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# coding: utf-8
# pylint: disable= arguments-differ
"""ResNets, implemented in Gluon."""
from __future__ import division
__all__ = ['ResNetV1', 'ResNetV2',
'BasicBlockV1', 'BasicBlockV2',
'BottleneckV1', 'BottleneckV2',
'resnet18_v1', 'resnet34_v1', 'resnet50_v1', 'resnet101_v1', 'resnet152_v1',
'resnet18_v2', 'resnet34_v2', 'resnet50_v2', 'resnet101_v2', 'resnet152_v2',
'get_resnet']
from ....context import cpu
from ...block import HybridBlock
from ... import nn
# Helpers
def _conv3x3(channels, stride, in_channels):
return nn.Conv2D(channels, kernel_size=3, strides=stride, padding=1,
use_bias=False, in_channels=in_channels)
# Blocks
[docs]class BasicBlockV1(HybridBlock):
r"""BasicBlock V1 from `"Deep Residual Learning for Image Recognition"
`_ paper.
This is used for ResNet V1 for 18, 34 layers.
Parameters
----------
channels : int
Number of output channels.
stride : int
Stride size.
downsample : bool, default False
Whether to downsample the input.
in_channels : int, default 0
Number of input channels. Default is 0, to infer from the graph.
"""
def __init__(self, channels, stride, downsample=False, in_channels=0, **kwargs):
super(BasicBlockV1, self).__init__(**kwargs)
self.body = nn.HybridSequential(prefix='')
self.body.add(_conv3x3(channels, stride, in_channels))
self.body.add(nn.BatchNorm())
self.body.add(nn.Activation('relu'))
self.body.add(_conv3x3(channels, 1, channels))
self.body.add(nn.BatchNorm())
if downsample:
self.downsample = nn.HybridSequential(prefix='')
self.downsample.add(nn.Conv2D(channels, kernel_size=1, strides=stride,
use_bias=False, in_channels=in_channels))
self.downsample.add(nn.BatchNorm())
else:
self.downsample = None
def hybrid_forward(self, F, x):
residual = x
x = self.body(x)
if self.downsample:
residual = self.downsample(residual)
x = F.Activation(residual+x, act_type='relu')
return x
[docs]class BottleneckV1(HybridBlock):
r"""Bottleneck V1 from `"Deep Residual Learning for Image Recognition"
`_ paper.
This is used for ResNet V1 for 50, 101, 152 layers.
Parameters
----------
channels : int
Number of output channels.
stride : int
Stride size.
downsample : bool, default False
Whether to downsample the input.
in_channels : int, default 0
Number of input channels. Default is 0, to infer from the graph.
"""
def __init__(self, channels, stride, downsample=False, in_channels=0, **kwargs):
super(BottleneckV1, self).__init__(**kwargs)
self.body = nn.HybridSequential(prefix='')
self.body.add(nn.Conv2D(channels//4, kernel_size=1, strides=1))
self.body.add(nn.BatchNorm())
self.body.add(nn.Activation('relu'))
self.body.add(_conv3x3(channels//4, stride, channels//4))
self.body.add(nn.BatchNorm())
self.body.add(nn.Activation('relu'))
self.body.add(nn.Conv2D(channels, kernel_size=1, strides=1))
self.body.add(nn.BatchNorm())
if downsample:
self.downsample = nn.HybridSequential(prefix='')
self.downsample.add(nn.Conv2D(channels, kernel_size=1, strides=stride,
use_bias=False, in_channels=in_channels))
self.downsample.add(nn.BatchNorm())
else:
self.downsample = None
def hybrid_forward(self, F, x):
residual = x
x = self.body(x)
if self.downsample:
residual = self.downsample(residual)
x = F.Activation(x + residual, act_type='relu')
return x
[docs]class BasicBlockV2(HybridBlock):
r"""BasicBlock V2 from
`"Identity Mappings in Deep Residual Networks"
`_ paper.
This is used for ResNet V2 for 18, 34 layers.
Parameters
----------
channels : int
Number of output channels.
stride : int
Stride size.
downsample : bool, default False
Whether to downsample the input.
in_channels : int, default 0
Number of input channels. Default is 0, to infer from the graph.
"""
def __init__(self, channels, stride, downsample=False, in_channels=0, **kwargs):
super(BasicBlockV2, self).__init__(**kwargs)
self.bn1 = nn.BatchNorm()
self.conv1 = _conv3x3(channels, stride, in_channels)
self.bn2 = nn.BatchNorm()
self.conv2 = _conv3x3(channels, 1, channels)
if downsample:
self.downsample = nn.Conv2D(channels, 1, stride, use_bias=False,
in_channels=in_channels)
else:
self.downsample = None
def hybrid_forward(self, F, x):
residual = x
x = self.bn1(x)
x = F.Activation(x, act_type='relu')
if self.downsample:
residual = self.downsample(x)
x = self.conv1(x)
x = self.bn2(x)
x = F.Activation(x, act_type='relu')
x = self.conv2(x)
return x + residual
[docs]class BottleneckV2(HybridBlock):
r"""Bottleneck V2 from
`"Identity Mappings in Deep Residual Networks"
`_ paper.
This is used for ResNet V2 for 50, 101, 152 layers.
Parameters
----------
channels : int
Number of output channels.
stride : int
Stride size.
downsample : bool, default False
Whether to downsample the input.
in_channels : int, default 0
Number of input channels. Default is 0, to infer from the graph.
"""
def __init__(self, channels, stride, downsample=False, in_channels=0, **kwargs):
super(BottleneckV2, self).__init__(**kwargs)
self.bn1 = nn.BatchNorm()
self.conv1 = nn.Conv2D(channels//4, kernel_size=1, strides=1, use_bias=False)
self.bn2 = nn.BatchNorm()
self.conv2 = _conv3x3(channels//4, stride, channels//4)
self.bn3 = nn.BatchNorm()
self.conv3 = nn.Conv2D(channels, kernel_size=1, strides=1, use_bias=False)
if downsample:
self.downsample = nn.Conv2D(channels, 1, stride, use_bias=False,
in_channels=in_channels)
else:
self.downsample = None
def hybrid_forward(self, F, x):
residual = x
x = self.bn1(x)
x = F.Activation(x, act_type='relu')
if self.downsample:
residual = self.downsample(x)
x = self.conv1(x)
x = self.bn2(x)
x = F.Activation(x, act_type='relu')
x = self.conv2(x)
x = self.bn3(x)
x = F.Activation(x, act_type='relu')
x = self.conv3(x)
return x + residual
# Nets
[docs]class ResNetV1(HybridBlock):
r"""ResNet V1 model from
`"Deep Residual Learning for Image Recognition"
`_ paper.
Parameters
----------
block : HybridBlock
Class for the residual block. Options are BasicBlockV1, BottleneckV1.
layers : list of int
Numbers of layers in each block
channels : list of int
Numbers of channels in each block. Length should be one larger than layers list.
classes : int, default 1000
Number of classification classes.
thumbnail : bool, default False
Enable thumbnail.
"""
def __init__(self, block, layers, channels, classes=1000, thumbnail=False, **kwargs):
super(ResNetV1, self).__init__(**kwargs)
assert len(layers) == len(channels) - 1
with self.name_scope():
self.features = nn.HybridSequential(prefix='')
if thumbnail:
self.features.add(_conv3x3(channels[0], 1, 3))
else:
self.features.add(nn.Conv2D(channels[0], 7, 2, 3, use_bias=False,
in_channels=3))
self.features.add(nn.BatchNorm())
self.features.add(nn.Activation('relu'))
self.features.add(nn.MaxPool2D(3, 2, 1))
for i, num_layer in enumerate(layers):
stride = 1 if i == 0 else 2
self.features.add(self._make_layer(block, num_layer, channels[i+1],
stride, i+1, in_channels=channels[i]))
self.classifier = nn.HybridSequential(prefix='')
self.classifier.add(nn.GlobalAvgPool2D())
self.classifier.add(nn.Flatten())
self.classifier.add(nn.Dense(classes, in_units=channels[-1]))
def _make_layer(self, block, layers, channels, stride, stage_index, in_channels=0):
layer = nn.HybridSequential(prefix='stage%d_'%stage_index)
with layer.name_scope():
layer.add(block(channels, stride, channels != in_channels, in_channels=in_channels,
prefix=''))
for _ in range(layers-1):
layer.add(block(channels, 1, False, in_channels=channels, prefix=''))
return layer
def hybrid_forward(self, F, x):
x = self.features(x)
x = self.classifier(x)
return x
[docs]class ResNetV2(HybridBlock):
r"""ResNet V2 model from
`"Identity Mappings in Deep Residual Networks"
`_ paper.
Parameters
----------
block : HybridBlock
Class for the residual block. Options are BasicBlockV1, BottleneckV1.
layers : list of int
Numbers of layers in each block
channels : list of int
Numbers of channels in each block. Length should be one larger than layers list.
classes : int, default 1000
Number of classification classes.
thumbnail : bool, default False
Enable thumbnail.
"""
def __init__(self, block, layers, channels, classes=1000, thumbnail=False, **kwargs):
super(ResNetV2, self).__init__(**kwargs)
assert len(layers) == len(channels) - 1
with self.name_scope():
self.features = nn.HybridSequential(prefix='')
self.features.add(nn.BatchNorm(scale=False, center=False))
if thumbnail:
self.features.add(_conv3x3(channels[0], 1, 3))
else:
self.features.add(nn.Conv2D(channels[0], 7, 2, 3, use_bias=False,
in_channels=3))
self.features.add(nn.BatchNorm())
self.features.add(nn.Activation('relu'))
self.features.add(nn.MaxPool2D(3, 2, 1))
in_channels = channels[0]
for i, num_layer in enumerate(layers):
stride = 1 if i == 0 else 2
self.features.add(self._make_layer(block, num_layer, channels[i+1],
stride, i+1, in_channels=in_channels))
in_channels = channels[i+1]
self.classifier = nn.HybridSequential(prefix='')
self.classifier.add(nn.BatchNorm())
self.classifier.add(nn.Activation('relu'))
self.classifier.add(nn.GlobalAvgPool2D())
self.classifier.add(nn.Flatten())
self.classifier.add(nn.Dense(classes, in_units=in_channels))
def _make_layer(self, block, layers, channels, stride, stage_index, in_channels=0):
layer = nn.HybridSequential(prefix='stage%d_'%stage_index)
with layer.name_scope():
layer.add(block(channels, stride, channels != in_channels, in_channels=in_channels,
prefix=''))
for _ in range(layers-1):
layer.add(block(channels, 1, False, in_channels=channels, prefix=''))
return layer
def hybrid_forward(self, F, x):
x = self.features(x)
x = self.classifier(x)
return x
# Specification
resnet_spec = {18: ('basic_block', [2, 2, 2, 2], [64, 64, 128, 256, 512]),
34: ('basic_block', [3, 4, 6, 3], [64, 64, 128, 256, 512]),
50: ('bottle_neck', [3, 4, 6, 3], [64, 256, 512, 1024, 2048]),
101: ('bottle_neck', [3, 4, 23, 3], [64, 256, 512, 1024, 2048]),
152: ('bottle_neck', [3, 8, 36, 3], [64, 256, 512, 1024, 2048])}
resnet_net_versions = [ResNetV1, ResNetV2]
resnet_block_versions = [{'basic_block': BasicBlockV1, 'bottle_neck': BottleneckV1},
{'basic_block': BasicBlockV2, 'bottle_neck': BottleneckV2}]
# Constructor
def get_resnet(version, num_layers, pretrained=False, ctx=cpu(), **kwargs):
r"""ResNet V1 model from `"Deep Residual Learning for Image Recognition"
`_ paper.
ResNet V2 model from `"Identity Mappings in Deep Residual Networks"
`_ paper.
Parameters
----------
version : int
Version of ResNet. Options are 1, 2.
num_layers : int
Numbers of layers. Options are 18, 34, 50, 101, 152.
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.
"""
block_type, layers, channels = resnet_spec[num_layers]
resnet_class = resnet_net_versions[version-1]
block_class = resnet_block_versions[version-1][block_type]
net = resnet_class(block_class, layers, channels, **kwargs)
if pretrained:
from ..model_store import get_model_file
net.load_params(get_model_file('resnet%d_v%d'%(num_layers, version)), ctx=ctx)
return net
def resnet18_v1(**kwargs):
r"""ResNet-18 V1 model from `"Deep Residual Learning for Image Recognition"
`_ 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.
"""
return get_resnet(1, 18, **kwargs)
def resnet34_v1(**kwargs):
r"""ResNet-34 V1 model from `"Deep Residual Learning for Image Recognition"
`_ 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.
"""
return get_resnet(1, 34, **kwargs)
def resnet50_v1(**kwargs):
r"""ResNet-50 V1 model from `"Deep Residual Learning for Image Recognition"
`_ 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.
"""
return get_resnet(1, 50, **kwargs)
def resnet101_v1(**kwargs):
r"""ResNet-101 V1 model from `"Deep Residual Learning for Image Recognition"
`_ 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.
"""
return get_resnet(1, 101, **kwargs)
def resnet152_v1(**kwargs):
r"""ResNet-152 V1 model from `"Deep Residual Learning for Image Recognition"
`_ 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.
"""
return get_resnet(1, 152, **kwargs)
def resnet18_v2(**kwargs):
r"""ResNet-18 V2 model from `"Identity Mappings in Deep Residual Networks"
`_ 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.
"""
return get_resnet(2, 18, **kwargs)
def resnet34_v2(**kwargs):
r"""ResNet-34 V2 model from `"Identity Mappings in Deep Residual Networks"
`_ 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.
"""
return get_resnet(2, 34, **kwargs)
def resnet50_v2(**kwargs):
r"""ResNet-50 V2 model from `"Identity Mappings in Deep Residual Networks"
`_ 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.
"""
return get_resnet(2, 50, **kwargs)
def resnet101_v2(**kwargs):
r"""ResNet-101 V2 model from `"Identity Mappings in Deep Residual Networks"
`_ 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.
"""
return get_resnet(2, 101, **kwargs)
def resnet152_v2(**kwargs):
r"""ResNet-152 V2 model from `"Identity Mappings in Deep Residual Networks"
`_ 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.
"""
return get_resnet(2, 152, **kwargs)
