# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
from paddle import nn, ParamAttr
from paddle.nn import functional as F
import numpy as np
class ConvBNLayer(nn.Layer):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
groups=1,
act=None,
name=None):
super(ConvBNLayer, self).__init__()
self.conv = nn.Conv2D(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
padding=(kernel_size - 1) // 2,
groups=groups,
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
bn_name = "bn_" + name
self.bn = nn.BatchNorm(
out_channels,
act=act,
param_attr=ParamAttr(name=bn_name + '_scale'),
bias_attr=ParamAttr(bn_name + '_offset'),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return x
class LocalizationNetwork(nn.Layer):
def __init__(self, in_channels, num_fiducial, loc_lr, model_name):
super(LocalizationNetwork, self).__init__()
self.F = num_fiducial
F = num_fiducial
if model_name == "large":
num_filters_list = [64, 128, 256, 512]
fc_dim = 256
else:
num_filters_list = [16, 32, 64, 128]
fc_dim = 64
self.block_list = []
for fno in range(0, len(num_filters_list)):
num_filters = num_filters_list[fno]
name = "loc_conv%d" % fno
conv = self.add_sublayer(
name,
ConvBNLayer(
in_channels=in_channels,
out_channels=num_filters,
kernel_size=3,
act='relu',
name=name))
self.block_list.append(conv)
if fno == len(num_filters_list) - 1:
pool = nn.AdaptiveAvgPool2D(1)
else:
pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
in_channels = num_filters
self.block_list.append(pool)
name = "loc_fc1"
stdv = 1.0 / math.sqrt(num_filters_list[-1] * 1.0)
self.fc1 = nn.Linear(
in_channels,
fc_dim,
weight_attr=ParamAttr(
learning_rate=loc_lr,
name=name + "_w",
initializer=nn.initializer.Uniform(-stdv, stdv)),
bias_attr=ParamAttr(name=name + '.b_0'),
name=name)
# Init fc2 in LocalizationNetwork
initial_bias = self.get_initial_fiducials()
initial_bias = initial_bias.reshape(-1)
name = "loc_fc2"
param_attr = ParamAttr(
learning_rate=loc_lr,
initializer=nn.initializer.Assign(np.zeros([fc_dim, F * 2])),
name=name + "_w")
bias_attr = ParamAttr(
learning_rate=loc_lr,
initializer=nn.initializer.Assign(initial_bias),
name=name + "_b")
self.fc2 = nn.Linear(
fc_dim,
F * 2,
weight_attr=param_attr,
bias_attr=bias_attr,
name=name)
self.out_channels = F * 2
def forward(self, x):
"""
Estimating parameters of geometric transformation
Args:
image: input
Return:
batch_C_prime: the matrix of the geometric transformation
"""
B = x.shape[0]
i = 0
for block in self.block_list:
x = block(x)
x = x.squeeze(axis=2).squeeze(axis=2)
x = self.fc1(x)
x = F.relu(x)
x = self.fc2(x)
x = x.reshape(shape=[-1, self.F, 2])
return x
def get_initial_fiducials(self):
""" see RARE paper Fig. 6 (a) """
F = self.F
ctrl_pts_x = np.linspace(-1.0, 1.0, int(F / 2))
ctrl_pts_y_top = np.linspace(0.0, -1.0, num=int(F / 2))
ctrl_pts_y_bottom = np.linspace(1.0, 0.0, num=int(F / 2))
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)
initial_bias = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0)
return initial_bias
class GridGenerator(nn.Layer):
def __init__(self, in_channels, num_fiducial):
super(GridGenerator, self).__init__()
self.eps = 1e-6
self.F = num_fiducial
name = "ex_fc"
initializer = nn.initializer.Constant(value=0.0)
param_attr = ParamAttr(
learning_rate=0.0, initializer=initializer, name=name + "_w")
bias_attr = ParamAttr(
learning_rate=0.0, initializer=initializer, name=name + "_b")
self.fc = nn.Linear(
in_channels,
6,
weight_attr=param_attr,
bias_attr=bias_attr,
name=name)
def forward(self, batch_C_prime, I_r_size):
"""
Generate the grid for the grid_sampler.
Args:
batch_C_prime: the matrix of the geometric transformation
I_r_size: the shape of the input image
Return:
batch_P_prime: the grid for the grid_sampler
"""
C = self.build_C_paddle()
P = self.build_P_paddle(I_r_size)
inv_delta_C_tensor = self.build_inv_delta_C_paddle(C).astype('float32')
P_hat_tensor = self.build_P_hat_paddle(
C, paddle.to_tensor(P)).astype('float32')
inv_delta_C_tensor.stop_gradient = True
P_hat_tensor.stop_gradient = True
batch_C_ex_part_tensor = self.get_expand_tensor(batch_C_prime)
batch_C_ex_part_tensor.stop_gradient = True
batch_C_prime_with_zeros = paddle.concat(
[batch_C_prime, batch_C_ex_part_tensor], axis=1)
batch_T = paddle.matmul(inv_delta_C_tensor, batch_C_prime_with_zeros)
batch_P_prime = paddle.matmul(P_hat_tensor, batch_T)
return batch_P_prime
def build_C_paddle(self):
""" Return coordinates of fiducial points in I_r; C """
F = self.F
ctrl_pts_x = paddle.linspace(-1.0, 1.0, int(F / 2), dtype='float64')
ctrl_pts_y_top = -1 * paddle.ones([int(F / 2)], dtype='float64')
ctrl_pts_y_bottom = paddle.ones([int(F / 2)], dtype='float64')
ctrl_pts_top = paddle.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1)
ctrl_pts_bottom = paddle.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1)
C = paddle.concat([ctrl_pts_top, ctrl_pts_bottom], axis=0)
return C # F x 2
def build_P_paddle(self, I_r_size):
I_r_height, I_r_width = I_r_size
I_r_grid_x = (paddle.arange(
-I_r_width, I_r_width, 2, dtype='float64') + 1.0
) / paddle.to_tensor(np.array([I_r_width]))
I_r_grid_y = (paddle.arange(
-I_r_height, I_r_height, 2, dtype='float64') + 1.0
) / paddle.to_tensor(np.array([I_r_height]))
# P: self.I_r_width x self.I_r_height x 2
P = paddle.stack(paddle.meshgrid(I_r_grid_x, I_r_grid_y), axis=2)
P = paddle.transpose(P, perm=[1, 0, 2])
# n (= self.I_r_width x self.I_r_height) x 2
return P.reshape([-1, 2])
def build_inv_delta_C_paddle(self, C):
""" Return inv_delta_C which is needed to calculate T """
F = self.F
hat_C = paddle.zeros((F, F), dtype='float64') # F x F
for i in range(0, F):
for j in range(i, F):
if i == j:
hat_C[i, j] = 1
else:
r = paddle.norm(C[i] - C[j])
hat_C[i, j] = r
hat_C[j, i] = r
hat_C = (hat_C**2) * paddle.log(hat_C)
delta_C = paddle.concat( # F+3 x F+3
[
paddle.concat(
[paddle.ones(
(F, 1), dtype='float64'), C, hat_C], axis=1), # F x F+3
paddle.concat(
[
paddle.zeros(
(2, 3), dtype='float64'), paddle.transpose(
C, perm=[1, 0])
],
axis=1), # 2 x F+3
paddle.concat(
[
paddle.zeros(
(1, 3), dtype='float64'), paddle.ones(
(1, F), dtype='float64')
],
axis=1) # 1 x F+3
],
axis=0)
inv_delta_C = paddle.inverse(delta_C)
return inv_delta_C # F+3 x F+3
def build_P_hat_paddle(self, C, P):
F = self.F
eps = self.eps
n = P.shape[0] # n (= self.I_r_width x self.I_r_height)
# P_tile: n x 2 -> n x 1 x 2 -> n x F x 2
P_tile = paddle.tile(paddle.unsqueeze(P, axis=1), (1, F, 1))
C_tile = paddle.unsqueeze(C, axis=0) # 1 x F x 2
P_diff = P_tile - C_tile # n x F x 2
# rbf_norm: n x F
rbf_norm = paddle.norm(P_diff, p=2, axis=2, keepdim=False)
# rbf: n x F
rbf = paddle.multiply(
paddle.square(rbf_norm), paddle.log(rbf_norm + eps))
P_hat = paddle.concat(
[paddle.ones(
(n, 1), dtype='float64'), P, rbf], axis=1)
return P_hat # n x F+3
def get_expand_tensor(self, batch_C_prime):
B, H, C = batch_C_prime.shape
batch_C_prime = batch_C_prime.reshape([B, H * C])
batch_C_ex_part_tensor = self.fc(batch_C_prime)
batch_C_ex_part_tensor = batch_C_ex_part_tensor.reshape([-1, 3, 2])
return batch_C_ex_part_tensor
class TPS(nn.Layer):
def __init__(self, in_channels, num_fiducial, loc_lr, model_name):
super(TPS, self).__init__()
self.loc_net = LocalizationNetwork(in_channels, num_fiducial, loc_lr,
model_name)
self.grid_generator = GridGenerator(self.loc_net.out_channels,
num_fiducial)
self.out_channels = in_channels
def forward(self, image):
image.stop_gradient = False
batch_C_prime = self.loc_net(image)
batch_P_prime = self.grid_generator(batch_C_prime, image.shape[2:])
batch_P_prime = batch_P_prime.reshape(
[-1, image.shape[2], image.shape[3], 2])
batch_I_r = F.grid_sample(x=image, grid=batch_P_prime)
return batch_I_r