Source code for gluoncv.model_zoo.fcn
"""Fully Convolutional Network with Stride of 8"""
from __future__ import division
from mxnet.gluon import nn
from mxnet.context import cpu
from mxnet.gluon.nn import HybridBlock
from .segbase import SegBaseModel
# pylint: disable=unused-argument,abstract-method,missing-docstring
__all__ = ['FCN', 'get_fcn', 'get_fcn_resnet50_voc', 'get_fcn_resnet101_voc',
'get_fcn_resnet101_coco', 'get_fcn_resnet50_ade', 'get_fcn_resnet101_ade']
[docs]class FCN(SegBaseModel):
r"""Fully Convolutional Networks for Semantic Segmentation
Parameters
----------
nclass : int
Number of categories for the training dataset.
backbone : string
Pre-trained dilated backbone network type (default:'resnet50'; 'resnet50',
'resnet101' or 'resnet152').
norm_layer : object
Normalization layer used in backbone network (default: :class:`mxnet.gluon.nn.BatchNorm`;
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
pretrained_base : bool or str
Refers to if the FCN backbone or the encoder is pretrained or not. If `True`,
model weights of a model that was trained on ImageNet is loaded.
Reference:
Long, Jonathan, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks
for semantic segmentation." *CVPR*, 2015
Examples
--------
>>> model = FCN(nclass=21, backbone='resnet50')
>>> print(model)
"""
# pylint: disable=arguments-differ
def __init__(self, nclass, backbone='resnet50', aux=True, ctx=cpu(), pretrained_base=True,
base_size=520, crop_size=480, **kwargs):
super(FCN, self).__init__(nclass, aux, backbone, ctx=ctx, base_size=base_size,
crop_size=crop_size, pretrained_base=pretrained_base, **kwargs)
with self.name_scope():
self.head = _FCNHead(2048, nclass, **kwargs)
self.head.initialize(ctx=ctx)
self.head.collect_params().setattr('lr_mult', 10)
if self.aux:
self.auxlayer = _FCNHead(1024, nclass, **kwargs)
self.auxlayer.initialize(ctx=ctx)
self.auxlayer.collect_params().setattr('lr_mult', 10)
[docs] def hybrid_forward(self, F, x):
c3, c4 = self.base_forward(x)
outputs = []
x = self.head(c4)
x = F.contrib.BilinearResize2D(x, **self._up_kwargs)
outputs.append(x)
if self.aux:
auxout = self.auxlayer(c3)
auxout = F.contrib.BilinearResize2D(auxout, **self._up_kwargs)
outputs.append(auxout)
return tuple(outputs)
class _FCNHead(HybridBlock):
# pylint: disable=redefined-outer-name
def __init__(self, in_channels, channels, norm_layer=nn.BatchNorm, norm_kwargs=None, **kwargs):
super(_FCNHead, self).__init__()
with self.name_scope():
self.block = nn.HybridSequential()
inter_channels = in_channels // 4
with self.block.name_scope():
self.block.add(nn.Conv2D(in_channels=in_channels, channels=inter_channels,
kernel_size=3, padding=1, use_bias=False))
self.block.add(norm_layer(in_channels=inter_channels,
**({} if norm_kwargs is None else norm_kwargs)))
self.block.add(nn.Activation('relu'))
self.block.add(nn.Dropout(0.1))
self.block.add(nn.Conv2D(in_channels=inter_channels, channels=channels,
kernel_size=1))
# pylint: disable=arguments-differ
def hybrid_forward(self, F, x):
return self.block(x)
[docs]def get_fcn(dataset='pascal_voc', backbone='resnet50', pretrained=False,
root='~/.mxnet/models', ctx=cpu(0), pretrained_base=True, **kwargs):
r"""FCN model from the paper `"Fully Convolutional Network for semantic segmentation"
<https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf>`_
Parameters
----------
dataset : str, default pascal_voc
The dataset that model pretrained on. (pascal_voc, ade20k)
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '~/.mxnet/models'
Location for keeping the model parameters.
pretrained_base : bool or str, default True
This will load pretrained backbone network, that was trained on ImageNet.
Examples
--------
>>> model = get_fcn(dataset='pascal_voc', backbone='resnet50', pretrained=False)
>>> print(model)
"""
acronyms = {
'pascal_voc': 'voc',
'pascal_aug': 'voc',
'ade20k': 'ade',
'coco': 'coco',
}
from ..data import datasets
# infer number of classes
model = FCN(datasets[dataset].NUM_CLASS, backbone=backbone, pretrained_base=pretrained_base,
ctx=ctx, **kwargs)
model.classes = datasets[dataset].CLASSES
if pretrained:
from .model_store import get_model_file
model.load_parameters(get_model_file(
'fcn_%s_%s'%(backbone, acronyms[dataset]), tag=pretrained, root=root), ctx=ctx)
return model
[docs]def get_fcn_resnet50_voc(**kwargs):
r"""FCN model with base network ResNet-50 pre-trained on Pascal VOC dataset
from the paper `"Fully Convolutional Network for semantic segmentation"
<https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf>`_
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '~/.mxnet/models'
Location for keeping the model parameters.
Examples
--------
>>> model = get_fcn_resnet50_voc(pretrained=True)
>>> print(model)
"""
return get_fcn('pascal_voc', 'resnet50', **kwargs)
[docs]def get_fcn_resnet101_coco(**kwargs):
r"""FCN model with base network ResNet-101 pre-trained on Pascal VOC dataset
from the paper `"Fully Convolutional Network for semantic segmentation"
<https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf>`_
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '~/.mxnet/models'
Location for keeping the model parameters.
Examples
--------
>>> model = get_fcn_resnet101_coco(pretrained=True)
>>> print(model)
"""
return get_fcn('coco', 'resnet101', **kwargs)
[docs]def get_fcn_resnet101_voc(**kwargs):
r"""FCN model with base network ResNet-101 pre-trained on Pascal VOC dataset
from the paper `"Fully Convolutional Network for semantic segmentation"
<https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf>`_
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '~/.mxnet/models'
Location for keeping the model parameters.
Examples
--------
>>> model = get_fcn_resnet101_voc(pretrained=True)
>>> print(model)
"""
return get_fcn('pascal_voc', 'resnet101', **kwargs)
[docs]def get_fcn_resnet50_ade(**kwargs):
r"""FCN model with base network ResNet-50 pre-trained on ADE20K dataset
from the paper `"Fully Convolutional Network for semantic segmentation"
<https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf>`_
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '~/.mxnet/models'
Location for keeping the model parameters.
Examples
--------
>>> model = get_fcn_resnet50_ade(pretrained=True)
>>> print(model)
"""
return get_fcn('ade20k', 'resnet50', **kwargs)
[docs]def get_fcn_resnet101_ade(**kwargs):
r"""FCN model with base network ResNet-50 pre-trained on ADE20K dataset
from the paper `"Fully Convolutional Network for semantic segmentation"
<https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf>`_
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '~/.mxnet/models'
Location for keeping the model parameters.
Examples
--------
>>> model = get_fcn_resnet50_ade(pretrained=True)
>>> print(model)
"""
return get_fcn('ade20k', 'resnet101', **kwargs)