554 lines
17 KiB
Python
554 lines
17 KiB
Python
##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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import torch
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import torch.nn as nn
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__all__ = ["OPS", "RAW_OP_CLASSES", "ResNetBasicblock", "SearchSpaceNames"]
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OPS = {
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"none": lambda C_in, C_out, stride, affine, track_running_stats: Zero(
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C_in, C_out, stride
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),
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"avg_pool_3x3": lambda C_in, C_out, stride, affine, track_running_stats: POOLING(
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C_in, C_out, stride, "avg", affine, track_running_stats
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),
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"max_pool_3x3": lambda C_in, C_out, stride, affine, track_running_stats: POOLING(
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C_in, C_out, stride, "max", affine, track_running_stats
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),
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"nor_conv_7x7": lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN(
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C_in,
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C_out,
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(7, 7),
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(stride, stride),
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(3, 3),
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(1, 1),
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affine,
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track_running_stats,
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),
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"nor_conv_3x3": lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN(
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C_in,
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C_out,
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(3, 3),
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(stride, stride),
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(1, 1),
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(1, 1),
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affine,
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track_running_stats,
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),
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"nor_conv_1x1": lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN(
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C_in,
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C_out,
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(1, 1),
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(stride, stride),
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(0, 0),
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(1, 1),
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affine,
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track_running_stats,
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),
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"dua_sepc_3x3": lambda C_in, C_out, stride, affine, track_running_stats: DualSepConv(
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C_in,
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C_out,
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(3, 3),
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(stride, stride),
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(1, 1),
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(1, 1),
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affine,
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track_running_stats,
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),
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"dua_sepc_5x5": lambda C_in, C_out, stride, affine, track_running_stats: DualSepConv(
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C_in,
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C_out,
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(5, 5),
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(stride, stride),
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(2, 2),
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(1, 1),
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affine,
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track_running_stats,
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),
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"dil_sepc_3x3": lambda C_in, C_out, stride, affine, track_running_stats: SepConv(
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C_in,
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C_out,
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(3, 3),
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(stride, stride),
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(2, 2),
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(2, 2),
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affine,
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track_running_stats,
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),
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"dil_sepc_5x5": lambda C_in, C_out, stride, affine, track_running_stats: SepConv(
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C_in,
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C_out,
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(5, 5),
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(stride, stride),
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(4, 4),
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(2, 2),
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affine,
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track_running_stats,
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),
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"skip_connect": lambda C_in, C_out, stride, affine, track_running_stats: Identity()
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if stride == 1 and C_in == C_out
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else FactorizedReduce(C_in, C_out, stride, affine, track_running_stats),
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}
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CONNECT_NAS_BENCHMARK = ["none", "skip_connect", "nor_conv_3x3"]
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NAS_BENCH_201 = ["none", "skip_connect", "nor_conv_1x1", "nor_conv_3x3", "avg_pool_3x3"]
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DARTS_SPACE = [
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"none",
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"skip_connect",
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"dua_sepc_3x3",
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"dua_sepc_5x5",
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"dil_sepc_3x3",
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"dil_sepc_5x5",
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"avg_pool_3x3",
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"max_pool_3x3",
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]
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SearchSpaceNames = {
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"connect-nas": CONNECT_NAS_BENCHMARK,
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"nats-bench": NAS_BENCH_201,
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"nas-bench-201": NAS_BENCH_201,
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"darts": DARTS_SPACE,
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}
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class ReLUConvBN(nn.Module):
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def __init__(
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self,
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C_in,
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C_out,
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kernel_size,
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stride,
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padding,
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dilation,
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affine,
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track_running_stats=True,
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):
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super(ReLUConvBN, self).__init__()
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self.op = nn.Sequential(
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nn.ReLU(inplace=False),
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nn.Conv2d(
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C_in,
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C_out,
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kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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bias=not affine,
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),
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nn.BatchNorm2d(
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C_out, affine=affine, track_running_stats=track_running_stats
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),
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)
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def forward(self, x):
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return self.op(x)
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class SepConv(nn.Module):
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def __init__(
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self,
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C_in,
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C_out,
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kernel_size,
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stride,
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padding,
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dilation,
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affine,
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track_running_stats=True,
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):
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super(SepConv, self).__init__()
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self.op = nn.Sequential(
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nn.ReLU(inplace=False),
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nn.Conv2d(
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C_in,
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C_in,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=C_in,
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bias=False,
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),
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nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=not affine),
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nn.BatchNorm2d(
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C_out, affine=affine, track_running_stats=track_running_stats
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),
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)
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def forward(self, x):
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return self.op(x)
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class DualSepConv(nn.Module):
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def __init__(
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self,
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C_in,
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C_out,
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kernel_size,
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stride,
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padding,
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dilation,
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affine,
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track_running_stats=True,
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):
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super(DualSepConv, self).__init__()
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self.op_a = SepConv(
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C_in,
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C_in,
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kernel_size,
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stride,
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padding,
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dilation,
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affine,
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track_running_stats,
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)
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self.op_b = SepConv(
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C_in, C_out, kernel_size, 1, padding, dilation, affine, track_running_stats
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)
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def forward(self, x):
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x = self.op_a(x)
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x = self.op_b(x)
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return x
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class ResNetBasicblock(nn.Module):
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def __init__(self, inplanes, planes, stride, affine=True, track_running_stats=True):
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super(ResNetBasicblock, self).__init__()
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assert stride == 1 or stride == 2, "invalid stride {:}".format(stride)
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self.conv_a = ReLUConvBN(
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inplanes, planes, 3, stride, 1, 1, affine, track_running_stats
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)
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self.conv_b = ReLUConvBN(
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planes, planes, 3, 1, 1, 1, affine, track_running_stats
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)
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if stride == 2:
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self.downsample = nn.Sequential(
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nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
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nn.Conv2d(
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inplanes, planes, kernel_size=1, stride=1, padding=0, bias=False
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),
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)
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elif inplanes != planes:
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self.downsample = ReLUConvBN(
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inplanes, planes, 1, 1, 0, 1, affine, track_running_stats
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)
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else:
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self.downsample = None
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self.in_dim = inplanes
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self.out_dim = planes
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self.stride = stride
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self.num_conv = 2
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def extra_repr(self):
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string = "{name}(inC={in_dim}, outC={out_dim}, stride={stride})".format(
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name=self.__class__.__name__, **self.__dict__
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)
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return string
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def forward(self, inputs):
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basicblock = self.conv_a(inputs)
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basicblock = self.conv_b(basicblock)
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if self.downsample is not None:
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residual = self.downsample(inputs)
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else:
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residual = inputs
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return residual + basicblock
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class POOLING(nn.Module):
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def __init__(
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self, C_in, C_out, stride, mode, affine=True, track_running_stats=True
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):
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super(POOLING, self).__init__()
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if C_in == C_out:
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self.preprocess = None
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else:
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self.preprocess = ReLUConvBN(
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C_in, C_out, 1, 1, 0, 1, affine, track_running_stats
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)
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if mode == "avg":
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self.op = nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False)
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elif mode == "max":
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self.op = nn.MaxPool2d(3, stride=stride, padding=1)
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else:
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raise ValueError("Invalid mode={:} in POOLING".format(mode))
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def forward(self, inputs):
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if self.preprocess:
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x = self.preprocess(inputs)
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else:
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x = inputs
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return self.op(x)
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class Identity(nn.Module):
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def __init__(self):
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super(Identity, self).__init__()
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def forward(self, x):
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return x
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class Zero(nn.Module):
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def __init__(self, C_in, C_out, stride):
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super(Zero, self).__init__()
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self.C_in = C_in
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self.C_out = C_out
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self.stride = stride
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self.is_zero = True
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def forward(self, x):
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if self.C_in == self.C_out:
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if self.stride == 1:
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return x.mul(0.0)
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else:
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return x[:, :, :: self.stride, :: self.stride].mul(0.0)
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else:
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shape = list(x.shape)
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shape[1] = self.C_out
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zeros = x.new_zeros(shape, dtype=x.dtype, device=x.device)
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return zeros
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def extra_repr(self):
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return "C_in={C_in}, C_out={C_out}, stride={stride}".format(**self.__dict__)
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class FactorizedReduce(nn.Module):
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def __init__(self, C_in, C_out, stride, affine, track_running_stats):
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super(FactorizedReduce, self).__init__()
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self.stride = stride
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self.C_in = C_in
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self.C_out = C_out
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self.relu = nn.ReLU(inplace=False)
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if stride == 2:
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# assert C_out % 2 == 0, 'C_out : {:}'.format(C_out)
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C_outs = [C_out // 2, C_out - C_out // 2]
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self.convs = nn.ModuleList()
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for i in range(2):
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self.convs.append(
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nn.Conv2d(
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C_in, C_outs[i], 1, stride=stride, padding=0, bias=not affine
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)
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)
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self.pad = nn.ConstantPad2d((0, 1, 0, 1), 0)
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elif stride == 1:
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self.conv = nn.Conv2d(
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C_in, C_out, 1, stride=stride, padding=0, bias=not affine
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)
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else:
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raise ValueError("Invalid stride : {:}".format(stride))
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self.bn = nn.BatchNorm2d(
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C_out, affine=affine, track_running_stats=track_running_stats
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)
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def forward(self, x):
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if self.stride == 2:
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x = self.relu(x)
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y = self.pad(x)
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out = torch.cat([self.convs[0](x), self.convs[1](y[:, :, 1:, 1:])], dim=1)
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else:
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out = self.conv(x)
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out = self.bn(out)
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return out
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def extra_repr(self):
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return "C_in={C_in}, C_out={C_out}, stride={stride}".format(**self.__dict__)
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# Auto-ReID: Searching for a Part-Aware ConvNet for Person Re-Identification, ICCV 2019
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class PartAwareOp(nn.Module):
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def __init__(self, C_in, C_out, stride, part=4):
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super().__init__()
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self.part = 4
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self.hidden = C_in // 3
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.local_conv_list = nn.ModuleList()
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for i in range(self.part):
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self.local_conv_list.append(
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nn.Sequential(
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nn.ReLU(),
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nn.Conv2d(C_in, self.hidden, 1),
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nn.BatchNorm2d(self.hidden, affine=True),
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)
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)
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self.W_K = nn.Linear(self.hidden, self.hidden)
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self.W_Q = nn.Linear(self.hidden, self.hidden)
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if stride == 2:
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self.last = FactorizedReduce(C_in + self.hidden, C_out, 2)
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elif stride == 1:
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self.last = FactorizedReduce(C_in + self.hidden, C_out, 1)
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else:
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raise ValueError("Invalid Stride : {:}".format(stride))
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def forward(self, x):
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batch, C, H, W = x.size()
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assert H >= self.part, "input size too small : {:} vs {:}".format(
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x.shape, self.part
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)
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IHs = [0]
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for i in range(self.part):
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IHs.append(min(H, int((i + 1) * (float(H) / self.part))))
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local_feat_list = []
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for i in range(self.part):
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feature = x[:, :, IHs[i] : IHs[i + 1], :]
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xfeax = self.avg_pool(feature)
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xfea = self.local_conv_list[i](xfeax)
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local_feat_list.append(xfea)
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part_feature = torch.cat(local_feat_list, dim=2).view(batch, -1, self.part)
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part_feature = part_feature.transpose(1, 2).contiguous()
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part_K = self.W_K(part_feature)
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part_Q = self.W_Q(part_feature).transpose(1, 2).contiguous()
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weight_att = torch.bmm(part_K, part_Q)
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attention = torch.softmax(weight_att, dim=2)
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aggreateF = torch.bmm(attention, part_feature).transpose(1, 2).contiguous()
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features = []
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for i in range(self.part):
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feature = aggreateF[:, :, i : i + 1].expand(
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batch, self.hidden, IHs[i + 1] - IHs[i]
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)
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feature = feature.view(batch, self.hidden, IHs[i + 1] - IHs[i], 1)
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features.append(feature)
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features = torch.cat(features, dim=2).expand(batch, self.hidden, H, W)
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final_fea = torch.cat((x, features), dim=1)
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outputs = self.last(final_fea)
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return outputs
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def drop_path(x, drop_prob):
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if drop_prob > 0.0:
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keep_prob = 1.0 - drop_prob
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mask = x.new_zeros(x.size(0), 1, 1, 1)
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mask = mask.bernoulli_(keep_prob)
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x = torch.div(x, keep_prob)
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x.mul_(mask)
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return x
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# Searching for A Robust Neural Architecture in Four GPU Hours
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class GDAS_Reduction_Cell(nn.Module):
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def __init__(
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self, C_prev_prev, C_prev, C, reduction_prev, affine, track_running_stats
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):
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super(GDAS_Reduction_Cell, self).__init__()
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if reduction_prev:
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self.preprocess0 = FactorizedReduce(
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C_prev_prev, C, 2, affine, track_running_stats
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)
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else:
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self.preprocess0 = ReLUConvBN(
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C_prev_prev, C, 1, 1, 0, 1, affine, track_running_stats
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)
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self.preprocess1 = ReLUConvBN(
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C_prev, C, 1, 1, 0, 1, affine, track_running_stats
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)
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self.reduction = True
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self.ops1 = nn.ModuleList(
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[
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nn.Sequential(
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nn.ReLU(inplace=False),
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nn.Conv2d(
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C,
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C,
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(1, 3),
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stride=(1, 2),
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padding=(0, 1),
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groups=8,
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bias=not affine,
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),
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nn.Conv2d(
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C,
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C,
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(3, 1),
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stride=(2, 1),
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padding=(1, 0),
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groups=8,
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bias=not affine,
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),
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nn.BatchNorm2d(
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C, affine=affine, track_running_stats=track_running_stats
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),
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nn.ReLU(inplace=False),
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nn.Conv2d(C, C, 1, stride=1, padding=0, bias=not affine),
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nn.BatchNorm2d(
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C, affine=affine, track_running_stats=track_running_stats
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),
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),
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nn.Sequential(
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nn.ReLU(inplace=False),
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nn.Conv2d(
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C,
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C,
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(1, 3),
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stride=(1, 2),
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padding=(0, 1),
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groups=8,
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bias=not affine,
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),
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nn.Conv2d(
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C,
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C,
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(3, 1),
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stride=(2, 1),
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padding=(1, 0),
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groups=8,
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bias=not affine,
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),
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nn.BatchNorm2d(
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C, affine=affine, track_running_stats=track_running_stats
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),
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nn.ReLU(inplace=False),
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nn.Conv2d(C, C, 1, stride=1, padding=0, bias=not affine),
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nn.BatchNorm2d(
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C, affine=affine, track_running_stats=track_running_stats
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),
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),
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]
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)
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self.ops2 = nn.ModuleList(
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[
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nn.Sequential(
|
|
nn.MaxPool2d(3, stride=2, padding=1),
|
|
nn.BatchNorm2d(
|
|
C, affine=affine, track_running_stats=track_running_stats
|
|
),
|
|
),
|
|
nn.Sequential(
|
|
nn.MaxPool2d(3, stride=2, padding=1),
|
|
nn.BatchNorm2d(
|
|
C, affine=affine, track_running_stats=track_running_stats
|
|
),
|
|
),
|
|
]
|
|
)
|
|
|
|
@property
|
|
def multiplier(self):
|
|
return 4
|
|
|
|
def forward(self, s0, s1, drop_prob=-1):
|
|
s0 = self.preprocess0(s0)
|
|
s1 = self.preprocess1(s1)
|
|
|
|
X0 = self.ops1[0](s0)
|
|
X1 = self.ops1[1](s1)
|
|
if self.training and drop_prob > 0.0:
|
|
X0, X1 = drop_path(X0, drop_prob), drop_path(X1, drop_prob)
|
|
|
|
# X2 = self.ops2[0] (X0+X1)
|
|
X2 = self.ops2[0](s0)
|
|
X3 = self.ops2[1](s1)
|
|
if self.training and drop_prob > 0.0:
|
|
X2, X3 = drop_path(X2, drop_prob), drop_path(X3, drop_prob)
|
|
return torch.cat([X0, X1, X2, X3], dim=1)
|
|
|
|
|
|
# To manage the useful classes in this file.
|
|
RAW_OP_CLASSES = {"gdas_reduction": GDAS_Reduction_Cell}
|