def __init__(self, in_chs, out_chs, kernel_channels=3,
strides=3, padding='valid', pool=False):
"""AlexBlock
Args:
in_chs (Any):
out_chs (Any):
kernel_channels (Any):
strides (Any):
padding:
pool:
Returns:
None
"""
super(AlexBlock, self).__init__()
in_chs = trans_tuple(in_chs)
out_chs = trans_tuple(out_chs)
kernel_channels = trans_tuple(kernel_channels, num_param=len(out_chs))
strides = trans_tuple(strides, num_param=len(out_chs))
self._layers = list([[], []])
for in_ch, out_ch, kernel_size, stride in zip(in_chs, out_chs, kernel_channels, strides):
for i in range(2):
self._layers[i].append(
FeatureExtractor(in_ch, out_ch, kernel_size, stride, padding=padding, bn=True, activation='relu'))
if pool:
self._layers[i].append(MaxPool2D(3, 1, 'same'))
self.addLayers(self._layers[0] + self._layers[1])
def __init__(self, in_ch, out_ch):
"""InceptionBlock_v1A
__init__ 构造函数
Args:
in_ch (None): 输入维度
out_ch (None): 输出维度
"""
super(InceptionBlock_v1A, self).__init__(out_ch)
self.inc_list = [
ConvSameBnRelu2D(in_ch, self.out_ch, 1, stride=2),
ConvSameBnRelu2D(in_ch, self.out_ch, 3, stride=2),
ConvSameBnRelu2D(in_ch, self.out_ch, 5, stride=2),
MaxPool2D(3, stride=2, padding='same')
]
self.addLayers(self.inc_list)
def __init__(self, in_ch, out_ch, kernel_size=3, num_layer=2, pool_size=1, pool_stride=1):
"""VGGPoolBlock
Args:
in_ch:
out_ch:
num_layer:
kernel_size:
Returns:
None
"""
super(VGGPoolBlock, self).__init__()
self.add_module('fe1', ConvSameBnRelu2D(in_ch, out_ch, 3))
if 3 <= num_layer:
self.add_module('fe2', ConvSameBnRelu2D(out_ch, out_ch, 3))
self.add_module(f'fe{num_layer}', ConvSameBnRelu2D(out_ch, out_ch, kernel_size))
self.add_module('max_pool', MaxPool2D(pool_size, pool_stride, padding='same'))
def make_shortcut(in_ch, out_ch, pool_size, residual_path='equal'):
"""make_shortcut
Conv, MaxPool2D, Equ
Args:
in_ch:
out_ch:
pool_size:
residual_path: 'conv', 'pool', 'equal', Default 'equal'
Returns:
None
"""
if residual_path == 'conv':
shortcut = FeatureExtractor(in_ch, out_ch, 1, padding='same', strides=1)
elif residual_path == 'pool':
shortcut = MaxPool2D(pool_size, stride=2, padding='same')
else:
shortcut = Equ()
return shortcut
def __init__(self, in_ch, out_ch, kernel_n=3, stride=2):
"""IncResBlock_v4B
c1-s2, c3-s2, c5-s2, pool-s2
c1-s2: Conv1-s2
c3-s2: Conv1 -> Conv3-s2
c5-s2: Conv1 -> Conv5-s2
pool-s2: MaxPooling-s2 -> Conv1
call: concat([c1-s2(x), c3-s2(x), c5-s2(x), prob-s2(x)], -3)
Args:
in_ch:
out_ch:
kernel_n:
Returns:
None
"""
super(IncResBlock_v4B, self).__init__(out_ch)
self.inc_list = [
nn.Sequential(
ConvSameBnRelu2D(in_ch, self.out_ch, kernel_size=1, stride=stride)),
nn.Sequential(
ConvSameBnRelu2D(in_ch, self.out_ch, 1),
ConvSameBnRelu2D(self.out_ch, self.out_ch, kernel_size=(1, kernel_n), stride=(stride, 1)),
ConvSameBnRelu2D(self.out_ch, self.out_ch, kernel_size=(kernel_n, 1), stride=(1, stride))),
nn.Sequential(
ConvSameBnRelu2D(in_ch, self.out_ch, 1),
ConvSameBnRelu2D(self.out_ch, self.out_ch, kernel_size=(1, kernel_n)),
ConvSameBnRelu2D(self.out_ch, self.out_ch, kernel_size=(kernel_n, 1)),
ConvSameBnRelu2D(self.out_ch, self.out_ch, kernel_size=(1, kernel_n), stride=(stride, 1)),
ConvSameBnRelu2D(self.out_ch, self.out_ch, kernel_size=(kernel_n, 1), stride=(1, stride))),
nn.Sequential(
MaxPool2D(3, stride=stride, padding='same'),
ConvSameBnRelu2D(in_ch, self.out_ch, 1))
]
self.addLayers(self.inc_list)
def __init__(self, in_ch, out_ch):
"""IncResBlock_v4A
c1-s2, c3-s2, c5-s2, pool-s2
c1-s2: Conv1-s2
c3-s2: Conv1 -> Conv3-s2
c5-s2: Conv1 -> Conv5-s2
pool-s2: MaxPooling-s2 -> Conv1
call: concat([c1-s2(x), c3-s2(x), c5-s2(x), prob-s2(x)], -3)
Args:
in_ch:
out_ch:
Returns:
None
"""
super(IncResBlock_v4A, self).__init__(out_ch)
self.inc_list = [
nn.Sequential(
ConvSameBnRelu2D(in_ch, self.out_ch, stride=2)),
nn.Sequential(
ConvSameBnRelu2D(in_ch, self.out_ch, 1),
ConvSameBnRelu2D(self.out_ch, self.out_ch, 3, stride=2)),
nn.Sequential(
ConvSameBnRelu2D(in_ch, self.out_ch, 1),
ConvSameBnRelu2D(self.out_ch, self.out_ch, 3),
ConvSameBnRelu2D(self.out_ch, self.out_ch, 3, stride=2)),
nn.Sequential(
MaxPool2D(3, stride=2, padding='same'),
ConvSameBnRelu2D(in_ch, self.out_ch, 1)),
]
self.addLayers(self.inc_list)
def __init__(self, in_ch, out_ch):
"""ReductionBlock_v4B
c1-s2, c3-s2, c5-s2, pool-s2
c1-s2: Conv1-s2
c3-s2: Conv1 -> Conv3-s2
c5-s2: Conv1 -> Conv5-s2
pool-s2: MaxPooling-s2 -> Conv1
call: concat([c1-s2(x), c3-s2(x), c5-s2(x), prob-s2(x)], -3)
Args:
in_ch:
out_ch:
Returns:
None
"""
super(ReductionBlock_v4B, self).__init__(out_ch)
self.inc_list = [
nn.Sequential(
ConvSameBnRelu2D(in_ch, 256, 1),
ConvSameBnRelu2D(256, 384, 3, stride=2)),
nn.Sequential(
ConvSameBnRelu2D(in_ch, 256, 1),
ConvSameBnRelu2D(256, 288, 3, stride=2)),
nn.Sequential(
ConvSameBnRelu2D(in_ch, 256, 1),
ConvSameBnRelu2D(256, 288, 3),
FeatureExtractor(288, 320, 3, stride=2)),
nn.Sequential(MaxPool2D(3, stride=2, padding='same')),
]
self.addLayers(self.inc_list)