def __init__(self,
n_layer,
n_class=None,
pretrained_model=None,
mean=None,
initialW=None,
fc_kwargs={},
arch='fb'):
if arch == 'fb':
stride_first = False
conv1_no_bias = True
elif arch == 'he':
stride_first = True
# Kaiming He uses bias only for ResNet50
conv1_no_bias = n_layer != 50
else:
raise ValueError('arch is expected to be one of [\'he\', \'fb\']')
blocks = self._blocks[n_layer]
param, path = utils.prepare_pretrained_model(
{
'n_class': n_class,
'mean': mean
}, pretrained_model, self._models[arch][n_layer], {
'n_class': 1000,
'mean': _imagenet_mean
})
self.mean = param['mean']
if initialW is None:
initialW = initializers.HeNormal(scale=1., fan_option='fan_out')
if 'initialW' not in fc_kwargs:
fc_kwargs['initialW'] = initializers.Normal(scale=0.01)
if pretrained_model:
# As a sampling process is time-consuming,
# we employ a zero initializer for faster computation.
initialW = initializers.constant.Zero()
fc_kwargs['initialW'] = initializers.constant.Zero()
kwargs = {'initialW': initialW, 'stride_first': stride_first}
super(ResNet, self).__init__()
with self.init_scope():
self.conv1 = Conv2DBNActiv(None,
64,
7,
2,
3,
nobias=conv1_no_bias,
initialW=initialW)
self.pool1 = lambda x: F.max_pooling_2d(x, ksize=3, stride=2)
self.res2 = ResBlock(blocks[0], None, 64, 256, 1, **kwargs)
self.res3 = ResBlock(blocks[1], None, 128, 512, 2, **kwargs)
self.res4 = ResBlock(blocks[2], None, 256, 1024, 2, **kwargs)
self.res5 = ResBlock(blocks[3], None, 512, 2048, 2, **kwargs)
self.pool5 = lambda x: F.average(x, axis=(2, 3))
self.fc6 = L.Linear(None, param['n_class'], **fc_kwargs)
self.prob = F.softmax
if path:
chainer.serializers.load_npz(path, self)
def __init__(self, initialW=None):
super(ResNet101Extractor, self).__init__()
if initialW is None:
initialW = chainer.initializers.HeNormal()
kwargs = {
'initialW': initialW,
'bn_kwargs': {
'eps': 1e-5
},
'stride_first': True
}
with self.init_scope():
# ResNet
self.conv1 = Conv2DBNActiv(3,
64,
7,
2,
3,
nobias=True,
initialW=initialW)
self.pool1 = lambda x: F.max_pooling_2d(x, ksize=3, stride=2)
self.res2 = ResBlock(3, 64, 64, 256, 1, **kwargs)
self.res3 = ResBlock(4, 256, 128, 512, 2, **kwargs)
self.res4 = ResBlock(23, 512, 256, 1024, 2, **kwargs)
self.res5 = ResBlock(3, 1024, 512, 2048, 1, 2, **kwargs)
def __init__(self,
n_layer,
n_class=None,
pretrained_model=None,
mean=None,
initialW=None,
fc_kwargs={},
arch='fb'):
if arch == 'fb':
if pretrained_model == 'imagenet':
raise ValueError(
'Pretrained weights for Facebook ResNet models '
'are not supported. Please set mode to \'he\'.')
stride_first = False
conv1_no_bias = True
elif arch == 'he':
stride_first = True
conv1_no_bias = False
else:
raise ValueError('arch is expected to be one of [\'he\', \'fb\']')
_models = self._models[arch][n_layer]
blocks = self._blocks[n_layer]
if n_class is None:
if pretrained_model in _models:
n_class = _models[pretrained_model]['n_class']
else:
n_class = 1000
if mean is None:
if pretrained_model in _models:
mean = _models[pretrained_model]['mean']
else:
mean = _imagenet_mean
self.mean = mean
if initialW is None:
initialW = HeNormal(scale=1., fan_option='fan_out')
if 'initialW' not in fc_kwargs:
fc_kwargs['initialW'] = initializers.Normal(scale=0.01)
if pretrained_model:
# As a sampling process is time-consuming,
# we employ a zero initializer for faster computation.
initialW = initializers.constant.Zero()
fc_kwargs['initialW'] = initializers.constant.Zero()
kwargs = {'initialW': initialW, 'stride_first': stride_first}
super(ResNet, self).__init__()
with self.init_scope():
self.conv1 = Conv2DBNActiv(None,
64,
7,
2,
3,
nobias=conv1_no_bias,
initialW=initialW)
self.pool1 = lambda x: F.max_pooling_2d(x, ksize=3, stride=2)
self.res2 = ResBlock(blocks[0], None, 64, 256, 1, **kwargs)
self.res3 = ResBlock(blocks[1], None, 128, 512, 2, **kwargs)
self.res4 = ResBlock(blocks[2], None, 256, 1024, 2, **kwargs)
self.res5 = ResBlock(blocks[3], None, 512, 2048, 2, **kwargs)
self.pool5 = _global_average_pooling_2d
self.fc6 = L.Linear(None, n_class, **fc_kwargs)
self.prob = F.softmax
if pretrained_model in _models:
if not _available:
warnings.warn(
'cv2 is not installed on your environment. '
'The scores of ResNets reported in the '
'README of the ChainerCV\'s classification '
'example are calculated using OpenCV as the '
'backend. With Pillow as the '
'backend, the scores would change.', RuntimeWarning)
path = download_model(_models[pretrained_model]['url'])
chainer.serializers.load_npz(path, self)
elif pretrained_model:
chainer.serializers.load_npz(pretrained_model, self)
def __init__(self,
n_layer,
n_class=None,
pretrained_model=None,
mean=None,
initialW=None,
arch='fb'):
if arch == 'fb':
if pretrained_model == 'imagenet':
raise ValueError(
'Pretrained weights for Facebook ResNet models '
'are not supported. Please set mode to \'he\'.')
stride_first = False
conv1_no_bias = True
elif arch == 'he':
stride_first = True
conv1_no_bias = False
else:
raise ValueError('arch is expected to be one of [\'he\', \'fb\']')
_models = self._models[arch][n_layer]
blocks = self._blocks[n_layer]
if n_class is None:
if pretrained_model in _models:
n_class = _models[pretrained_model]['n_class']
else:
n_class = 1000
if mean is None:
if pretrained_model in _models:
mean = _models[pretrained_model]['mean']
else:
mean = _imagenet_mean
self.mean = mean
if initialW is None:
conv_initialW = HeNormal(scale=1., fan_option='fan_out')
fc_initialW = initializers.Normal(scale=0.01)
if pretrained_model:
# As a sampling process is time-consuming,
# we employ a zero initializer for faster computation.
initialW = initializers.constant.Zero()
kwargs = {'initialW': conv_initialW, 'stride_first': stride_first}
super(ResNet, self).__init__()
with self.init_scope():
self.conv1 = Conv2DBNActiv(None,
64,
7,
2,
3,
nobias=conv1_no_bias,
initialW=conv_initialW)
self.pool1 = lambda x: F.max_pooling_2d(x, ksize=3, stride=2)
self.res2 = ResBlock(blocks[0], None, 64, 256, 1, **kwargs)
self.res3 = ResBlock(blocks[1], None, 128, 512, 2, **kwargs)
self.res4 = ResBlock(blocks[2], None, 256, 1024, 2, **kwargs)
self.res5 = ResBlock(blocks[3], None, 512, 2048, 2, **kwargs)
self.pool5 = _global_average_pooling_2d
self.fc6 = L.Linear(None, n_class, initialW=fc_initialW)
self.prob = F.softmax
if pretrained_model in _models:
path = download_model(_models[pretrained_model]['url'])
chainer.serializers.load_npz(path, self)
elif pretrained_model:
chainer.serializers.load_npz(pretrained_model, self)