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CoordConv.py
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CoordConv.py
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import torch
import torch.nn as nn
import torch.nn.modules.conv as conv
from hparams import create_hparams
hparams = create_hparams()
class AddCoords(nn.Module):
def __init__(self, rank, with_r=False):
super(AddCoords, self).__init__()
self.rank = rank
self.with_r = with_r
def forward(self, input_tensor):
"""
:param input_tensor: shape (N, C_in, H, W)
:return:
"""
if self.rank == 1:
batch_size_shape, channel_in_shape, dim_x = input_tensor.shape
xx_range = torch.arange(dim_x, dtype=torch.int32)
xx_channel = xx_range[None, None, :]
xx_channel = xx_channel.float() / (dim_x - 1)
xx_channel = xx_channel * 2 - 1
xx_channel = xx_channel.repeat(batch_size_shape, 1, 1)
if torch.cuda.is_available:
input_tensor = input_tensor.cuda()
xx_channel = xx_channel.cuda()
out = torch.cat([input_tensor, xx_channel], dim=1)
if self.with_r:
rr = torch.sqrt(torch.pow(xx_channel - 0.5, 2))
out = torch.cat([out, rr], dim=1)
elif self.rank == 2:
batch_size_shape, channel_in_shape, dim_y, dim_x = input_tensor.shape
xx_ones = torch.ones([1, 1, 1, dim_x], dtype=torch.int32)
yy_ones = torch.ones([1, 1, 1, dim_y], dtype=torch.int32)
xx_range = torch.arange(dim_y, dtype=torch.int32)
yy_range = torch.arange(dim_x, dtype=torch.int32)
xx_range = xx_range[None, None, :, None]
yy_range = yy_range[None, None, :, None]
xx_channel = torch.matmul(xx_range, xx_ones)
yy_channel = torch.matmul(yy_range, yy_ones)
# transpose y
yy_channel = yy_channel.permute(0, 1, 3, 2)
xx_channel = xx_channel.float() / (dim_y - 1)
yy_channel = yy_channel.float() / (dim_x - 1)
xx_channel = xx_channel * 2 - 1
yy_channel = yy_channel * 2 - 1
xx_channel = xx_channel.repeat(batch_size_shape, 1, 1, 1)
yy_channel = yy_channel.repeat(batch_size_shape, 1, 1, 1)
if torch.cuda.is_available:
input_tensor = input_tensor.cuda()
xx_channel = xx_channel.cuda()
yy_channel = yy_channel.cuda()
if hparams.fp16_run:
input_tensor = input_tensor.half()
xx_channel = xx_channel.half()
yy_channel = yy_channel.half()
out = torch.cat([input_tensor, xx_channel, yy_channel], dim=1)
if self.with_r:
rr = torch.sqrt(torch.pow(xx_channel - 0.5, 2) + torch.pow(yy_channel - 0.5, 2))
out = torch.cat([out, rr], dim=1)
elif self.rank == 3:
batch_size_shape, channel_in_shape, dim_z, dim_y, dim_x = input_tensor.shape
xx_ones = torch.ones([1, 1, 1, 1, dim_x], dtype=torch.int32)
yy_ones = torch.ones([1, 1, 1, 1, dim_y], dtype=torch.int32)
zz_ones = torch.ones([1, 1, 1, 1, dim_z], dtype=torch.int32)
xy_range = torch.arange(dim_y, dtype=torch.int32)
xy_range = xy_range[None, None, None, :, None]
yz_range = torch.arange(dim_z, dtype=torch.int32)
yz_range = yz_range[None, None, None, :, None]
zx_range = torch.arange(dim_x, dtype=torch.int32)
zx_range = zx_range[None, None, None, :, None]
xy_channel = torch.matmul(xy_range, xx_ones)
xx_channel = torch.cat([xy_channel + i for i in range(dim_z)], dim=2)
yz_channel = torch.matmul(yz_range, yy_ones)
yz_channel = yz_channel.permute(0, 1, 3, 4, 2)
yy_channel = torch.cat([yz_channel + i for i in range(dim_x)], dim=4)
zx_channel = torch.matmul(zx_range, zz_ones)
zx_channel = zx_channel.permute(0, 1, 4, 2, 3)
zz_channel = torch.cat([zx_channel + i for i in range(dim_y)], dim=3)
if torch.cuda.is_available:
input_tensor = input_tensor.cuda()
xx_channel = xx_channel.cuda()
yy_channel = yy_channel.cuda()
zz_channel = zz_channel.cuda()
out = torch.cat([input_tensor, xx_channel, yy_channel, zz_channel], dim=1)
if self.with_r:
rr = torch.sqrt(torch.pow(xx_channel - 0.5, 2) +
torch.pow(yy_channel - 0.5, 2) +
torch.pow(zz_channel - 0.5, 2))
out = torch.cat([out, rr], dim=1)
else:
raise NotImplementedError
return out
class CoordConv1d(conv.Conv1d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True, with_r=False):
super(CoordConv1d, self).__init__(in_channels, out_channels, kernel_size,
stride, padding, dilation, groups, bias)
self.rank = 1
self.addcoords = AddCoords(self.rank, with_r)
self.conv = nn.Conv1d(in_channels + self.rank + int(with_r), out_channels,
kernel_size, stride, padding, dilation, groups, bias)
def forward(self, input_tensor):
"""
input_tensor_shape: (N, C_in,H,W)
output_tensor_shape: N,C_out,H_out,W_out）
:return: CoordConv2d Result
"""
out = self.addcoords(input_tensor)
out = self.conv(out)
return out
class CoordConv2d(conv.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True, with_r=False):
super(CoordConv2d, self).__init__(in_channels, out_channels, kernel_size,
stride, padding, dilation, groups, bias)
self.rank = 2
self.addcoords = AddCoords(self.rank, with_r)
self.conv = nn.Conv2d(in_channels + self.rank + int(with_r), out_channels,
kernel_size, stride, padding, dilation, groups, bias)
def forward(self, input_tensor):
"""
input_tensor_shape: (N, C_in,H,W)
output_tensor_shape: N,C_out,H_out,W_out）
:return: CoordConv2d Result
"""
out = self.addcoords(input_tensor)
out = self.conv(out)
return out
class CoordConv3d(conv.Conv3d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1,
padding=0, dilation=1, groups=1, bias=True, with_r=False):
super(CoordConv3d, self).__init__(in_channels, out_channels, kernel_size,
stride, padding, dilation, groups, bias)
self.rank = 3
self.addcoords = AddCoords(self.rank, with_r)
self.conv = nn.Conv3d(in_channels + self.rank + int(with_r), out_channels,
kernel_size, stride, padding, dilation, groups, bias)
def forward(self, input_tensor):
"""
input_tensor_shape: (N, C_in,H,W)
output_tensor_shape: N,C_out,H_out,W_out）
:return: CoordConv2d Result
"""
out = self.addcoords(input_tensor)
out = self.conv(out)
return out