Source code for gluoncv.model_zoo.action_recognition.p3d
"""P3D, implemented in Gluon. https://arxiv.org/abs/1711.10305.
Code adapted from https://github.com/qijiezhao/pseudo-3d-pytorch."""
# pylint: disable=arguments-differ,unused-argument,line-too-long
__all__ = ['P3D', 'p3d_resnet50_kinetics400', 'p3d_resnet101_kinetics400']
from mxnet import init
from mxnet.context import cpu
from mxnet.gluon.block import HybridBlock
from mxnet.gluon import nn
from mxnet.gluon.nn import BatchNorm
def conv1x3x3(in_planes, out_planes, spatial_stride=1, temporal_stride=1, dilation=1):
"""1x3x3 convolution with padding"""
return nn.Conv3D(in_channels=in_planes,
channels=out_planes,
kernel_size=(1, 3, 3),
strides=(temporal_stride, spatial_stride, spatial_stride),
padding=(0, dilation, dilation),
dilation=dilation,
use_bias=False)
def conv3x1x1(in_planes, out_planes, spatial_stride=1, temporal_stride=1, dilation=1):
"""3x1x1 convolution with padding"""
return nn.Conv3D(in_channels=in_planes,
channels=out_planes,
kernel_size=(3, 1, 1),
strides=(temporal_stride, spatial_stride, spatial_stride),
padding=(dilation, 0, 0),
dilation=dilation,
use_bias=False)
class Bottleneck(HybridBlock):
r"""ResBlock for P3D
Parameters
----------
inplanes : int.
Input channels of each block.
planes : int.
Output channels of each block.
spatial_stride : int, default is 1.
Stride in spatial dimension of convolutional layers in a block.
temporal_stride : int, default is 1.
Stride in temporal dimension of convolutional layers in a block.
dilation : int, default is 1.
Dilation of convolutional layers in a block.
downsample : bool.
Whether to contain a downsampling layer in the block.
num_layers : int.
The current depth of this layer.
depth_3d : int.
Number of 3D layers in the network. For example,
a P3D with ResNet50 backbone has a depth_3d of 13, which is the sum of 3, 4 and 6.
block_design : tuple of str.
Different designs for each block, from 'A', 'B' or 'C'.
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`.
layer_name : str, default is ''.
Give a name to current block.
"""
expansion = 4
def __init__(self,
inplanes,
planes,
spatial_stride=1,
temporal_stride=1,
dilation=1,
downsample=None,
num_layers=0,
depth_3d=13,
block_design=('A', 'B', 'C'),
norm_layer=BatchNorm,
norm_kwargs=None,
layer_name='',
**kwargs):
super(Bottleneck, self).__init__()
self.inplanes = inplanes
self.planes = planes
self.spatial_tride = spatial_stride
self.temporal_tride = temporal_stride
self.dilation = dilation
self.downsample = downsample
self.num_layers = num_layers
self.depth_3d = depth_3d
self.block_design = block_design
self.cycle = len(self.block_design)
if self.downsample is not None:
strides = (1, 2, 2)
else:
strides = spatial_stride
with self.name_scope():
# first block
if self.num_layers < self.depth_3d:
if self.num_layers == 0:
strides = 1
self.conv1 = nn.Conv3D(in_channels=inplanes,
channels=planes,
kernel_size=1,
strides=strides,
use_bias=False)
self.bn1 = norm_layer(in_channels=planes,
**({} if norm_kwargs is None else norm_kwargs))
else:
if self.num_layers == self.depth_3d:
strides = 2
else:
strides = 1
self.conv1 = nn.Conv2D(in_channels=inplanes,
channels=planes,
kernel_size=1,
strides=strides,
use_bias=False)
self.bn1 = norm_layer(in_channels=planes,
**({} if norm_kwargs is None else norm_kwargs))
# second block
self.block_id = int(num_layers)
self.design = list(self.block_design)[self.block_id % self.cycle]
if self.num_layers < self.depth_3d:
self.conv2 = conv1x3x3(planes, planes)
self.bn2 = norm_layer(in_channels=planes,
**({} if norm_kwargs is None else norm_kwargs))
self.conv_temporal = conv3x1x1(planes, planes)
self.bn_temporal = norm_layer(in_channels=planes,
**({} if norm_kwargs is None else norm_kwargs))
else:
self.conv2 = nn.Conv2D(in_channels=planes, channels=planes, kernel_size=3,
strides=1, padding=1, use_bias=False)
self.bn2 = norm_layer(in_channels=planes,
**({} if norm_kwargs is None else norm_kwargs))
# third block
if self.num_layers < self.depth_3d:
self.conv3 = nn.Conv3D(in_channels=planes,
channels=planes * self.expansion,
kernel_size=1,
use_bias=False)
self.bn3 = norm_layer(in_channels=planes * self.expansion,
**({} if norm_kwargs is None else norm_kwargs))
else:
self.conv3 = nn.Conv2D(in_channels=planes,
channels=planes * self.expansion,
kernel_size=1,
use_bias=False)
self.bn3 = norm_layer(in_channels=planes * self.expansion,
**({} if norm_kwargs is None else norm_kwargs))
self.relu = nn.Activation('relu')
def P3DA(self, x):
"""P3D-A unit
Stacked architecture by making temporal 1D filters (T)
follow spatial 2D filters (S) in a cascaded manner.
"""
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
x = self.conv_temporal(x)
x = self.bn_temporal(x)
x = self.relu(x)
return x
def P3DB(self, x):
"""P3D-B unit
Parallel architecture by making temporal 1D filters (T)
and spatial 2D filters (S) in different pathways.
"""
spatial_x = self.conv2(x)
spatial_x = self.bn2(spatial_x)
spatial_x = self.relu(spatial_x)
temporal_x = self.conv_temporal(x)
temporal_x = self.bn_temporal(temporal_x)
temporal_x = self.relu(temporal_x)
return spatial_x + temporal_x
def P3DC(self, x):
"""P3D-C unit
A compromise design between P3D-A and P3D-B.
"""
spatial_x = self.conv2(x)
spatial_x = self.bn2(spatial_x)
spatial_x = self.relu(spatial_x)
st_residual_x = self.conv_temporal(spatial_x)
st_residual_x = self.bn_temporal(st_residual_x)
st_residual_x = self.relu(st_residual_x)
return spatial_x + st_residual_x
def hybrid_forward(self, F, x):
"""Hybrid forward of a ResBlock in P3D."""
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
if self.block_id < self.depth_3d:
if self.design == 'A':
out = self.P3DA(out)
elif self.design == 'B':
out = self.P3DB(out)
elif self.design == 'C':
out = self.P3DC(out)
else:
print('We do not support %s building block for P3D networks. \
Please try A, B or C.' % self.design)
else:
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out = F.Activation(out + identity, act_type='relu')
return out
[docs]class P3D(HybridBlock):
r"""
The Pseudo 3D network (P3D).
Learning Spatio-Temporal Representation with Pseudo-3D Residual Networks.
ICCV, 2017. https://arxiv.org/abs/1711.10305
Parameters
----------
nclass : int
Number of classes in the training dataset.
block : Block, default is `Bottleneck`.
Class for the residual block.
layers : list of int
Numbers of layers in each block
block_design : tuple of str.
Different designs for each block, from 'A', 'B' or 'C'.
dropout_ratio : float, default is 0.5.
The dropout rate of a dropout layer.
The larger the value, the more strength to prevent overfitting.
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.
feat_ext : bool.
Whether to extract features before dense classification layer or
do a complete forward pass.
init_std : float, default is 0.001.
Standard deviation value when initialize the dense layers.
ctx : Context, default CPU.
The context in which to load the pretrained weights.
partial_bn : bool, default False.
Freeze all batch normalization layers during training except the first layer.
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, nclass, block, layers, shortcut_type='B',
block_design=('A', 'B', 'C'), dropout_ratio=0.5,
num_segments=1, num_crop=1, feat_ext=False,
init_std=0.001, ctx=None, partial_bn=False,
norm_layer=BatchNorm, norm_kwargs=None, **kwargs):
super(P3D, self).__init__()
self.shortcut_type = shortcut_type
self.block_design = block_design
self.partial_bn = partial_bn
self.dropout_ratio = dropout_ratio
self.init_std = init_std
self.num_segments = num_segments
self.num_crop = num_crop
self.feat_ext = feat_ext
self.inplanes = 64
self.feat_dim = 512 * block.expansion
with self.name_scope():
self.conv1 = nn.Conv3D(in_channels=3, channels=64, kernel_size=(1, 7, 7),
strides=(1, 2, 2), padding=(0, 3, 3), use_bias=False)
self.bn1 = norm_layer(in_channels=64, **({} if norm_kwargs is None else norm_kwargs))
self.relu = nn.Activation('relu')
self.pool = nn.MaxPool3D(pool_size=(2, 3, 3), strides=2, padding=(0, 1, 1))
self.pool2 = nn.MaxPool3D(pool_size=(2, 1, 1), strides=(2, 1, 1), padding=0)
if self.partial_bn:
if norm_kwargs is not None:
norm_kwargs['use_global_stats'] = True
else:
norm_kwargs = {}
norm_kwargs['use_global_stats'] = True
# 3D layers are only for (layers1, layers2 and layers3), layers4 is C2D
self.depth_3d = sum(layers[:3])
self.layer_cnt = 0
self.layer1 = self._make_res_layer(block=block,
planes=64,
blocks=layers[0],
layer_name='layer1_')
self.layer2 = self._make_res_layer(block=block,
planes=128,
blocks=layers[1],
spatial_stride=2,
layer_name='layer2_')
self.layer3 = self._make_res_layer(block=block,
planes=256,
blocks=layers[2],
spatial_stride=2,
layer_name='layer3_')
self.layer4 = self._make_res_layer(block=block,
planes=512,
blocks=layers[3],
spatial_stride=2,
layer_name='layer4_')
self.avgpool = nn.GlobalAvgPool2D()
self.dropout = nn.Dropout(rate=self.dropout_ratio)
self.fc = nn.Dense(in_units=self.feat_dim, units=nclass,
weight_initializer=init.Normal(sigma=self.init_std))
def _make_res_layer(self,
block,
planes,
blocks,
shortcut_type='B',
block_design=('A', 'B', 'C'),
spatial_stride=1,
temporal_stride=1,
norm_layer=BatchNorm,
norm_kwargs=None,
layer_name=''):
"""Build each stage of a ResNet"""
downsample = None
if self.layer_cnt < self.depth_3d:
if spatial_stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.HybridSequential(prefix=layer_name + 'downsample_')
with downsample.name_scope():
downsample.add(nn.Conv3D(in_channels=self.inplanes,
channels=planes * block.expansion,
kernel_size=1,
strides=(temporal_stride, spatial_stride, spatial_stride),
use_bias=False))
downsample.add(norm_layer(in_channels=planes * block.expansion,
**({} if norm_kwargs is None else norm_kwargs)))
else:
if spatial_stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.HybridSequential(prefix=layer_name + 'downsample_')
with downsample.name_scope():
downsample.add(nn.Conv2D(in_channels=self.inplanes,
channels=planes * block.expansion,
kernel_size=1,
strides=spatial_stride,
use_bias=False))
downsample.add(norm_layer(in_channels=planes * block.expansion,
**({} if norm_kwargs is None else norm_kwargs)))
layers = nn.HybridSequential(prefix=layer_name)
with layers.name_scope():
layers.add(block(inplanes=self.inplanes,
planes=planes,
spatial_stride=spatial_stride,
temporal_stride=temporal_stride,
downsample=downsample,
num_layers=self.layer_cnt,
depth_3d=self.depth_3d,
block_design=block_design))
self.layer_cnt += 1
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.add(block(inplanes=self.inplanes,
planes=planes,
spatial_stride=1,
temporal_stride=1,
num_layers=self.layer_cnt,
depth_3d=self.depth_3d,
block_design=block_design))
self.layer_cnt += 1
return layers
[docs] def hybrid_forward(self, F, x):
"""Hybrid forward of P3D net"""
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.pool(x)
x = self.pool2(self.layer1(x))
x = self.pool2(self.layer2(x))
x = self.pool2(self.layer3(x))
x = F.reshape(x, (-1, 0, 1, 0, 0)) # 0 keeps the original dimension
x = F.squeeze(x, axis=2)
x = self.layer4(x)
x = self.avgpool(x)
x = F.squeeze(x, axis=(2, 3))
# segmental consensus
x = F.reshape(x, shape=(-1, self.num_segments * self.num_crop, self.feat_dim))
x = F.mean(x, axis=1)
if self.feat_ext:
return x
x = self.fc(self.dropout(x))
return x
[docs]def p3d_resnet50_kinetics400(nclass=400, pretrained=False, pretrained_base=True,
root='~/.mxnet/models', num_segments=1, num_crop=1,
feat_ext=False, ctx=cpu(), **kwargs):
r"""The Pseudo 3D network (P3D) with ResNet50 backbone 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.
feat_ext : bool.
Whether to extract features before dense classification layer or
do a complete forward pass.
"""
model = P3D(nclass=nclass,
block=Bottleneck,
layers=[3, 4, 6, 3],
num_segments=num_segments,
num_crop=num_crop,
feat_ext=feat_ext,
ctx=ctx,
**kwargs)
model.initialize(init.MSRAPrelu(), ctx=ctx)
if pretrained:
from ..model_store import get_model_file
model.load_parameters(get_model_file('p3d_resnet50_kinetics400',
tag=pretrained, root=root), ctx=ctx)
from ...data import Kinetics400Attr
attrib = Kinetics400Attr()
model.classes = attrib.classes
model.collect_params().reset_ctx(ctx)
return model
[docs]def p3d_resnet101_kinetics400(nclass=400, pretrained=False, pretrained_base=True,
root='~/.mxnet/models', num_segments=1, num_crop=1,
feat_ext=False, ctx=cpu(), **kwargs):
r"""The Pseudo 3D network (P3D) with ResNet101 backbone 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.
feat_ext : bool.
Whether to extract features before dense classification layer or
do a complete forward pass.
"""
model = P3D(nclass=nclass,
block=Bottleneck,
layers=[3, 4, 23, 3],
num_segments=num_segments,
num_crop=num_crop,
feat_ext=feat_ext,
ctx=ctx,
**kwargs)
model.initialize(init.MSRAPrelu(), ctx=ctx)
if pretrained:
from ..model_store import get_model_file
model.load_parameters(get_model_file('p3d_resnet101_kinetics400',
tag=pretrained, root=root), ctx=ctx)
from ...data import Kinetics400Attr
attrib = Kinetics400Attr()
model.classes = attrib.classes
model.collect_params().reset_ctx(ctx)
return model