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import itertools
import math
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F
def conv3x3_block(in_planes, out_planes, stride=1):
"""3x3 convolution with padding."""
conv_layer = nn.Conv2d(in_planes,
out_planes,
kernel_size=3,
stride=1,
padding=1)
block = nn.Sequential(
conv_layer,
nn.BatchNorm2d(out_planes),
nn.ReLU(inplace=True),
)
return block
class STNHead(nn.Module):
def __init__(self, in_planes, num_ctrlpoints, activation='none'):
super(STNHead, self).__init__()
self.in_planes = in_planes
self.num_ctrlpoints = num_ctrlpoints
self.activation = activation
self.stn_convnet = nn.Sequential(
conv3x3_block(in_planes, 32), # 32*64
nn.MaxPool2d(kernel_size=2, stride=2),
conv3x3_block(32, 64), # 16*32
nn.MaxPool2d(kernel_size=2, stride=2),
conv3x3_block(64, 128), # 8*16
nn.MaxPool2d(kernel_size=2, stride=2),
conv3x3_block(128, 256), # 4*8
nn.MaxPool2d(kernel_size=2, stride=2),
conv3x3_block(256, 256), # 2*4,
nn.MaxPool2d(kernel_size=2, stride=2),
conv3x3_block(256, 256)) # 1*2
self.stn_fc1 = nn.Sequential(nn.Linear(2 * 256, 512),
nn.BatchNorm1d(512),
nn.ReLU(inplace=True))
self.stn_fc2 = nn.Linear(512, num_ctrlpoints * 2)
self.init_weights(self.stn_convnet)
self.init_weights(self.stn_fc1)
self.init_stn(self.stn_fc2)
def init_weights(self, module):
for m in module.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
m.weight.data.normal_(0, 0.001)
m.bias.data.zero_()
def init_stn(self, stn_fc2):
margin = 0.01
sampling_num_per_side = int(self.num_ctrlpoints / 2)
ctrl_pts_x = np.linspace(margin, 1. - margin, sampling_num_per_side)
ctrl_pts_y_top = np.ones(sampling_num_per_side) * margin
ctrl_pts_y_bottom = np.ones(sampling_num_per_side) * (1 - margin)
ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1)
ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1)
ctrl_points = np.concatenate([ctrl_pts_top, ctrl_pts_bottom],
axis=0).astype(np.float32)
if self.activation == 'none':
pass
elif self.activation == 'sigmoid':
ctrl_points = -np.log(1. / ctrl_points - 1.)
stn_fc2.weight.data.zero_()
stn_fc2.bias.data = torch.Tensor(ctrl_points).view(-1)
def forward(self, x):
x = self.stn_convnet(x)
batch_size, _, h, w = x.size()
x = x.view(batch_size, -1)
img_feat = self.stn_fc1(x)
x = self.stn_fc2(0.1 * img_feat)
if self.activation == 'sigmoid':
x = F.sigmoid(x)
x = x.view(-1, self.num_ctrlpoints, 2)
return x
def grid_sample(input, grid, canvas=None):
output = F.grid_sample(input, grid)
if canvas is None:
return output
else:
input_mask = input.data.new(input.size()).fill_(1)
output_mask = F.grid_sample(input_mask, grid)
padded_output = output * output_mask + canvas * (1 - output_mask)
return padded_output
# phi(x1, x2) = r^2 * log(r), where r = ||x1 - x2||_2
def compute_partial_repr(input_points, control_points):
N = input_points.size(0)
M = control_points.size(0)
pairwise_diff = input_points.view(N, 1, 2) - control_points.view(1, M, 2)
# original implementation, very slow
# pairwise_dist = torch.sum(pairwise_diff ** 2, dim = 2) # square of distance
pairwise_diff_square = pairwise_diff * pairwise_diff
pairwise_dist = pairwise_diff_square[:, :, 0] + pairwise_diff_square[:, :,
1]
repr_matrix = 0.5 * pairwise_dist * torch.log(pairwise_dist)
# fix numerical error for 0 * log(0), substitute all nan with 0
mask = repr_matrix != repr_matrix
repr_matrix.masked_fill_(mask, 0)
return repr_matrix
# output_ctrl_pts are specified, according to our task.
def build_output_control_points(num_control_points, margins):
margin_x, margin_y = margins
num_ctrl_pts_per_side = num_control_points // 2
ctrl_pts_x = np.linspace(margin_x, 1.0 - margin_x, num_ctrl_pts_per_side)
ctrl_pts_y_top = np.ones(num_ctrl_pts_per_side) * margin_y
ctrl_pts_y_bottom = np.ones(num_ctrl_pts_per_side) * (1.0 - margin_y)
ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1)
ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1)
# ctrl_pts_top = ctrl_pts_top[1:-1,:]
# ctrl_pts_bottom = ctrl_pts_bottom[1:-1,:]
output_ctrl_pts_arr = np.concatenate([ctrl_pts_top, ctrl_pts_bottom],
axis=0)
output_ctrl_pts = torch.Tensor(output_ctrl_pts_arr)
return output_ctrl_pts
class TPSSpatialTransformer(nn.Module):
def __init__(
self,
output_image_size,
num_control_points,
margins,
):
super(TPSSpatialTransformer, self).__init__()
self.output_image_size = output_image_size
self.num_control_points = num_control_points
self.margins = margins
self.target_height, self.target_width = output_image_size
target_control_points = build_output_control_points(
num_control_points, margins)
N = num_control_points
# N = N - 4
# create padded kernel matrix
forward_kernel = torch.zeros(N + 3, N + 3)
target_control_partial_repr = compute_partial_repr(
target_control_points, target_control_points)
forward_kernel[:N, :N].copy_(target_control_partial_repr)
forward_kernel[:N, -3].fill_(1)
forward_kernel[-3, :N].fill_(1)
forward_kernel[:N, -2:].copy_(target_control_points)
forward_kernel[-2:, :N].copy_(target_control_points.transpose(0, 1))
# compute inverse matrix
inverse_kernel = torch.inverse(forward_kernel)
# create target cordinate matrix
HW = self.target_height * self.target_width
target_coordinate = list(
itertools.product(range(self.target_height),
range(self.target_width)))
target_coordinate = torch.Tensor(target_coordinate) # HW x 2
Y, X = target_coordinate.split(1, dim=1)
Y = Y / (self.target_height - 1)
X = X / (self.target_width - 1)
target_coordinate = torch.cat([X, Y],
dim=1) # convert from (y, x) to (x, y)
target_coordinate_partial_repr = compute_partial_repr(
target_coordinate, target_control_points)
target_coordinate_repr = torch.cat([
target_coordinate_partial_repr,
torch.ones(HW, 1), target_coordinate
],
dim=1)
# register precomputed matrices
self.register_buffer('inverse_kernel', inverse_kernel)
self.register_buffer('padding_matrix', torch.zeros(3, 2))
self.register_buffer('target_coordinate_repr', target_coordinate_repr)
self.register_buffer('target_control_points', target_control_points)
def forward(self, input, source_control_points):
assert source_control_points.ndimension() == 3
assert source_control_points.size(1) == self.num_control_points
assert source_control_points.size(2) == 2
batch_size = source_control_points.size(0)
Y = torch.cat([
source_control_points,
self.padding_matrix.expand(batch_size, 3, 2)
], 1)
mapping_matrix = torch.matmul(self.inverse_kernel, Y)
source_coordinate = torch.matmul(self.target_coordinate_repr,
mapping_matrix)
grid = source_coordinate.view(-1, self.target_height,
self.target_width, 2)
grid = torch.clamp(
grid, 0, 1) # the source_control_points may be out of [0, 1].
# the input to grid_sample is normalized [-1, 1], but what we get is [0, 1]
grid = 2.0 * grid - 1.0
output_maps = grid_sample(input, grid, canvas=None)
return output_maps
class Aster_TPS(nn.Module):
def __init__(
self,
in_channels,
tps_inputsize=[32, 64],
tps_outputsize=[32, 100],
num_control_points=20,
tps_margins=[0.05, 0.05],
) -> None:
super().__init__()
self.in_channels = in_channels
#TODO
self.out_channels = in_channels
self.tps_inputsize = tps_inputsize
self.num_control_points = num_control_points
self.stn_head = STNHead(
in_planes=3,
num_ctrlpoints=num_control_points,
)
self.tps = TPSSpatialTransformer(
output_image_size=tps_outputsize,
num_control_points=num_control_points,
margins=tps_margins,
)
def forward(self, img):
stn_input = F.interpolate(img,
self.tps_inputsize,
mode='bilinear',
align_corners=True)
ctrl_points = self.stn_head(stn_input)
img = self.tps(img, ctrl_points)
return img
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