Source code for nnabla.models.imagenet.densenet

# 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 DenseNet(ImageNetBase): ''' 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 output from last denseblock.. * ``'lastconv+relu'``: Network up to ``'lastconv'`` followed by ReLU activation. References: * `Huang et al., Densely Connected Convolutional Networks. <https://arxiv.org/abs/1608.06993>`_ ''' _KEY_VARIABLE = { 'classifier': 'DenseNet/Affine', 'pool': 'DenseNet/AveragePooling_4', 'lastconv': 'DenseNet/BatchNormalization_5', 'lastconv+relu': 'DenseNet/ReLU_5', } def __init__(self): # Load nnp self._load_nnp('DenseNet-161.nnp', 'DenseNet-161/DenseNet-161.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, 'DenseNet/AveragePooling_4') 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( 'Train', batch_size=batch_size, callback=callback) if returns_net: return net return list(net.outputs.values())[0]