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import torch
import torch.nn as nn
class AddCoordsTh(nn.Module):
def __init__(self, x_dim=64, y_dim=64, with_r=False, with_boundary=False):
super(AddCoordsTh, self).__init__()
self.x_dim = x_dim
self.y_dim = y_dim
self.with_r = with_r
self.with_boundary = with_boundary
def forward(self, input_tensor, heatmap=None):
"""
input_tensor: (batch, c, x_dim, y_dim)
"""
batch_size_tensor = input_tensor.shape[0]
xx_ones = torch.ones([1, self.y_dim], dtype=torch.int32).to(input_tensor.device)
xx_ones = xx_ones.unsqueeze(-1)
xx_range = torch.arange(self.x_dim, dtype=torch.int32).unsqueeze(0).to(input_tensor.device)
xx_range = xx_range.unsqueeze(1)
xx_channel = torch.matmul(xx_ones.float(), xx_range.float())
xx_channel = xx_channel.unsqueeze(-1)
yy_ones = torch.ones([1, self.x_dim], dtype=torch.int32).to(input_tensor.device)
yy_ones = yy_ones.unsqueeze(1)
yy_range = torch.arange(self.y_dim, dtype=torch.int32).unsqueeze(0).to(input_tensor.device)
yy_range = yy_range.unsqueeze(-1)
yy_channel = torch.matmul(yy_range.float(), yy_ones.float())
yy_channel = yy_channel.unsqueeze(-1)
xx_channel = xx_channel.permute(0, 3, 2, 1)
yy_channel = yy_channel.permute(0, 3, 2, 1)
xx_channel = xx_channel / (self.x_dim - 1)
yy_channel = yy_channel / (self.y_dim - 1)
xx_channel = xx_channel * 2 - 1
yy_channel = yy_channel * 2 - 1
xx_channel = xx_channel.repeat(batch_size_tensor, 1, 1, 1)
yy_channel = yy_channel.repeat(batch_size_tensor, 1, 1, 1)
if self.with_boundary and type(heatmap) != type(None):
boundary_channel = torch.clamp(heatmap[:, -1:, :, :], 0.0, 1.0)
zero_tensor = torch.zeros_like(xx_channel)
xx_boundary_channel = torch.where(boundary_channel > 0.05, xx_channel, zero_tensor)
yy_boundary_channel = torch.where(boundary_channel > 0.05, yy_channel, zero_tensor)
if self.with_boundary and type(heatmap) != type(None):
xx_boundary_channel = xx_boundary_channel.to(input_tensor.device)
yy_boundary_channel = yy_boundary_channel.to(input_tensor.device)
ret = torch.cat([input_tensor, xx_channel, yy_channel], dim=1)
if self.with_r:
rr = torch.sqrt(torch.pow(xx_channel, 2) + torch.pow(yy_channel, 2))
rr = rr / torch.max(rr)
ret = torch.cat([ret, rr], dim=1)
if self.with_boundary and type(heatmap) != type(None):
ret = torch.cat([ret, xx_boundary_channel, yy_boundary_channel], dim=1)
return ret
class CoordConvTh(nn.Module):
"""CoordConv layer as in the paper."""
def __init__(self, x_dim, y_dim, with_r, with_boundary, in_channels, first_one=False, *args, **kwargs):
super(CoordConvTh, self).__init__()
self.addcoords = AddCoordsTh(x_dim=x_dim, y_dim=y_dim, with_r=with_r, with_boundary=with_boundary)
in_channels += 2
if with_r:
in_channels += 1
if with_boundary and not first_one:
in_channels += 2
self.conv = nn.Conv2d(in_channels=in_channels, *args, **kwargs)
def forward(self, input_tensor, heatmap=None):
ret = self.addcoords(input_tensor, heatmap)
last_channel = ret[:, -2:, :, :]
ret = self.conv(ret)
return ret, last_channel
"""
An alternative implementation for PyTorch with auto-infering the x-y dimensions.
"""
class AddCoords(nn.Module):
def __init__(self, with_r=False):
super().__init__()
self.with_r = with_r
def forward(self, input_tensor):
"""
Args:
input_tensor: shape(batch, channel, x_dim, y_dim)
"""
batch_size, _, x_dim, y_dim = input_tensor.size()
xx_channel = torch.arange(x_dim).repeat(1, y_dim, 1)
yy_channel = torch.arange(y_dim).repeat(1, x_dim, 1).transpose(1, 2)
xx_channel = xx_channel / (x_dim - 1)
yy_channel = yy_channel / (y_dim - 1)
xx_channel = xx_channel * 2 - 1
yy_channel = yy_channel * 2 - 1
xx_channel = xx_channel.repeat(batch_size, 1, 1, 1).transpose(2, 3)
yy_channel = yy_channel.repeat(batch_size, 1, 1, 1).transpose(2, 3)
if input_tensor.is_cuda:
xx_channel = xx_channel.to(input_tensor.device)
yy_channel = yy_channel.to(input_tensor.device)
ret = torch.cat([input_tensor, xx_channel.type_as(input_tensor), yy_channel.type_as(input_tensor)], dim=1)
if self.with_r:
rr = torch.sqrt(torch.pow(xx_channel - 0.5, 2) + torch.pow(yy_channel - 0.5, 2))
if input_tensor.is_cuda:
rr = rr.to(input_tensor.device)
ret = torch.cat([ret, rr], dim=1)
return ret
class CoordConv(nn.Module):
def __init__(self, in_channels, out_channels, with_r=False, **kwargs):
super().__init__()
self.addcoords = AddCoords(with_r=with_r)
self.conv = nn.Conv2d(in_channels + 2, out_channels, **kwargs)
def forward(self, x):
ret = self.addcoords(x)
ret = self.conv(ret)
return ret
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