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# coding: utf-8
# pylint: disable= arguments-differ
"""Inception, implemented in Gluon."""
__all__ = ['Inception3', 'inception_v3']
from ....context import cpu
from ...block import HybridBlock
from ... import nn
from ..custom_layers import HybridConcurrent
# Helpers
def _make_basic_conv(**kwargs):
out = nn.HybridSequential(prefix='')
out.add(nn.Conv2D(use_bias=False, **kwargs))
out.add(nn.BatchNorm(epsilon=0.001))
out.add(nn.Activation('relu'))
return out
def _make_branch(use_pool, *conv_settings):
out = nn.HybridSequential(prefix='')
if use_pool == 'avg':
out.add(nn.AvgPool2D(pool_size=3, strides=1, padding=1))
elif use_pool == 'max':
out.add(nn.MaxPool2D(pool_size=3, strides=2))
setting_names = ['channels', 'kernel_size', 'strides', 'padding']
for setting in conv_settings:
kwargs = {}
for i, value in enumerate(setting):
if value is not None:
kwargs[setting_names[i]] = value
out.add(_make_basic_conv(**kwargs))
return out
def _make_A(pool_features, prefix):
out = HybridConcurrent(concat_dim=1, prefix=prefix)
with out.name_scope():
out.add(_make_branch(None,
(64, 1, None, None)))
out.add(_make_branch(None,
(48, 1, None, None),
(64, 5, None, 2)))
out.add(_make_branch(None,
(64, 1, None, None),
(96, 3, None, 1),
(96, 3, None, 1)))
out.add(_make_branch('avg',
(pool_features, 1, None, None)))
return out
def _make_B(prefix):
out = HybridConcurrent(concat_dim=1, prefix=prefix)
with out.name_scope():
out.add(_make_branch(None,
(384, 3, 2, None)))
out.add(_make_branch(None,
(64, 1, None, None),
(96, 3, None, 1),
(96, 3, 2, None)))
out.add(_make_branch('max'))
return out
def _make_C(channels_7x7, prefix):
out = HybridConcurrent(concat_dim=1, prefix=prefix)
with out.name_scope():
out.add(_make_branch(None,
(192, 1, None, None)))
out.add(_make_branch(None,
(channels_7x7, 1, None, None),
(channels_7x7, (1, 7), None, (0, 3)),
(192, (7, 1), None, (3, 0))))
out.add(_make_branch(None,
(channels_7x7, 1, None, None),
(channels_7x7, (7, 1), None, (3, 0)),
(channels_7x7, (1, 7), None, (0, 3)),
(channels_7x7, (7, 1), None, (3, 0)),
(192, (1, 7), None, (0, 3))))
out.add(_make_branch('avg',
(192, 1, None, None)))
return out
def _make_D(prefix):
out = HybridConcurrent(concat_dim=1, prefix=prefix)
with out.name_scope():
out.add(_make_branch(None,
(192, 1, None, None),
(320, 3, 2, None)))
out.add(_make_branch(None,
(192, 1, None, None),
(192, (1, 7), None, (0, 3)),
(192, (7, 1), None, (3, 0)),
(192, 3, 2, None)))
out.add(_make_branch('max'))
return out
def _make_E(prefix):
out = HybridConcurrent(concat_dim=1, prefix=prefix)
with out.name_scope():
out.add(_make_branch(None,
(320, 1, None, None)))
branch_3x3 = nn.HybridSequential(prefix='')
out.add(branch_3x3)
branch_3x3.add(_make_branch(None,
(384, 1, None, None)))
branch_3x3_split = HybridConcurrent(concat_dim=1, prefix='')
branch_3x3_split.add(_make_branch(None,
(384, (1, 3), None, (0, 1))))
branch_3x3_split.add(_make_branch(None,
(384, (3, 1), None, (1, 0))))
branch_3x3.add(branch_3x3_split)
branch_3x3dbl = nn.HybridSequential(prefix='')
out.add(branch_3x3dbl)
branch_3x3dbl.add(_make_branch(None,
(448, 1, None, None),
(384, 3, None, 1)))
branch_3x3dbl_split = HybridConcurrent(concat_dim=1, prefix='')
branch_3x3dbl.add(branch_3x3dbl_split)
branch_3x3dbl_split.add(_make_branch(None,
(384, (1, 3), None, (0, 1))))
branch_3x3dbl_split.add(_make_branch(None,
(384, (3, 1), None, (1, 0))))
out.add(_make_branch('avg',
(192, 1, None, None)))
return out
def make_aux(classes):
out = nn.HybridSequential(prefix='')
out.add(nn.AvgPool2D(pool_size=5, strides=3))
out.add(_make_basic_conv(channels=128, kernel_size=1))
out.add(_make_basic_conv(channels=768, kernel_size=5))
out.add(nn.Flatten())
out.add(nn.Dense(classes))
return out
# Net
[docs]class Inception3(HybridBlock):
r"""Inception v3 model from
`"Rethinking the Inception Architecture for Computer Vision"
`_ paper.
Parameters
----------
classes : int, default 1000
Number of classification classes.
"""
def __init__(self, classes=1000, **kwargs):
super(Inception3, self).__init__(**kwargs)
# self.use_aux_logits = use_aux_logits
with self.name_scope():
self.features = nn.HybridSequential(prefix='')
self.features.add(_make_basic_conv(channels=32, kernel_size=3, strides=2))
self.features.add(_make_basic_conv(channels=32, kernel_size=3))
self.features.add(_make_basic_conv(channels=64, kernel_size=3, padding=1))
self.features.add(nn.MaxPool2D(pool_size=3, strides=2))
self.features.add(_make_basic_conv(channels=80, kernel_size=1))
self.features.add(_make_basic_conv(channels=192, kernel_size=3))
self.features.add(nn.MaxPool2D(pool_size=3, strides=2))
self.features.add(_make_A(32, 'A1_'))
self.features.add(_make_A(64, 'A2_'))
self.features.add(_make_A(64, 'A3_'))
self.features.add(_make_B('B_'))
self.features.add(_make_C(128, 'C1_'))
self.features.add(_make_C(160, 'C2_'))
self.features.add(_make_C(160, 'C3_'))
self.features.add(_make_C(192, 'C4_'))
self.classifier = nn.HybridSequential(prefix='')
self.classifier.add(_make_D('D_'))
self.classifier.add(_make_E('E1_'))
self.classifier.add(_make_E('E2_'))
self.classifier.add(nn.AvgPool2D(pool_size=8))
self.classifier.add(nn.Dropout(0.5))
self.classifier.add(nn.Dense(classes))
def hybrid_forward(self, F, x):
x = self.features(x)
x = self.classifier(x)
return x
# Constructor
[docs]def inception_v3(pretrained=False, ctx=cpu(), root='~/.mxnet/models', **kwargs):
r"""Inception v3 model from
`"Rethinking the Inception Architecture for Computer Vision"
`_ paper.
Parameters
----------
pretrained : bool, default False
Whether to load the pretrained weights for model.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '~/.mxnet/models'
Location for keeping the model parameters.
"""
net = Inception3(**kwargs)
if pretrained:
from ..model_store import get_model_file
net.load_params(get_model_file('inceptionv3', root=root), ctx=ctx)
return net
