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Merge pull request #412 from sony/feature/20190129-nnabla-models-ince…
…ptionv3 Add InceptionV3 model
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from .vgg import VGG | ||
from .nin import NIN | ||
from .densenet import DenseNet | ||
from .inception import InceptionV3 |
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# Copyright (c) 2017 Sony Corporation. All Rights Reserved. | ||
# | ||
# 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 | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
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from __future__ import absolute_import | ||
import nnabla as nn | ||
from nnabla.utils.nnp_graph import NnpNetworkPass | ||
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from nnabla import logger | ||
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from .base import ImageNetBase | ||
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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>`_ | ||
''' | ||
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_KEY_VARIABLE = { | ||
'classifier': 'Affine', | ||
'pool': 'AveragePooling_2', | ||
'prepool': 'Inception_11/Concatenate', | ||
'_aux_classifier': 'Affine_2', | ||
'_include_no_aux': 'Conv_6/Convolution' | ||
} | ||
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def __init__(self): | ||
self._load_nnp('Inception-v3.nnp', 'Inception-v3/Inception-v3.nnp') | ||
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def _input_shape(self): | ||
return (3, 299, 299) | ||
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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." | ||
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input_var = self.get_input_var(input_var) | ||
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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") | ||
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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] |
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