Source code for nnabla.models.imagenet.resnet

# Copyright (c) 2017 Sony Corporation. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (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 ResNet(ImageNetBase): ''' ResNet architectures for 18, 34, 50, 101, and 152 of number of layers. Args: num_layers (int): Number of layers chosen from 18, 34, 50, 101, and 152. 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: * `He et al, Deep Residual Learning for Image Recognition. <https://arxiv.org/abs/1512.03385>`_ ''' _KEY_VARIABLE = { 'classifier': 'Affine', 'pool': 'AveragePooling', 'lastconv': 'Add2_7_RepeatStart_4[{index}]', 'lastconv+relu': 'ReLU_25_RepeatStart_4[{index}]', } def __init__(self, num_layers=18): # Check validity of num_layers set_num_layers = set((18, 34, 50, 101, 152)) assert num_layers in set_num_layers, "num_layers must be chosen from {}".format( set_num_layers) self.num_layers = num_layers # Load nnp self._load_nnp('Resnet-{}.nnp'.format(num_layers), 'Resnet-{0}/Resnet-{0}.nnp'.format(num_layers)) 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) index = 0 if self.num_layers == 18 else 1 self.use_up_to(use_up_to, callback, index=index) 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]