def __init__(self, inchannel, outchannel, innerchannel, stride=1):
"""Init PruneBasicBlock."""
super(PruneBasicBlock, self).__init__()
conv_block = PruneBasicConv(inchannel, outchannel, innerchannel, stride)
shortcut = ShortCut(inchannel, outchannel, self.expansion, stride)
self.block = Add(conv_block, shortcut)
self.relu3 = ops.Relu()
def __init__(self,
inchannel,
outchannel,
groups=1,
base_width=64,
stride=1,
norm_layer={"norm_type": 'BN'},
Conv2d='Conv2d'):
"""Create BottleneckBlock layers.
:param inchannel: input channel.
:type inchannel: int
:param outchannel: output channel.
:type outchannel: int
:param stride: the number to jump, default 1
:type stride: int
"""
super(BottleneckBlock, self).__init__()
bottle_conv = BottleConv(inchannel=inchannel,
outchannel=outchannel,
expansion=self.expansion,
stride=stride,
groups=groups,
base_width=base_width,
norm_layer=norm_layer,
Conv2d=Conv2d)
shortcut = ShortCut(inchannel=inchannel,
outchannel=outchannel,
expansion=self.expansion,
stride=stride,
norm_layer=norm_layer)
self.block = Add(bottle_conv, shortcut)
self.relu = ops.Relu()
def __init__(self, in_channel, out_channel, upscale, rgb_mean, blocks,
candidates, cib_range, method, code, block_range):
"""Construct the MtMSR class.
:param net_desc: config of the searched structure
"""
super(MtMSR, self).__init__()
logging.info("start init MTMSR")
current_channel = in_channel
layers = list()
for i, block_name in enumerate(blocks):
if isinstance(block_name, list):
layers.append(ChannelIncreaseBlock(block_name,
current_channel))
current_channel *= len(block_name)
else:
if block_name == "res2":
layers.append(
ResidualBlock(kernel_size=2,
base_channel=current_channel))
elif block_name == "res3":
layers.append(
ResidualBlock(kernel_size=3,
base_channel=current_channel))
layers.extend([
conv(current_channel, out_channel * upscale**2),
ops.PixelShuffle(upscale)
])
initialize_weights(layers[-2], 0.1)
self.sub_mean = ops.MeanShift(1.0, rgb_mean)
body = Sequential(*layers)
upsample = ops.InterpolateScale(scale_factor=upscale)
self.add = Add(body, upsample)
self.head = ops.MeanShift(1.0, rgb_mean, sign=1)
def __init__(self,
inchannel,
outchannel,
groups=1,
base_width=64,
stride=1):
"""Create BasicBlock layers.
:param inchannel: input channel.
:type inchannel: int
:param outchannel: output channel.
:type outchannel: int
:param stride: the number to jump, default 1
:type stride: int
"""
super(BasicBlock, self).__init__()
base_conv = BasicConv(inchannel=inchannel,
outchannel=outchannel,
stride=stride,
groups=groups,
base_width=base_width)
shortcut = ShortCut(inchannel=inchannel,
outchannel=outchannel,
expansion=self.expansion,
stride=stride)
self.block = Add(base_conv, shortcut)
self.relu = ops.Relu()