with_aux_tower: return list(net.outputs.values()) else: return list(net.outputs.values())[0]