480 lines
15 KiB
Python
480 lines
15 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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# reference: https://arxiv.org/abs/1602.07261
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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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from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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MODEL_URLS = {
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"InceptionV4":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV4_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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padding=0,
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groups=1,
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act='relu',
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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=padding,
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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 = name + "_bn"
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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 InceptionStem(nn.Layer):
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def __init__(self):
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super(InceptionStem, self).__init__()
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self._conv_1 = ConvBNLayer(
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3, 32, 3, stride=2, act="relu", name="conv1_3x3_s2")
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self._conv_2 = ConvBNLayer(32, 32, 3, act="relu", name="conv2_3x3_s1")
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self._conv_3 = ConvBNLayer(
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32, 64, 3, padding=1, act="relu", name="conv3_3x3_s1")
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self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
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self._conv2 = ConvBNLayer(
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64, 96, 3, stride=2, act="relu", name="inception_stem1_3x3_s2")
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self._conv1_1 = ConvBNLayer(
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160, 64, 1, act="relu", name="inception_stem2_3x3_reduce")
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self._conv1_2 = ConvBNLayer(
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64, 96, 3, act="relu", name="inception_stem2_3x3")
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self._conv2_1 = ConvBNLayer(
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160, 64, 1, act="relu", name="inception_stem2_1x7_reduce")
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self._conv2_2 = ConvBNLayer(
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64,
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64, (7, 1),
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padding=(3, 0),
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act="relu",
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name="inception_stem2_1x7")
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self._conv2_3 = ConvBNLayer(
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64,
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64, (1, 7),
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padding=(0, 3),
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act="relu",
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name="inception_stem2_7x1")
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self._conv2_4 = ConvBNLayer(
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64, 96, 3, act="relu", name="inception_stem2_3x3_2")
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self._conv3 = ConvBNLayer(
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192, 192, 3, stride=2, act="relu", name="inception_stem3_3x3_s2")
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def forward(self, inputs):
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conv = self._conv_1(inputs)
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conv = self._conv_2(conv)
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conv = self._conv_3(conv)
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pool1 = self._pool(conv)
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conv2 = self._conv2(conv)
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concat = paddle.concat([pool1, conv2], axis=1)
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conv1 = self._conv1_1(concat)
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conv1 = self._conv1_2(conv1)
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conv2 = self._conv2_1(concat)
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conv2 = self._conv2_2(conv2)
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conv2 = self._conv2_3(conv2)
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conv2 = self._conv2_4(conv2)
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concat = paddle.concat([conv1, conv2], axis=1)
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conv1 = self._conv3(concat)
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pool1 = self._pool(concat)
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concat = paddle.concat([conv1, pool1], axis=1)
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return concat
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class InceptionA(nn.Layer):
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def __init__(self, name):
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super(InceptionA, self).__init__()
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self._pool = AvgPool2D(kernel_size=3, stride=1, padding=1)
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self._conv1 = ConvBNLayer(
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384, 96, 1, act="relu", name="inception_a" + name + "_1x1")
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self._conv2 = ConvBNLayer(
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384, 96, 1, act="relu", name="inception_a" + name + "_1x1_2")
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self._conv3_1 = ConvBNLayer(
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384, 64, 1, act="relu", name="inception_a" + name + "_3x3_reduce")
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self._conv3_2 = ConvBNLayer(
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64,
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96,
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3,
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padding=1,
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act="relu",
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name="inception_a" + name + "_3x3")
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self._conv4_1 = ConvBNLayer(
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384,
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64,
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1,
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act="relu",
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name="inception_a" + name + "_3x3_2_reduce")
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self._conv4_2 = ConvBNLayer(
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64,
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96,
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3,
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padding=1,
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act="relu",
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name="inception_a" + name + "_3x3_2")
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self._conv4_3 = ConvBNLayer(
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96,
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96,
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3,
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padding=1,
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act="relu",
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name="inception_a" + name + "_3x3_3")
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def forward(self, inputs):
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pool1 = self._pool(inputs)
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conv1 = self._conv1(pool1)
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conv2 = self._conv2(inputs)
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conv3 = self._conv3_1(inputs)
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conv3 = self._conv3_2(conv3)
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conv4 = self._conv4_1(inputs)
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conv4 = self._conv4_2(conv4)
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conv4 = self._conv4_3(conv4)
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concat = paddle.concat([conv1, conv2, conv3, conv4], axis=1)
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return concat
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class ReductionA(nn.Layer):
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def __init__(self):
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super(ReductionA, self).__init__()
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self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
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self._conv2 = ConvBNLayer(
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384, 384, 3, stride=2, act="relu", name="reduction_a_3x3")
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self._conv3_1 = ConvBNLayer(
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384, 192, 1, act="relu", name="reduction_a_3x3_2_reduce")
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self._conv3_2 = ConvBNLayer(
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192, 224, 3, padding=1, act="relu", name="reduction_a_3x3_2")
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self._conv3_3 = ConvBNLayer(
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224, 256, 3, stride=2, act="relu", name="reduction_a_3x3_3")
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def forward(self, inputs):
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pool1 = self._pool(inputs)
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conv2 = self._conv2(inputs)
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conv3 = self._conv3_1(inputs)
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conv3 = self._conv3_2(conv3)
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conv3 = self._conv3_3(conv3)
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concat = paddle.concat([pool1, conv2, conv3], axis=1)
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return concat
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class InceptionB(nn.Layer):
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def __init__(self, name=None):
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super(InceptionB, self).__init__()
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self._pool = AvgPool2D(kernel_size=3, stride=1, padding=1)
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self._conv1 = ConvBNLayer(
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1024, 128, 1, act="relu", name="inception_b" + name + "_1x1")
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self._conv2 = ConvBNLayer(
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1024, 384, 1, act="relu", name="inception_b" + name + "_1x1_2")
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self._conv3_1 = ConvBNLayer(
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1024,
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192,
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1,
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act="relu",
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name="inception_b" + name + "_1x7_reduce")
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self._conv3_2 = ConvBNLayer(
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192,
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224, (1, 7),
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padding=(0, 3),
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act="relu",
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name="inception_b" + name + "_1x7")
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self._conv3_3 = ConvBNLayer(
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224,
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256, (7, 1),
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padding=(3, 0),
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act="relu",
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name="inception_b" + name + "_7x1")
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self._conv4_1 = ConvBNLayer(
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1024,
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192,
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1,
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act="relu",
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name="inception_b" + name + "_7x1_2_reduce")
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self._conv4_2 = ConvBNLayer(
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192,
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192, (1, 7),
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padding=(0, 3),
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act="relu",
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name="inception_b" + name + "_1x7_2")
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self._conv4_3 = ConvBNLayer(
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192,
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224, (7, 1),
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padding=(3, 0),
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act="relu",
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name="inception_b" + name + "_7x1_2")
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self._conv4_4 = ConvBNLayer(
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224,
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224, (1, 7),
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padding=(0, 3),
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act="relu",
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name="inception_b" + name + "_1x7_3")
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self._conv4_5 = ConvBNLayer(
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224,
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256, (7, 1),
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padding=(3, 0),
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act="relu",
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name="inception_b" + name + "_7x1_3")
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def forward(self, inputs):
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pool1 = self._pool(inputs)
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conv1 = self._conv1(pool1)
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conv2 = self._conv2(inputs)
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conv3 = self._conv3_1(inputs)
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conv3 = self._conv3_2(conv3)
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conv3 = self._conv3_3(conv3)
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conv4 = self._conv4_1(inputs)
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conv4 = self._conv4_2(conv4)
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conv4 = self._conv4_3(conv4)
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conv4 = self._conv4_4(conv4)
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conv4 = self._conv4_5(conv4)
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concat = paddle.concat([conv1, conv2, conv3, conv4], axis=1)
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return concat
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class ReductionB(nn.Layer):
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def __init__(self):
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super(ReductionB, self).__init__()
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self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
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self._conv2_1 = ConvBNLayer(
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1024, 192, 1, act="relu", name="reduction_b_3x3_reduce")
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self._conv2_2 = ConvBNLayer(
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192, 192, 3, stride=2, act="relu", name="reduction_b_3x3")
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self._conv3_1 = ConvBNLayer(
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1024, 256, 1, act="relu", name="reduction_b_1x7_reduce")
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self._conv3_2 = ConvBNLayer(
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256,
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256, (1, 7),
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padding=(0, 3),
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act="relu",
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name="reduction_b_1x7")
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self._conv3_3 = ConvBNLayer(
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256,
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320, (7, 1),
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padding=(3, 0),
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act="relu",
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name="reduction_b_7x1")
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self._conv3_4 = ConvBNLayer(
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320, 320, 3, stride=2, act="relu", name="reduction_b_3x3_2")
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def forward(self, inputs):
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pool1 = self._pool(inputs)
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conv2 = self._conv2_1(inputs)
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conv2 = self._conv2_2(conv2)
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conv3 = self._conv3_1(inputs)
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conv3 = self._conv3_2(conv3)
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conv3 = self._conv3_3(conv3)
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conv3 = self._conv3_4(conv3)
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concat = paddle.concat([pool1, conv2, conv3], axis=1)
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return concat
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class InceptionC(nn.Layer):
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def __init__(self, name=None):
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super(InceptionC, self).__init__()
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self._pool = AvgPool2D(kernel_size=3, stride=1, padding=1)
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self._conv1 = ConvBNLayer(
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1536, 256, 1, act="relu", name="inception_c" + name + "_1x1")
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self._conv2 = ConvBNLayer(
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1536, 256, 1, act="relu", name="inception_c" + name + "_1x1_2")
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self._conv3_0 = ConvBNLayer(
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1536, 384, 1, act="relu", name="inception_c" + name + "_1x1_3")
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self._conv3_1 = ConvBNLayer(
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384,
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256, (1, 3),
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padding=(0, 1),
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act="relu",
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name="inception_c" + name + "_1x3")
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self._conv3_2 = ConvBNLayer(
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384,
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256, (3, 1),
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padding=(1, 0),
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act="relu",
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name="inception_c" + name + "_3x1")
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self._conv4_0 = ConvBNLayer(
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1536, 384, 1, act="relu", name="inception_c" + name + "_1x1_4")
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self._conv4_00 = ConvBNLayer(
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384,
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448, (1, 3),
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padding=(0, 1),
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act="relu",
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name="inception_c" + name + "_1x3_2")
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self._conv4_000 = ConvBNLayer(
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448,
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512, (3, 1),
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padding=(1, 0),
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act="relu",
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name="inception_c" + name + "_3x1_2")
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self._conv4_1 = ConvBNLayer(
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512,
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256, (1, 3),
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padding=(0, 1),
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act="relu",
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name="inception_c" + name + "_1x3_3")
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self._conv4_2 = ConvBNLayer(
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512,
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256, (3, 1),
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padding=(1, 0),
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act="relu",
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name="inception_c" + name + "_3x1_3")
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def forward(self, inputs):
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pool1 = self._pool(inputs)
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conv1 = self._conv1(pool1)
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conv2 = self._conv2(inputs)
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conv3 = self._conv3_0(inputs)
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conv3_1 = self._conv3_1(conv3)
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conv3_2 = self._conv3_2(conv3)
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conv4 = self._conv4_0(inputs)
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conv4 = self._conv4_00(conv4)
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conv4 = self._conv4_000(conv4)
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conv4_1 = self._conv4_1(conv4)
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conv4_2 = self._conv4_2(conv4)
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concat = paddle.concat(
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[conv1, conv2, conv3_1, conv3_2, conv4_1, conv4_2], axis=1)
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return concat
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class InceptionV4DY(nn.Layer):
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def __init__(self, class_num=1000):
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super(InceptionV4DY, self).__init__()
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self._inception_stem = InceptionStem()
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self._inceptionA_1 = InceptionA(name="1")
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self._inceptionA_2 = InceptionA(name="2")
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self._inceptionA_3 = InceptionA(name="3")
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self._inceptionA_4 = InceptionA(name="4")
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self._reductionA = ReductionA()
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self._inceptionB_1 = InceptionB(name="1")
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self._inceptionB_2 = InceptionB(name="2")
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self._inceptionB_3 = InceptionB(name="3")
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self._inceptionB_4 = InceptionB(name="4")
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self._inceptionB_5 = InceptionB(name="5")
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self._inceptionB_6 = InceptionB(name="6")
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self._inceptionB_7 = InceptionB(name="7")
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self._reductionB = ReductionB()
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self._inceptionC_1 = InceptionC(name="1")
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self._inceptionC_2 = InceptionC(name="2")
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self._inceptionC_3 = InceptionC(name="3")
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self.avg_pool = AdaptiveAvgPool2D(1)
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self._drop = Dropout(p=0.2, mode="downscale_in_infer")
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stdv = 1.0 / math.sqrt(1536 * 1.0)
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self.out = Linear(
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1536,
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class_num,
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weight_attr=ParamAttr(
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initializer=Uniform(-stdv, stdv), name="final_fc_weights"),
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bias_attr=ParamAttr(name="final_fc_offset"))
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def forward(self, inputs):
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x = self._inception_stem(inputs)
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x = self._inceptionA_1(x)
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x = self._inceptionA_2(x)
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x = self._inceptionA_3(x)
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x = self._inceptionA_4(x)
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x = self._reductionA(x)
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x = self._inceptionB_1(x)
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x = self._inceptionB_2(x)
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x = self._inceptionB_3(x)
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x = self._inceptionB_4(x)
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x = self._inceptionB_5(x)
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x = self._inceptionB_6(x)
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x = self._inceptionB_7(x)
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x = self._reductionB(x)
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x = self._inceptionC_1(x)
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x = self._inceptionC_2(x)
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x = self._inceptionC_3(x)
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x = self.avg_pool(x)
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x = paddle.squeeze(x, axis=[2, 3])
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x = self._drop(x)
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x = self.out(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 InceptionV4(pretrained=False, use_ssld=False, **kwargs):
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model = InceptionV4DY(**kwargs)
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_load_pretrained(
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pretrained, model, MODEL_URLS["InceptionV4"], use_ssld=use_ssld)
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return model
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