Use black for lib/models
This commit is contained in:
D-X-Y
@@ -4,315 +4,550 @@
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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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__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(C_in, C_out, stride),
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'avg_pool_3x3' : lambda C_in, C_out, stride, affine, track_running_stats: POOLING(C_in, C_out, stride, 'avg', affine, track_running_stats),
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'max_pool_3x3' : lambda C_in, C_out, stride, affine, track_running_stats: POOLING(C_in, C_out, stride, 'max', affine, track_running_stats),
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'nor_conv_7x7' : lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN(C_in, C_out, (7,7), (stride,stride), (3,3), (1,1), affine, track_running_stats),
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'nor_conv_3x3' : lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN(C_in, C_out, (3,3), (stride,stride), (1,1), (1,1), affine, track_running_stats),
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'nor_conv_1x1' : lambda C_in, C_out, stride, affine, track_running_stats: ReLUConvBN(C_in, C_out, (1,1), (stride,stride), (0,0), (1,1), affine, track_running_stats),
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'dua_sepc_3x3' : lambda C_in, C_out, stride, affine, track_running_stats: DualSepConv(C_in, C_out, (3,3), (stride,stride), (1,1), (1,1), affine, track_running_stats),
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'dua_sepc_5x5' : lambda C_in, C_out, stride, affine, track_running_stats: DualSepConv(C_in, C_out, (5,5), (stride,stride), (2,2), (1,1), affine, track_running_stats),
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'dil_sepc_3x3' : lambda C_in, C_out, stride, affine, track_running_stats: SepConv(C_in, C_out, (3,3), (stride,stride), (2,2), (2,2), affine, track_running_stats),
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'dil_sepc_5x5' : lambda C_in, C_out, stride, affine, track_running_stats: SepConv(C_in, C_out, (5,5), (stride,stride), (4,4), (2,2), affine, track_running_stats),
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'skip_connect' : lambda C_in, C_out, stride, affine, track_running_stats: Identity() if stride == 1 and C_in == C_out else FactorizedReduce(C_in, C_out, stride, affine, track_running_stats),
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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 = ['none', 'skip_connect', 'dua_sepc_3x3', 'dua_sepc_5x5', 'dil_sepc_3x3', 'dil_sepc_5x5', 'avg_pool_3x3', 'max_pool_3x3']
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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 = {'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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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 __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine, track_running_stats=True):
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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(C_in, C_out, kernel_size, stride=stride, padding=padding, dilation=dilation, bias=not affine),
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nn.BatchNorm2d(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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return self.op(x)
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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__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine, track_running_stats=True):
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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(C_in, C_in, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=C_in, bias=False),
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nn.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=not affine),
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nn.BatchNorm2d(C_out, affine=affine, track_running_stats=track_running_stats),
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)
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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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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__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine, track_running_stats=True):
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super(DualSepConv, self).__init__()
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self.op_a = SepConv(C_in, C_in , kernel_size, stride, padding, dilation, affine, track_running_stats)
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self.op_b = SepConv(C_in, C_out, kernel_size, 1, padding, dilation, affine, track_running_stats)
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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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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 __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(inplanes, planes, 3, stride, 1, 1, affine, track_running_stats)
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self.conv_b = ReLUConvBN( planes, planes, 3, 1, 1, 1, affine, track_running_stats)
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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(inplanes, planes, kernel_size=1, stride=1, padding=0, bias=False))
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elif inplanes != planes:
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self.downsample = ReLUConvBN(inplanes, planes, 1, 1, 0, 1, affine, track_running_stats)
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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 extra_repr(self):
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string = '{name}(inC={in_dim}, outC={out_dim}, stride={stride})'.format(name=self.__class__.__name__, **self.__dict__)
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return string
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def forward(self, inputs):
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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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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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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 __init__(self, C_in, C_out, stride, mode, affine=True, track_running_stats=True):
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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(C_in, C_out, 1, 1, 0, 1, affine, track_running_stats)
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if mode == 'avg' : self.op = nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False)
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elif mode == 'max': self.op = nn.MaxPool2d(3, stride=stride, padding=1)
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else : raise ValueError('Invalid mode={:} in POOLING'.format(mode))
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def forward(self, inputs):
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if self.preprocess: x = self.preprocess(inputs)
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else : x = inputs
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return self.op(x)
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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 __init__(self):
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super(Identity, self).__init__()
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||||
|
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def forward(self, x):
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return x
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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 __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 forward(self, x):
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if self.C_in == self.C_out:
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if self.stride == 1: return x.mul(0.)
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else : return x[:,:,::self.stride,::self.stride].mul(0.)
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else:
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shape = list(x.shape)
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shape[1] = self.C_out
|
||||
zeros = x.new_zeros(shape, dtype=x.dtype, device=x.device)
|
||||
return zeros
|
||||
|
||||
def extra_repr(self):
|
||||
return 'C_in={C_in}, C_out={C_out}, stride={stride}'.format(**self.__dict__)
|
||||
def extra_repr(self):
|
||||
return "C_in={C_in}, C_out={C_out}, stride={stride}".format(**self.__dict__)
|
||||
|
||||
|
||||
class FactorizedReduce(nn.Module):
|
||||
def __init__(self, C_in, C_out, stride, affine, track_running_stats):
|
||||
super(FactorizedReduce, self).__init__()
|
||||
self.stride = stride
|
||||
self.C_in = C_in
|
||||
self.C_out = C_out
|
||||
self.relu = nn.ReLU(inplace=False)
|
||||
if stride == 2:
|
||||
# assert C_out % 2 == 0, 'C_out : {:}'.format(C_out)
|
||||
C_outs = [C_out // 2, C_out - C_out // 2]
|
||||
self.convs = nn.ModuleList()
|
||||
for i in range(2):
|
||||
self.convs.append(
|
||||
nn.Conv2d(
|
||||
C_in, C_outs[i], 1, stride=stride, padding=0, bias=not affine
|
||||
)
|
||||
)
|
||||
self.pad = nn.ConstantPad2d((0, 1, 0, 1), 0)
|
||||
elif stride == 1:
|
||||
self.conv = nn.Conv2d(
|
||||
C_in, C_out, 1, stride=stride, padding=0, bias=not affine
|
||||
)
|
||||
else:
|
||||
raise ValueError("Invalid stride : {:}".format(stride))
|
||||
self.bn = nn.BatchNorm2d(
|
||||
C_out, affine=affine, track_running_stats=track_running_stats
|
||||
)
|
||||
|
||||
def __init__(self, C_in, C_out, stride, affine, track_running_stats):
|
||||
super(FactorizedReduce, self).__init__()
|
||||
self.stride = stride
|
||||
self.C_in = C_in
|
||||
self.C_out = C_out
|
||||
self.relu = nn.ReLU(inplace=False)
|
||||
if stride == 2:
|
||||
#assert C_out % 2 == 0, 'C_out : {:}'.format(C_out)
|
||||
C_outs = [C_out // 2, C_out - C_out // 2]
|
||||
self.convs = nn.ModuleList()
|
||||
for i in range(2):
|
||||
self.convs.append(nn.Conv2d(C_in, C_outs[i], 1, stride=stride, padding=0, bias=not affine))
|
||||
self.pad = nn.ConstantPad2d((0, 1, 0, 1), 0)
|
||||
elif stride == 1:
|
||||
self.conv = nn.Conv2d(C_in, C_out, 1, stride=stride, padding=0, bias=not affine)
|
||||
else:
|
||||
raise ValueError('Invalid stride : {:}'.format(stride))
|
||||
self.bn = nn.BatchNorm2d(C_out, affine=affine, track_running_stats=track_running_stats)
|
||||
def forward(self, x):
|
||||
if self.stride == 2:
|
||||
x = self.relu(x)
|
||||
y = self.pad(x)
|
||||
out = torch.cat([self.convs[0](x), self.convs[1](y[:, :, 1:, 1:])], dim=1)
|
||||
else:
|
||||
out = self.conv(x)
|
||||
out = self.bn(out)
|
||||
return out
|
||||
|
||||
def forward(self, x):
|
||||
if self.stride == 2:
|
||||
x = self.relu(x)
|
||||
y = self.pad(x)
|
||||
out = torch.cat([self.convs[0](x), self.convs[1](y[:,:,1:,1:])], dim=1)
|
||||
else:
|
||||
out = self.conv(x)
|
||||
out = self.bn(out)
|
||||
return out
|
||||
|
||||
def extra_repr(self):
|
||||
return 'C_in={C_in}, C_out={C_out}, stride={stride}'.format(**self.__dict__)
|
||||
def extra_repr(self):
|
||||
return "C_in={C_in}, C_out={C_out}, stride={stride}".format(**self.__dict__)
|
||||
|
||||
|
||||
# Auto-ReID: Searching for a Part-Aware ConvNet for Person Re-Identification, ICCV 2019
|
||||
class PartAwareOp(nn.Module):
|
||||
|
||||
def __init__(self, C_in, C_out, stride, part=4):
|
||||
super().__init__()
|
||||
self.part = 4
|
||||
self.hidden = C_in // 3
|
||||
self.avg_pool = nn.AdaptiveAvgPool2d(1)
|
||||
self.local_conv_list = nn.ModuleList()
|
||||
for i in range(self.part):
|
||||
self.local_conv_list.append(
|
||||
nn.Sequential(nn.ReLU(), nn.Conv2d(C_in, self.hidden, 1), nn.BatchNorm2d(self.hidden, affine=True))
|
||||
def __init__(self, C_in, C_out, stride, part=4):
|
||||
super().__init__()
|
||||
self.part = 4
|
||||
self.hidden = C_in // 3
|
||||
self.avg_pool = nn.AdaptiveAvgPool2d(1)
|
||||
self.local_conv_list = nn.ModuleList()
|
||||
for i in range(self.part):
|
||||
self.local_conv_list.append(
|
||||
nn.Sequential(
|
||||
nn.ReLU(),
|
||||
nn.Conv2d(C_in, self.hidden, 1),
|
||||
nn.BatchNorm2d(self.hidden, affine=True),
|
||||
)
|
||||
)
|
||||
self.W_K = nn.Linear(self.hidden, self.hidden)
|
||||
self.W_Q = nn.Linear(self.hidden, self.hidden)
|
||||
self.W_K = nn.Linear(self.hidden, self.hidden)
|
||||
self.W_Q = nn.Linear(self.hidden, self.hidden)
|
||||
|
||||
if stride == 2 : self.last = FactorizedReduce(C_in + self.hidden, C_out, 2)
|
||||
elif stride == 1: self.last = FactorizedReduce(C_in + self.hidden, C_out, 1)
|
||||
else: raise ValueError('Invalid Stride : {:}'.format(stride))
|
||||
if stride == 2:
|
||||
self.last = FactorizedReduce(C_in + self.hidden, C_out, 2)
|
||||
elif stride == 1:
|
||||
self.last = FactorizedReduce(C_in + self.hidden, C_out, 1)
|
||||
else:
|
||||
raise ValueError("Invalid Stride : {:}".format(stride))
|
||||
|
||||
def forward(self, x):
|
||||
batch, C, H, W = x.size()
|
||||
assert H >= self.part, 'input size too small : {:} vs {:}'.format(x.shape, self.part)
|
||||
IHs = [0]
|
||||
for i in range(self.part): IHs.append( min(H, int((i+1)*(float(H)/self.part))) )
|
||||
local_feat_list = []
|
||||
for i in range(self.part):
|
||||
feature = x[:, :, IHs[i]:IHs[i+1], :]
|
||||
xfeax = self.avg_pool(feature)
|
||||
xfea = self.local_conv_list[i]( xfeax )
|
||||
local_feat_list.append( xfea )
|
||||
part_feature = torch.cat(local_feat_list, dim=2).view(batch, -1, self.part)
|
||||
part_feature = part_feature.transpose(1,2).contiguous()
|
||||
part_K = self.W_K(part_feature)
|
||||
part_Q = self.W_Q(part_feature).transpose(1,2).contiguous()
|
||||
weight_att = torch.bmm(part_K, part_Q)
|
||||
attention = torch.softmax(weight_att, dim=2)
|
||||
aggreateF = torch.bmm(attention, part_feature).transpose(1,2).contiguous()
|
||||
features = []
|
||||
for i in range(self.part):
|
||||
feature = aggreateF[:, :, i:i+1].expand(batch, self.hidden, IHs[i+1]-IHs[i])
|
||||
feature = feature.view(batch, self.hidden, IHs[i+1]-IHs[i], 1)
|
||||
features.append( feature )
|
||||
features = torch.cat(features, dim=2).expand(batch, self.hidden, H, W)
|
||||
final_fea = torch.cat((x,features), dim=1)
|
||||
outputs = self.last( final_fea )
|
||||
return outputs
|
||||
def forward(self, x):
|
||||
batch, C, H, W = x.size()
|
||||
assert H >= self.part, "input size too small : {:} vs {:}".format(
|
||||
x.shape, self.part
|
||||
)
|
||||
IHs = [0]
|
||||
for i in range(self.part):
|
||||
IHs.append(min(H, int((i + 1) * (float(H) / self.part))))
|
||||
local_feat_list = []
|
||||
for i in range(self.part):
|
||||
feature = x[:, :, IHs[i] : IHs[i + 1], :]
|
||||
xfeax = self.avg_pool(feature)
|
||||
xfea = self.local_conv_list[i](xfeax)
|
||||
local_feat_list.append(xfea)
|
||||
part_feature = torch.cat(local_feat_list, dim=2).view(batch, -1, self.part)
|
||||
part_feature = part_feature.transpose(1, 2).contiguous()
|
||||
part_K = self.W_K(part_feature)
|
||||
part_Q = self.W_Q(part_feature).transpose(1, 2).contiguous()
|
||||
weight_att = torch.bmm(part_K, part_Q)
|
||||
attention = torch.softmax(weight_att, dim=2)
|
||||
aggreateF = torch.bmm(attention, part_feature).transpose(1, 2).contiguous()
|
||||
features = []
|
||||
for i in range(self.part):
|
||||
feature = aggreateF[:, :, i : i + 1].expand(
|
||||
batch, self.hidden, IHs[i + 1] - IHs[i]
|
||||
)
|
||||
feature = feature.view(batch, self.hidden, IHs[i + 1] - IHs[i], 1)
|
||||
features.append(feature)
|
||||
features = torch.cat(features, dim=2).expand(batch, self.hidden, H, W)
|
||||
final_fea = torch.cat((x, features), dim=1)
|
||||
outputs = self.last(final_fea)
|
||||
return outputs
|
||||
|
||||
|
||||
def drop_path(x, drop_prob):
|
||||
if drop_prob > 0.:
|
||||
keep_prob = 1. - drop_prob
|
||||
mask = x.new_zeros(x.size(0), 1, 1, 1)
|
||||
mask = mask.bernoulli_(keep_prob)
|
||||
x = torch.div(x, keep_prob)
|
||||
x.mul_(mask)
|
||||
return x
|
||||
if drop_prob > 0.0:
|
||||
keep_prob = 1.0 - drop_prob
|
||||
mask = x.new_zeros(x.size(0), 1, 1, 1)
|
||||
mask = mask.bernoulli_(keep_prob)
|
||||
x = torch.div(x, keep_prob)
|
||||
x.mul_(mask)
|
||||
return x
|
||||
|
||||
|
||||
# Searching for A Robust Neural Architecture in Four GPU Hours
|
||||
class GDAS_Reduction_Cell(nn.Module):
|
||||
def __init__(
|
||||
self, C_prev_prev, C_prev, C, reduction_prev, affine, track_running_stats
|
||||
):
|
||||
super(GDAS_Reduction_Cell, self).__init__()
|
||||
if reduction_prev:
|
||||
self.preprocess0 = FactorizedReduce(
|
||||
C_prev_prev, C, 2, affine, track_running_stats
|
||||
)
|
||||
else:
|
||||
self.preprocess0 = ReLUConvBN(
|
||||
C_prev_prev, C, 1, 1, 0, 1, affine, track_running_stats
|
||||
)
|
||||
self.preprocess1 = ReLUConvBN(
|
||||
C_prev, C, 1, 1, 0, 1, affine, track_running_stats
|
||||
)
|
||||
|
||||
def __init__(self, C_prev_prev, C_prev, C, reduction_prev, affine, track_running_stats):
|
||||
super(GDAS_Reduction_Cell, self).__init__()
|
||||
if reduction_prev:
|
||||
self.preprocess0 = FactorizedReduce(C_prev_prev, C, 2, affine, track_running_stats)
|
||||
else:
|
||||
self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0, 1, affine, track_running_stats)
|
||||
self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0, 1, affine, track_running_stats)
|
||||
self.reduction = True
|
||||
self.ops1 = nn.ModuleList(
|
||||
[
|
||||
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
|
||||
),
|
||||
),
|
||||
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.reduction = True
|
||||
self.ops1 = nn.ModuleList(
|
||||
[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)),
|
||||
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
|
||||
),
|
||||
),
|
||||
]
|
||||
)
|
||||
|
||||
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
|
||||
|
||||
@property
|
||||
def multiplier(self):
|
||||
return 4
|
||||
def forward(self, s0, s1, drop_prob=-1):
|
||||
s0 = self.preprocess0(s0)
|
||||
s1 = self.preprocess1(s1)
|
||||
|
||||
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)
|
||||
|
||||
X0 = self.ops1[0] (s0)
|
||||
X1 = self.ops1[1] (s1)
|
||||
if self.training and drop_prob > 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.:
|
||||
X2, X3 = drop_path(X2, drop_prob), drop_path(X3, drop_prob)
|
||||
return torch.cat([X0, X1, X2, X3], dim=1)
|
||||
# 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
|
||||
}
|
||||
|
||||
RAW_OP_CLASSES = {"gdas_reduction": GDAS_Reduction_Cell}
|
||||
|
||||
Reference in New Issue
Block a user