Method
__init__
(self,
in_channels,
out_channels,
types='Pool',
stride=[2, 1],
sub_norm='nn.LayerNorm',
act=None)
Source from the content-addressed store, hash-verified
| 340 | |
| 341 | class SubSample(nn.Module): |
| 342 | def __init__(self, |
| 343 | in_channels, |
| 344 | out_channels, |
| 345 | types='Pool', |
| 346 | stride=[2, 1], |
| 347 | sub_norm='nn.LayerNorm', |
| 348 | act=None): |
| 349 | super().__init__() |
| 350 | self.types = types |
| 351 | if types == 'Pool': |
| 352 | self.avgpool = nn.AvgPool2d( |
| 353 | kernel_size=[3, 5], stride=stride, padding=[1, 2]) |
| 354 | self.maxpool = nn.MaxPool2d( |
| 355 | kernel_size=[3, 5], stride=stride, padding=[1, 2]) |
| 356 | self.proj = nn.Linear(in_channels, out_channels) |
| 357 | else: |
| 358 | self.conv = nn.Conv2d( |
| 359 | in_channels, |
| 360 | out_channels, |
| 361 | kernel_size=3, |
| 362 | stride=stride, |
| 363 | padding=1) |
| 364 | |
| 365 | self.norm = eval(sub_norm)(out_channels) |
| 366 | if act is not None: |
| 367 | self.act = act() |
| 368 | else: |
| 369 | self.act = None |
| 370 | |
| 371 | def forward(self, x): |
| 372 | |
Callers
nothing calls this directly
Tested by
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