Source code for nnabla.models.imagenet.squeezenet

# 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.
# You may obtain a copy of the License at
# 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
# limitations under the License.

from __future__ import absolute_import

from nnabla.utils.nnp_graph import NnpNetworkPass

from .base import ImageNetBase

[docs]class SqueezeNet(ImageNetBase): """ SqueezeNet model for architecture-v1.0 and v1.1 . Args: version (str): Version chosen from 'v1.0' and 'v1.1'. 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: * `Iandola, Forrest N. et al., SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. <>`_ * `Iandola, Forrest N. et al., SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size. <>`_ * `DeepScale/SqueezeNet on GitHub <>`_ """ _KEY_VARIABLE = { 'classifier': '{prefix}Reshape', 'pool': '{prefix}AveragePooling', 'lastconv': '{prefix}Convolution_2', 'lastconv+relu': '{prefix}ReLU_2', } def __init__(self, version='v1.1'): # Check versions assert version in ( 'v1.0', 'v1.1'), "version must be chosen from {'v1.0', 'v1.1'}" self.version = version self._prefix = '' if version == 'v1.1': self._prefix = 'SqueezeNet/' # Load nnp self._load_nnp('SqueezeNet-{}.nnp'.format(version[1:]), 'SqueezeNet-{0}/SqueezeNet-{0}.nnp'.format(version[1:])) def _input_shape(self): return (3, 227, 227) 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): input_var = self.get_input_var(input_var) callback = NnpNetworkPass(verbose) callback.remove_and_rewire('ImageAugmentationX') callback.set_variable('TrainingInput', input_var) self.configure_global_average_pooling( callback, force_global_pooling, check_global_pooling, '{}AveragePooling'.format(self._prefix)) callback.set_batch_normalization_batch_stat_all(training) self.use_up_to(use_up_to, callback, prefix=self._prefix) if not training: callback.remove_and_rewire('{}Dropout'.format(self._prefix)) 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 SqueezeNetV10(SqueezeNet): """SquezeNetV10 An alias of :obj:`SqueezeNet` `('v1.0')`. """ def __init__(self): super(SqueezeNetV10, self).__init__('v1.0')
[docs]class SqueezeNetV11(SqueezeNet): """SquezeNetV11 An alias of :obj:`SqueezeNet` `('v1.1')`. """ def __init__(self): super(SqueezeNetV11, self).__init__('v1.1')