Source code for gluoncv.model_zoo.action_recognition.actionrec_inceptionv1
# pylint: disable=line-too-long,too-many-lines,missing-docstring,arguments-differ,unused-argument
import mxnet as mx
from mxnet import init
from mxnet.gluon import nn
from mxnet.gluon.nn import HybridBlock
from gluoncv.model_zoo.googlenet import googlenet
__all__ = ['inceptionv1_ucf101', 'inceptionv1_hmdb51', 'inceptionv1_kinetics400',
'inceptionv1_sthsthv2']
class ActionRecInceptionV1(HybridBlock):
r"""Inception v1 model for video action recognition
Christian Szegedy, etal, Going Deeper with Convolutions, CVPR 2015
https://arxiv.org/abs/1409.4842
Limin Wang, etal, Towards Good Practices for Very Deep Two-Stream ConvNets, arXiv 2015
https://arxiv.org/abs/1507.02159
Limin Wang, etal, Temporal Segment Networks: Towards Good Practices for Deep Action Recognition, ECCV 2016
https://arxiv.org/abs/1608.00859
Parameters
----------
nclass : int
Number of classes in the training dataset.
pretrained_base : bool or str, optional, default is True.
Load pretrained base network, the extra layers are randomized. Note that
if pretrained is `True`, this has no effect.
partial_bn : bool, default False.
Freeze all batch normalization layers during training except the first layer.
dropout_ratio : float, default is 0.5.
The dropout rate of a dropout layer.
The larger the value, the more strength to prevent overfitting.
init_std : float, default is 0.001.
Standard deviation value when initialize the dense layers.
num_segments : int, default is 1.
Number of segments used to evenly divide a video.
num_crop : int, default is 1.
Number of crops used during evaluation, choices are 1, 3 or 10.
Input: a single video frame or N images from N segments when num_segments > 1
Output: a single predicted action label
"""
def __init__(self, nclass, pretrained_base=True,
partial_bn=True, dropout_ratio=0.5, init_std=0.001,
num_segments=1, num_crop=1, **kwargs):
super(ActionRecInceptionV1, self).__init__()
self.dropout_ratio = dropout_ratio
self.init_std = init_std
self.num_segments = num_segments
self.num_crop = num_crop
self.feat_dim = 1024
pretrained_model = googlenet(pretrained=pretrained_base, partial_bn=partial_bn, **kwargs)
self.conv1 = pretrained_model.conv1
self.maxpool1 = pretrained_model.maxpool1
self.conv2 = pretrained_model.conv2
self.conv3 = pretrained_model.conv3
self.maxpool2 = pretrained_model.maxpool2
self.inception3a = pretrained_model.inception3a
self.inception3b = pretrained_model.inception3b
self.maxpool3 = pretrained_model.maxpool3
self.inception4a = pretrained_model.inception4a
self.inception4b = pretrained_model.inception4b
self.inception4c = pretrained_model.inception4c
self.inception4d = pretrained_model.inception4d
self.inception4e = pretrained_model.inception4e
self.maxpool4 = pretrained_model.maxpool4
self.inception5a = pretrained_model.inception5a
self.inception5b = pretrained_model.inception5b
self.avgpool = nn.AvgPool2D(pool_size=7)
self.dropout = nn.Dropout(self.dropout_ratio)
self.output = nn.Dense(units=nclass, in_units=self.feat_dim,
weight_initializer=init.Normal(sigma=self.init_std))
self.output.initialize()
def hybrid_forward(self, F, x):
x = self.conv1(x)
x = self.maxpool1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.maxpool2(x)
x = self.inception3a(x)
x = self.inception3b(x)
x = self.maxpool3(x)
x = self.inception4a(x)
x = self.inception4b(x)
x = self.inception4c(x)
x = self.inception4d(x)
x = self.inception4e(x)
x = self.maxpool4(x)
x = self.inception5a(x)
x = self.inception5b(x)
x = self.avgpool(x)
x = self.dropout(x)
# segmental consensus
x = F.reshape(x, shape=(-1, self.num_segments * self.num_crop, self.feat_dim))
x = F.mean(x, axis=1)
x = self.output(x)
return x
[docs]def inceptionv1_ucf101(nclass=101, pretrained=False, pretrained_base=True,
use_tsn=False, num_segments=1, num_crop=1, partial_bn=True,
ctx=mx.cpu(), root='~/.mxnet/models', **kwargs):
r"""InceptionV1 model trained on UCF101 dataset.
Parameters
----------
nclass : int.
Number of categories in the dataset.
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.
pretrained_base : bool or str, optional, default is True.
Load pretrained base network, the extra layers are randomized. Note that
if pretrained is `True`, this has no effect.
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.
num_segments : int, default is 1.
Number of segments used to evenly divide a video.
num_crop : int, default is 1.
Number of crops used during evaluation, choices are 1, 3 or 10.
partial_bn : bool, default False.
Freeze all batch normalization layers during training except the first layer.
"""
model = ActionRecInceptionV1(nclass=nclass,
partial_bn=partial_bn,
pretrained_base=pretrained_base,
num_segments=num_segments,
num_crop=num_crop,
dropout_ratio=0.8,
init_std=0.001)
if pretrained:
from ..model_store import get_model_file
model.load_parameters(get_model_file('inceptionv1_ucf101',
tag=pretrained, root=root))
from ...data import UCF101Attr
attrib = UCF101Attr()
model.classes = attrib.classes
model.collect_params().reset_ctx(ctx)
return model
[docs]def inceptionv1_hmdb51(nclass=51, pretrained=False, pretrained_base=True,
use_tsn=False, num_segments=1, num_crop=1, partial_bn=True,
ctx=mx.cpu(), root='~/.mxnet/models', **kwargs):
r"""InceptionV1 model trained on HMDB51 dataset.
Parameters
----------
nclass : int.
Number of categories in the dataset.
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.
pretrained_base : bool or str, optional, default is True.
Load pretrained base network, the extra layers are randomized. Note that
if pretrained is `True`, this has no effect.
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.
num_segments : int, default is 1.
Number of segments used to evenly divide a video.
num_crop : int, default is 1.
Number of crops used during evaluation, choices are 1, 3 or 10.
partial_bn : bool, default False.
Freeze all batch normalization layers during training except the first layer.
"""
model = ActionRecInceptionV1(nclass=nclass,
partial_bn=partial_bn,
pretrained_base=pretrained_base,
num_segments=num_segments,
num_crop=num_crop,
dropout_ratio=0.8,
init_std=0.001)
if pretrained:
from ..model_store import get_model_file
model.load_parameters(get_model_file('inceptionv1_hmdb51',
tag=pretrained, root=root))
from ...data import HMDB51Attr
attrib = HMDB51Attr()
model.classes = attrib.classes
model.collect_params().reset_ctx(ctx)
return model
[docs]def inceptionv1_kinetics400(nclass=400, pretrained=False, pretrained_base=True,
tsn=False, num_segments=1, num_crop=1, partial_bn=True,
ctx=mx.cpu(), root='~/.mxnet/models', **kwargs):
r"""InceptionV1 model trained on Kinetics400 dataset.
Parameters
----------
nclass : int.
Number of categories in the dataset.
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.
pretrained_base : bool or str, optional, default is True.
Load pretrained base network, the extra layers are randomized. Note that
if pretrained is `True`, this has no effect.
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.
num_segments : int, default is 1.
Number of segments used to evenly divide a video.
num_crop : int, default is 1.
Number of crops used during evaluation, choices are 1, 3 or 10.
partial_bn : bool, default False.
Freeze all batch normalization layers during training except the first layer.
"""
model = ActionRecInceptionV1(nclass=nclass,
partial_bn=partial_bn,
pretrained_base=pretrained_base,
num_segments=num_segments,
num_crop=num_crop,
dropout_ratio=0.5,
init_std=0.01)
if pretrained:
from ..model_store import get_model_file
model.load_parameters(get_model_file('inceptionv1_kinetics400',
tag=pretrained, root=root))
from ...data import Kinetics400Attr
attrib = Kinetics400Attr()
model.classes = attrib.classes
model.collect_params().reset_ctx(ctx)
return model
[docs]def inceptionv1_sthsthv2(nclass=174, pretrained=False, pretrained_base=True,
tsn=False, num_segments=1, num_crop=1, partial_bn=True,
ctx=mx.cpu(), root='~/.mxnet/models', **kwargs):
r"""InceptionV1 model trained on Something-Something-V2 dataset.
Parameters
----------
nclass : int.
Number of categories in the dataset.
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.
pretrained_base : bool or str, optional, default is True.
Load pretrained base network, the extra layers are randomized. Note that
if pretrained is `True`, this has no effect.
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.
num_segments : int, default is 1.
Number of segments used to evenly divide a video.
num_crop : int, default is 1.
Number of crops used during evaluation, choices are 1, 3 or 10.
partial_bn : bool, default False.
Freeze all batch normalization layers during training except the first layer.
"""
model = ActionRecInceptionV1(nclass=nclass,
partial_bn=partial_bn,
pretrained_base=pretrained_base,
num_segments=num_segments,
num_crop=num_crop,
dropout_ratio=0.5,
init_std=0.01)
if pretrained:
from ..model_store import get_model_file
model.load_parameters(get_model_file('inceptionv1_sthsthv2',
tag=pretrained, root=root))
from ...data import SomethingSomethingV2Attr
attrib = SomethingSomethingV2Attr()
model.classes = attrib.classes
model.collect_params().reset_ctx(ctx)
return model