124 lines
3.9 KiB
Python
Executable File
124 lines
3.9 KiB
Python
Executable File
#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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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 paddle.fluid as fluid
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from ..common_functions import conv_bn_layer, deconv_bn_layer
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from collections import OrderedDict
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class EASTHead(object):
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"""
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EAST: An Efficient and Accurate Scene Text Detector
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see arxiv: https://arxiv.org/abs/1704.03155
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args:
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params(dict): the super parameters for network build
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"""
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def __init__(self, params):
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self.model_name = params['model_name']
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def unet_fusion(self, inputs):
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f = inputs[::-1]
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if self.model_name == "large":
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num_outputs = [128, 128, 128, 128]
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else:
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num_outputs = [64, 64, 64, 64]
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g = [None, None, None, None]
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h = [None, None, None, None]
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for i in range(4):
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if i == 0:
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h[i] = f[i]
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else:
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h[i] = fluid.layers.concat([g[i - 1], f[i]], axis=1)
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h[i] = conv_bn_layer(
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input=h[i],
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num_filters=num_outputs[i],
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filter_size=3,
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stride=1,
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act='relu',
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name="unet_h_%d" % (i))
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if i <= 2:
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#can be replaced with unpool
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g[i] = deconv_bn_layer(
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input=h[i],
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num_filters=num_outputs[i],
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name="unet_g_%d" % (i))
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else:
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g[i] = conv_bn_layer(
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input=h[i],
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num_filters=num_outputs[i],
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filter_size=3,
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stride=1,
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act='relu',
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name="unet_g_%d" % (i))
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return g[3]
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def detector_header(self, f_common):
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if self.model_name == "large":
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num_outputs = [128, 64, 1, 8]
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else:
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num_outputs = [64, 32, 1, 8]
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f_det = conv_bn_layer(
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input=f_common,
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num_filters=num_outputs[0],
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filter_size=3,
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stride=1,
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act='relu',
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name="det_head1")
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f_det = conv_bn_layer(
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input=f_det,
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num_filters=num_outputs[1],
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filter_size=3,
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stride=1,
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act='relu',
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name="det_head2")
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#f_score
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f_score = conv_bn_layer(
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input=f_det,
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num_filters=num_outputs[2],
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filter_size=1,
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stride=1,
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act=None,
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name="f_score")
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f_score = fluid.layers.sigmoid(f_score)
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#f_geo
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f_geo = conv_bn_layer(
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input=f_det,
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num_filters=num_outputs[3],
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filter_size=1,
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stride=1,
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act=None,
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name="f_geo")
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f_geo = (fluid.layers.sigmoid(f_geo) - 0.5) * 2 * 800
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return f_score, f_geo
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def __call__(self, inputs):
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"""
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Fuse different levels of feature map from backbone and predict results
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Args:
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inputs(list): feature maps from backbone
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Return: predicts
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"""
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f_common = self.unet_fusion(inputs)
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f_score, f_geo = self.detector_header(f_common)
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predicts = OrderedDict()
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predicts['f_score'] = f_score
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predicts['f_geo'] = f_geo
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return predicts
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