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downsampler.py
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downsampler.py
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import torch
import torch.nn.parallel
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from IPython import embed
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.*(filt_size-1)/2), int(np.ceil(1.*(filt_size-1)/2)), int(1.*(filt_size-1)/2), int(np.ceil(1.*(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.)
self.channels = channels
# print('Filter size [%i]'%filt_size)
if(self.filt_size==1):
a = np.array([1.,])
elif(self.filt_size==2):
a = np.array([1., 1.])
elif(self.filt_size==3):
a = np.array([1., 2., 1.])
elif(self.filt_size==4):
a = np.array([1., 3., 3., 1.])
elif(self.filt_size==5):
a = np.array([1., 4., 6., 4., 1.])
elif(self.filt_size==6):
a = np.array([1., 5., 10., 10., 5., 1.])
elif(self.filt_size==7):
a = np.array([1., 6., 15., 20., 15., 6., 1.])
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
class Downsample1D(nn.Module):
def __init__(self, pad_type='reflect', filt_size=3, stride=2, channels=None, pad_off=0):
super(Downsample1D, self).__init__()
self.filt_size = filt_size
self.pad_off = pad_off
self.pad_sizes = [int(1. * (filt_size - 1) / 2), int(np.ceil(1. * (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.)
self.channels = channels
# print('Filter size [%i]' % filt_size)
if(self.filt_size == 1):
a = np.array([1., ])
elif(self.filt_size == 2):
a = np.array([1., 1.])
elif(self.filt_size == 3):
a = np.array([1., 2., 1.])
elif(self.filt_size == 4):
a = np.array([1., 3., 3., 1.])
elif(self.filt_size == 5):
a = np.array([1., 4., 6., 4., 1.])
elif(self.filt_size == 6):
a = np.array([1., 5., 10., 10., 5., 1.])
elif(self.filt_size == 7):
a = np.array([1., 6., 15., 20., 15., 6., 1.])
filt = torch.Tensor(a)
filt = filt / torch.sum(filt)
self.register_buffer('filt', filt[None, None, :].repeat((self.channels, 1, 1)))
self.pad = get_pad_layer_1d(pad_type)(self.pad_sizes)
def forward(self, inp):
if(self.filt_size == 1):
if(self.pad_off == 0):
return inp[:, :, ::self.stride]
else:
return self.pad(inp)[:, :, ::self.stride]
else:
return F.conv1d(self.pad(inp), self.filt, stride=self.stride, groups=inp.shape[1])
def get_pad_layer_1d(pad_type):
if(pad_type in ['refl', 'reflect']):
PadLayer = nn.ReflectionPad1d
elif(pad_type in ['repl', 'replicate']):
PadLayer = nn.ReplicationPad1d
elif(pad_type == 'zero'):
PadLayer = nn.ZeroPad1d
else:
print('Pad type [%s] not recognized' % pad_type)
return PadLayer