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layers.py
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layers.py
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from utils import *
import pdb
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn.utils import weight_norm as wn
import numpy as np
class nin(nn.Module):
def __init__(self, dim_in, dim_out):
super(nin, self).__init__()
self.lin_a = wn(nn.Linear(dim_in, dim_out))
self.dim_out = dim_out
def forward(self, x):
og_x = x
# assumes pytorch ordering
""" a network in network layer (1x1 CONV) """
# TODO : try with original ordering
x = x.permute(0, 2, 3, 1)
shp = [int(y) for y in x.size()]
out = self.lin_a(x.contiguous().view(shp[0]*shp[1]*shp[2], shp[3]))
shp[-1] = self.dim_out
out = out.view(shp)
return out.permute(0, 3, 1, 2)
class down_shifted_conv2d(nn.Module):
def __init__(self, num_filters_in, num_filters_out, filter_size=(2,3), stride=(1,1),
shift_output_down=False, norm='weight_norm'):
super(down_shifted_conv2d, self).__init__()
assert norm in [None, 'batch_norm', 'weight_norm']
self.conv = nn.Conv2d(num_filters_in, num_filters_out, filter_size, stride)
self.shift_output_down = shift_output_down
self.norm = norm
self.pad = nn.ZeroPad2d((int((filter_size[1] - 1) / 2), # pad left
int((filter_size[1] - 1) / 2), # pad right
filter_size[0] - 1, # pad top
0) ) # pad down
if norm == 'weight_norm':
self.conv = wn(self.conv)
elif norm == 'batch_norm':
self.bn = nn.BatchNorm2d(num_filters_out)
if shift_output_down :
self.down_shift = lambda x : down_shift(x, pad=nn.ZeroPad2d((0, 0, 1, 0)))
def forward(self, x):
x = self.pad(x)
x = self.conv(x)
x = self.bn(x) if self.norm == 'batch_norm' else x
return self.down_shift(x) if self.shift_output_down else x
class down_shifted_deconv2d(nn.Module):
def __init__(self, num_filters_in, num_filters_out, filter_size=(2,3), stride=(1,1)):
super(down_shifted_deconv2d, self).__init__()
self.deconv = wn(nn.ConvTranspose2d(num_filters_in, num_filters_out, filter_size, stride,
output_padding=1))
self.filter_size = filter_size
self.stride = stride
def forward(self, x):
x = self.deconv(x)
xs = [int(y) for y in x.size()]
return x[:, :, :(xs[2] - self.filter_size[0] + 1),
int((self.filter_size[1] - 1) / 2):(xs[3] - int((self.filter_size[1] - 1) / 2))]
class down_right_shifted_conv2d(nn.Module):
def __init__(self, num_filters_in, num_filters_out, filter_size=(2,2), stride=(1,1),
shift_output_right=False, norm='weight_norm'):
super(down_right_shifted_conv2d, self).__init__()
assert norm in [None, 'batch_norm', 'weight_norm']
self.pad = nn.ZeroPad2d((filter_size[1] - 1, 0, filter_size[0] - 1, 0))
self.conv = nn.Conv2d(num_filters_in, num_filters_out, filter_size, stride=stride)
self.shift_output_right = shift_output_right
self.norm = norm
if norm == 'weight_norm':
self.conv = wn(self.conv)
elif norm == 'batch_norm':
self.bn = nn.BatchNorm2d(num_filters_out)
if shift_output_right :
self.right_shift = lambda x : right_shift(x, pad=nn.ZeroPad2d((1, 0, 0, 0)))
def forward(self, x):
x = self.pad(x)
x = self.conv(x)
x = self.bn(x) if self.norm == 'batch_norm' else x
return self.right_shift(x) if self.shift_output_right else x
class down_right_shifted_deconv2d(nn.Module):
def __init__(self, num_filters_in, num_filters_out, filter_size=(2,2), stride=(1,1),
shift_output_right=False):
super(down_right_shifted_deconv2d, self).__init__()
self.deconv = wn(nn.ConvTranspose2d(num_filters_in, num_filters_out, filter_size,
stride, output_padding=1))
self.filter_size = filter_size
self.stride = stride
def forward(self, x):
x = self.deconv(x)
xs = [int(y) for y in x.size()]
x = x[:, :, :(xs[2] - self.filter_size[0] + 1):, :(xs[3] - self.filter_size[1] + 1)]
return x
'''
skip connection parameter : 0 = no skip connection
1 = skip connection where skip input size === input size
2 = skip connection where skip input size === 2 * input size
'''
class gated_resnet(nn.Module):
def __init__(self, num_filters, conv_op, nonlinearity=concat_elu, skip_connection=0):
super(gated_resnet, self).__init__()
self.skip_connection = skip_connection
self.nonlinearity = nonlinearity
self.conv_input = conv_op(2 * num_filters, num_filters) # cuz of concat elu
if skip_connection != 0 :
self.nin_skip = nin(2 * skip_connection * num_filters, num_filters)
self.dropout = nn.Dropout2d(0.5)
self.conv_out = conv_op(2 * num_filters, 2 * num_filters)
def forward(self, og_x, a=None):
x = self.conv_input(self.nonlinearity(og_x))
if a is not None :
x += self.nin_skip(self.nonlinearity(a))
x = self.nonlinearity(x)
x = self.dropout(x)
x = self.conv_out(x)
a, b = torch.chunk(x, 2, dim=1)
c3 = a * F.sigmoid(b)
return og_x + c3