148 lines
5.0 KiB
Python
148 lines
5.0 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This code is refer from:
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https://github.com/ayumiymk/aster.pytorch/blob/master/lib/models/stn_head.py
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import math
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import paddle
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from paddle import nn, ParamAttr
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from paddle.nn import functional as F
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import numpy as np
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from .tps_spatial_transformer import TPSSpatialTransformer
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def conv3x3_block(in_channels, out_channels, stride=1):
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n = 3 * 3 * out_channels
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w = math.sqrt(2.0 / n)
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conv_layer = nn.Conv2D(
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in_channels,
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out_channels,
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kernel_size=3,
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stride=stride,
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padding=1,
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weight_attr=nn.initializer.Normal(mean=0.0, std=w),
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bias_attr=nn.initializer.Constant(0),
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)
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block = nn.Sequential(conv_layer, nn.BatchNorm2D(out_channels), nn.ReLU())
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return block
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class STN(nn.Layer):
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def __init__(self, in_channels, num_ctrlpoints, activation="none"):
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super(STN, self).__init__()
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self.in_channels = in_channels
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self.num_ctrlpoints = num_ctrlpoints
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self.activation = activation
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self.stn_convnet = nn.Sequential(
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conv3x3_block(in_channels, 32), # 32x64
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nn.MaxPool2D(kernel_size=2, stride=2),
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conv3x3_block(32, 64), # 16x32
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nn.MaxPool2D(kernel_size=2, stride=2),
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conv3x3_block(64, 128), # 8*16
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nn.MaxPool2D(kernel_size=2, stride=2),
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conv3x3_block(128, 256), # 4*8
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nn.MaxPool2D(kernel_size=2, stride=2),
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conv3x3_block(256, 256), # 2*4,
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nn.MaxPool2D(kernel_size=2, stride=2),
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conv3x3_block(256, 256),
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) # 1*2
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self.stn_fc1 = nn.Sequential(
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nn.Linear(
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2 * 256,
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512,
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weight_attr=nn.initializer.Normal(0, 0.001),
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bias_attr=nn.initializer.Constant(0),
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),
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nn.BatchNorm1D(512),
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nn.ReLU(),
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)
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fc2_bias = self.init_stn()
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self.stn_fc2 = nn.Linear(
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512,
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num_ctrlpoints * 2,
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weight_attr=nn.initializer.Constant(0.0),
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bias_attr=nn.initializer.Assign(fc2_bias),
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)
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def init_stn(self):
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margin = 0.01
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sampling_num_per_side = int(self.num_ctrlpoints / 2)
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ctrl_pts_x = np.linspace(margin, 1.0 - margin, sampling_num_per_side)
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ctrl_pts_y_top = np.ones(sampling_num_per_side) * margin
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ctrl_pts_y_bottom = np.ones(sampling_num_per_side) * (1 - margin)
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ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1)
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ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1)
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ctrl_points = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0).astype(
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np.float32
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)
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if self.activation == "none":
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pass
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elif self.activation == "sigmoid":
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ctrl_points = -np.log(1.0 / ctrl_points - 1.0)
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ctrl_points = paddle.to_tensor(ctrl_points)
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fc2_bias = paddle.reshape(
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ctrl_points, shape=[ctrl_points.shape[0] * ctrl_points.shape[1]]
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)
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return fc2_bias
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def forward(self, x):
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x = self.stn_convnet(x)
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batch_size, _, h, w = x.shape
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x = paddle.reshape(x, shape=(batch_size, -1))
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img_feat = self.stn_fc1(x)
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x = self.stn_fc2(0.1 * img_feat)
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if self.activation == "sigmoid":
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x = F.sigmoid(x)
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x = paddle.reshape(x, shape=[-1, self.num_ctrlpoints, 2])
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return img_feat, x
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class STN_ON(nn.Layer):
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def __init__(
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self,
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in_channels,
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tps_inputsize,
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tps_outputsize,
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num_control_points,
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tps_margins,
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stn_activation,
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):
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super(STN_ON, self).__init__()
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self.tps = TPSSpatialTransformer(
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output_image_size=tuple(tps_outputsize),
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num_control_points=num_control_points,
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margins=tuple(tps_margins),
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)
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self.stn_head = STN(
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in_channels=in_channels,
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num_ctrlpoints=num_control_points,
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activation=stn_activation,
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)
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self.tps_inputsize = tps_inputsize
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self.out_channels = in_channels
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def forward(self, image):
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stn_input = paddle.nn.functional.interpolate(
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image, self.tps_inputsize, mode="bilinear", align_corners=True
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)
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stn_img_feat, ctrl_points = self.stn_head(stn_input)
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x, _ = self.tps(image, ctrl_points)
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return x
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