Source code for nnabla.models.imagenet.shufflenet

# 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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# distributed under the License is distributed on an "AS IS" BASIS,
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from __future__ import absolute_import

from nnabla.utils.nnp_graph import NnpNetworkPass

from .base import ImageNetBase

[docs]class ShuffleNet(ImageNetBase): """ Model for architecture ShuffleNet, ShuffleNet-0.5x and ShufffleNet-2.0x. Args: Scaling Factor (str): To customize the network to a desired complexity, we can simply apply a scale factor on the number of channnels. This can be chosen from '10', '5' and '20'. 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: * `Zhang. et al., ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. <>`_ * `Ma, Zhang. et al., ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. <>`_ """ _KEY_VARIABLE = { 'classifier': 'Affine', 'pool': 'AveragePooling', 'lastconv': 'Add2_3_RepeatStart_3[2]', 'lastconv+relu': 'ReLU_8_RepeatStart_3[2]', } def __init__(self, scaling_factor=10): # Check validity of scaling factor set_scaling_factor = set((10, 5, 20)) assert scaling_factor in set_scaling_factor, "scaling_factor must be chosen from {}".format( set_scaling_factor) self.scaling_factor = scaling_factor # Load nnp if scaling_factor == 10: self._load_nnp('ShuffleNet.nnp', 'ShuffleNet/ShuffleNet.nnp') elif scaling_factor == 5: self._load_nnp('ShuffleNet-.05x.nnp', 'ShuffleNet-0.5x/ShuffleNet-0.5x.nnp') elif scaling_factor == 20: self._load_nnp('ShuffleNet-2.0x.nnp', 'ShuffleNet-2.0x/ShuffleNet-2.0x.nnp') def _input_shape(self): return (3, 224, 224) 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('ImageAugmentationX') callback.set_variable('InputX', input_var) self.configure_global_average_pooling( callback, force_global_pooling, check_global_pooling, 'AveragePooling') callback.set_batch_normalization_batch_stat_all(training) self.use_up_to(use_up_to, callback) if not training: 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]
[docs]class ShuffleNet10(ShuffleNet): """ShuffleNet10 An alias of :obj:`ShuffleNet` `(10)`. """ def __init__(self): super(ShuffleNet10, self).__init__(10)
[docs]class ShuffleNet05(ShuffleNet): """ShuffleNet05 An alias of :obj:`ShuffleNet` `(5)`. """ def __init__(self): super(ShuffleNet05, self).__init__(5)
[docs]class ShuffleNet20(ShuffleNet): """ShuffleNet20 An alias of :obj:`ShuffleNet` `(20)`. """ def __init__(self): super(ShuffleNet20, self).__init__(20)