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| # Copyright (c) Microsoft Corporation. | |
| # Licensed under the MIT License. | |
| 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): | |
| # https://github.com/adobe/antialiased-cnns | |
| 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 | |
| # print('Filter size [%i]'%filt_size) | |
| 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): | |
| 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 | |