| |
| |
|
|
| import torch |
| import torch.nn.parallel |
| import numpy as np |
| import torch.nn as nn |
| import torch.nn.functional as F |
|
|
|
|
| class Downsample(nn.Module): |
| |
|
|
| def __init__(self, pad_type="reflect", filt_size=3, stride=2, channels=None, pad_off=0): |
| super(Downsample, self).__init__() |
| self.filt_size = filt_size |
| self.pad_off = pad_off |
| self.pad_sizes = [ |
| int(1.0 * (filt_size - 1) / 2), |
| int(np.ceil(1.0 * (filt_size - 1) / 2)), |
| int(1.0 * (filt_size - 1) / 2), |
| int(np.ceil(1.0 * (filt_size - 1) / 2)), |
| ] |
| self.pad_sizes = [pad_size + pad_off for pad_size in self.pad_sizes] |
| self.stride = stride |
| self.off = int((self.stride - 1) / 2.0) |
| self.channels = channels |
|
|
| |
| if self.filt_size == 1: |
| a = np.array([1.0,]) |
| elif self.filt_size == 2: |
| a = np.array([1.0, 1.0]) |
| elif self.filt_size == 3: |
| a = np.array([1.0, 2.0, 1.0]) |
| elif self.filt_size == 4: |
| a = np.array([1.0, 3.0, 3.0, 1.0]) |
| elif self.filt_size == 5: |
| a = np.array([1.0, 4.0, 6.0, 4.0, 1.0]) |
| elif self.filt_size == 6: |
| a = np.array([1.0, 5.0, 10.0, 10.0, 5.0, 1.0]) |
| elif self.filt_size == 7: |
| a = np.array([1.0, 6.0, 15.0, 20.0, 15.0, 6.0, 1.0]) |
|
|
| filt = torch.Tensor(a[:, None] * a[None, :]) |
| filt = filt / torch.sum(filt) |
| self.register_buffer("filt", filt[None, None, :, :].repeat((self.channels, 1, 1, 1))) |
|
|
| self.pad = get_pad_layer(pad_type)(self.pad_sizes) |
|
|
| def forward(self, inp): |
| if self.filt_size == 1: |
| if self.pad_off == 0: |
| return inp[:, :, :: self.stride, :: self.stride] |
| else: |
| return self.pad(inp)[:, :, :: self.stride, :: self.stride] |
| else: |
| return F.conv2d(self.pad(inp), self.filt, stride=self.stride, groups=inp.shape[1]) |
|
|
|
|
| def get_pad_layer(pad_type): |
| PadLayer = None |
| if pad_type in ["refl", "reflect"]: |
| PadLayer = nn.ReflectionPad2d |
| elif pad_type in ["repl", "replicate"]: |
| PadLayer = nn.ReplicationPad2d |
| elif pad_type == "zero": |
| PadLayer = nn.ZeroPad2d |
| else: |
| print("Pad type [%s] not recognized" % pad_type) |
| return PadLayer |
|
|
