394 lines
13 KiB
Python
394 lines
13 KiB
Python
# copyright (c) 2021 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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# reference: https://arxiv.org/abs/1610.02357
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import paddle
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from paddle import ParamAttr
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
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from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
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from paddle.nn.initializer import Uniform
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import math
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import sys
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from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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MODEL_URLS = {
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"Xception41":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_pretrained.pdparams",
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"Xception65":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_pretrained.pdparams",
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"Xception71":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception71_pretrained.pdparams"
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}
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__all__ = list(MODEL_URLS.keys())
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class ConvBNLayer(nn.Layer):
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def __init__(self,
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num_channels,
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num_filters,
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filter_size,
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stride=1,
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groups=1,
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act=None,
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name=None):
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super(ConvBNLayer, self).__init__()
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self._conv = Conv2D(
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in_channels=num_channels,
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out_channels=num_filters,
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kernel_size=filter_size,
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stride=stride,
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padding=(filter_size - 1) // 2,
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groups=groups,
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weight_attr=ParamAttr(name=name + "_weights"),
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bias_attr=False)
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bn_name = "bn_" + name
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self._batch_norm = BatchNorm(
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num_filters,
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act=act,
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param_attr=ParamAttr(name=bn_name + "_scale"),
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bias_attr=ParamAttr(name=bn_name + "_offset"),
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moving_mean_name=bn_name + '_mean',
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moving_variance_name=bn_name + '_variance')
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def forward(self, inputs):
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y = self._conv(inputs)
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y = self._batch_norm(y)
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return y
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class SeparableConv(nn.Layer):
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def __init__(self, input_channels, output_channels, stride=1, name=None):
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super(SeparableConv, self).__init__()
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self._pointwise_conv = ConvBNLayer(
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input_channels, output_channels, 1, name=name + "_sep")
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self._depthwise_conv = ConvBNLayer(
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output_channels,
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output_channels,
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3,
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stride=stride,
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groups=output_channels,
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name=name + "_dw")
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def forward(self, inputs):
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x = self._pointwise_conv(inputs)
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x = self._depthwise_conv(x)
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return x
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class EntryFlowBottleneckBlock(nn.Layer):
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def __init__(self,
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input_channels,
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output_channels,
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stride=2,
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name=None,
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relu_first=False):
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super(EntryFlowBottleneckBlock, self).__init__()
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self.relu_first = relu_first
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self._short = Conv2D(
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in_channels=input_channels,
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out_channels=output_channels,
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kernel_size=1,
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stride=stride,
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padding=0,
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weight_attr=ParamAttr(name + "_branch1_weights"),
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bias_attr=False)
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self._conv1 = SeparableConv(
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input_channels,
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output_channels,
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stride=1,
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name=name + "_branch2a_weights")
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self._conv2 = SeparableConv(
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output_channels,
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output_channels,
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stride=1,
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name=name + "_branch2b_weights")
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self._pool = MaxPool2D(kernel_size=3, stride=stride, padding=1)
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def forward(self, inputs):
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conv0 = inputs
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short = self._short(inputs)
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if self.relu_first:
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conv0 = F.relu(conv0)
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conv1 = self._conv1(conv0)
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conv2 = F.relu(conv1)
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conv2 = self._conv2(conv2)
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pool = self._pool(conv2)
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return paddle.add(x=short, y=pool)
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class EntryFlow(nn.Layer):
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def __init__(self, block_num=3):
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super(EntryFlow, self).__init__()
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name = "entry_flow"
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self.block_num = block_num
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self._conv1 = ConvBNLayer(
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3, 32, 3, stride=2, act="relu", name=name + "_conv1")
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self._conv2 = ConvBNLayer(32, 64, 3, act="relu", name=name + "_conv2")
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if block_num == 3:
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self._conv_0 = EntryFlowBottleneckBlock(
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64, 128, stride=2, name=name + "_0", relu_first=False)
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self._conv_1 = EntryFlowBottleneckBlock(
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128, 256, stride=2, name=name + "_1", relu_first=True)
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self._conv_2 = EntryFlowBottleneckBlock(
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256, 728, stride=2, name=name + "_2", relu_first=True)
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elif block_num == 5:
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self._conv_0 = EntryFlowBottleneckBlock(
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64, 128, stride=2, name=name + "_0", relu_first=False)
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self._conv_1 = EntryFlowBottleneckBlock(
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128, 256, stride=1, name=name + "_1", relu_first=True)
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self._conv_2 = EntryFlowBottleneckBlock(
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256, 256, stride=2, name=name + "_2", relu_first=True)
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self._conv_3 = EntryFlowBottleneckBlock(
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256, 728, stride=1, name=name + "_3", relu_first=True)
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self._conv_4 = EntryFlowBottleneckBlock(
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728, 728, stride=2, name=name + "_4", relu_first=True)
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else:
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sys.exit(-1)
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def forward(self, inputs):
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x = self._conv1(inputs)
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x = self._conv2(x)
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if self.block_num == 3:
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x = self._conv_0(x)
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x = self._conv_1(x)
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x = self._conv_2(x)
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elif self.block_num == 5:
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x = self._conv_0(x)
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x = self._conv_1(x)
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x = self._conv_2(x)
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x = self._conv_3(x)
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x = self._conv_4(x)
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return x
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class MiddleFlowBottleneckBlock(nn.Layer):
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def __init__(self, input_channels, output_channels, name):
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super(MiddleFlowBottleneckBlock, self).__init__()
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self._conv_0 = SeparableConv(
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input_channels,
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output_channels,
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stride=1,
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name=name + "_branch2a_weights")
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self._conv_1 = SeparableConv(
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output_channels,
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output_channels,
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stride=1,
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name=name + "_branch2b_weights")
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self._conv_2 = SeparableConv(
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output_channels,
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output_channels,
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stride=1,
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name=name + "_branch2c_weights")
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def forward(self, inputs):
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conv0 = F.relu(inputs)
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conv0 = self._conv_0(conv0)
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conv1 = F.relu(conv0)
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conv1 = self._conv_1(conv1)
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conv2 = F.relu(conv1)
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conv2 = self._conv_2(conv2)
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return paddle.add(x=inputs, y=conv2)
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class MiddleFlow(nn.Layer):
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def __init__(self, block_num=8):
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super(MiddleFlow, self).__init__()
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self.block_num = block_num
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self._conv_0 = MiddleFlowBottleneckBlock(
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728, 728, name="middle_flow_0")
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self._conv_1 = MiddleFlowBottleneckBlock(
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728, 728, name="middle_flow_1")
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self._conv_2 = MiddleFlowBottleneckBlock(
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728, 728, name="middle_flow_2")
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self._conv_3 = MiddleFlowBottleneckBlock(
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728, 728, name="middle_flow_3")
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self._conv_4 = MiddleFlowBottleneckBlock(
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728, 728, name="middle_flow_4")
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self._conv_5 = MiddleFlowBottleneckBlock(
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728, 728, name="middle_flow_5")
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self._conv_6 = MiddleFlowBottleneckBlock(
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728, 728, name="middle_flow_6")
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self._conv_7 = MiddleFlowBottleneckBlock(
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728, 728, name="middle_flow_7")
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if block_num == 16:
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self._conv_8 = MiddleFlowBottleneckBlock(
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728, 728, name="middle_flow_8")
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self._conv_9 = MiddleFlowBottleneckBlock(
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728, 728, name="middle_flow_9")
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self._conv_10 = MiddleFlowBottleneckBlock(
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728, 728, name="middle_flow_10")
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self._conv_11 = MiddleFlowBottleneckBlock(
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728, 728, name="middle_flow_11")
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self._conv_12 = MiddleFlowBottleneckBlock(
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728, 728, name="middle_flow_12")
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self._conv_13 = MiddleFlowBottleneckBlock(
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728, 728, name="middle_flow_13")
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self._conv_14 = MiddleFlowBottleneckBlock(
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728, 728, name="middle_flow_14")
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self._conv_15 = MiddleFlowBottleneckBlock(
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728, 728, name="middle_flow_15")
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def forward(self, inputs):
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x = self._conv_0(inputs)
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x = self._conv_1(x)
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x = self._conv_2(x)
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x = self._conv_3(x)
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x = self._conv_4(x)
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x = self._conv_5(x)
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x = self._conv_6(x)
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x = self._conv_7(x)
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if self.block_num == 16:
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x = self._conv_8(x)
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x = self._conv_9(x)
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x = self._conv_10(x)
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x = self._conv_11(x)
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x = self._conv_12(x)
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x = self._conv_13(x)
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x = self._conv_14(x)
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x = self._conv_15(x)
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return x
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class ExitFlowBottleneckBlock(nn.Layer):
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def __init__(self, input_channels, output_channels1, output_channels2,
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name):
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super(ExitFlowBottleneckBlock, self).__init__()
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self._short = Conv2D(
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in_channels=input_channels,
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out_channels=output_channels2,
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kernel_size=1,
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stride=2,
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padding=0,
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weight_attr=ParamAttr(name + "_branch1_weights"),
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bias_attr=False)
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self._conv_1 = SeparableConv(
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input_channels,
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output_channels1,
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stride=1,
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name=name + "_branch2a_weights")
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self._conv_2 = SeparableConv(
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output_channels1,
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output_channels2,
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stride=1,
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name=name + "_branch2b_weights")
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self._pool = MaxPool2D(kernel_size=3, stride=2, padding=1)
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def forward(self, inputs):
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short = self._short(inputs)
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conv0 = F.relu(inputs)
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conv1 = self._conv_1(conv0)
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conv2 = F.relu(conv1)
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conv2 = self._conv_2(conv2)
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pool = self._pool(conv2)
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return paddle.add(x=short, y=pool)
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class ExitFlow(nn.Layer):
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def __init__(self, class_num):
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super(ExitFlow, self).__init__()
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name = "exit_flow"
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self._conv_0 = ExitFlowBottleneckBlock(
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728, 728, 1024, name=name + "_1")
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self._conv_1 = SeparableConv(1024, 1536, stride=1, name=name + "_2")
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self._conv_2 = SeparableConv(1536, 2048, stride=1, name=name + "_3")
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self._pool = AdaptiveAvgPool2D(1)
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stdv = 1.0 / math.sqrt(2048 * 1.0)
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self._out = Linear(
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2048,
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class_num,
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weight_attr=ParamAttr(
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name="fc_weights", initializer=Uniform(-stdv, stdv)),
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bias_attr=ParamAttr(name="fc_offset"))
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def forward(self, inputs):
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conv0 = self._conv_0(inputs)
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conv1 = self._conv_1(conv0)
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conv1 = F.relu(conv1)
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conv2 = self._conv_2(conv1)
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conv2 = F.relu(conv2)
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pool = self._pool(conv2)
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pool = paddle.flatten(pool, start_axis=1, stop_axis=-1)
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out = self._out(pool)
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return out
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class Xception(nn.Layer):
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def __init__(self,
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entry_flow_block_num=3,
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middle_flow_block_num=8,
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class_num=1000):
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super(Xception, self).__init__()
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self.entry_flow_block_num = entry_flow_block_num
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self.middle_flow_block_num = middle_flow_block_num
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self._entry_flow = EntryFlow(entry_flow_block_num)
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self._middle_flow = MiddleFlow(middle_flow_block_num)
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self._exit_flow = ExitFlow(class_num)
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def forward(self, inputs):
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x = self._entry_flow(inputs)
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x = self._middle_flow(x)
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x = self._exit_flow(x)
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return x
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def _load_pretrained(pretrained, model, model_url, use_ssld=False):
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if pretrained is False:
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pass
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elif pretrained is True:
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load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
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elif isinstance(pretrained, str):
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load_dygraph_pretrain(model, pretrained)
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else:
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raise RuntimeError(
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"pretrained type is not available. Please use `string` or `boolean` type."
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)
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def Xception41(pretrained=False, use_ssld=False, **kwargs):
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model = Xception(entry_flow_block_num=3, middle_flow_block_num=8, **kwargs)
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_load_pretrained(
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pretrained, model, MODEL_URLS["Xception41"], use_ssld=use_ssld)
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return model
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def Xception65(pretrained=False, use_ssld=False, **kwargs):
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model = Xception(
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entry_flow_block_num=3, middle_flow_block_num=16, **kwargs)
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_load_pretrained(
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pretrained, model, MODEL_URLS["Xception65"], use_ssld=use_ssld)
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return model
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def Xception71(pretrained=False, use_ssld=False, **kwargs):
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model = Xception(
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entry_flow_block_num=5, middle_flow_block_num=16, **kwargs)
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_load_pretrained(
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pretrained, model, MODEL_URLS["Xception71"], use_ssld=use_ssld)
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return model
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