Source code for gluoncv.model_zoo.pspnet
# pylint: disable=unused-argument
"""Pyramid Scene Parsing Network"""
from mxnet.gluon import nn
from mxnet.context import cpu
from mxnet.gluon.nn import HybridBlock
from .segbase import SegBaseModel
from .fcn import _FCNHead
# pylint: disable-all
__all__ = ['PSPNet', 'get_psp', 'get_psp_resnet101_coco', 'get_psp_resnet101_voc',
'get_psp_resnet50_ade', 'get_psp_resnet101_ade', 'get_psp_resnet101_citys']
[docs]class PSPNet(SegBaseModel):
r"""Pyramid Scene Parsing Network
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`;
for Synchronized Cross-GPU BachNormalization).
aux : bool
Auxiliary loss.
Reference:
Zhao, Hengshuang, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia.
"Pyramid scene parsing network." *CVPR*, 2017
"""
def __init__(self, nclass, backbone='resnet50', aux=True, ctx=cpu(), pretrained_base=True,
base_size=520, crop_size=480, **kwargs):
super(PSPNet, 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 = _PSPHead(nclass, feature_map_height=self._up_kwargs['height']//8,
feature_map_width=self._up_kwargs['width']//8, **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)
print('self.crop_size', self.crop_size)
[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)
def demo(self, x):
return self.predict(x)
def predict(self, x):
h, w = x.shape[2:]
self._up_kwargs['height'] = h
self._up_kwargs['width'] = w
c3, c4 = self.base_forward(x)
outputs = []
x = self.head.demo(c4)
import mxnet.ndarray as F
pred = F.contrib.BilinearResize2D(x, **self._up_kwargs)
return pred
def _PSP1x1Conv(in_channels, out_channels, norm_layer, norm_kwargs):
block = nn.HybridSequential()
with block.name_scope():
block.add(nn.Conv2D(in_channels=in_channels, channels=out_channels,
kernel_size=1, use_bias=False))
block.add(norm_layer(in_channels=out_channels, **({} if norm_kwargs is None else norm_kwargs)))
block.add(nn.Activation('relu'))
return block
class _PyramidPooling(HybridBlock):
def __init__(self, in_channels, height=60, width=60, **kwargs):
super(_PyramidPooling, self).__init__()
out_channels = int(in_channels/4)
self._up_kwargs = {'height': height, 'width': width}
with self.name_scope():
self.conv1 = _PSP1x1Conv(in_channels, out_channels, **kwargs)
self.conv2 = _PSP1x1Conv(in_channels, out_channels, **kwargs)
self.conv3 = _PSP1x1Conv(in_channels, out_channels, **kwargs)
self.conv4 = _PSP1x1Conv(in_channels, out_channels, **kwargs)
def pool(self, F, x, size):
return F.contrib.AdaptiveAvgPooling2D(x, output_size=size)
def upsample(self, F, x):
return F.contrib.BilinearResize2D(x, **self._up_kwargs)
def hybrid_forward(self, F, x):
feat1 = self.upsample(F, self.conv1(self.pool(F, x, 1)))
feat2 = self.upsample(F, self.conv2(self.pool(F, x, 2)))
feat3 = self.upsample(F, self.conv3(self.pool(F, x, 3)))
feat4 = self.upsample(F, self.conv4(self.pool(F, x, 6)))
return F.concat(x, feat1, feat2, feat3, feat4, dim=1)
def demo(self, x):
self._up_kwargs['height'] = x.shape[2]
self._up_kwargs['width'] = x.shape[3]
import mxnet.ndarray as F
feat1 = self.upsample(F, self.conv1(self.pool(F, x, 1)))
feat2 = self.upsample(F, self.conv2(self.pool(F, x, 2)))
feat3 = self.upsample(F, self.conv3(self.pool(F, x, 3)))
feat4 = self.upsample(F, self.conv4(self.pool(F, x, 6)))
return F.concat(x, feat1, feat2, feat3, feat4, dim=1)
class _PSPHead(HybridBlock):
def __init__(self, nclass, norm_layer=nn.BatchNorm, norm_kwargs=None,
feature_map_height=60, feature_map_width=60, **kwargs):
super(_PSPHead, self).__init__()
self.psp = _PyramidPooling(2048, height=feature_map_height, width=feature_map_width,
norm_layer=norm_layer,
norm_kwargs=norm_kwargs)
with self.name_scope():
self.block = nn.HybridSequential(prefix='')
self.block.add(nn.Conv2D(in_channels=4096, channels=512,
kernel_size=3, padding=1, use_bias=False))
self.block.add(norm_layer(in_channels=512, **({} 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=512, channels=nclass,
kernel_size=1))
def hybrid_forward(self, F, x):
x = self.psp(x)
return self.block(x)
def demo(self, x):
x = self.psp.demo(x)
return self.block(x)
[docs]def get_psp(dataset='pascal_voc', backbone='resnet50', pretrained=False,
root='~/.mxnet/models', ctx=cpu(0), pretrained_base=True, **kwargs):
r"""Pyramid Scene Parsing Network
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',
'citys': 'citys',
}
from ..data import datasets
# infer number of classes
model = PSPNet(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('psp_%s_%s'%(backbone, acronyms[dataset]),
tag=pretrained, root=root), ctx=ctx)
return model
[docs]def get_psp_resnet101_coco(**kwargs):
r"""Pyramid Scene Parsing Network
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_psp_resnet101_coco(pretrained=True)
>>> print(model)
"""
return get_psp('coco', 'resnet101', **kwargs)
[docs]def get_psp_resnet101_voc(**kwargs):
r"""Pyramid Scene Parsing Network
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_psp_resnet101_voc(pretrained=True)
>>> print(model)
"""
return get_psp('pascal_voc', 'resnet101', **kwargs)
[docs]def get_psp_resnet50_ade(**kwargs):
r"""Pyramid Scene Parsing Network
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_psp_resnet50_ade(pretrained=True)
>>> print(model)
"""
return get_psp('ade20k', 'resnet50', **kwargs)
[docs]def get_psp_resnet101_ade(**kwargs):
r"""Pyramid Scene Parsing Network
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_psp_resnet101_ade(pretrained=True)
>>> print(model)
"""
return get_psp('ade20k', 'resnet101', **kwargs)
[docs]def get_psp_resnet101_citys(**kwargs):
r"""Pyramid Scene Parsing Network
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_psp_resnet101_ade(pretrained=True)
>>> print(model)
"""
return get_psp('citys', 'resnet101', **kwargs)