def __init__(self,
num_classes,
depth,
pretrained=None,
pretrained_base=True,
feat_ext=False,
num_segments=1,
num_crop=1,
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=1,
conv1_stride_t=1,
pool1_kernel_t=1,
pool1_stride_t=1,
frozen_stages=-1,
inflate_freq=(0, 0, 1, 1),
inflate_stride=(1, 1, 1, 1),
inflate_style='3x1x1',
nonlocal_stages=(-1, ),
nonlocal_freq=(0, 0, 0, 0),
nonlocal_cfg=None,
bn_eval=False,
bn_frozen=False,
partial_bn=False,
dropout_ratio=0.5,
init_std=0.01,
norm_layer=BatchNorm,
norm_kwargs=None,
ctx=None,
**kwargs):
super(ResNet_SlowFast, self).__init__()
if depth not in self.arch_settings:
raise KeyError('invalid depth {} for resnet'.format(depth))
self.num_classes = num_classes
self.depth = depth
self.pretrained = pretrained
self.pretrained_base = pretrained_base
self.feat_ext = feat_ext
self.num_segments = num_segments
self.num_crop = num_crop
self.num_stages = num_stages
assert 1 <= 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.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.feat_ext = feat_ext
self.dropout_ratio = dropout_ratio
self.init_std = init_std
self.block, stage_blocks = self.arch_settings[depth]
self.stage_blocks = stage_blocks[:num_stages]
self.inplanes = 64
self.first_stage = nn.HybridSequential(prefix='')
self.first_stage.add(
nn.Conv3D(in_channels=3,
channels=64,
kernel_size=(conv1_kernel_t, 7, 7),
strides=(conv1_stride_t, 2, 2),
padding=((conv1_kernel_t - 1) // 2, 3, 3),
use_bias=False))
self.first_stage.add(
norm_layer(in_channels=64,
**({} if norm_kwargs is None else norm_kwargs)))
self.first_stage.add(nn.Activation('relu'))
self.first_stage.add(
nn.MaxPool3D(pool_size=(pool1_kernel_t, 3, 3),
strides=(pool1_stride_t, 2, 2),
padding=(pool1_kernel_t // 2, 1, 1)))
self.res_layers = nn.HybridSequential(prefix='')
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
layer_name = 'layer{}_'.format(i + 1)
res_layer = make_res_layer(self.block,
self.inplanes,
planes,
num_blocks,
spatial_stride=spatial_stride,
temporal_stride=temporal_stride,
dilation=dilation,
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,
norm_layer=norm_layer,
norm_kwargs=norm_kwargs,
layer_name=layer_name)
self.inplanes = planes * self.block.expansion
self.res_layers.add(res_layer)
self.feat_dim = self.block.expansion * 64 * 2**(
len(self.stage_blocks) - 1)
self.st_avg = nn.GlobalAvgPool3D()
self.head = nn.HybridSequential(prefix='')
self.head.add(nn.Dropout(rate=self.dropout_ratio))
self.fc = nn.Dense(in_units=self.feat_dim,
units=num_classes,
weight_initializer=init.Normal(sigma=self.init_std))
self.head.add(self.fc)
self.init_weights(ctx)
# 保存更新过的moving_mean和moving_var
Y, self.moving_mean, self.moving_var = batch_norm(X,
self.gamma.data(),
self.beta.data(),
self.moving_mean,
self.moving_var,
eps=1e-5,
momentum=0.9)
return Y
# In[3]:
net = nn.Sequential()
net.add(nn.Conv2D(6, kernel_size=5), BatchNorm(6, num_dims=4),
nn.Activation('sigmoid'), nn.MaxPool2D(pool_size=2, strides=2),
nn.Conv2D(16, kernel_size=5), BatchNorm(16, num_dims=4),
nn.Activation('sigmoid'), nn.MaxPool2D(pool_size=2, strides=2),
nn.Dense(120), BatchNorm(120, num_dims=2), nn.Activation('sigmoid'),
nn.Dense(84), BatchNorm(84, num_dims=2), nn.Activation('sigmoid'),
nn.Dense(10))
# In[ ]:
lr, num_epochs, batch_size, ctx = 1.0, 5, 256, d2l.try_gpu()
net.initialize(ctx=ctx, init=init.Xavier())
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': lr})
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
d2l.train_ch5(net, train_iter, test_iter, batch_size, trainer, ctx, num_epochs)
# In[5]:
def conv2d_block(num_filters, kernel_size):
block = nn.HybridSequential()
block.add(nn.Conv2D(num_filters, (kernel_size, kernel_size)))
block.add(nn.BatchNorm())
block.add(nn.Activation(activation='relu'))
return block
def __init__(self,
channels,
init_block_channels,
stem_blocks_channels,
in_channels=3,
classes=1000,
**kwargs):
super(NASNet, self).__init__(**kwargs)
with self.name_scope():
self.features = nasnet_dual_path_sequential(
return_two=False,
first_ordinals=1,
last_ordinals=2,
prefix='')
self.features.add(NASNetInitBlock(
in_channels=in_channels,
out_channels=init_block_channels))
in_channels = init_block_channels
out_channels = stem_blocks_channels[0]
self.features.add(Stem1Unit(
in_channels=in_channels,
out_channels=out_channels))
prev_in_channels = in_channels
in_channels = out_channels
out_channels = stem_blocks_channels[1]
self.features.add(Stem2Unit(
in_channels=in_channels,
prev_in_channels=prev_in_channels,
out_channels=out_channels))
prev_in_channels = in_channels
in_channels = out_channels
for i, channels_per_stage in enumerate(channels):
stage = nasnet_dual_path_sequential(prefix='stage{}_'.format(i + 1))
with stage.name_scope():
for j, out_channels in enumerate(channels_per_stage):
if (j == 0) and (i != 0):
unit = ReductionUnit
elif ((i == 0) and (j == 0)) or ((i != 0) and (j == 1)):
unit = FirstUnit
else:
unit = NormalUnit
stage.add(unit(
in_channels=in_channels,
prev_in_channels=prev_in_channels,
out_channels=out_channels))
prev_in_channels = in_channels
in_channels = out_channels
self.features.add(stage)
self.features.add(nn.Activation('relu'))
self.features.add(nn.AvgPool2D(
pool_size=7,
strides=1))
self.output = nn.HybridSequential(prefix='')
self.output.add(nn.Flatten())
self.output.add(nn.Dropout(rate=0.5))
self.output.add(nn.Dense(
units=classes,
in_units=in_channels))
def conv_block(num_channels):
blk = nn.Sequential()
blk.add(nn.BatchNorm(), nn.Activation('relu'),
nn.Conv2D(num_channels, kernel_size=3, padding=1))
return blk
def Act():
if config.net_act == 'prelu':
return nn.PReLU()
else:
return nn.Activation(config.net_act)
netD.add(nn.BatchNorm())
netD.add(nn.LeakyReLU(.2))
netD.add(nn.Conv2D(channels=512, kernel_size=6, strides=2, padding=2))
netD.add(nn.BatchNorm())
netD.add(nn.LeakyReLU(.2))
netD.add(nn.Conv2D(channels=1024, kernel_size=6, strides=2, padding=2))
netD.add(nn.BatchNorm())
netD.add(nn.LeakyReLU(.2))
netD.add(nn.Conv2D(channels=1, kernel_size=6))
#===============generator==================
netG = nn.Sequential()
with netG.name_scope():
netG.add(nn.Conv2DTranspose(channels=1024, kernel_size=6))
netG.add(nn.BatchNorm())
netG.add(nn.Activation(activation='relu'))
netG.add(
nn.Conv2DTranspose(channels=512, kernel_size=6, strides=2, padding=2))
netG.add(nn.BatchNorm())
netG.add(nn.Activation(activation='relu'))
netG.add(
nn.Conv2DTranspose(channels=256, kernel_size=6, strides=2, padding=2))
netG.add(nn.BatchNorm())
netG.add(nn.Activation(activation='relu'))
netG.add(
nn.Conv2DTranspose(channels=128, kernel_size=6, strides=2, padding=2))
netG.add(nn.BatchNorm())
netG.add(nn.Activation(activation='relu'))
netG.add(
nn.Conv2DTranspose(channels=3, kernel_size=6, strides=2, padding=2))
netG.add(nn.BatchNorm())
def __init__(self,
levels,
channels,
classes=1000,
block=BasicBlock,
momentum=0.9,
norm_layer=BatchNorm,
norm_kwargs=None,
residual_root=False,
linear_root=False,
use_feature=False,
**kwargs):
super(DLA, self).__init__(**kwargs)
if norm_kwargs is None:
norm_kwargs = {}
norm_kwargs['momentum'] = momentum
self._use_feature = use_feature
self.channels = channels
self.base_layer = nn.HybridSequential('base')
self.base_layer.add(
nn.Conv2D(in_channels=3,
channels=channels[0],
kernel_size=7,
strides=1,
padding=3,
use_bias=False))
self.base_layer.add(norm_layer(in_channels=channels[0], **norm_kwargs))
self.base_layer.add(nn.Activation('relu'))
self.level0 = self._make_conv_level(channels[0], channels[0],
levels[0], norm_layer, norm_kwargs)
self.level1 = self._make_conv_level(channels[0],
channels[1],
levels[1],
norm_layer,
norm_kwargs,
stride=2)
self.level2 = Tree(levels[2],
block,
channels[1],
channels[2],
2,
level_root=False,
root_residual=residual_root,
norm_layer=norm_layer,
norm_kwargs=norm_kwargs,
prefix='level2_')
self.level3 = Tree(levels[3],
block,
channels[2],
channels[3],
2,
level_root=True,
root_residual=residual_root,
norm_layer=norm_layer,
norm_kwargs=norm_kwargs,
prefix='level3_')
self.level4 = Tree(levels[4],
block,
channels[3],
channels[4],
2,
level_root=True,
root_residual=residual_root,
norm_layer=norm_layer,
norm_kwargs=norm_kwargs,
prefix='level4_')
self.level5 = Tree(levels[5],
block,
channels[4],
channels[5],
2,
level_root=True,
root_residual=residual_root,
norm_layer=norm_layer,
norm_kwargs=norm_kwargs,
prefix='level5_')
if not self._use_feature:
self.global_avg_pool = nn.GlobalAvgPool2D()
self.fc = nn.Dense(units=classes)
def conv_block(channels):
out = nn.Sequential()
out.add(nn.BatchNorm(), nn.Activation('relu'),
nn.Conv2D(channels, kernel_size=3, padding=1))
return out
def conv_1x1_bn(channels, activation=nn.Activation('relu')):
out = nn.HybridSequential()
out.add(nn.Conv2D(channels, 1, 1, 0, use_bias=False),
nn.BatchNorm(scale=True), activation)
return out
def __init__(self, in_channels, bn_use_global_stats=False, **kwargs):
super(PreActivation, self).__init__(**kwargs)
with self.name_scope():
self.bn = nn.BatchNorm(in_channels=in_channels,
use_global_stats=bn_use_global_stats)
self.activ = nn.Activation("relu")
def conv_bn(channels, filter_size, stride, activation=nn.Activation('relu')):
out = nn.HybridSequential()
out.add(nn.Conv2D(channels, 3, stride, 1, use_bias=False),
nn.BatchNorm(scale=True), activation)
return out
def __init__(self, features, top_features, classes, box_features=None,
short=600, max_size=1000, min_stage=4, max_stage=4, train_patterns=None,
nms_thresh=0.3, nms_topk=400, post_nms=100, roi_mode='align', roi_size=(14, 14), strides=16,
clip=None, rpn_channel=1024, base_size=16, scales=(8, 16, 32),
ratios=(0.5, 1, 2), alloc_size=(128, 128), rpn_nms_thresh=0.7,
rpn_train_pre_nms=12000, rpn_train_post_nms=2000, rpn_test_pre_nms=6000,
rpn_test_post_nms=300, rpn_min_size=16, per_device_batch_size=1, num_sample=128,
pos_iou_thresh=0.5, pos_ratio=0.25, max_num_gt=300, additional_output=False,
force_nms=False, minimal_opset=False, **kwargs):
super(DoubleHeadRCNN, self).__init__(
features=features, top_features=top_features, classes=classes,
box_features=box_features, short=short, max_size=max_size,
train_patterns=train_patterns, nms_thresh=nms_thresh, nms_topk=nms_topk, post_nms=post_nms,
roi_mode=roi_mode, roi_size=roi_size, strides=strides, clip=clip, force_nms=force_nms,
minimal_opset=minimal_opset, **kwargs)
if max_stage - min_stage > 1 and isinstance(strides, (int, float)):
raise ValueError('Multi level detected but strides is of a single number:', strides)
if rpn_train_post_nms > rpn_train_pre_nms:
rpn_train_post_nms = rpn_train_pre_nms
if rpn_test_post_nms > rpn_test_pre_nms:
rpn_test_post_nms = rpn_test_pre_nms
self.ashape = alloc_size[0]
self._min_stage = min_stage
self._max_stage = max_stage
self.num_stages = max_stage - min_stage + 1
if self.num_stages > 1:
assert len(scales) == len(strides) == self.num_stages, \
"The num_stages (%d) must match number of scales (%d) and strides (%d)" \
% (self.num_stages, len(scales), len(strides))
self._batch_size = per_device_batch_size
self._num_sample = num_sample
self._rpn_test_post_nms = rpn_test_post_nms
if minimal_opset:
self._target_generator = None
else:
self._target_generator = lambda: RCNNTargetGenerator(self.num_class,
int(num_sample * pos_ratio),
self._batch_size)
self._additional_output = additional_output
with self.name_scope():
self.rpn = RPN(
channels=rpn_channel, strides=strides, base_size=base_size,
scales=scales, ratios=ratios, alloc_size=alloc_size,
clip=clip, nms_thresh=rpn_nms_thresh, train_pre_nms=rpn_train_pre_nms,
train_post_nms=rpn_train_post_nms, test_pre_nms=rpn_test_pre_nms,
test_post_nms=rpn_test_post_nms, min_size=rpn_min_size,
multi_level=self.num_stages > 1, per_level_nms=False,
minimal_opset=minimal_opset)
self.sampler = RCNNTargetSampler(num_image=self._batch_size,
num_proposal=rpn_train_post_nms, num_sample=num_sample,
pos_iou_thresh=pos_iou_thresh, pos_ratio=pos_ratio,
max_num_gt=max_num_gt)
# double head branch with class and box
self.class_features = nn.HybridSequential(prefix='double_fc_')
with self.class_features.name_scope():
for _ in range(2):
self.class_features.add(nn.Dense(1024, weight_initializer=mx.init.Normal(0.01)))
self.class_features.add(nn.Activation('relu'))
self.newbox_features = nn.HybridSequential(prefix='double_')
with self.newbox_features.name_scope():
for _ in range(2):
self.newbox_features.add(BottleneckV1(channels=1024, stride=1))
def __init__(self,
inplanes,
planes,
spatial_stride=1,
temporal_stride=1,
dilation=1,
downsample=None,
if_inflate=True,
inflate_style='3x1x1',
if_nonlocal=True,
nonlocal_cfg=None,
norm_layer=BatchNorm,
norm_kwargs=None,
layer_name='',
**kwargs):
super(Bottleneck, self).__init__()
assert inflate_style in ['3x1x1', '3x3x3']
self.inplanes = inplanes
self.planes = planes
self.conv1_stride = 1
self.conv2_stride = spatial_stride
self.conv1_stride_t = 1
self.conv2_stride_t = temporal_stride
self.layer_name = layer_name
if if_inflate:
if inflate_style == '3x1x1':
self.conv1 = nn.Conv3D(in_channels=inplanes,
channels=planes,
kernel_size=(3, 1, 1),
strides=(self.conv1_stride_t,
self.conv1_stride,
self.conv1_stride),
padding=(1, 0, 0),
use_bias=False)
self.conv2 = nn.Conv3D(in_channels=planes,
channels=planes,
kernel_size=(1, 3, 3),
strides=(self.conv2_stride_t,
self.conv2_stride,
self.conv2_stride),
padding=(0, dilation, dilation),
dilation=(1, dilation, dilation),
use_bias=False)
else:
self.conv1 = nn.Conv3D(in_channels=inplanes,
channels=planes,
kernel_size=1,
strides=(self.conv1_stride_t,
self.conv1_stride,
self.conv1_stride),
use_bias=False)
self.conv2 = nn.Conv3D(in_channels=planes,
channels=planes,
kernel_size=3,
strides=(self.conv2_stride_t,
self.conv2_stride,
self.conv2_stride),
padding=(1, dilation, dilation),
dilation=(1, dilation, dilation),
use_bias=False)
else:
self.conv1 = nn.Conv3D(in_channels=inplanes,
channels=planes,
kernel_size=1,
strides=(1, self.conv1_stride,
self.conv1_stride),
use_bias=False)
self.conv2 = nn.Conv3D(in_channels=planes,
channels=planes,
kernel_size=(1, 3, 3),
strides=(1, self.conv2_stride,
self.conv2_stride),
padding=(0, dilation, dilation),
dilation=(1, dilation, dilation),
use_bias=False)
self.bn1 = norm_layer(in_channels=planes,
**({} if norm_kwargs is None else norm_kwargs))
self.bn2 = norm_layer(in_channels=planes,
**({} if norm_kwargs is None else norm_kwargs))
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))
self.relu = nn.Activation('relu')
self.downsample = downsample
self.spatial_tride = spatial_stride
self.temporal_tride = temporal_stride
self.dilation = dilation
if if_nonlocal and nonlocal_cfg is not None:
nonlocal_cfg_ = nonlocal_cfg.copy()
nonlocal_cfg_['in_channels'] = planes * self.expansion
self.nonlocal_block = build_nonlocal_block(nonlocal_cfg_)
else:
self.nonlocal_block = None
use_gpu = True
ctx = mx.gpu() if use_gpu else mx.cpu()
#%%
# build the generator
nc = 3 # number of channel
ngf = 64 # final image width X height
netG = nn.Sequential() # sequential model
# simple generator. Use any models but should upscale the late
# nt variable(randome vectors) to 64 * 64 * 3 channel image
with netG.name_scope():
# input is random_z (batchsize X 150 X 1), going into a tranposed convolution
netG.add(nn.Conv2DTranspose(ngf * 8, 4, 1, 0))
netG.add(nn.BatchNorm())
netG.add(nn.Activation('relu'))
# output size. (ngf*8) x 4 x 4
netG.add(nn.Conv2DTranspose(ngf * 4, 4, 2, 1))
netG.add(nn.BatchNorm())
netG.add(nn.Activation('relu'))
# output size. (ngf*8) x 8 x 8
netG.add(nn.Conv2DTranspose(ngf * 2, 4, 2, 1))
netG.add(nn.BatchNorm())
netG.add(nn.Activation('relu'))
# output size. (ngf*8) x 16 x 16
netG.add(nn.Conv2DTranspose(ngf, 4, 2, 1))
netG.add(nn.BatchNorm())
netG.add(nn.Activation('relu'))
# output size. (ngf*8) x 32 x 32
netG.add(nn.Conv2DTranspose(nc, 4, 2, 1))
netG.add(
def __init__(self,
block,
layers,
classes=1000,
dilated=False,
norm_layer=BatchNorm,
norm_kwargs=None,
last_gamma=False,
deep_stem=False,
stem_width=32,
avg_down=False,
final_drop=0.0,
use_global_stats=False,
name_prefix='',
**kwargs):
self.inplanes = stem_width * 2 if deep_stem else 64
super(ResNetV1b, self).__init__(prefix=name_prefix)
norm_kwargs = norm_kwargs if norm_kwargs is not None else {}
if use_global_stats:
norm_kwargs['use_global_stats'] = True
self.norm_kwargs = norm_kwargs
with self.name_scope():
if not deep_stem:
self.conv1 = nn.Conv2D(channels=64,
kernel_size=7,
strides=2,
padding=3,
use_bias=False)
else:
self.conv1 = nn.HybridSequential(prefix='conv1')
self.conv1.add(
nn.Conv2D(channels=stem_width,
kernel_size=3,
strides=2,
padding=1,
use_bias=False))
self.conv1.add(
norm_layer(in_channels=stem_width, **norm_kwargs))
self.conv1.add(nn.Activation('relu'))
self.conv1.add(
nn.Conv2D(channels=stem_width,
kernel_size=3,
strides=1,
padding=1,
use_bias=False))
self.conv1.add(
norm_layer(in_channels=stem_width, **norm_kwargs))
self.conv1.add(nn.Activation('relu'))
self.conv1.add(
nn.Conv2D(channels=stem_width * 2,
kernel_size=3,
strides=1,
padding=1,
use_bias=False))
self.bn1 = norm_layer(
in_channels=64 if not deep_stem else stem_width * 2,
**norm_kwargs)
self.relu = nn.Activation('relu')
self.maxpool = nn.MaxPool2D(pool_size=3, strides=2, padding=1)
self.layer1 = self._make_layer(1,
block,
64,
layers[0],
avg_down=avg_down,
norm_layer=norm_layer,
last_gamma=last_gamma)
self.layer2 = self._make_layer(2,
block,
128,
layers[1],
strides=2,
avg_down=avg_down,
norm_layer=norm_layer,
last_gamma=last_gamma)
if dilated:
self.layer3 = self._make_layer(3,
block,
256,
layers[2],
strides=1,
dilation=2,
avg_down=avg_down,
norm_layer=norm_layer,
last_gamma=last_gamma)
self.layer4 = self._make_layer(4,
block,
512,
layers[3],
strides=1,
dilation=4,
avg_down=avg_down,
norm_layer=norm_layer,
last_gamma=last_gamma)
else:
self.layer3 = self._make_layer(3,
block,
256,
layers[2],
strides=2,
avg_down=avg_down,
norm_layer=norm_layer,
last_gamma=last_gamma)
self.layer4 = self._make_layer(4,
block,
512,
layers[3],
strides=2,
avg_down=avg_down,
norm_layer=norm_layer,
last_gamma=last_gamma)
self.avgpool = nn.GlobalAvgPool2D()
self.flat = nn.Flatten()
self.drop = None
if final_drop > 0.0:
self.drop = nn.Dropout(final_drop)
def __init__(self,
bits,
bits_a,
num_init_features,
growth_rate,
block_config,
reduction,
bn_size,
modifier=[],
thumbnail=False,
dropout=0,
classes=1000,
**kwargs):
assert len(modifier) == 0
super(DenseNetX, self).__init__(**kwargs)
with self.name_scope():
self.fp_features = nn.HybridSequential(prefix='')
if thumbnail:
self.fp_features.add(
nn.Conv2D(num_init_features,
kernel_size=3,
strides=1,
padding=1,
in_channels=0,
use_bias=False))
else:
self.fp_features.add(
nn.Conv2D(num_init_features,
kernel_size=7,
strides=2,
padding=3,
use_bias=False))
self.fp_features.add(nn.BatchNorm())
self.fp_features.add(nn.Activation('relu'))
self.fp_features.add(
nn.MaxPool2D(pool_size=3, strides=2, padding=1))
# Add dense blocks
num_features = num_init_features
self.features1 = nn.HybridSequential(prefix='')
self.features2 = nn.HybridSequential(prefix='')
add_to = self.features1
for i, num_layers in enumerate(block_config):
add_to.add(
_make_dense_block(bits, bits_a, num_layers, bn_size,
growth_rate, dropout, i + 1))
num_features = num_features + num_layers * growth_rate
if i != len(block_config) - 1:
features_after_transition = num_features // reduction[i]
# make it to be multiples of 32
features_after_transition = int(
round(features_after_transition / 32)) * 32
if i == 0:
add_to.add(nn.BatchNorm())
add_to.add(nn.QActivation(bits=bits_a))
add_to.add(
nn.QConv2D(features_after_transition,
bits=bits,
kernel_size=1))
add_to = self.features2
add_to.add(nn.AvgPool2D(pool_size=2, strides=2))
else:
add_to.add(nn.BatchNorm())
add_to.add(nn.QActivation(bits=bits_a))
add_to.add(
nn.QConv2D(features_after_transition,
bits=bits,
kernel_size=1))
add_to.add(nn.AvgPool2D(pool_size=2, strides=2))
num_features = features_after_transition
add_to.add(nn.BatchNorm())
add_to.add(nn.Activation('relu'))
add_to.add(nn.AvgPool2D(pool_size=4 if thumbnail else 7))
add_to.add(nn.Flatten())
self.output = nn.Dense(classes)
def __init__(self,
units,
act=nn.Activation('tanh'),
normalized=False,
dropout=0.0,
weight_initializer=None,
bias_initializer='zeros',
prefix=None,
params=None):
# Define a temporary class to implement the normalized version
# TODO(sxjscience) Find a better solution
class _NormalizedScoreProj(HybridBlock):
def __init__(self,
in_units,
weight_initializer=None,
prefix=None,
params=None):
super(_NormalizedScoreProj, self).__init__(prefix=prefix,
params=params)
self.g = self.params.get('g',
shape=(1, ),
init=mx.init.Constant(
1.0 / math.sqrt(in_units)),
allow_deferred_init=True)
self.v = self.params.get('v',
shape=(1, in_units),
init=weight_initializer,
allow_deferred_init=True)
def hybrid_forward(self, F, x, g, v): # pylint: disable=arguments-differ
v = F.broadcast_div(v, F.sqrt(F.dot(v, v, transpose_b=True)))
weight = F.broadcast_mul(g, v)
out = F.FullyConnected(x,
weight,
None,
no_bias=True,
num_hidden=1,
flatten=False,
name='fwd')
return out
super(MLPAttentionCell, self).__init__(prefix=prefix, params=params)
self._units = units
self._act = act
self._normalized = normalized
self._dropout = dropout
with self.name_scope():
self._dropout_layer = nn.Dropout(dropout)
self._query_mid_layer = nn.Dense(
units=self._units,
flatten=False,
use_bias=True,
weight_initializer=weight_initializer,
bias_initializer=bias_initializer,
prefix='query_')
self._key_mid_layer = nn.Dense(
units=self._units,
flatten=False,
use_bias=False,
weight_initializer=weight_initializer,
prefix='key_')
if self._normalized:
self._attention_score = \
_NormalizedScoreProj(in_units=units,
weight_initializer=weight_initializer,
prefix='score_')
else:
self._attention_score = nn.Dense(
units=1,
in_units=self._units,
flatten=False,
use_bias=False,
weight_initializer=weight_initializer,
prefix='score_')
def _make_basic_conv(in_channels, channels, norm_layer=BatchNorm, norm_kwargs=None, **kwargs):
out = nn.HybridSequential(prefix='')
out.add(nn.Conv3D(in_channels=in_channels, channels=channels, use_bias=False, **kwargs))
out.add(norm_layer(in_channels=channels, epsilon=0.001, **({} if norm_kwargs is None else norm_kwargs)))
out.add(nn.Activation('relu'))
return out
def __init__(self,
scale,
m,
classes=10,
norm_layer=BatchNorm,
norm_kwargs=None,
**kwargs):
super(cifar_ResidualAttentionModel, self).__init__(**kwargs)
assert len(scale) == 3 and len(m) == 3
m1, m2, m3 = m
with self.name_scope():
self.conv1 = nn.HybridSequential()
with self.conv1.name_scope():
self.conv1.add(
nn.Conv2D(32,
kernel_size=3,
strides=1,
padding=1,
use_bias=False))
self.conv1.add(
norm_layer(**({} if norm_kwargs is None else norm_kwargs)))
self.conv1.add(nn.Activation('relu'))
# 32 x 32
# self.mpool1 = nn.MaxPool2D(pool_size=2, strides=2, padding=0)
self.residual_block1 = ResidualBlock(128, in_channels=32)
self.attention_module1 = nn.HybridSequential()
_add_block(self.attention_module1,
AttentionModule_stage2,
m1,
128,
size1=32,
size2=16,
scale=scale,
norm_layer=norm_layer,
norm_kwargs=norm_kwargs)
self.residual_block2 = ResidualBlock(256,
in_channels=128,
stride=2)
self.attention_module2 = nn.HybridSequential()
_add_block(self.attention_module2,
AttentionModule_stage3,
m2,
256,
size1=16,
scale=scale,
norm_layer=norm_layer,
norm_kwargs=norm_kwargs)
self.residual_block3 = ResidualBlock(512,
in_channels=256,
stride=2)
self.attention_module3 = nn.HybridSequential()
_add_block(self.attention_module3,
AttentionModule_stage4,
m3,
512,
scale=scale,
norm_layer=norm_layer,
norm_kwargs=norm_kwargs)
self.residual_block4 = ResidualBlock(1024, in_channels=512)
self.residual_block5 = ResidualBlock(1024)
self.residual_block6 = ResidualBlock(1024)
self.mpool2 = nn.HybridSequential()
with self.mpool2.name_scope():
self.mpool2.add(
norm_layer(**({} if norm_kwargs is None else norm_kwargs)))
self.mpool2.add(nn.Activation('relu'))
self.mpool2.add(nn.AvgPool2D(pool_size=8, strides=1))
self.fc = nn.Conv2D(classes, kernel_size=1)
def __init__(self, **kwargs):
super(GRP_DSOD320, self).__init__(**kwargs)
growth_rate = 48
dropout = 0
nchannels = 128
with self.name_scope():
self.net0 = nn.HybridSequential()
self.net0.add(
nn.Conv2D(64,
3,
strides=2,
padding=1,
use_bias=False,
weight_initializer='xavier'),
nn.BatchNorm(epsilon=1e-4), nn.Activation('relu'),
nn.Conv2D(64,
3,
strides=1,
padding=1,
use_bias=False,
weight_initializer='xavier'),
nn.BatchNorm(epsilon=1e-4), nn.Activation('relu'),
nn.Conv2D(128,
3,
strides=1,
padding=1,
use_bias=False,
weight_initializer='xavier'),
nn.BatchNorm(epsilon=1e-4), nn.Activation('relu'))
self.net1 = nn.HybridSequential()
self.net1.add(
nn.MaxPool2D(pool_size=(2, 2), strides=(2, 2), ceil_mode=True))
times = 1
for i in range(6):
self.net1.add(bl_layer_block(growth_rate, dropout, 4))
nchannels += growth_rate
nchannels = int(nchannels / times)
self.net1.add(transition_w_o_block(nchannels, dropout))
self.net2 = nn.HybridSequential()
self.net2.add(
nn.MaxPool2D(pool_size=(2, 2), strides=(2, 2), ceil_mode=True))
for i in range(8):
self.net2.add(bl_layer_block(growth_rate, dropout, 4))
nchannels += growth_rate
nchannels = int(nchannels / times)
self.net2.add(transition_w_o_block(nchannels, dropout))
self.extra0 = nn.HybridSequential()
self.extra0.add(
nn.MaxPool2D(pool_size=(4, 4), strides=(4, 4), ceil_mode=True),
conv_block(kernel_size=1,
channels=128,
stride=1,
pad=0,
dropout=dropout))
self.extra1 = nn.HybridSequential()
self.extra1.add(
nn.MaxPool2D(pool_size=(2, 2), strides=(2, 2), ceil_mode=True),
conv_block(kernel_size=1,
channels=128,
stride=1,
pad=0,
dropout=dropout))
self.net3 = nn.HybridSequential()
self.net3.add(
nn.MaxPool2D(pool_size=(2, 2), strides=(2, 2), ceil_mode=True))
for i in range(8):
self.net3.add(bl_layer_block(growth_rate, dropout, 4))
nchannels += growth_rate
nchannels = int(nchannels / times)
self.net3.add(transition_w_o_block(nchannels, dropout))
for i in range(8):
self.net3.add(bl_layer_block(growth_rate, dropout, 4))
nchannels += growth_rate
self.net3.add(transition_w_o_block(171, dropout))
self.first = nn.HybridSequential()
self.first.add(
nn.MaxPool2D(pool_size=(2, 2), strides=(2, 2), ceil_mode=True),
conv_block(kernel_size=1,
channels=171,
stride=1,
pad=0,
dropout=dropout))
self.net4 = nn.HybridSequential()
self.net4.add(bl_layer_block2(86, dropout, 1))
self.net5 = nn.HybridSequential()
self.net5.add(bl_layer_block2(86, dropout, 1))
self.net6 = nn.HybridSequential()
self.net6.add(bl_layer_block2(86, dropout, 1))
self.net7 = nn.HybridSequential()
self.net7.add(bl_layer_block2(128, dropout, 1))
self.Recurrent1 = nn.HybridSequential()
self.Recurrent1.add(recurrent_layer(2, 128, dropout))
self.Recurrent2 = nn.HybridSequential()
self.Recurrent2.add(recurrent_layer(2, 171, dropout))
self.Recurrent3 = nn.HybridSequential()
self.Recurrent3.add(recurrent_layer(2, 86, dropout))
self.Recurrent4 = nn.HybridSequential()
self.Recurrent4.add(recurrent_layer(1, 86, dropout))
self.Recurrent5 = nn.HybridSequential()
self.Recurrent5.add(recurrent_layer(1, 86, dropout))
def __init__(
self,
bin_values: mx.nd.NDArray,
n_residue: int,
n_skip: int,
dilation_depth: int,
n_stacks: int,
act_type: str,
cardinality: List[int],
embedding_dimension: int,
pred_length: int,
**kwargs,
):
super().__init__(**kwargs)
self.dilation_depth = dilation_depth
self.pred_length = pred_length
self.mu = len(bin_values)
self.dilations = WaveNet._get_dilations(
dilation_depth=dilation_depth, n_stacks=n_stacks
)
self.receptive_field = WaveNet.get_receptive_field(
dilation_depth=dilation_depth, n_stacks=n_stacks
)
self.trim_lengths = [
sum(self.dilations) - sum(self.dilations[: i + 1])
for i, _ in enumerate(self.dilations)
]
with self.name_scope():
self.feature_embedder = FeatureEmbedder(
cardinalities=cardinality,
embedding_dims=[embedding_dimension for _ in cardinality],
)
self.post_transform = LookupValues(bin_values)
self.target_embed = nn.Embedding(
input_dim=self.mu, output_dim=n_residue
)
self.residuals = nn.HybridSequential()
for i, d in enumerate(self.dilations):
is_not_last = i + 1 < len(self.dilations)
self.residuals.add(
CausalDilatedResidue(
n_residue=n_residue,
n_skip=n_skip,
dilation=d,
return_dense_out=is_not_last,
kernel_size=2,
)
)
# heuristic assuming ~5 features
std = 1.0 / math.sqrt(n_residue + 5)
self.conv_project = nn.Conv1D(
channels=n_residue,
kernel_size=1,
use_bias=True,
weight_initializer=mx.init.Uniform(std),
bias_initializer="zero",
)
self.conv1 = conv1d(
in_channels=n_skip, channels=n_skip, kernel_size=1
)
self.conv2 = conv1d(
in_channels=n_skip, channels=self.mu, kernel_size=1
)
self.output_act = (
nn.ELU()
if act_type == "elu"
else nn.Activation(act_type=act_type)
)
self.cross_entropy_loss = gluon.loss.SoftmaxCrossEntropyLoss()
return X
#Transition_block to decrease the complexity
def transition_block(num_channels):
blk = nn.Sequential()
blk.add(nn.BatchNorm(), nn.Activation('relu'),
nn.Conv2D(num_channels, kernel_size=1),
nn.AvgPool2D(pool_size=2, strides=2))
return blk
#Build DenseNet
net = nn.Sequential()
net.add(nn.Conv2D(64, kernel_size=7, strides=2, padding=3), nn.BatchNorm(),
nn.Activation('relu'), nn.MaxPool2D(pool_size=3, strides=2, padding=1))
num_channels, growth_rate = 64, 32 #
num_convs_in_dense_blocks = [4, 4, 4, 4]
for i, num_convs in enumerate(num_convs_in_dense_blocks):
net.add(DenseBlock(num_convs, growth_rate))
# Record channels of last block
num_channels += num_convs * growth_rate
# Add transition block between dense blocks
if i != len(num_convs_in_dense_blocks) - 1:
num_channels //= 2
net.add(transition_block(num_channels))
net.add(nn.BatchNorm(), nn.Activation('relu'), nn.GlobalAvgPool2D(),
nn.Dense(10))
def __init__(self,
repeat=6,
penultimate_filters=4032,
stem_filters=96,
filters_multiplier=2,
classes=1000,
use_aux=True):
super(NASNetALarge, self).__init__()
filters = penultimate_filters // 24
self.conv0 = nn.HybridSequential(prefix='')
self.conv0.add(
nn.Conv2D(stem_filters, 3, padding=0, strides=2, use_bias=False))
self.conv0.add(nn.BatchNorm(momentum=0.1, epsilon=0.001))
self.cell_stem_0 = CellStem0(stem_filters,
num_filters=filters //
(filters_multiplier**2))
self.cell_stem_1 = CellStem1(num_filters=filters // filters_multiplier)
self.norm_1 = nn.HybridSequential(prefix='')
self.norm_1.add(
FirstCell(out_channels_left=filters // 2,
out_channels_right=filters))
for _ in range(repeat - 1):
self.norm_1.add(
NormalCell(out_channels_left=filters,
out_channels_right=filters))
self.reduction_cell_0 = ReductionCell0(out_channels_left=2 * filters,
out_channels_right=2 * filters)
self.norm_2 = nn.HybridSequential(prefix='')
self.norm_2.add(
FirstCell(out_channels_left=filters,
out_channels_right=2 * filters))
for _ in range(repeat - 1):
self.norm_2.add(
NormalCell(out_channels_left=2 * filters,
out_channels_right=2 * filters))
if use_aux:
self.out_aux = nn.HybridSequential(prefix='')
self.out_aux.add(
nn.Conv2D(filters // 3, kernel_size=1, use_bias=False))
self.out_aux.add(nn.BatchNorm(epsilon=0.001))
self.out_aux.add(nn.Activation('relu'))
self.out_aux.add(
nn.Conv2D(2 * filters, kernel_size=5, use_bias=False))
self.out_aux.add(nn.BatchNorm(epsilon=0.001))
self.out_aux.add(nn.Activation('relu'))
self.out_aux.add(nn.Dense(classes))
else:
self.out_aux = None
self.reduction_cell_1 = ReductionCell1(out_channels_left=4 * filters,
out_channels_right=4 * filters)
self.norm_3 = nn.HybridSequential(prefix='')
self.norm_3.add(
FirstCell(out_channels_left=2 * filters,
out_channels_right=4 * filters))
for _ in range(repeat - 1):
self.norm_3.add(
NormalCell(out_channels_left=4 * filters,
out_channels_right=4 * filters))
self.out = nn.HybridSequential(prefix='')
self.out.add(nn.Activation('relu'))
self.out.add(nn.GlobalAvgPool2D())
self.out.add(nn.Dropout(0.5))
self.out.add(nn.Dense(classes))
def transition_block(num_channels):
blk = nn.Sequential()
blk.add(nn.BatchNorm(), nn.Activation('relu'),
nn.Conv2D(num_channels, kernel_size=1),
nn.AvgPool2D(pool_size=2, strides=2))
return blk
def __init__(self, channels, **kwargs):
super(PreActivation, self).__init__(**kwargs)
with self.name_scope():
self.bn = dpn_batch_norm(channels=channels)
self.activ = nn.Activation('relu')
def forward(self, X):
# 如果X不在内存上，将moving_mean和moving_var复制到X所在显存上
if self.moving_mean.context != X.context:
self.moving_mean = self.moving_mean.copyto(X.context)
self.moving_var = self.moving_var.copyto(X.context)
# 保存更新过的moving_mean和moving_var
Y, self.moving_mean, self.moving_var = batch_norm(X,
self.gamma.data(),
self.beta.data(),
self.moving_mean,
self.moving_var,
eps=1e-5,
momentum=0.9)
return Y
net = nn.Sequential()
net.add(nn.Conv2D(6, kernel_size=5), BatchNorm(6, num_dims=4),
nn.Activation('sigmoid'), nn.MaxPool2D(pool_size=2, strides=2),
nn.Conv2D(16, kernel_size=5), BatchNorm(16, num_dims=4),
nn.Activation('sigmoid'), nn.MaxPool2D(pool_size=2, strides=2),
nn.Dense(120), BatchNorm(120, num_dims=2), nn.Activation('sigmoid'),
nn.Dense(84), BatchNorm(84, num_dims=2), nn.Activation('sigmoid'),
nn.Dense(10))
lr, num_epochs, batch_size, ctx = 1.0, 5, 256, d2l.try_gpu()
net.initialize(ctx=ctx, init=init.Xavier())
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': lr})
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
d2l.train_ch5(net, train_iter, test_iter, batch_size, trainer, ctx, num_epochs)
def _make_basic_conv(**kwargs):
out = nn.HybridSequential(prefix='')
out.add(nn.QConv2D(use_bias=False, **kwargs))
out.add(nn.BatchNorm(epsilon=0.001))
out.add(nn.Activation('relu'))
return out
def __init__(self,
num_filter,
stride,
dim_match,
isBin=False,
prefix='',
**kwargs):
super(ResidualUnit, self).__init__(**kwargs)
self.dim_match = dim_match
self.features = nn.HybridSequential()
self.bn1 = nn.BatchNorm(use_global_stats=use_global_stats,
prefix=prefix + '_nb1_')
self.act1 = nn.Activation('relu')
self.scale = nn.Conv2D(channels=num_filter,
kernel_size=(1, 1),
strides=stride,
use_bias=False,
prefix=prefix + '_sc_')
if isBin:
self.features.add(
nn.QConv2D(channels=int(num_filter * 0.25),
kernel_size=(1, 1),
strides=(1, 1),
padding=(0, 0),
use_bias=False,
apply_scaling=True,
prefix=prefix + '_conv1_'))
self.features.add(
nn.BatchNorm(use_global_stats=use_global_stats,
prefix=prefix + '_nb2_'))
self.features.add(nn.Activation('relu'))
self.features.add(
nn.QConv2D(channels=int(num_filter * 0.25),
kernel_size=(3, 3),
strides=stride,
padding=(1, 1),
use_bias=False,
apply_scaling=True,
prefix=prefix + '_conv2_'))
self.features.add(
nn.BatchNorm(use_global_stats=use_global_stats,
prefix=prefix + '_nb3_'))
self.features.add(nn.Activation('relu'))
self.features.add(
nn.QConv2D(channels=num_filter,
kernel_size=(1, 1),
strides=(1, 1),
padding=(0, 0),
use_bias=False,
apply_scaling=True,
prefix=prefix + '_conv3_'))
else:
self.features.add(
nn.Conv2D(channels=int(num_filter * 0.25),
kernel_size=(1, 1),
strides=(1, 1),
padding=(0, 0),
use_bias=False,
prefix=prefix + '_conv1_'))
self.features.add(
nn.BatchNorm(use_global_stats=use_global_stats,
prefix=prefix + '_nb2_'))
self.features.add(nn.Activation('relu'))
self.features.add(
nn.Conv2D(channels=int(num_filter * 0.25),
kernel_size=(3, 3),
strides=stride,
padding=(1, 1),
use_bias=False,
prefix=prefix + '_conv2_'))
self.features.add(
nn.BatchNorm(use_global_stats=use_global_stats,
prefix=prefix + '_nb3_'))
self.features.add(nn.Activation('relu'))
self.features.add(
nn.Conv2D(channels=num_filter,
kernel_size=(1, 1),
strides=(1, 1),
padding=(0, 0),
use_bias=False,
prefix=prefix + '_conv3_'))
def __init__(
self,
num_scenes,
num_actions,
model_depth,
final_spatial_kernel=7,
final_temporal_kernel=2,
with_bias=False,
):
super(R2Plus2D_MT, self).__init__()
self.comp_count = 0
self.base = nn.Sequential(prefix='base_')
with self.base.name_scope():
self.base.add(
nn.Conv3D(channels=45,
kernel_size=(1, 7, 7),
strides=(1, 2, 2),
padding=(0, 3, 3),
use_bias=with_bias), nn.BatchNorm(),
nn.Activation(activation='relu'),
nn.Conv3D(channels=64,
kernel_size=(3, 1, 1),
strides=(1, 1, 1),
padding=(1, 0, 0),
use_bias=with_bias), nn.BatchNorm(),
nn.Activation(activation='relu'))
self.base_name = self.set_base_name()
(n2, n3, n4, n5) = BLOCK_CONFIG[model_depth]
self.conv2_name = []
self.conv2 = nn.Sequential(prefix='conv2_')
with self.conv2.name_scope():
for _ in range(n2):
self.conv2_name.extend(
self.add_comp_count_index(change_channels=False,
comp_index=self.comp_count,
prefix=self.conv2.prefix))
self.conv2.add(
R3DBlock(input_filter=64,
num_filter=64,
comp_index=self.comp_count,
use_bias=with_bias))
self.comp_count += 1
#self.conv3
self.conv3_name = []
self.conv3 = nn.Sequential(prefix='conv3_')
with self.conv3.name_scope():
print("this in conv3 comp_count is ", self.comp_count)
self.conv3_name.extend(
self.add_comp_count_index(change_channels=True,
downsampling=True,
comp_index=self.comp_count))
self.conv3.add(
R3DBlock(input_filter=64,
num_filter=128,
comp_index=self.comp_count,
downsampling=True,
use_bias=with_bias))
self.comp_count += 1
for _ in range(n3 - 1):
self.conv3_name.extend(
self.add_comp_count_index(change_channels=False,
downsampling=False,
comp_index=self.comp_count))
self.conv3.add(
R3DBlock(input_filter=128,
num_filter=128,
comp_index=self.comp_count,
use_bias=with_bias))
self.comp_count += 1
# self.conv4
self.conv4_name = []
self.conv4 = nn.Sequential(prefix='conv4_')
with self.conv4.name_scope():
self.conv4_name.extend(
self.add_comp_count_index(change_channels=True,
downsampling=True,
comp_index=self.comp_count))
self.conv4.add(
R3DBlock(128,
256,
comp_index=self.comp_count,
downsampling=True,
use_bias=with_bias))
self.comp_count += 1
for _ in range(n4 - 1):
self.conv4_name.extend(
self.add_comp_count_index(change_channels=False,
downsampling=False,
comp_index=self.comp_count))
self.conv4.add(
R3DBlock(256,
256,
comp_index=self.comp_count,
use_bias=with_bias))
self.comp_count += 1
#conv5
self.conv5_name = []
self.conv5 = nn.Sequential(prefix='conv5_')
with self.conv5.name_scope():
self.conv5_name.extend(
self.add_comp_count_index(change_channels=True,
downsampling=True,
comp_index=self.comp_count))
self.conv5.add(
R3DBlock(256,
512,
comp_index=self.comp_count,
downsampling=True,
use_bias=with_bias))
self.comp_count += 1
for _ in range(n5 - 1):
self.conv5_name.extend(
self.add_comp_count_index(comp_index=self.comp_count))
self.conv5.add(
R3DBlock(512, 512, self.comp_count, use_bias=with_bias))
self.comp_count += 1
# final output of conv5 is [512,t/8,7,7] #512x1x7x7
# for static scene tagging
self.scene_conv = nn.Sequential()
self.scene_conv.add(
nn.Conv3D(256, kernel_size=(1, 3, 3), strides=(1, 2, 2)),
nn.BatchNorm(),
nn.Activation('relu')) # shape 256*1*2*2 # reshape(1024)
self.scene_drop = nn.Dropout(rate=0.3)
self.scene_output = nn.Dense(num_scenes)
# for action classification
self.action_conv = nn.Sequential()
self.action_conv.add(
nn.Conv3D(512,
kernel_size=(1, 3, 3),
strides=(1, 1, 1),
padding=(0, 1, 1)), nn.BatchNorm(),
nn.Activation('relu'))
self.action_avg = nn.AvgPool3D(pool_size=(final_temporal_kernel,
final_spatial_kernel,
final_spatial_kernel),
strides=(1, 1, 1),
padding=(0, 0, 0))
self.action_output = nn.Dense(units=num_actions)
self.dense0_name = ['final_fc_weight', 'final_fc_bias']
