321 lines
11 KiB
Python
321 lines
11 KiB
Python
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#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 contextlib
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import paddle
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import math
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import paddle.fluid as fluid
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from .model_libs import scope, name_scope
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from .model_libs import bn, bn_relu, relu
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from .model_libs import conv
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from .model_libs import seperate_conv
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__all__ = ['Xception41_deeplab', 'Xception65_deeplab', 'Xception71_deeplab']
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def check_data(data, number):
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if type(data) == int:
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return [data] * number
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assert len(data) == number
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return data
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def check_stride(s, os):
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if s <= os:
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return True
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else:
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return False
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def check_points(count, points):
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if points is None:
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return False
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else:
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if isinstance(points, list):
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return (True if count in points else False)
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else:
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return (True if count == points else False)
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class Xception():
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def __init__(self, backbone="xception_65"):
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self.bottleneck_params = self.gen_bottleneck_params(backbone)
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self.backbone = backbone
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def gen_bottleneck_params(self, backbone='xception_65'):
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if backbone == 'xception_65':
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bottleneck_params = {
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"entry_flow": (3, [2, 2, 2], [128, 256, 728]),
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"middle_flow": (16, 1, 728),
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"exit_flow":
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(2, [2, 1], [[728, 1024, 1024], [1536, 1536, 2048]])
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}
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elif backbone == 'xception_41':
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bottleneck_params = {
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"entry_flow": (3, [2, 2, 2], [128, 256, 728]),
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"middle_flow": (8, 1, 728),
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"exit_flow":
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(2, [2, 1], [[728, 1024, 1024], [1536, 1536, 2048]])
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}
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elif backbone == 'xception_71':
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bottleneck_params = {
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"entry_flow": (5, [2, 1, 2, 1, 2], [128, 256, 256, 728, 728]),
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"middle_flow": (16, 1, 728),
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"exit_flow":
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(2, [2, 1], [[728, 1024, 1024], [1536, 1536, 2048]])
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}
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else:
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raise Exception(
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"xception backbont only support xception_41/xception_65/xception_71"
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)
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return bottleneck_params
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def net(self,
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input,
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output_stride=32,
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class_dim=1000,
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end_points=None,
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decode_points=None):
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self.stride = 2
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self.block_point = 0
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self.output_stride = output_stride
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self.decode_points = decode_points
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self.short_cuts = dict()
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with scope(self.backbone):
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# Entry flow
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data = self.entry_flow(input)
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if check_points(self.block_point, end_points):
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return data, self.short_cuts
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# Middle flow
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data = self.middle_flow(data)
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if check_points(self.block_point, end_points):
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return data, self.short_cuts
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# Exit flow
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data = self.exit_flow(data)
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if check_points(self.block_point, end_points):
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return data, self.short_cuts
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data = fluid.layers.reduce_mean(data, [2, 3], keep_dim=True)
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data = fluid.layers.dropout(data, 0.5)
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stdv = 1.0 / math.sqrt(data.shape[1] * 1.0)
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with scope("logit"):
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out = fluid.layers.fc(
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input=data,
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size=class_dim,
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param_attr=fluid.param_attr.ParamAttr(
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name='fc_weights',
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initializer=fluid.initializer.Uniform(-stdv, stdv)),
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bias_attr=fluid.param_attr.ParamAttr(name='fc_bias'))
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return out
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def entry_flow(self, data):
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param_attr = fluid.ParamAttr(
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name=name_scope + 'weights',
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regularizer=None,
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initializer=fluid.initializer.TruncatedNormal(
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loc=0.0, scale=0.09))
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with scope("entry_flow"):
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with scope("conv1"):
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data = bn_relu(
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conv(
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data,
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32,
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3,
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stride=2,
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padding=1,
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param_attr=param_attr))
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with scope("conv2"):
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data = bn_relu(
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conv(
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data,
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64,
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3,
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stride=1,
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padding=1,
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param_attr=param_attr))
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# get entry flow params
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block_num = self.bottleneck_params["entry_flow"][0]
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strides = self.bottleneck_params["entry_flow"][1]
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chns = self.bottleneck_params["entry_flow"][2]
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strides = check_data(strides, block_num)
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chns = check_data(chns, block_num)
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# params to control your flow
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s = self.stride
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block_point = self.block_point
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output_stride = self.output_stride
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with scope("entry_flow"):
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for i in range(block_num):
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block_point = block_point + 1
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with scope("block" + str(i + 1)):
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stride = strides[i] if check_stride(s * strides[i],
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output_stride) else 1
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data, short_cuts = self.xception_block(data, chns[i],
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[1, 1, stride])
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s = s * stride
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if check_points(block_point, self.decode_points):
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self.short_cuts[block_point] = short_cuts[1]
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self.stride = s
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self.block_point = block_point
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return data
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def middle_flow(self, data):
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block_num = self.bottleneck_params["middle_flow"][0]
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strides = self.bottleneck_params["middle_flow"][1]
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chns = self.bottleneck_params["middle_flow"][2]
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strides = check_data(strides, block_num)
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chns = check_data(chns, block_num)
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# params to control your flow
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s = self.stride
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block_point = self.block_point
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output_stride = self.output_stride
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with scope("middle_flow"):
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for i in range(block_num):
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block_point = block_point + 1
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with scope("block" + str(i + 1)):
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stride = strides[i] if check_stride(s * strides[i],
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output_stride) else 1
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data, short_cuts = self.xception_block(
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data, chns[i], [1, 1, strides[i]], skip_conv=False)
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s = s * stride
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if check_points(block_point, self.decode_points):
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self.short_cuts[block_point] = short_cuts[1]
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self.stride = s
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self.block_point = block_point
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return data
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def exit_flow(self, data):
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block_num = self.bottleneck_params["exit_flow"][0]
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strides = self.bottleneck_params["exit_flow"][1]
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chns = self.bottleneck_params["exit_flow"][2]
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strides = check_data(strides, block_num)
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chns = check_data(chns, block_num)
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assert (block_num == 2)
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# params to control your flow
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s = self.stride
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block_point = self.block_point
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output_stride = self.output_stride
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with scope("exit_flow"):
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with scope('block1'):
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block_point += 1
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stride = strides[0] if check_stride(s * strides[0],
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output_stride) else 1
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data, short_cuts = self.xception_block(data, chns[0],
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[1, 1, stride])
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s = s * stride
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if check_points(block_point, self.decode_points):
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self.short_cuts[block_point] = short_cuts[1]
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with scope('block2'):
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block_point += 1
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stride = strides[1] if check_stride(s * strides[1],
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output_stride) else 1
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data, short_cuts = self.xception_block(
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data,
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chns[1], [1, 1, stride],
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dilation=2,
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has_skip=False,
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activation_fn_in_separable_conv=True)
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s = s * stride
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if check_points(block_point, self.decode_points):
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self.short_cuts[block_point] = short_cuts[1]
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self.stride = s
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self.block_point = block_point
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return data
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def xception_block(self,
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input,
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channels,
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strides=1,
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filters=3,
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dilation=1,
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skip_conv=True,
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has_skip=True,
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activation_fn_in_separable_conv=False):
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repeat_number = 3
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channels = check_data(channels, repeat_number)
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filters = check_data(filters, repeat_number)
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strides = check_data(strides, repeat_number)
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data = input
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results = []
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for i in range(repeat_number):
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with scope('separable_conv' + str(i + 1)):
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if not activation_fn_in_separable_conv:
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data = relu(data)
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data = seperate_conv(
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data,
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channels[i],
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strides[i],
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filters[i],
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dilation=dilation)
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else:
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data = seperate_conv(
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data,
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channels[i],
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strides[i],
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filters[i],
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dilation=dilation,
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act=relu)
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results.append(data)
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if not has_skip:
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return data, results
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if skip_conv:
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param_attr = fluid.ParamAttr(
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name=name_scope + 'weights',
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regularizer=None,
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initializer=fluid.initializer.TruncatedNormal(
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loc=0.0, scale=0.09))
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with scope('shortcut'):
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skip = bn(
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conv(
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input,
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channels[-1],
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1,
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strides[-1],
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groups=1,
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padding=0,
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param_attr=param_attr))
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else:
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skip = input
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return data + skip, results
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def Xception41_deeplab():
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model = Xception("xception_41")
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return model
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def Xception65_deeplab():
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model = Xception("xception_65")
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return model
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def Xception71_deeplab():
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model = Xception("xception_71")
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return model
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