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################################################## |
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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, |
|
|
|
bias=not affine, |
|
|
|
), |
|
|
|
nn.BatchNorm2d( |
|
|
|
C, affine=affine, track_running_stats=track_running_stats |
|
|
|
), |
|
|
|
nn.ReLU(inplace=False), |
|
|
|
nn.Conv2d(C, C, 1, stride=1, padding=0, bias=not affine), |
|
|
|
nn.BatchNorm2d( |
|
|
|
C, affine=affine, track_running_stats=track_running_stats |
|
|
|
), |
|
|
|
), |
|
|
|
nn.Sequential( |
|
|
|
nn.ReLU(inplace=False), |
|
|
|
nn.Conv2d( |
|
|
|
C, |
|
|
|
C, |
|
|
|
(1, 3), |
|
|
|
stride=(1, 2), |
|
|
|
padding=(0, 1), |
|
|
|
groups=8, |
|
|
|
bias=not affine, |
|
|
|
), |
|
|
|
nn.Conv2d( |
|
|
|
C, |
|
|
|
C, |
|
|
|
(3, 1), |
|
|
|
stride=(2, 1), |
|
|
|
padding=(1, 0), |
|
|
|
groups=8, |
|
|
|
bias=not affine, |
|
|
|
), |
|
|
|
nn.BatchNorm2d( |
|
|
|
C, affine=affine, track_running_stats=track_running_stats |
|
|
|
), |
|
|
|
nn.ReLU(inplace=False), |
|
|
|
nn.Conv2d(C, C, 1, stride=1, padding=0, bias=not affine), |
|
|
|
nn.BatchNorm2d( |
|
|
|
C, affine=affine, track_running_stats=track_running_stats |
|
|
|
), |
|
|
|
), |
|
|
|
] |
|
|
|
) |
|
|
|
|
|
|
|
self.ops2 = nn.ModuleList( |
|
|
|
[ |
|
|
|
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} |
