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# 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.
"""
This code is refer from:
https://github.com/ayumiymk/aster.pytorch/blob/master/lib/models/stn_head.py
"""
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
from .tps_spatial_transformer import TPSSpatialTransformer
def conv3x3_block(in_channels, out_channels, stride=1):
n = 3 * 3 * out_channels
w = math.sqrt(2. / n)
conv_layer = nn.Conv2D(
in_channels,
out_channels,
kernel_size=3,
stride=stride,
padding=1,
weight_attr=nn.initializer.Normal(
mean=0.0, std=w),
bias_attr=nn.initializer.Constant(0))
block = nn.Sequential(conv_layer, nn.BatchNorm2D(out_channels), nn.ReLU())
return block
class STN(nn.Layer):
def __init__(self, in_channels, num_ctrlpoints, activation='none'):
super(STN, self).__init__()
self.in_channels = in_channels
self.num_ctrlpoints = num_ctrlpoints
self.activation = activation
self.stn_convnet = nn.Sequential(
conv3x3_block(in_channels, 32), #32x64
nn.MaxPool2D(
kernel_size=2, stride=2),
conv3x3_block(32, 64), #16x32
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,
weight_attr=nn.initializer.Normal(0, 0.001),
bias_attr=nn.initializer.Constant(0)),
nn.BatchNorm1D(512),
nn.ReLU())
fc2_bias = self.init_stn()
self.stn_fc2 = nn.Linear(
512,
num_ctrlpoints * 2,
weight_attr=nn.initializer.Constant(0.0),
bias_attr=nn.initializer.Assign(fc2_bias))
def init_stn(self):
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.)
ctrl_points = paddle.to_tensor(ctrl_points)
fc2_bias = paddle.reshape(
ctrl_points, shape=[ctrl_points.shape[0] * ctrl_points.shape[1]])
return fc2_bias
def forward(self, x):
x = self.stn_convnet(x)
batch_size, _, h, w = x.shape
x = paddle.reshape(x, shape=(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 = paddle.reshape(x, shape=[-1, self.num_ctrlpoints, 2])
return img_feat, x
class STN_ON(nn.Layer):
def __init__(self, in_channels, tps_inputsize, tps_outputsize,
num_control_points, tps_margins, stn_activation):
super(STN_ON, self).__init__()
self.tps = TPSSpatialTransformer(
output_image_size=tuple(tps_outputsize),
num_control_points=num_control_points,
margins=tuple(tps_margins))
self.stn_head = STN(in_channels=in_channels,
num_ctrlpoints=num_control_points,
activation=stn_activation)
self.tps_inputsize = tps_inputsize
self.out_channels = in_channels
def forward(self, image):
stn_input = paddle.nn.functional.interpolate(
image, self.tps_inputsize, mode="bilinear", align_corners=True)
stn_img_feat, ctrl_points = self.stn_head(stn_input)
x, _ = self.tps(image, ctrl_points)
return x
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