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Posted to issues@mxnet.apache.org by GitBox <gi...@apache.org> on 2020/12/27 04:27:17 UTC
[GitHub] [incubator-mxnet] chinakook edited a comment on issue #19649: Results are significant different between RTX 2080Ti and RTX 3090
chinakook edited a comment on issue #19649:
URL: https://github.com/apache/incubator-mxnet/issues/19649#issuecomment-751424572
The result also varies in mxnet_cu110-2.0.0b20201226
Result 1 on RTX 3090 on mxnet_cu110-2.0.0b20201226
```
1.41821623e+00 -6.14694595e-01 -1.21822190e+00 1.47472918e+00
1.08678900e-01 -1.53905892e+00 -2.19664723e-01 9.48607504e-01
9.76179004e-01 1.70066428e+00 8.15666854e-01 -1.23275781e+00
1.59943473e+00 6.92619503e-01 -1.52998209e+00 -1.63329318e-01
-7.86948949e-02 2.69214898e-01 -6.79625511e-01 1.63082540e-01
1.30359614e+00 3.54878873e-01 3.44506621e-01 -1.63622832e+00
-1.83121693e+00 -2.71499276e+00 -1.90867770e+00 -1.56530845e+00
-2.34865284e+00 -8.75126600e-01 -1.44264027e-02 2.31574321e+00
```
Result 2 on RTX 3090 on mxnet_cu110-2.0.0b20201226
```
1.41812336e+00 -6.14903927e-01 -1.21819293e+00 1.47481430e+00
1.08835243e-01 -1.53912401e+00 -2.19649285e-01 9.48447049e-01
9.76122022e-01 1.70034528e+00 8.15561593e-01 -1.23293483e+00
1.59933603e+00 6.92907691e-01 -1.53025889e+00 -1.63300052e-01
-7.87986293e-02 2.69500673e-01 -6.79565012e-01 1.62798882e-01
1.30361140e+00 3.54955018e-01 3.44288290e-01 -1.63627052e+00
-1.83101904e+00 -2.71485925e+00 -1.90862215e+00 -1.56534243e+00
-2.34861803e+00 -8.75208020e-01 -1.46629252e-02 2.31575775e+00
```
Test script for mxnet 2.0.0 master, use resent18_v1 to test between the results on cpu and gpu to address the problem.
```python
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
# coding: utf-8
# pylint: disable= arguments-differ,unused-argument,missing-docstring,too-many-lines
"""ResNets, implemented in Gluon."""
from __future__ import division
import gluoncv as gcv
__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',
'se_resnet18_v1', 'se_resnet34_v1', 'se_resnet50_v1',
'se_resnet101_v1', 'se_resnet152_v1',
'se_resnet18_v2', 'se_resnet34_v2', 'se_resnet50_v2',
'se_resnet101_v2', 'se_resnet152_v2',
'get_resnet']
from mxnet.context import cpu
from mxnet.gluon.block import HybridBlock
from mxnet.gluon import nn
from mxnet.gluon.nn import BatchNorm
from mxnet import base
from mxnet.util import is_np_array
# 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
class BasicBlockV1(HybridBlock):
r"""BasicBlock V1 from `"Deep Residual Learning for Image Recognition"
<http://arxiv.org/abs/1512.03385>`_ 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.
last_gamma : bool, default False
Whether to initialize the gamma of the last BatchNorm layer in each bottleneck to zero.
use_se : bool, default False
Whether to use Squeeze-and-Excitation module
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
def __init__(self, channels, stride, downsample=False, in_channels=0,
last_gamma=False, use_se=False, norm_layer=BatchNorm, norm_kwargs=None, **kwargs):
super(BasicBlockV1, self).__init__(**kwargs)
self.body = nn.HybridSequential()
self.body.add(_conv3x3(channels, stride, in_channels))
self.body.add(norm_layer(**({} if norm_kwargs is None else norm_kwargs)))
self.body.add(nn.Activation('relu'))
self.body.add(_conv3x3(channels, 1, channels))
if not last_gamma:
self.body.add(norm_layer(**({} if norm_kwargs is None else norm_kwargs)))
else:
self.body.add(norm_layer(gamma_initializer='zeros',
**({} if norm_kwargs is None else norm_kwargs)))
if use_se:
self.se = nn.HybridSequential()
self.se.add(nn.Dense(channels // 16, use_bias=False))
self.se.add(nn.Activation('relu'))
self.se.add(nn.Dense(channels, use_bias=False))
self.se.add(nn.Activation('sigmoid'))
else:
self.se = None
if downsample:
self.downsample = nn.HybridSequential()
self.downsample.add(nn.Conv2D(channels, kernel_size=1, strides=stride,
use_bias=False, in_channels=in_channels))
self.downsample.add(norm_layer(**({} if norm_kwargs is None else norm_kwargs)))
else:
self.downsample = None
def hybrid_forward(self, F, x):
residual = x
x = self.body(x)
if self.se:
w = F.contrib.AdaptiveAvgPooling2D(x, output_size=1)
w = self.se(w)
x = F.broadcast_mul(x, w.expand_dims(axis=2).expand_dims(axis=2))
if self.downsample:
residual = self.downsample(residual)
act = F.npx.activation if is_np_array() else F.Activation
x = act(residual+x, act_type='relu')
return x
class BottleneckV1(HybridBlock):
r"""Bottleneck V1 from `"Deep Residual Learning for Image Recognition"
<http://arxiv.org/abs/1512.03385>`_ 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.
last_gamma : bool, default False
Whether to initialize the gamma of the last BatchNorm layer in each bottleneck to zero.
use_se : bool, default False
Whether to use Squeeze-and-Excitation module
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
def __init__(self, channels, stride, downsample=False, in_channels=0,
last_gamma=False, use_se=False, norm_layer=BatchNorm, norm_kwargs=None, **kwargs):
super(BottleneckV1, self).__init__(**kwargs)
self.body = nn.HybridSequential()
self.body.add(nn.Conv2D(channels//4, kernel_size=1, strides=1, use_bias=False))
self.body.add(norm_layer(**({} if norm_kwargs is None else norm_kwargs)))
self.body.add(nn.Activation('relu'))
self.body.add(_conv3x3(channels//4, stride, channels//4))
self.body.add(norm_layer(**({} if norm_kwargs is None else norm_kwargs)))
self.body.add(nn.Activation('relu'))
self.body.add(nn.Conv2D(channels, kernel_size=1, strides=1, use_bias=False))
if use_se:
self.se = nn.HybridSequential()
self.se.add(nn.Dense(channels // 16, use_bias=False))
self.se.add(nn.Activation('relu'))
self.se.add(nn.Dense(channels, use_bias=False))
self.se.add(nn.Activation('sigmoid'))
else:
self.se = None
if not last_gamma:
self.body.add(norm_layer(**({} if norm_kwargs is None else norm_kwargs)))
else:
self.body.add(norm_layer(gamma_initializer='zeros',
**({} if norm_kwargs is None else norm_kwargs)))
if downsample:
self.downsample = nn.HybridSequential()
self.downsample.add(nn.Conv2D(channels, kernel_size=1, strides=stride,
use_bias=False, in_channels=in_channels))
self.downsample.add(norm_layer(**({} if norm_kwargs is None else norm_kwargs)))
else:
self.downsample = None
def hybrid_forward(self, F, x):
residual = x
x = self.body(x)
if self.se:
w = F.contrib.AdaptiveAvgPooling2D(x, output_size=1)
w = self.se(w)
x = F.broadcast_mul(x, w.expand_dims(axis=2).expand_dims(axis=2))
if self.downsample:
residual = self.downsample(residual)
act = F.npx.activation if is_np_array() else F.Activation
x = act(x + residual, act_type='relu')
return x
class BasicBlockV2(HybridBlock):
r"""BasicBlock V2 from
`"Identity Mappings in Deep Residual Networks"
<https://arxiv.org/abs/1603.05027>`_ 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.
last_gamma : bool, default False
Whether to initialize the gamma of the last BatchNorm layer in each bottleneck to zero.
use_se : bool, default False
Whether to use Squeeze-and-Excitation module
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
def __init__(self, channels, stride, downsample=False, in_channels=0,
last_gamma=False, use_se=False,
norm_layer=BatchNorm, norm_kwargs=None, **kwargs):
super(BasicBlockV2, self).__init__(**kwargs)
self.bn1 = norm_layer(**({} if norm_kwargs is None else norm_kwargs))
self.conv1 = _conv3x3(channels, stride, in_channels)
if not last_gamma:
self.bn2 = norm_layer(**({} if norm_kwargs is None else norm_kwargs))
else:
self.bn2 = norm_layer(gamma_initializer='zeros',
**({} if norm_kwargs is None else norm_kwargs))
self.conv2 = _conv3x3(channels, 1, channels)
if use_se:
self.se = nn.HybridSequential()
self.se.add(nn.Dense(channels // 16, use_bias=False))
self.se.add(nn.Activation('relu'))
self.se.add(nn.Dense(channels, use_bias=False))
self.se.add(nn.Activation('sigmoid'))
else:
self.se = None
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)
act = F.npx.activation if is_np_array() else F.Activation
x = act(x, act_type='relu')
if self.downsample:
residual = self.downsample(x)
x = self.conv1(x)
x = self.bn2(x)
x = act(x, act_type='relu')
x = self.conv2(x)
if self.se:
w = F.contrib.AdaptiveAvgPooling2D(x, output_size=1)
w = self.se(w)
x = F.broadcast_mul(x, w.expand_dims(axis=2).expand_dims(axis=2))
return x + residual
class BottleneckV2(HybridBlock):
r"""Bottleneck V2 from
`"Identity Mappings in Deep Residual Networks"
<https://arxiv.org/abs/1603.05027>`_ 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.
last_gamma : bool, default False
Whether to initialize the gamma of the last BatchNorm layer in each bottleneck to zero.
use_se : bool, default False
Whether to use Squeeze-and-Excitation module
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
def __init__(self, channels, stride, downsample=False, in_channels=0,
last_gamma=False, use_se=False, norm_layer=BatchNorm, norm_kwargs=None, **kwargs):
super(BottleneckV2, self).__init__(**kwargs)
self.bn1 = norm_layer(**({} if norm_kwargs is None else norm_kwargs))
self.conv1 = nn.Conv2D(channels//4, kernel_size=1, strides=1, use_bias=False)
self.bn2 = norm_layer(**({} if norm_kwargs is None else norm_kwargs))
self.conv2 = _conv3x3(channels//4, stride, channels//4)
if not last_gamma:
self.bn3 = norm_layer(**({} if norm_kwargs is None else norm_kwargs))
else:
self.bn3 = norm_layer(gamma_initializer='zeros',
**({} if norm_kwargs is None else norm_kwargs))
self.conv3 = nn.Conv2D(channels, kernel_size=1, strides=1, use_bias=False)
if use_se:
self.se = nn.HybridSequential()
self.se.add(nn.Dense(channels // 16, use_bias=False))
self.se.add(nn.Activation('relu'))
self.se.add(nn.Dense(channels, use_bias=False))
self.se.add(nn.Activation('sigmoid'))
else:
self.se = None
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)
act = F.npx.activation if is_np_array() else F.Activation
x = act(x, act_type='relu')
if self.downsample:
residual = self.downsample(x)
x = self.conv1(x)
x = self.bn2(x)
x = act(x, act_type='relu')
x = self.conv2(x)
x = self.bn3(x)
x = act(x, act_type='relu')
x = self.conv3(x)
if self.se:
w = F.contrib.AdaptiveAvgPooling2D(x, output_size=1)
w = self.se(w)
x = F.broadcast_mul(x, w.expand_dims(axis=2).expand_dims(axis=2))
return x + residual
# Nets
class ResNetV1(HybridBlock):
r"""ResNet V1 model from
`"Deep Residual Learning for Image Recognition"
<http://arxiv.org/abs/1512.03385>`_ 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.
last_gamma : bool, default False
Whether to initialize the gamma of the last BatchNorm layer in each bottleneck to zero.
use_se : bool, default False
Whether to use Squeeze-and-Excitation module
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
def __init__(self, block, layers, channels, classes=1000, thumbnail=False,
last_gamma=False, use_se=False, norm_layer=BatchNorm, norm_kwargs=None, **kwargs):
super(ResNetV1, self).__init__(**kwargs)
assert len(layers) == len(channels) - 1
self.features = nn.HybridSequential()
if thumbnail:
self.features.add(_conv3x3(channels[0], 1, 0))
else:
self.features.add(nn.Conv2D(channels[0], 7, 2, 3, use_bias=False))
self.features.add(norm_layer(**({} if norm_kwargs is None else norm_kwargs)))
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],
last_gamma=last_gamma, use_se=use_se,
norm_layer=norm_layer, norm_kwargs=norm_kwargs))
self.features.add(nn.GlobalAvgPool2D())
self.output = nn.Dense(classes, in_units=channels[-1])
def _make_layer(self, block, layers, channels, stride, stage_index, in_channels=0,
last_gamma=False, use_se=False, norm_layer=BatchNorm, norm_kwargs=None):
layer = nn.HybridSequential()
layer.add(block(channels, stride, channels != in_channels, in_channels=in_channels,
last_gamma=last_gamma, use_se=use_se,
norm_layer=norm_layer, norm_kwargs=norm_kwargs))
for _ in range(layers-1):
layer.add(block(channels, 1, False, in_channels=channels,
last_gamma=last_gamma, use_se=use_se,
norm_layer=norm_layer, norm_kwargs=norm_kwargs))
return layer
def hybrid_forward(self, F, x):
x = self.features(x)
x = self.output(x)
return x
class ResNetV2(HybridBlock):
r"""ResNet V2 model from
`"Identity Mappings in Deep Residual Networks"
<https://arxiv.org/abs/1603.05027>`_ 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.
last_gamma : bool, default False
Whether to initialize the gamma of the last BatchNorm layer in each bottleneck to zero.
use_se : bool, default False
Whether to use Squeeze-and-Excitation module
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
def __init__(self, block, layers, channels, classes=1000, thumbnail=False,
last_gamma=False, use_se=False, norm_layer=BatchNorm, norm_kwargs=None, **kwargs):
super(ResNetV2, self).__init__(**kwargs)
assert len(layers) == len(channels) - 1
self.features = nn.HybridSequential()
self.features.add(norm_layer(scale=False, center=False,
**({} if norm_kwargs is None else norm_kwargs)))
if thumbnail:
self.features.add(_conv3x3(channels[0], 1, 0))
else:
self.features.add(nn.Conv2D(channels[0], 7, 2, 3, use_bias=False))
self.features.add(norm_layer(**({} if norm_kwargs is None else norm_kwargs)))
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,
last_gamma=last_gamma, use_se=use_se,
norm_layer=norm_layer, norm_kwargs=norm_kwargs))
in_channels = channels[i+1]
self.features.add(norm_layer(**({} if norm_kwargs is None else norm_kwargs)))
self.features.add(nn.Activation('relu'))
self.features.add(nn.GlobalAvgPool2D())
self.features.add(nn.Flatten())
self.output = nn.Dense(classes, in_units=in_channels)
def _make_layer(self, block, layers, channels, stride, stage_index, in_channels=0,
last_gamma=False, use_se=False, norm_layer=BatchNorm, norm_kwargs=None):
layer = nn.HybridSequential()
layer.add(block(channels, stride, channels != in_channels, in_channels=in_channels,
last_gamma=last_gamma, use_se=use_se,
norm_layer=norm_layer, norm_kwargs=norm_kwargs))
for _ in range(layers-1):
layer.add(block(channels, 1, False, in_channels=channels,
last_gamma=last_gamma, use_se=use_se,
norm_layer=norm_layer, norm_kwargs=norm_kwargs))
return layer
def hybrid_forward(self, F, x):
x = self.features(x)
x = self.output(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(),
root='~/.mxnet/models', use_se=False, **kwargs):
r"""ResNet V1 model from `"Deep Residual Learning for Image Recognition"
<http://arxiv.org/abs/1512.03385>`_ paper.
ResNet V2 model from `"Identity Mappings in Deep Residual Networks"
<https://arxiv.org/abs/1603.05027>`_ 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 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 $MXNET_HOME/models
Location for keeping the model parameters.
use_se : bool, default False
Whether to use Squeeze-and-Excitation module
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
assert num_layers in resnet_spec, \
"Invalid number of layers: %d. Options are %s"%(
num_layers, str(resnet_spec.keys()))
block_type, layers, channels = resnet_spec[num_layers]
assert 1 <= version <= 2, \
"Invalid resnet version: %d. Options are 1 and 2."%version
resnet_class = resnet_net_versions[version-1]
block_class = resnet_block_versions[version-1][block_type]
net = resnet_class(block_class, layers, channels, use_se=use_se, **kwargs)
if pretrained:
from gluoncv.model_zoo.model_store import get_model_file
if not use_se:
net.load_parameters(get_model_file('resnet%d_v%d'%(num_layers, version),
tag=pretrained, root=root), ctx=ctx)
else:
net.load_parameters(get_model_file('se_resnet%d_v%d'%(num_layers, version),
tag=pretrained, root=root), ctx=ctx)
from gluoncv.data import ImageNet1kAttr
attrib = ImageNet1kAttr()
net.synset = attrib.synset
net.classes = attrib.classes
net.classes_long = attrib.classes_long
return net
def resnet18_v1(**kwargs):
r"""ResNet-18 V1 model from `"Deep Residual Learning for Image Recognition"
<http://arxiv.org/abs/1512.03385>`_ paper.
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
return get_resnet(1, 18, use_se=False, **kwargs)
def resnet34_v1(**kwargs):
r"""ResNet-34 V1 model from `"Deep Residual Learning for Image Recognition"
<http://arxiv.org/abs/1512.03385>`_ paper.
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 '$MXNET_HOME/models'
Location for keeping the model parameters.
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
return get_resnet(1, 34, use_se=False, **kwargs)
def resnet50_v1(**kwargs):
r"""ResNet-50 V1 model from `"Deep Residual Learning for Image Recognition"
<http://arxiv.org/abs/1512.03385>`_ paper.
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
return get_resnet(1, 50, use_se=False, **kwargs)
def resnet101_v1(**kwargs):
r"""ResNet-101 V1 model from `"Deep Residual Learning for Image Recognition"
<http://arxiv.org/abs/1512.03385>`_ paper.
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 '$MXNET_HOME/models'
Location for keeping the model parameters.
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
return get_resnet(1, 101, use_se=False, **kwargs)
def resnet152_v1(**kwargs):
r"""ResNet-152 V1 model from `"Deep Residual Learning for Image Recognition"
<http://arxiv.org/abs/1512.03385>`_ paper.
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
return get_resnet(1, 152, use_se=False, **kwargs)
def resnet18_v2(**kwargs):
r"""ResNet-18 V2 model from `"Identity Mappings in Deep Residual Networks"
<https://arxiv.org/abs/1603.05027>`_ paper.
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 '$MXNET_HOME/models'
Location for keeping the model parameters.
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
return get_resnet(2, 18, use_se=False, **kwargs)
def resnet34_v2(**kwargs):
r"""ResNet-34 V2 model from `"Identity Mappings in Deep Residual Networks"
<https://arxiv.org/abs/1603.05027>`_ paper.
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
return get_resnet(2, 34, use_se=False, **kwargs)
def resnet50_v2(**kwargs):
r"""ResNet-50 V2 model from `"Identity Mappings in Deep Residual Networks"
<https://arxiv.org/abs/1603.05027>`_ paper.
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 '$MXNET_HOME/models'
Location for keeping the model parameters.
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
return get_resnet(2, 50, use_se=False, **kwargs)
def resnet101_v2(**kwargs):
r"""ResNet-101 V2 model from `"Identity Mappings in Deep Residual Networks"
<https://arxiv.org/abs/1603.05027>`_ paper.
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
return get_resnet(2, 101, use_se=False, **kwargs)
def resnet152_v2(**kwargs):
r"""ResNet-152 V2 model from `"Identity Mappings in Deep Residual Networks"
<https://arxiv.org/abs/1603.05027>`_ paper.
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 '$MXNET_HOME/models'
Location for keeping the model parameters.
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
return get_resnet(2, 152, use_se=False, **kwargs)
# SE-ResNet
def se_resnet18_v1(**kwargs):
r"""SE-ResNet-18 V1 model from `"Squeeze-and-Excitation Networks"
<https://arxiv.org/abs/1709.01507>`_ paper.
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
return get_resnet(1, 18, use_se=True, **kwargs)
def se_resnet34_v1(**kwargs):
r"""SE-ResNet-34 V1 model from `"Squeeze-and-Excitation Networks"
<https://arxiv.org/abs/1709.01507>`_ paper.
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 '$MXNET_HOME/models'
Location for keeping the model parameters.
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
return get_resnet(1, 34, use_se=True, **kwargs)
def se_resnet50_v1(**kwargs):
r"""SE-ResNet-50 V1 model from `"Squeeze-and-Excitation Networks"
<https://arxiv.org/abs/1709.01507>`_ paper.
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
return get_resnet(1, 50, use_se=True, **kwargs)
def se_resnet101_v1(**kwargs):
r"""SE-ResNet-101 V1 model from `"Squeeze-and-Excitation Networks"
<https://arxiv.org/abs/1709.01507>`_ paper.
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 '$MXNET_HOME/models'
Location for keeping the model parameters.
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
return get_resnet(1, 101, use_se=True, **kwargs)
def se_resnet152_v1(**kwargs):
r"""SE-ResNet-152 V1 model from `"Squeeze-and-Excitation Networks"
<https://arxiv.org/abs/1709.01507>`_ paper.
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
return get_resnet(1, 152, use_se=True, **kwargs)
def se_resnet18_v2(**kwargs):
r"""SE-ResNet-18 V2 model from `"Squeeze-and-Excitation Networks"
<https://arxiv.org/abs/1709.01507>`_ paper.
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 '$MXNET_HOME/models'
Location for keeping the model parameters.
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
return get_resnet(2, 18, use_se=True, **kwargs)
def se_resnet34_v2(**kwargs):
r"""SE-ResNet-34 V2 model from `"Squeeze-and-Excitation Networks"
<https://arxiv.org/abs/1709.01507>`_ paper.
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
return get_resnet(2, 34, use_se=True, **kwargs)
def se_resnet50_v2(**kwargs):
r"""SE-ResNet-50 V2 model from `"Squeeze-and-Excitation Networks"
<https://arxiv.org/abs/1709.01507>`_ paper.
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 '$MXNET_HOME/models'
Location for keeping the model parameters.
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
return get_resnet(2, 50, use_se=True, **kwargs)
def se_resnet101_v2(**kwargs):
r"""SE-ResNet-101 V2 model from `"Squeeze-and-Excitation Networks"
<https://arxiv.org/abs/1709.01507>`_ paper.
Parameters
----------
pretrained : bool or str
Boolean value controls whether to load the default pretrained weights for model.
String value represents the hashtag for a certain version of pretrained weights.
ctx : Context, default CPU
The context in which to load the pretrained weights.
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
return get_resnet(2, 101, use_se=True, **kwargs)
def se_resnet152_v2(**kwargs):
r"""SE-ResNet-152 V2 model from `"Squeeze-and-Excitation Networks"
<https://arxiv.org/abs/1709.01507>`_ paper.
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_HOME/models'
Location for keeping the model parameters.
norm_layer : object
Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
norm_kwargs : dict
Additional `norm_layer` arguments, for example `num_devices=4`
for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
"""
return get_resnet(2, 152, use_se=True, **kwargs)
```
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