206 lines
7.4 KiB
Python
206 lines
7.4 KiB
Python
#copyright (c) 2019 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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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.fluid as fluid
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class DBHead(object):
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"""
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Differentiable Binarization (DB) for text detection:
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see https://arxiv.org/abs/1911.08947
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args:
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params(dict): super parameters for build DB network
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"""
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def __init__(self, params):
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self.k = params['k']
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self.inner_channels = params['inner_channels']
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self.C, self.H, self.W = params['image_shape']
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print(self.C, self.H, self.W)
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def binarize(self, x):
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conv1 = fluid.layers.conv2d(
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input=x,
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num_filters=self.inner_channels // 4,
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filter_size=3,
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padding=1,
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param_attr=fluid.initializer.MSRAInitializer(uniform=False),
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bias_attr=False)
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conv_bn1 = fluid.layers.batch_norm(
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input=conv1,
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param_attr=fluid.initializer.ConstantInitializer(value=1.0),
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bias_attr=fluid.initializer.ConstantInitializer(value=1e-4),
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act="relu")
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conv2 = fluid.layers.conv2d_transpose(
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input=conv_bn1,
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num_filters=self.inner_channels // 4,
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filter_size=2,
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stride=2,
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param_attr=fluid.initializer.MSRAInitializer(uniform=False),
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bias_attr=self._get_bias_attr(0.0004, conv_bn1.shape[1], "conv2"),
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act=None)
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conv_bn2 = fluid.layers.batch_norm(
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input=conv2,
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param_attr=fluid.initializer.ConstantInitializer(value=1.0),
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bias_attr=fluid.initializer.ConstantInitializer(value=1e-4),
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act="relu")
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conv3 = fluid.layers.conv2d_transpose(
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input=conv_bn2,
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num_filters=1,
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filter_size=2,
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stride=2,
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param_attr=fluid.initializer.MSRAInitializer(uniform=False),
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bias_attr=self._get_bias_attr(0.0004, conv_bn2.shape[1], "conv3"),
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act=None)
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out = fluid.layers.sigmoid(conv3)
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return out
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def thresh(self, x):
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conv1 = fluid.layers.conv2d(
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input=x,
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num_filters=self.inner_channels // 4,
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filter_size=3,
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padding=1,
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param_attr=fluid.initializer.MSRAInitializer(uniform=False),
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bias_attr=False)
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conv_bn1 = fluid.layers.batch_norm(
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input=conv1,
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param_attr=fluid.initializer.ConstantInitializer(value=1.0),
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bias_attr=fluid.initializer.ConstantInitializer(value=1e-4),
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act="relu")
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conv2 = fluid.layers.conv2d_transpose(
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input=conv_bn1,
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num_filters=self.inner_channels // 4,
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filter_size=2,
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stride=2,
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param_attr=fluid.initializer.MSRAInitializer(uniform=False),
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bias_attr=self._get_bias_attr(0.0004, conv_bn1.shape[1], "conv2"),
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act=None)
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conv_bn2 = fluid.layers.batch_norm(
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input=conv2,
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param_attr=fluid.initializer.ConstantInitializer(value=1.0),
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bias_attr=fluid.initializer.ConstantInitializer(value=1e-4),
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act="relu")
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conv3 = fluid.layers.conv2d_transpose(
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input=conv_bn2,
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num_filters=1,
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filter_size=2,
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stride=2,
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param_attr=fluid.initializer.MSRAInitializer(uniform=False),
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bias_attr=self._get_bias_attr(0.0004, conv_bn2.shape[1], "conv3"),
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act=None)
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out = fluid.layers.sigmoid(conv3)
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return out
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def _get_bias_attr(self, l2_decay, k, name, gradient_clip=None):
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regularizer = fluid.regularizer.L2Decay(l2_decay)
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stdv = 1.0 / math.sqrt(k * 1.0)
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initializer = fluid.initializer.Uniform(-stdv, stdv)
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bias_attr = fluid.ParamAttr(
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regularizer=regularizer,
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initializer=initializer,
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name=name + "_b_attr")
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return bias_attr
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def step_function(self, x, y):
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return fluid.layers.reciprocal(1 + fluid.layers.exp(-self.k * (x - y)))
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def __call__(self, conv_features, mode="train"):
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c2, c3, c4, c5 = conv_features
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param_attr = fluid.initializer.MSRAInitializer(uniform=False)
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in5 = fluid.layers.conv2d(
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input=c5,
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num_filters=self.inner_channels,
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filter_size=1,
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param_attr=param_attr,
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bias_attr=False)
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in4 = fluid.layers.conv2d(
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input=c4,
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num_filters=self.inner_channels,
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filter_size=1,
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param_attr=param_attr,
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bias_attr=False)
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in3 = fluid.layers.conv2d(
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input=c3,
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num_filters=self.inner_channels,
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filter_size=1,
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param_attr=param_attr,
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bias_attr=False)
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in2 = fluid.layers.conv2d(
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input=c2,
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num_filters=self.inner_channels,
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filter_size=1,
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param_attr=param_attr,
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bias_attr=False)
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out4 = fluid.layers.elementwise_add(
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x=fluid.layers.resize_nearest(
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input=in5, scale=2), y=in4) # 1/16
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out3 = fluid.layers.elementwise_add(
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x=fluid.layers.resize_nearest(
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input=out4, scale=2), y=in3) # 1/8
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out2 = fluid.layers.elementwise_add(
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x=fluid.layers.resize_nearest(
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input=out3, scale=2), y=in2) # 1/4
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p5 = fluid.layers.conv2d(
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input=in5,
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num_filters=self.inner_channels // 4,
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filter_size=3,
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padding=1,
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param_attr=param_attr,
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bias_attr=False)
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p5 = fluid.layers.resize_nearest(input=p5, scale=8)
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p4 = fluid.layers.conv2d(
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input=out4,
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num_filters=self.inner_channels // 4,
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filter_size=3,
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padding=1,
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param_attr=param_attr,
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bias_attr=False)
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p4 = fluid.layers.resize_nearest(input=p4, scale=4)
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p3 = fluid.layers.conv2d(
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input=out3,
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num_filters=self.inner_channels // 4,
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filter_size=3,
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padding=1,
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param_attr=param_attr,
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bias_attr=False)
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p3 = fluid.layers.resize_nearest(input=p3, scale=2)
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p2 = fluid.layers.conv2d(
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input=out2,
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num_filters=self.inner_channels // 4,
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filter_size=3,
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padding=1,
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param_attr=param_attr,
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bias_attr=False)
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fuse = fluid.layers.concat(input=[p5, p4, p3, p2], axis=1)
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shrink_maps = self.binarize(fuse)
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if mode != "train":
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return {"maps": shrink_maps}
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threshold_maps = self.thresh(fuse)
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binary_maps = self.step_function(shrink_maps, threshold_maps)
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y = fluid.layers.concat(
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input=[shrink_maps, threshold_maps, binary_maps], axis=1)
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predicts = {}
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predicts['maps'] = y
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return predicts
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