560 lines
17 KiB
Python
560 lines
17 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/1512.00567v3
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from __future__ import absolute_import, division, print_function
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import math
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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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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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from ..base.theseus_layer import TheseusLayer
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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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"InceptionV3":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/InceptionV3_pretrained.pdparams"
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}
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MODEL_STAGES_PATTERN = {
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"InceptionV3": [
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"inception_block_list[2]", "inception_block_list[3]",
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"inception_block_list[7]", "inception_block_list[8]",
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"inception_block_list[10]"
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]
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}
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__all__ = MODEL_URLS.keys()
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'''
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InceptionV3 config: dict.
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key: inception blocks of InceptionV3.
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values: conv num in different blocks.
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'''
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NET_CONFIG = {
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"inception_a": [[192, 256, 288], [32, 64, 64]],
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"inception_b": [288],
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"inception_c": [[768, 768, 768, 768], [128, 160, 160, 192]],
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"inception_d": [768],
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"inception_e": [1280, 2048]
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}
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class ConvBNLayer(TheseusLayer):
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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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super().__init__()
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self.act = act
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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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bias_attr=False)
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self.bn = BatchNorm(num_filters)
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self.relu = nn.ReLU()
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def forward(self, x):
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x = self.conv(x)
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x = self.bn(x)
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if self.act:
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x = self.relu(x)
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return x
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class InceptionStem(TheseusLayer):
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def __init__(self):
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super().__init__()
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self.conv_1a_3x3 = ConvBNLayer(
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num_channels=3,
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num_filters=32,
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filter_size=3,
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stride=2,
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act="relu")
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self.conv_2a_3x3 = ConvBNLayer(
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num_channels=32,
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num_filters=32,
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filter_size=3,
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stride=1,
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act="relu")
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self.conv_2b_3x3 = ConvBNLayer(
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num_channels=32,
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num_filters=64,
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filter_size=3,
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padding=1,
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act="relu")
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self.max_pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
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self.conv_3b_1x1 = ConvBNLayer(
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num_channels=64, num_filters=80, filter_size=1, act="relu")
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self.conv_4a_3x3 = ConvBNLayer(
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num_channels=80, num_filters=192, filter_size=3, act="relu")
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def forward(self, x):
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x = self.conv_1a_3x3(x)
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x = self.conv_2a_3x3(x)
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x = self.conv_2b_3x3(x)
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x = self.max_pool(x)
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x = self.conv_3b_1x1(x)
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x = self.conv_4a_3x3(x)
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x = self.max_pool(x)
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return x
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class InceptionA(TheseusLayer):
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def __init__(self, num_channels, pool_features):
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super().__init__()
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self.branch1x1 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=64,
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filter_size=1,
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act="relu")
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self.branch5x5_1 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=48,
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filter_size=1,
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act="relu")
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self.branch5x5_2 = ConvBNLayer(
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num_channels=48,
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num_filters=64,
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filter_size=5,
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padding=2,
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act="relu")
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self.branch3x3dbl_1 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=64,
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filter_size=1,
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act="relu")
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self.branch3x3dbl_2 = ConvBNLayer(
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num_channels=64,
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num_filters=96,
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filter_size=3,
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padding=1,
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act="relu")
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self.branch3x3dbl_3 = ConvBNLayer(
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num_channels=96,
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num_filters=96,
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filter_size=3,
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padding=1,
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act="relu")
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self.branch_pool = AvgPool2D(
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kernel_size=3, stride=1, padding=1, exclusive=False)
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self.branch_pool_conv = ConvBNLayer(
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num_channels=num_channels,
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num_filters=pool_features,
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filter_size=1,
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act="relu")
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def forward(self, x):
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branch1x1 = self.branch1x1(x)
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branch5x5 = self.branch5x5_1(x)
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branch5x5 = self.branch5x5_2(branch5x5)
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branch3x3dbl = self.branch3x3dbl_1(x)
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branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
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branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
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branch_pool = self.branch_pool(x)
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branch_pool = self.branch_pool_conv(branch_pool)
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x = paddle.concat(
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[branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=1)
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return x
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class InceptionB(TheseusLayer):
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def __init__(self, num_channels):
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super().__init__()
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self.branch3x3 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=384,
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filter_size=3,
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stride=2,
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act="relu")
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self.branch3x3dbl_1 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=64,
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filter_size=1,
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act="relu")
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self.branch3x3dbl_2 = ConvBNLayer(
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num_channels=64,
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num_filters=96,
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filter_size=3,
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padding=1,
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act="relu")
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self.branch3x3dbl_3 = ConvBNLayer(
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num_channels=96,
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num_filters=96,
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filter_size=3,
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stride=2,
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act="relu")
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self.branch_pool = MaxPool2D(kernel_size=3, stride=2)
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def forward(self, x):
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branch3x3 = self.branch3x3(x)
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branch3x3dbl = self.branch3x3dbl_1(x)
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branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
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branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
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branch_pool = self.branch_pool(x)
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x = paddle.concat([branch3x3, branch3x3dbl, branch_pool], axis=1)
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return x
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class InceptionC(TheseusLayer):
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def __init__(self, num_channels, channels_7x7):
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super().__init__()
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self.branch1x1 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=192,
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filter_size=1,
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act="relu")
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self.branch7x7_1 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=channels_7x7,
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filter_size=1,
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stride=1,
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act="relu")
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self.branch7x7_2 = ConvBNLayer(
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num_channels=channels_7x7,
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num_filters=channels_7x7,
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filter_size=(1, 7),
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stride=1,
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padding=(0, 3),
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act="relu")
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self.branch7x7_3 = ConvBNLayer(
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num_channels=channels_7x7,
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num_filters=192,
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filter_size=(7, 1),
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stride=1,
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padding=(3, 0),
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act="relu")
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self.branch7x7dbl_1 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=channels_7x7,
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filter_size=1,
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act="relu")
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self.branch7x7dbl_2 = ConvBNLayer(
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num_channels=channels_7x7,
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num_filters=channels_7x7,
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filter_size=(7, 1),
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padding=(3, 0),
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act="relu")
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self.branch7x7dbl_3 = ConvBNLayer(
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num_channels=channels_7x7,
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num_filters=channels_7x7,
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filter_size=(1, 7),
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padding=(0, 3),
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act="relu")
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self.branch7x7dbl_4 = ConvBNLayer(
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num_channels=channels_7x7,
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num_filters=channels_7x7,
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filter_size=(7, 1),
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padding=(3, 0),
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act="relu")
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self.branch7x7dbl_5 = ConvBNLayer(
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num_channels=channels_7x7,
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num_filters=192,
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filter_size=(1, 7),
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padding=(0, 3),
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act="relu")
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self.branch_pool = AvgPool2D(
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kernel_size=3, stride=1, padding=1, exclusive=False)
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self.branch_pool_conv = ConvBNLayer(
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num_channels=num_channels,
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num_filters=192,
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filter_size=1,
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act="relu")
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def forward(self, x):
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branch1x1 = self.branch1x1(x)
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branch7x7 = self.branch7x7_1(x)
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branch7x7 = self.branch7x7_2(branch7x7)
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branch7x7 = self.branch7x7_3(branch7x7)
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branch7x7dbl = self.branch7x7dbl_1(x)
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branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
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branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
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branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
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branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
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branch_pool = self.branch_pool(x)
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branch_pool = self.branch_pool_conv(branch_pool)
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x = paddle.concat(
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[branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1)
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return x
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class InceptionD(TheseusLayer):
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def __init__(self, num_channels):
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super().__init__()
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self.branch3x3_1 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=192,
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filter_size=1,
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act="relu")
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self.branch3x3_2 = ConvBNLayer(
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num_channels=192,
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num_filters=320,
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filter_size=3,
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stride=2,
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act="relu")
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self.branch7x7x3_1 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=192,
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filter_size=1,
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act="relu")
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self.branch7x7x3_2 = ConvBNLayer(
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num_channels=192,
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num_filters=192,
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filter_size=(1, 7),
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padding=(0, 3),
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act="relu")
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self.branch7x7x3_3 = ConvBNLayer(
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num_channels=192,
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num_filters=192,
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filter_size=(7, 1),
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padding=(3, 0),
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act="relu")
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self.branch7x7x3_4 = ConvBNLayer(
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num_channels=192,
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num_filters=192,
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filter_size=3,
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stride=2,
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act="relu")
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self.branch_pool = MaxPool2D(kernel_size=3, stride=2)
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def forward(self, x):
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branch3x3 = self.branch3x3_1(x)
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branch3x3 = self.branch3x3_2(branch3x3)
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branch7x7x3 = self.branch7x7x3_1(x)
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branch7x7x3 = self.branch7x7x3_2(branch7x7x3)
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branch7x7x3 = self.branch7x7x3_3(branch7x7x3)
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branch7x7x3 = self.branch7x7x3_4(branch7x7x3)
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branch_pool = self.branch_pool(x)
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x = paddle.concat([branch3x3, branch7x7x3, branch_pool], axis=1)
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return x
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class InceptionE(TheseusLayer):
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def __init__(self, num_channels):
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super().__init__()
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self.branch1x1 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=320,
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filter_size=1,
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act="relu")
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self.branch3x3_1 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=384,
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filter_size=1,
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act="relu")
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self.branch3x3_2a = ConvBNLayer(
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num_channels=384,
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num_filters=384,
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filter_size=(1, 3),
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padding=(0, 1),
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act="relu")
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self.branch3x3_2b = ConvBNLayer(
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num_channels=384,
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num_filters=384,
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filter_size=(3, 1),
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padding=(1, 0),
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act="relu")
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self.branch3x3dbl_1 = ConvBNLayer(
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num_channels=num_channels,
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num_filters=448,
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filter_size=1,
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act="relu")
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self.branch3x3dbl_2 = ConvBNLayer(
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num_channels=448,
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num_filters=384,
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filter_size=3,
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padding=1,
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act="relu")
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self.branch3x3dbl_3a = ConvBNLayer(
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num_channels=384,
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num_filters=384,
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filter_size=(1, 3),
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padding=(0, 1),
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act="relu")
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self.branch3x3dbl_3b = ConvBNLayer(
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num_channels=384,
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num_filters=384,
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filter_size=(3, 1),
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padding=(1, 0),
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act="relu")
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self.branch_pool = AvgPool2D(
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kernel_size=3, stride=1, padding=1, exclusive=False)
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self.branch_pool_conv = ConvBNLayer(
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num_channels=num_channels,
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num_filters=192,
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filter_size=1,
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act="relu")
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def forward(self, x):
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branch1x1 = self.branch1x1(x)
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branch3x3 = self.branch3x3_1(x)
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branch3x3 = [
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self.branch3x3_2a(branch3x3),
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self.branch3x3_2b(branch3x3),
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]
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branch3x3 = paddle.concat(branch3x3, axis=1)
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branch3x3dbl = self.branch3x3dbl_1(x)
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branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
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branch3x3dbl = [
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self.branch3x3dbl_3a(branch3x3dbl),
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self.branch3x3dbl_3b(branch3x3dbl),
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]
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branch3x3dbl = paddle.concat(branch3x3dbl, axis=1)
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branch_pool = self.branch_pool(x)
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branch_pool = self.branch_pool_conv(branch_pool)
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x = paddle.concat(
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[branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1)
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return x
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class Inception_V3(TheseusLayer):
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"""
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Inception_V3
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Args:
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config: dict. config of Inception_V3.
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class_num: int=1000. The number of classes.
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pretrained: (True or False) or path of pretrained_model. Whether to load the pretrained model.
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Returns:
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model: nn.Layer. Specific Inception_V3 model depends on args.
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"""
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def __init__(self,
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config,
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stages_pattern,
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class_num=1000,
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return_patterns=None,
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return_stages=None):
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super().__init__()
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self.inception_a_list = config["inception_a"]
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self.inception_c_list = config["inception_c"]
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self.inception_b_list = config["inception_b"]
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self.inception_d_list = config["inception_d"]
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self.inception_e_list = config["inception_e"]
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self.inception_stem = InceptionStem()
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self.inception_block_list = nn.LayerList()
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for i in range(len(self.inception_a_list[0])):
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inception_a = InceptionA(self.inception_a_list[0][i],
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self.inception_a_list[1][i])
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self.inception_block_list.append(inception_a)
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for i in range(len(self.inception_b_list)):
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inception_b = InceptionB(self.inception_b_list[i])
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self.inception_block_list.append(inception_b)
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for i in range(len(self.inception_c_list[0])):
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inception_c = InceptionC(self.inception_c_list[0][i],
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self.inception_c_list[1][i])
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self.inception_block_list.append(inception_c)
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for i in range(len(self.inception_d_list)):
|
|
inception_d = InceptionD(self.inception_d_list[i])
|
|
self.inception_block_list.append(inception_d)
|
|
|
|
for i in range(len(self.inception_e_list)):
|
|
inception_e = InceptionE(self.inception_e_list[i])
|
|
self.inception_block_list.append(inception_e)
|
|
|
|
self.avg_pool = AdaptiveAvgPool2D(1)
|
|
self.dropout = Dropout(p=0.2, mode="downscale_in_infer")
|
|
stdv = 1.0 / math.sqrt(2048 * 1.0)
|
|
self.fc = Linear(
|
|
2048,
|
|
class_num,
|
|
weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)),
|
|
bias_attr=ParamAttr())
|
|
|
|
super().init_res(
|
|
stages_pattern,
|
|
return_patterns=return_patterns,
|
|
return_stages=return_stages)
|
|
|
|
def forward(self, x):
|
|
x = self.inception_stem(x)
|
|
for inception_block in self.inception_block_list:
|
|
x = inception_block(x)
|
|
x = self.avg_pool(x)
|
|
x = paddle.reshape(x, shape=[-1, 2048])
|
|
x = self.dropout(x)
|
|
x = self.fc(x)
|
|
return x
|
|
|
|
|
|
def _load_pretrained(pretrained, model, model_url, use_ssld):
|
|
if pretrained is False:
|
|
pass
|
|
elif pretrained is True:
|
|
load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
|
|
elif isinstance(pretrained, str):
|
|
load_dygraph_pretrain(model, pretrained)
|
|
else:
|
|
raise RuntimeError(
|
|
"pretrained type is not available. Please use `string` or `boolean` type."
|
|
)
|
|
|
|
|
|
def InceptionV3(pretrained=False, use_ssld=False, **kwargs):
|
|
"""
|
|
InceptionV3
|
|
Args:
|
|
pretrained: bool=false or str. if `true` load pretrained parameters, `false` otherwise.
|
|
if str, means the path of the pretrained model.
|
|
use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
|
|
Returns:
|
|
model: nn.Layer. Specific `InceptionV3` model
|
|
"""
|
|
model = Inception_V3(
|
|
NET_CONFIG,
|
|
stages_pattern=MODEL_STAGES_PATTERN["InceptionV3"],
|
|
**kwargs)
|
|
_load_pretrained(pretrained, model, MODEL_URLS["InceptionV3"], use_ssld)
|
|
return model
|