def __init__(self, name_scope, groups=1):
super(Res3, self).__init__(name_scope)
self._conv1 = Conv3D(num_channels=96,
num_filters=128,
filter_size=3,
stride=1,
padding=1,
groups=groups,
act=None,
bias_attr=False)
self._batch_norm1 = BatchNorm(128, act='relu')
self._conv2 = Conv3D(num_channels=128,
num_filters=128,
filter_size=3,
stride=1,
padding=1,
groups=groups,
act=None,
bias_attr=False)
self._batch_norm2 = BatchNorm(128, act='relu')
self._conv3 = Conv3D(num_channels=128,
num_filters=128,
filter_size=3,
stride=1,
padding=1,
groups=groups,
act=None,
bias_attr=False)
self._batch_norm3 = BatchNorm(128, act='relu')
def __init__(self, name_scope, groups=1):
super(Res5, self).__init__(name_scope)
self._conv1 = Conv3D(num_channels=256,
num_filters=512,
filter_size=3,
stride=2,
padding=1,
groups=groups,
act=None,
bias_attr=False)
self._batch_norm1 = BatchNorm(512, act='relu')
self._conv2 = Conv3D(num_channels=512,
num_filters=512,
filter_size=3,
stride=1,
padding=1,
groups=groups,
act=None,
bias_attr=False)
self._conv3 = Conv3D(num_channels=256,
num_filters=512,
filter_size=3,
stride=2,
padding=1,
groups=groups,
act=None,
bias_attr=False)
self._batch_norm2 = BatchNorm(512, act='relu')
#########################################b
self._conv4 = Conv3D(num_channels=512,
num_filters=512,
filter_size=3,
stride=1,
padding=1,
groups=groups,
act=None,
bias_attr=False)
self._batch_norm3 = BatchNorm(512, act='relu')
self._conv5 = Conv3D(num_channels=512,
num_filters=512,
filter_size=3,
stride=1,
padding=1,
groups=groups,
act=None,
bias_attr=False)
self._batch_norm4 = BatchNorm(512, act='relu')
def __init__(self, dim_in, dim_out, batch_size, prefix, dim_inner, cfg, \
test_mode = False, max_pool_stride = 2):
super(spacetime_nonlocal, self).__init__()
self.cfg = cfg
self.prefix = prefix
self.dim_inner = dim_inner
self.max_pool_stride = max_pool_stride
self.conv3d_1 = Conv3D(
num_channels=dim_in,
num_filters=dim_inner,
filter_size=1,
param_attr=ParamAttr(initializer=fluid.initializer.Normal(loc=0.0, scale=cfg.NONLOCAL.conv_init_std)),
bias_attr=ParamAttr(initializer=fluid.initializer.Constant(value=0.)))
self.conv3d_2 = Conv3D(
num_channels=dim_in,
num_filters=dim_inner,
filter_size=1,
param_attr=ParamAttr(initializer=fluid.initializer.Normal(loc=0.0, scale=cfg.NONLOCAL.conv_init_std)),
bias_attr=ParamAttr(initializer=fluid.initializer.Constant(value=0.)))
self.conv3d_3 = Conv3D(
num_channels=dim_in,
num_filters=dim_inner,
filter_size=1,
param_attr=ParamAttr(initializer=fluid.initializer.Normal(loc=0.0, scale=cfg.NONLOCAL.conv_init_std)),
bias_attr=ParamAttr(initializer=fluid.initializer.Constant(value=0.)))
self.conv3d_4 = Conv3D(
num_channels=dim_inner,
num_filters=dim_out,
filter_size=1,
param_attr=ParamAttr(initializer=fluid.initializer.Normal(loc=0.0, scale=cfg.NONLOCAL.conv_init_std)),
bias_attr=ParamAttr(initializer=fluid.initializer.Constant(value=0.)))
self.bn = BatchNorm(
num_channels=dim_out,
is_test=test_mode,
momentum=cfg.NONLOCAL.bn_momentum,
epsilon=cfg.NONLOCAL.bn_epsilon,
param_attr=ParamAttr(
initializer=fluid.initializer.Constant(
value=cfg.NONLOCAL.bn_init_gamma),
regularizer=fluid.regularizer.L2Decay(
cfg.TRAIN.weight_decay_bn)),
bias_attr=ParamAttr(
regularizer=fluid.regularizer.L2Decay(
cfg.TRAIN.weight_decay_bn)))
def conv3x3x3(in_planes, out_planes, stride=1):
return Conv3D(in_planes,
out_planes,
filter_size=3,
stride=stride,
padding=1,
param_attr=ParamAttr(initializer=Xavier()))
def __init__(self,
inplanes,
planes,
kernel_size,
stride,
padding,
bias=False,
groups=1):
super(ConvModule, self).__init__()
self.conv = Conv3D(
num_channels=inplanes,
num_filters=planes,
filter_size=kernel_size,
stride=stride,
padding=padding,
bias_attr=bias,
groups=groups,
param_attr=fluid.initializer.XavierInitializer(uniform=True,
fan_in=None,
fan_out=None,
seed=0),
)
self.bn = BatchNorm(
num_channels=planes,
act='relu',
param_attr=fluid.initializer.ConstantInitializer(value=1.0,
force_cpu=False),
bias_attr=fluid.initializer.ConstantInitializer(value=0.0,
force_cpu=False))
def __init__(
self,
in_channels=[1024, 1024],
mid_channels=[1024, 1024],
out_channels=2048,
ds_scales=[(1, 1, 1), (1, 1, 1)],
):
super(LevelFusion, self).__init__()
self.ops = fluid.dygraph.LayerList()
num_ins = len(in_channels)
for i in range(num_ins):
op = Downampling(in_channels[i],
mid_channels[i],
kernel_size=(1, 1, 1),
stride=(1, 1, 1),
padding=(0, 0, 0),
bias=False,
groups=32,
norm=True,
activation=True,
downsample_position='before',
downsample_scale=ds_scales[i])
self.ops.append(op)
in_dims = np.sum(mid_channels)
self.fusion_conv = fluid.dygraph.Sequential(
Conv3D(in_dims, out_channels, 1, 1, 0, bias_attr=False),
BatchNorm(out_channels, act='relu'),
)
def __init__(
self,
inplanes,
planes,
kernel_size=(3, 1, 1),
stride=(1, 1, 1),
padding=(1, 0, 0),
bias=False,
groups=1,
norm=False,
activation=False,
downsample_position='after',
downsample_scale=(1, 2, 2),
):
super(Downampling, self).__init__()
self.conv = Conv3D(inplanes,
planes,
kernel_size,
stride,
padding,
bias_attr=bias,
groups=groups)
self.norm1 = norm
self.norm = BatchNorm(planes) if norm else None
self.activation = activation
assert (downsample_position in ['before', 'after'])
self.downsample_position = downsample_position
self.downsample_scale = downsample_scale
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
act='relu',
is_3d=False):
super(ConvBNLayer, self).__init__(name_scope)
self._conv = None
if is_3d:
self._conv = Conv3D(num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
act=None,
bias_attr=False)
else:
self._conv = Conv2D(num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
act=None,
bias_attr=False)
self._batch_norm = BatchNorm(num_filters, act=act)
def forward(self, x):
if not self.fcn_testing:
if len(x.shape) == 4:
x = fluid.layers.unsqueeze(x, axes=2)
assert x.shape[1] == self.in_channels
assert x.shape[2] == self.temporal_feature_size
assert x.shape[3] == self.spatial_feature_size
assert x.shape[4] == self.spatial_feature_size
if self.with_avg_pool:
x = fluid.layers.pool3d(x, pool_size=(self.temporal_feature_size, self.spatial_feature_size, self.spatial_feature_size), pool_type='avg',
pool_stride=(1, 1, 1), pool_padding=(0, 0, 0))
if self.dropout_ratio != 0:
x = fluid.layers.dropout(x, self.dropout_ratio)
x = fluid.layers.reshape(x, (x.shape[0], -1))
cls_score = self.fc_cls(x)
return cls_score
else:
if self.with_avg_pool:
x = fluid.layers.pool3d(x, pool_size=(self.temporal_feature_size, self.spatial_feature_size, self.spatial_feature_size), pool_type='avg',
pool_stride=(1, 1, 1), pool_padding=(0, 0, 0))
if self.new_cls is None:
self.new_cls = Conv3D(self.in_channels, self.num_classes, 1, 1, 0)
# 这里需要再想想
self.fc_cls = None
class_map = self.new_cls(x)
return class_map
def __init__(self, inplanes, planes, downsample_scale=8):
super(TemporalModulation, self).__init__()
self.conv = Conv3D(inplanes,
planes, (3, 1, 1), (1, 1, 1), (1, 0, 0),
bias_attr=False,
groups=32)
self.downsample_scale = downsample_scale
def conv1x1x1(in_planes, out_planes, stride=1):
return Conv3D(in_planes,
out_planes,
filter_size=1,
stride=stride,
bias_attr=False,
param_attr=fluid.initializer.MSRAInitializer(uniform=False,
fan_in=None,
seed=10))
def conv3d(in_planes, out_planes, filter_size=3, stride=1, padding=1):
# data = numpy.random.random((5, 3, 12, 32, 32)).astype('float32')
# conv3d = fluid.dygraph.nn.Conv3D(
# 'Conv3D', num_filters=2, filter_size=3, act="relu")
# ret = conv3d(fluid.dygraph.base.to_variable(data))
# 3*3 conv with padding
return Conv3D(
num_channels=in_planes,
num_filters=out_planes,
filter_size=filter_size,
stride=stride,
padding=padding,
)
def __init__(self,
block,
layers,
block_inplanes,
n_input_channels=3,
conv1_t_size=7,
conv1_t_stride=1,
no_max_pool=False,
shortcut_type='B',
widen_factor=1.0,
n_classes=400):
super(ResNet, self).__init__()
block_inplanes = [int(x * widen_factor) for x in block_inplanes]
self.in_planes = block_inplanes[0]
self.no_max_pool = no_max_pool
self.conv1 = Conv3D(n_input_channels,
self.in_planes,
filter_size=(conv1_t_size, 7, 7),
stride=(conv1_t_stride, 2, 2),
padding=(conv1_t_size // 2, 3, 3))
self.bn1 = BatchNorm(self.in_planes)
# self.maxpool = MaxPool3D(filter_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, block_inplanes[0], layers[0],
shortcut_type)
self.layer2 = self._make_layer(block,
block_inplanes[1],
layers[1],
shortcut_type,
stride=2)
self.layer3 = self._make_layer(block,
block_inplanes[2],
layers[2],
shortcut_type,
stride=2)
self.layer4 = self._make_layer(block,
block_inplanes[3],
layers[3],
shortcut_type,
stride=2)
# self.avgpool = AdaptiveAvgPool3d(1, 1, 1)
self.fc = Linear(block_inplanes[3] * block.expansion,
n_classes,
param_attr=ParamAttr(initializer=Xavier()),
act='sigmoid')
def __init__(self,
num_channels,
num_filters,
filter_size,
stride=1,
padding=0,
act=None):
super(Conv3DBNLayer, self).__init__()
self._conv = Conv3D(num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=padding,
act=act)
self._batch_norm = BatchNorm(num_filters, act=act)
def __init__(self,
name_scope,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
act=None):
super(ConvBNLayer, self).__init__(name_scope)
# 卷积操作
self._conv = Conv3D(num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
bias_attr=False)
# 残差块中经典的操作，一个卷积后面跟一个batch_norm
self._batch_norm = BatchNorm(num_filters, act=act)
def __init__(self, name, cfg, mode='train'):
super(NonLocal, self).__init__()
self.name = name
self.cfg = cfg
self.mode = mode
self.is_training = (mode == 'train')
self.linear = Linear(10, 10)
self.get_config()
self.use_temp_convs_set, self.temp_strides_set, self.pool_stride = resnet_video.obtain_arc(
cfg.MODEL.video_arc_choice, cfg[mode.upper()]['video_length'])
self.conv3d = Conv3D(
num_channels=3,
num_filters=64,
filter_size=[1 + self.use_temp_convs_set[0][0] * 2, 7, 7],
stride=[self.temp_strides_set[0][0], 2, 2],
padding=[self.use_temp_convs_set[0][0], 3, 3],
param_attr=ParamAttr(initializer=fluid.initializer.MSRA()),
bias_attr=False)
self.test_mode = False if (mode == 'train') else True
self.bn_conv1 = BatchNorm(
num_channels=64,
is_test=self.test_mode,
momentum=cfg.MODEL.bn_momentum,
epsilon=cfg.MODEL.bn_epsilon,
param_attr=ParamAttr(regularizer=fluid.regularizer.L2Decay(
cfg.TRAIN.weight_decay_bn)),
bias_attr=ParamAttr(regularizer=fluid.regularizer.L2Decay(
cfg.TRAIN.weight_decay_bn)),
moving_mean_name="bn_conv1_mean",
moving_variance_name="bn_conv1_variance")
self.fc = Linear(
2048,
cfg.MODEL.num_classes,
param_attr=ParamAttr(initializer=fluid.initializer.Normal(
loc=0.0, scale=cfg.MODEL.fc_init_std)),
bias_attr=ParamAttr(initializer=fluid.initializer.Constant(
value=0.)))
def __init__(
self,
name_scope,
num_channels,
num_filters,
filter_size=7,
stride=1,
padding=0,
groups=1,
act=None,
):
super(ConvBNLayer, self).__init__(name_scope)
self._conv = Conv3D(num_channels=num_channels,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=padding,
groups=groups,
act=None,
bias_attr=False)
self._batch_norm = BatchNorm(num_filters,
act=act) ##这是2D的BatchNorm，后面需要注意
def __init__(self,
inplanes,
planes,
spatial_stride=1,
temporal_stride=1,
dilation=1,
downsample=None,
style='pytorch',
if_inflate=True,
inflate_style='3x1x1',
if_nonlocal=True,
nonlocal_cfg=None,
with_cp=False):
super(Bottleneck, self).__init__()
assert style in ['pytorch', 'caffe']
assert inflate_style in ['3x1x1', '3x3x3']
self.inplanes = inplanes
self.planes = planes
if style == 'pytorch':
self.conv1_stride = 1
self.conv2_stride = spatial_stride
self.conv1_stride_t = 1
self.conv2_stride_t = temporal_stride
else:
self.conv1_stride = spatial_stride
self.conv2_stride = 1
self.conv1_stride_t = temporal_stride
self.conv2_stride_t = 1
if if_inflate:
if inflate_style == '3x1x1':
self.conv1 = Conv3D(num_channels=inplanes,
num_filters=planes,
filter_size=(3, 1, 1),
stride=(self.conv1_stride_t,
self.conv1_stride,
self.conv1_stride),
padding=(1, 0, 0),
bias_attr=False)
self.conv2 = Conv3D(num_channels=planes,
num_filters=planes,
filter_size=(1, 3, 3),
stride=(self.conv2_stride_t,
self.conv2_stride,
self.conv2_stride),
padding=(0, dilation, dilation),
dilation=(1, dilation, dilation),
bias_attr=False)
else:
self.conv1 = Conv3D(num_channels=inplanes,
num_filters=planes,
filter_size=1,
stride=(self.conv1_stride_t,
self.conv1_stride,
self.conv1_stride),
bias_attr=False)
self.conv2 = Conv3D(num_channels=planes,
num_filters=planes,
filter_size=3,
stride=(self.conv2_stride_t,
self.conv2_stride,
self.conv2_stride),
padding=(1, dilation, dilation),
dilation=(1, dilation, dilation),
bias_attr=False)
else:
self.conv1 = Conv3D(num_channels=inplanes,
num_filters=planes,
filter_size=1,
stride=(1, self.conv1_stride,
self.conv1_stride),
bias_attr=False)
self.conv2 = Conv3D(num_channels=planes,
num_filters=planes,
filter_size=(1, 3, 3),
stride=(1, self.conv2_stride,
self.conv2_stride),
padding=(0, dilation, dilation),
dilation=(1, dilation, dilation),
bias_attr=False)
self.bn1 = BatchNorm(planes, act='relu')
self.bn2 = BatchNorm(planes, act='relu')
self.conv3 = Conv3D(planes,
planes * self.expansion,
filter_size=1,
bias_attr=False)
self.bn3 = BatchNorm(planes * self.expansion)
self.downsample = downsample
self.spatial_tride = spatial_stride
self.temporal_tride = temporal_stride
self.dilation = dilation
self.with_cp = with_cp
if if_nonlocal and nonlocal_cfg is not None:
nonlocal_cfg_ = nonlocal_cfg.copy()
nonlocal_cfg_['in_channels'] = planes * self.expansion
self.nonlocal_block = None #build_nonlocal_block(nonlocal_cfg_)
else:
self.nonlocal_block = None
def make_res_layer(block,
inplanes,
planes,
blocks,
spatial_stride=1,
temporal_stride=1,
dilation=1,
style='pytorch',
inflate_freq=1,
inflate_style='3x1x1',
nonlocal_freq=1,
nonlocal_cfg=None,
with_cp=False):
inflate_freq = inflate_freq if not isinstance(
inflate_freq, int) else (inflate_freq, ) * blocks
nonlocal_freq = nonlocal_freq if not isinstance(
nonlocal_freq, int) else (nonlocal_freq, ) * blocks
assert len(inflate_freq) == blocks
assert len(nonlocal_freq) == blocks
downsample = None
if spatial_stride != 1 or inplanes != planes * block.expansion:
downsample = fluid.dygraph.Sequential(
Conv3D(num_channels=inplanes,
num_filters=planes * block.expansion,
filter_size=1,
stride=(temporal_stride, spatial_stride, spatial_stride),
bias_attr=False),
BatchNorm(planes * block.expansion),
)
layers = []
layers.append(
block(inplanes,
planes,
spatial_stride,
temporal_stride,
dilation,
downsample,
style=style,
if_inflate=(inflate_freq[0] == 1),
inflate_style=inflate_style,
if_nonlocal=(nonlocal_freq[0] == 1),
nonlocal_cfg=nonlocal_cfg,
with_cp=with_cp))
inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(
block(inplanes,
planes,
1,
1,
dilation,
style=style,
if_inflate=(inflate_freq[i] == 1),
inflate_style=inflate_style,
if_nonlocal=(nonlocal_freq[i] == 1),
nonlocal_cfg=nonlocal_cfg,
with_cp=with_cp))
return fluid.dygraph.Sequential(*layers)
def __init__(self,
depth,
pretrained=None,
pretrained2d=True,
num_stages=4,
spatial_strides=(1, 2, 2, 2),
temporal_strides=(1, 1, 1, 1),
dilations=(1, 1, 1, 1),
out_indices=(0, 1, 2, 3),
conv1_kernel_t=5,
conv1_stride_t=2,
pool1_kernel_t=1,
pool1_stride_t=2,
style='pytorch',
frozen_stages=-1,
inflate_freq=(1, 1, 1, 1),
inflate_stride=(1, 1, 1, 1),
inflate_style='3x1x1',
nonlocal_stages=(-1, ),
nonlocal_freq=(0, 1, 1, 0),
nonlocal_cfg=None,
bn_eval=False,
bn_frozen=False,
partial_bn=False,
with_cp=False):
super(ResNet_I3D, self).__init__()
if depth not in self.arch_settings:
raise KeyError('invalid depth {} for resnet'.format(depth))
self.depth = depth
self.pretrained = pretrained
self.pretrained2d = pretrained2d
self.num_stages = num_stages
assert num_stages >= 1 and num_stages <= 4
self.spatial_strides = spatial_strides
self.temporal_strides = temporal_strides
self.dilations = dilations
assert len(spatial_strides) == len(temporal_strides) == len(
dilations) == num_stages
self.out_indices = out_indices
assert max(out_indices) < num_stages
self.style = style
self.frozen_stages = frozen_stages
self.inflate_freqs = inflate_freq if not isinstance(
inflate_freq, int) else (inflate_freq, ) * num_stages
self.inflate_style = inflate_style
self.nonlocal_stages = nonlocal_stages
self.nonlocal_freqs = nonlocal_freq if not isinstance(
nonlocal_freq, int) else (nonlocal_freq, ) * num_stages
self.nonlocal_cfg = nonlocal_cfg
self.bn_eval = bn_eval
self.bn_frozen = bn_frozen
self.partial_bn = partial_bn
self.with_cp = with_cp
self.pool1_kernel_t = pool1_kernel_t
self.pool1_stride_t = pool1_stride_t
self.block, stage_blocks = self.arch_settings[depth]
self.stage_blocks = stage_blocks[:num_stages]
self.inplanes = 64
self.conv1 = Conv3D(3,
64,
filter_size=(conv1_kernel_t, 7, 7),
stride=(conv1_stride_t, 2, 2),
padding=((conv1_kernel_t - 1) // 2, 3, 3),
bias_attr=False)
self.bn1 = BatchNorm(64, act='relu')
#self.maxpool = nn.MaxPool3d(kernel_size=(pool1_kernel_t, 3, 3), stride=(pool1_stride_t, 2, 2),
# padding=(pool1_kernel_t // 2, 1, 1))
self.res_layers = []
for i, num_blocks in enumerate(self.stage_blocks):
spatial_stride = spatial_strides[i]
temporal_stride = temporal_strides[i]
dilation = dilations[i]
planes = 64 * 2**i
res_layer = make_res_layer(self.block,
self.inplanes,
planes,
num_blocks,
spatial_stride=spatial_stride,
temporal_stride=temporal_stride,
dilation=dilation,
style=self.style,
inflate_freq=self.inflate_freqs[i],
inflate_style=self.inflate_style,
nonlocal_freq=self.nonlocal_freqs[i],
nonlocal_cfg=self.nonlocal_cfg
if i in self.nonlocal_stages else None,
with_cp=with_cp)
self.inplanes = planes * self.block.expansion
layer_name = 'layer{}'.format(i + 1)
self.add_sublayer(layer_name, res_layer)
self.res_layers.append(layer_name)
self.feat_dim = self.block.expansion * 64 * 2**(
len(self.stage_blocks) - 1)
def __init__(self,
name_scope,
layers=50,
class_dim=102,
seg_num=10,
weight_devay=None):
super(ResNet3D, self).__init__(name_scope)
self.layers = layers
self.seg_num = seg_num
supported_layers = [50, 101, 152]
assert layers in supported_layers, "supported layers are {} but input layer is {}".format(
supported_layers, layers)
if layers == 50:
depth = [3, 4, 6, 3]
elif layers == 101:
depth = [3, 4, 23, 3]
elif layers == 152:
depth = [3, 8, 36, 3]
num_filters = [64, 128, 256, 512]
# 在进入残差网络之前，先对图片进行 7*7*7 的卷积和 3*3 最大池化
# self.conv = ConvBNLayer( self.full_name(), num_channels=3, num_filters=64, filter_size=7, stride=2, act='relu')
self.conv = Conv3D(num_channels=3,
num_filters=64,
filter_size=(7, 7, 7),
stride=(1, 2, 2),
padding=(7 // 2, 3, 3),
bias_attr=False)
self.bn = BatchNorm(num_channels=64, act='relu')
# pool3d 没有动态图的实现，自定义Pool3D类 继承layers.Layer，然后传参，构建forward
self.pool3d_max = Pool3D.Pool3D(pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
# 残差网络部分
self.bottleneck_block_list = []
num_channels = 64
for block in range(len(depth)):
shortcut = False
for i in range(depth[block]):
bottleneck_block = self.add_sublayer(
"bb_%d_%d" % (block, i),
BottleneckBlock(self.full_name(),
num_channels=num_channels,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut))
num_channels = bottleneck_block._num_channels_out
self.bottleneck_block_list.append(bottleneck_block)
shortcut = True
# 残差网络之后的 平均池化
self.pool3d_avg = Pool3D.Pool3D(pool_size=7,
pool_type='avg',
global_pooling=True)
import math
stdv = 1.0 / math.sqrt(2048 * 1.0)
# 最后的 fc
self.fc = Linear(
input_dim=num_channels,
output_dim=class_dim,
act='softmax',
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv)))
def __init__(self, name_scope):
super(ECO3dNet, self).__init__(name_scope)
self.res3a_2 = ConvBNLayer(self.full_name(),
num_channels=96,
num_filters=128,
filter_size=3,
stride=1,
act='relu',
is_3d=True)
self.res3b_1 = ConvBNLayer(self.full_name(),
num_channels=128,
num_filters=128,
filter_size=3,
stride=1,
act='relu',
is_3d=True)
self.res3b_2 = ConvBNLayer(self.full_name(),
num_channels=128,
num_filters=128,
filter_size=3,
stride=1,
act='relu',
is_3d=True)
self.res3b_2 = ConvBNLayer(self.full_name(),
num_channels=128,
num_filters=128,
filter_size=3,
stride=1,
act='relu',
is_3d=True)
self.res4a_1 = ConvBNLayer(self.full_name(),
num_channels=128,
num_filters=256,
filter_size=3,
stride=2,
act='relu',
is_3d=True)
self.res4a_2 = Conv3D(num_channels=256,
num_filters=256,
filter_size=3,
stride=1,
padding=1,
act=None,
bias_attr=False)
self.res4a_down = Conv3D(num_channels=128,
num_filters=256,
filter_size=3,
stride=2,
padding=1,
act=None,
bias_attr=False)
self.res_4a_bn = BatchNorm(256, act="relu")
self.res4b_1 = ConvBNLayer(self.full_name(),
num_channels=256,
num_filters=256,
filter_size=3,
stride=1,
act='relu',
is_3d=True)
self.res4b_2 = Conv3D(num_channels=256,
num_filters=256,
filter_size=3,
stride=1,
padding=1,
act=None,
bias_attr=False)
self.res_4b_bn = BatchNorm(256, act="relu")
self.res5a_1 = ConvBNLayer(self.full_name(),
num_channels=256,
num_filters=512,
filter_size=3,
stride=2,
act='relu',
is_3d=True)
self.res5a_2 = Conv3D(num_channels=512,
num_filters=512,
filter_size=3,
stride=1,
padding=1,
act=None,
bias_attr=False)
self.res5a_down = Conv3D(num_channels=256,
num_filters=512,
filter_size=3,
stride=2,
padding=1,
act=None,
bias_attr=False)
self.res5a_bn = BatchNorm(512, act="relu")
self.res5b_1 = ConvBNLayer(self.full_name(),
num_channels=512,
num_filters=512,
filter_size=3,
stride=1,
act='relu',
is_3d=True)
self.res5b_2 = Conv3D(num_channels=512,
num_filters=512,
filter_size=3,
stride=1,
padding=1,
act=None,
bias_attr=False)
self.res5b_bn = BatchNorm(512, act="relu")
def __init__(
self,
in_channels=[256, 512, 1024, 2048],
out_channels=256,
spatial_modulation_config=None,
temporal_modulation_config=None,
upsampling_config=None,
downsampling_config=None,
level_fusion_config=None,
aux_head_config=None,
):
super(TPN, self).__init__()
assert isinstance(in_channels, list)
assert isinstance(out_channels, int)
self.in_channels = in_channels
self.out_channels = out_channels
self.num_ins = len(in_channels)
spatial_modulation_config = spatial_modulation_config
temporal_modulation_config = temporal_modulation_config
upsampling_config = upsampling_config
downsampling_config = downsampling_config
aux_head_config = aux_head_config
level_fusion_config = level_fusion_config
self.temporal_modulation_ops = fluid.dygraph.LayerList()
self.upsampling_ops = fluid.dygraph.LayerList()
self.downsampling_ops = fluid.dygraph.LayerList()
self.level_fusion_op = LevelFusion(**level_fusion_config)
self.spatial_modulation = SpatialModulation(
**spatial_modulation_config)
for i in range(0, self.num_ins, 1):
inplanes = in_channels[-1]
planes = out_channels
if temporal_modulation_config is not None:
param = temporal_modulation_config.param
param.downsample_scale = temporal_modulation_config.scales[i]
param.inplanes = inplanes
param.planes = planes
print(inplanes, planes)
print(param)
temporal_modulation = TemporalModulation(**param)
self.temporal_modulation_ops.append(temporal_modulation)
if i < self.num_ins - 1:
if upsampling_config is not None:
upsampling = Upsampling(**upsampling_config)
self.upsampling_ops.append(upsampling)
if downsampling_config is not None:
param = downsampling_config.param
param.inplanes = planes
param.planes = planes
param.downsample_scale = downsampling_config.scales
downsampling = Downampling(**param)
self.downsampling_ops.append(downsampling)
out_dims = level_fusion_config.out_channels
# Two pyramids
self.level_fusion_op2 = LevelFusion(**level_fusion_config)
self.pyramid_fusion_op = fluid.dygraph.Sequential(
Conv3D(out_dims * 2, 2048, 1, 1, 0, bias_attr=False),
BatchNorm(2048, act='relu'))
if aux_head_config is not None:
aux_head_config.inplanes = self.in_channels[-2]
self.aux_head = AuxHead(**aux_head_config)
else:
self.aux_head = None
def __init__(self,
name_scope,
channels=1,
bottleneck=32,
params=[1, 1, 1, 1, 1],
n_iter=10,
act=None):
super(FlowLayer, self).__init__(name_scope)
self.name_scope = name_scope
self.n_iter = n_iter
self._batch_norm = fluid.BatchNorm(channels, act=act)
self._conv = Conv2D(32,
num_filters=32,
filter_size=1,
stride=1,
padding=0,
groups=1,
act=None,
bias_attr=False)
# self.bottleneck = nn.Conv3d(channels, bottleneck, stride=1, padding=0, bias=False, kernel_size=1)
self.bottleneck = Conv3D(bottleneck,
channels,
1,
stride=1,
padding=0,
bias_attr=None,
act=None)
# self.unbottleneck = nn.Conv3d(bottleneck*2, channels, stride=1, padding=0, bias=False, kernel_size=1)
self.unbottleneck = Conv3D(bottleneck * 2,
channels,
stride=1,
padding=0,
bias_attr=False,
filter_size=1)
# self.bn = nn.BatchNorm3d(channels)
self.bn = BatchNorm(channels)
#print(channels)
channels = bottleneck
if params[0]:
#self.img_grad = nn.Parameter(torch.FloatTensor([[[[-0.5,0,0.5]]]]).repeat(channels,channels,1,1))
#stop_gradient 属性为true，这意味这反向梯度不会被传递过这个数据变量。如果用户想传递反向梯度，可以设置 var.stop_gradient = False 。
#self.img_grad2 = nn.Parameter(torch.FloatTensor([[[[-0.5,0,0.5]]]]).transpose(3,2).repeat(channels,channels,1,1))
img_grad_temp = np.array([[[[-0.5, 0, 0.5]]]
]).astype("float32").repeat(channels, 1)
img_grad_temp2 = np.array([[[[-0.5, 0,
0.5]]]]).astype("float32").transpose(
0, 1, 3, 2).repeat(channels, 1)
img_grad_temp = np.reshape(img_grad_temp, [channels, 1, 1, 3])
img_grad_temp2 = np.reshape(img_grad_temp2, [channels, 1, 3, 1])
self.img_grad = self.create_parameter(shape=[channels, 1, 1, 3],
dtype="float32")
self.img_grad2 = self.create_parameter(shape=[channels, 1, 3, 1],
dtype="float32")
self.img_grad.set_value(img_grad_temp)
self.img_grad2.set_value(img_grad_temp2)
else:
#self.img_grad = nn.Parameter(torch.FloatTensor([[[[-0.5,0,0.5]]]]).repeat(channels,channels,1,1), requires_grad=False)
img_grad_temp = np.array([[[[-0.5, 0, 0.5]]]
]).astype("float32").repeat(channels, 1)
img_grad_temp2 = np.array([[[[-0.5, 0,
0.5]]]]).astype("float32").transpose(
0, 1, 3, 2).repeat(channels, 1)
img_grad_temp = np.reshape(img_grad_temp, [channels, 1, 1, 3])
img_grad_temp2 = np.reshape(img_grad_temp2, [channels, 1, 3, 1])
self.img_grad = self.create_parameter(shape=[channels, 1, 1, 3],
dtype="float32")
self.img_grad2 = self.create_parameter(shape=[channels, 1, 3, 1],
dtype="float32")
self.img_grad.set_value(img_grad_temp)
self.img_grad2.set_value(img_grad_temp2)
self.img_grad.stop_gradient = True
self.img_grad2.stop_gradient = True
self.conv2dimg_grad = Conv2D(
32,
num_filters=1,
filter_size=(1, 3),
stride=1,
padding=(0, 1),
groups=32,
act=None,
bias_attr=False) #param_attr=self.img_grad,
self.conv2dimg_grad2 = Conv2D(
32,
num_filters=1,
filter_size=(3, 1),
stride=1,
padding=(1, 0),
groups=32,
act=None,
bias_attr=False) #param_attr=self.img_grad2,
self.conv2dimg_grad.weight = self.img_grad
self.conv2dimg_grad2.weight = self.img_grad2
self.prelu = fluid.dygraph.PRelu(mode='all')
if params[1]:
#self.f_grad = nn.Parameter(torch.FloatTensor([[[[-1], [1]]]]).repeat(channels, channels, 1, 1))
#self.f_grad2 = nn.Parameter(torch.FloatTensor([[[[-1], [1]]]]).repeat(channels, channels, 1, 1))
#self.div = nn.Parameter(torch.FloatTensor([[[[-1], [1]]]]).repeat(channels, channels, 1, 1))
#self.div2 = nn.Parameter(torch.FloatTensor([[[[-1], [1]]]]).repeat(channels, channels, 1, 1))
img_grad_temp = np.array([[[[-1, 1]]]
]).astype("float32").repeat(channels,
axis=0)
img_grad_temp = np.reshape(img_grad_temp, [channels, 1, 1, 2])
img_grad_temp2 = np.array([[[[-1], [1]]]
]).astype("float32").repeat(channels,
axis=0)
img_grad_temp2 = np.reshape(img_grad_temp, [channels, 1, 2, 1])
self.f_grad = self.create_parameter(shape=[channels, 1, 1, 2],
dtype="float32")
self.f_grad2 = self.create_parameter(shape=[channels, 1, 2, 1],
dtype="float32")
self.div = self.create_parameter(shape=[channels, 1, 1, 2],
dtype="float32")
self.div2 = self.create_parameter(shape=[channels, 1, 2, 1],
dtype="float32")
self.f_grad.set_value(img_grad_temp)
self.f_grad2.set_value(img_grad_temp2)
self.div.set_value(img_grad_temp)
self.div2.set_value(img_grad_temp2)
else:
img_grad_temp = np.array([[[[-1], [1]]]
]).astype("float32").repeat(channels,
axis=0)
img_grad_temp = np.reshape(img_grad_temp, [channels, 1, 2, 1])
self.f_grad = self.create_parameter(shape=[channels, 1, 2, 1],
dtype="float32")
self.f_grad2 = self.create_parameter(shape=[channels, 1, 2, 1],
dtype="float32")
self.div = self.create_parameter(shape=[channels, 1, 2, 1],
dtype="float32")
self.div2 = self.create_parameter(shape=[channels, 1, 2, 1],
dtype="float32")
self.f_grad.set_value(img_grad_temp).stop_gradient = True
self.f_grad2.set_value(img_grad_temp).stop_gradient = True
self.div.set_value(img_grad_temp).stop_gradient = True
self.div2.set_value(img_grad_temp).stop_gradient = True
print('stop_gradient')
self.conv2df_grad = Conv2D(32,
num_filters=1,
filter_size=(1, 2),
stride=1,
padding=(0, 0),
groups=32,
act=None,
bias_attr=False) #param_attr=self.f_grad,
self.conv2df_grad2 = Conv2D(32,
num_filters=1,
filter_size=(2, 1),
stride=1,
padding=(0, 0),
groups=32,
act=None,
bias_attr=False) #param_attr=self.f_grad2,
self.conv2ddiv = Conv2D(32,
num_filters=1,
filter_size=(1, 2),
stride=1,
padding=(0, 0),
groups=32,
act=None,
bias_attr=False) #param_attr=self.div,
self.conv2ddiv2 = Conv2D(32,
num_filters=1,
filter_size=(2, 1),
stride=1,
padding=(0, 0),
groups=32,
act=None,
bias_attr=False) #param_attr=self.div2,
self.conv2df_grad.weight = self.f_grad
self.conv2df_grad2.weight = self.f_grad2
self.conv2ddiv.weight = self.div
self.conv2ddiv2.weight = self.div2
self.channels = channels
self.t1 = np.array([0.3]).astype("float32")
self.l1 = np.array([0.15]).astype("float32")
self.a1 = np.array([0.25]).astype("float32")
if params[2]: # XITA
#self.t = nn.Parameter(torch.FloatTensor([self.t]))
self.t = self.create_parameter(shape=[1], dtype="float32")
self.t.set_value(self.t1)
#print(self.t)
if params[3]: # TAU
#self.l = nn.Parameter(torch.FloatTensor([self.l]))
self.l = self.create_parameter(shape=[1], dtype="float32")
self.l.set_value(self.l1)
#print(self.l)
if params[4]: # LABADA
#self.a = nn.Parameter(torch.FloatTensor([self.a]))
self.a = self.create_parameter(shape=[1], dtype="float32")
self.a.set_value(self.a1)
def __init__(self):
super(C3D, self).__init__()
self.res3a_2 = Conv3DBNLayer(num_channels=96,
num_filters=128,
padding=1,
filter_size=3,
act='relu')
self.res3b_1 = Conv3DBNLayer(num_channels=128,
num_filters=128,
padding=1,
filter_size=3,
act='relu')
self.res3b_2 = Conv3DBNLayer(num_channels=128,
num_filters=128,
padding=1,
filter_size=3,
act='relu')
# res3b<=Eltwise<=res3b_2,res3a_2
self.res3b_bn = BatchNorm(num_channels=128, act='relu')
self.res4a_1 = Conv3DBNLayer(num_channels=128,
num_filters=256,
padding=1,
filter_size=3,
stride=2,
act='relu')
self.res4a_2 = Conv3D(num_channels=256,
num_filters=256,
padding=1,
filter_size=3)
# res4a_down<=Conv3d<=res3b_bn
self.res4a_down = Conv3D(num_channels=128,
num_filters=256,
padding=1,
filter_size=3,
stride=2)
# res4a<=Eltwise<=res4a_2,res4a_down
self.res4a = BatchNorm(num_channels=256, act='relu')
self.res4b_1 = Conv3DBNLayer(num_channels=256,
num_filters=256,
padding=1,
filter_size=3,
act='relu')
self.res4b_2 = Conv3DBNLayer(num_channels=256,
num_filters=256,
padding=1,
filter_size=3,
act='relu')
# res4b<=Eltwise<=res4b_2,res4a
self.res4b_bn = BatchNorm(num_channels=256, act='relu')
self.res5a_1 = Conv3DBNLayer(num_channels=256,
num_filters=512,
padding=1,
stride=2,
filter_size=3,
act='relu')
self.res5a_2 = Conv3D(num_channels=512,
num_filters=512,
padding=1,
filter_size=3,
act='relu')
# res5a_down<=Conv3d<=res4b_bn
self.res5a_down = Conv3D(num_channels=256,
num_filters=512,
padding=1,
stride=2,
filter_size=3)
# res5a<=Eltwise<=res5a_2,res5a_down
self.res5a = BatchNorm(num_channels=512, act='relu')
self.res5b_1 = Conv3DBNLayer(num_channels=512,
num_filters=512,
padding=1,
filter_size=3,
act='relu')
self.res5b_2 = Conv3D(num_channels=512,
num_filters=512,
padding=1,
filter_size=3)
# res5b<=Eltwise<=res5b_2,res5a
self.res5b = BatchNorm(num_channels=512, act='relu')
