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# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import 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 ppcls.arch.backbone.base.theseus_layer import TheseusLayer
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from ppcls.utils.save_load import load_dygraph_pretrain
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__all__ = ["InceptionV3"]
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2021-05-27 14:42:45 +08:00
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# InceptionV3 config
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# key: inception blocks
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# value: conv num in different blocks
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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(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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bias_attr=False)
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self.batch_norm = BatchNorm(
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num_filters,
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act=act)
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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(TheseusLayer):
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def __init__(self):
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super(InceptionStem, self).__init__()
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self.conv_1a_3x3 = ConvBNLayer(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(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(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.maxpool = MaxPool2D(kernel_size=3, stride=2, padding=0)
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self.conv_3b_1x1 = ConvBNLayer(num_channels=64,
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num_filters=80,
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filter_size=1,
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act="relu")
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self.conv_4a_3x3 = ConvBNLayer(num_channels=80,
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num_filters=192,
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filter_size=3,
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act="relu")
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def forward(self, x):
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y = self.conv_1a_3x3(x)
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y = self.conv_2a_3x3(y)
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y = self.conv_2b_3x3(y)
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y = self.maxpool(y)
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y = self.conv_3b_1x1(y)
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y = self.conv_4a_3x3(y)
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y = self.maxpool(y)
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return y
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class InceptionA(TheseusLayer):
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def __init__(self, num_channels, pool_features):
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super(InceptionA, self).__init__()
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self.branch1x1 = ConvBNLayer(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(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(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(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(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(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(kernel_size=3, stride=1, padding=1, exclusive=False)
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self.branch_pool_conv = ConvBNLayer(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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outputs = paddle.concat([branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=1)
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return outputs
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class InceptionB(TheseusLayer):
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def __init__(self, num_channels):
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super(InceptionB, self).__init__()
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self.branch3x3 = ConvBNLayer(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(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(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(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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outputs = paddle.concat([branch3x3, branch3x3dbl, branch_pool], axis=1)
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return outputs
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class InceptionC(TheseusLayer):
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def __init__(self, num_channels, channels_7x7):
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super(InceptionC, self).__init__()
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self.branch1x1 = ConvBNLayer(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(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(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(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(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(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(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(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(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(kernel_size=3, stride=1, padding=1, exclusive=False)
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self.branch_pool_conv = ConvBNLayer(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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outputs = paddle.concat([branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1)
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return outputs
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class InceptionD(TheseusLayer):
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def __init__(self, num_channels):
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super(InceptionD, self).__init__()
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self.branch3x3_1 = ConvBNLayer(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(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(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(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(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(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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outputs = paddle.concat([branch3x3, branch7x7x3, branch_pool], axis=1)
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return outputs
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class InceptionE(TheseusLayer):
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def __init__(self, num_channels):
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super(InceptionE, self).__init__()
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self.branch1x1 = ConvBNLayer(num_channels=num_channels,
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num_filters=320,
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filter_size=1,
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2021-05-26 19:43:27 +08:00
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act="relu")
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2021-05-26 15:22:01 +08:00
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self.branch3x3_1 = ConvBNLayer(num_channels=num_channels,
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num_filters=384,
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filter_size=1,
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2021-05-26 19:43:27 +08:00
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act="relu")
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2021-05-26 15:22:01 +08:00
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self.branch3x3_2a = ConvBNLayer(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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2021-05-26 19:43:27 +08:00
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act="relu")
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2021-05-26 15:22:01 +08:00
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self.branch3x3_2b = ConvBNLayer(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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2021-05-26 19:43:27 +08:00
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act="relu")
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2021-05-26 15:22:01 +08:00
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self.branch3x3dbl_1 = ConvBNLayer(num_channels=num_channels,
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num_filters=448,
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filter_size=1,
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2021-05-26 19:43:27 +08:00
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act="relu")
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2021-05-26 15:22:01 +08:00
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self.branch3x3dbl_2 = ConvBNLayer(num_channels=448,
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num_filters=384,
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filter_size=3,
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padding=1,
|
2021-05-26 19:43:27 +08:00
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act="relu")
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2021-05-26 15:22:01 +08:00
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self.branch3x3dbl_3a = ConvBNLayer(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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2021-05-26 19:43:27 +08:00
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|
act="relu")
|
2021-05-26 15:22:01 +08:00
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|
self.branch3x3dbl_3b = ConvBNLayer(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),
|
2021-05-26 19:43:27 +08:00
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|
act="relu")
|
2021-05-26 15:22:01 +08:00
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self.branch_pool = AvgPool2D(kernel_size=3, stride=1, padding=1, exclusive=False)
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self.branch_pool_conv = ConvBNLayer(num_channels=num_channels,
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num_filters=192,
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filter_size=1,
|
2021-05-26 19:43:27 +08:00
|
|
|
act="relu")
|
2021-05-26 15:22:01 +08:00
|
|
|
def forward(self, x):
|
|
|
|
|
branch1x1 = self.branch1x1(x)
|
|
|
|
|
|
|
|
|
|
branch3x3 = self.branch3x3_1(x)
|
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|
|
|
branch3x3 = [
|
|
|
|
|
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)
|
|
|
|
|
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
|
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|
|
|
branch3x3dbl = [
|
|
|
|
|
self.branch3x3dbl_3a(branch3x3dbl),
|
|
|
|
|
self.branch3x3dbl_3b(branch3x3dbl),
|
|
|
|
|
]
|
|
|
|
|
branch3x3dbl = paddle.concat(branch3x3dbl, axis=1)
|
|
|
|
|
|
|
|
|
|
branch_pool = self.branch_pool(x)
|
|
|
|
|
branch_pool = self.branch_pool_conv(branch_pool)
|
|
|
|
|
|
|
|
|
|
outputs = paddle.concat([branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1)
|
|
|
|
|
return outputs
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class Inception_V3(TheseusLayer):
|
2021-05-27 14:42:45 +08:00
|
|
|
|
|
|
|
|
def __init__(self,
|
|
|
|
|
config,
|
|
|
|
|
class_num=1000,
|
|
|
|
|
pretrained=False,
|
|
|
|
|
**kwargs):
|
2021-05-26 15:22:01 +08:00
|
|
|
super(Inception_V3, self).__init__()
|
|
|
|
|
|
|
|
|
|
self.inception_a_list = config['inception_a']
|
|
|
|
|
self.inception_c_list = config['inception_c']
|
|
|
|
|
self.inception_b_list = config['inception_b']
|
|
|
|
|
self.inception_d_list = config['inception_d']
|
|
|
|
|
self.inception_e_list = config ['inception_e']
|
|
|
|
|
|
|
|
|
|
self.inception_stem = InceptionStem()
|
|
|
|
|
|
2021-05-26 19:43:27 +08:00
|
|
|
self.inception_block_list = nn.LayerList()
|
2021-05-26 15:22:01 +08:00
|
|
|
for i in range(len(self.inception_a_list[0])):
|
|
|
|
|
inception_a = InceptionA(self.inception_a_list[0][i],
|
2021-05-26 19:43:27 +08:00
|
|
|
self.inception_a_list[1][i])
|
2021-05-26 15:22:01 +08:00
|
|
|
self.inception_block_list.append(inception_a)
|
|
|
|
|
|
|
|
|
|
for i in range(len(self.inception_b_list)):
|
2021-05-26 19:43:27 +08:00
|
|
|
inception_b = InceptionB(self.inception_b_list[i])
|
2021-05-26 15:22:01 +08:00
|
|
|
self.inception_block_list.append(inception_b)
|
|
|
|
|
|
|
|
|
|
for i in range(len(self.inception_c_list[0])):
|
|
|
|
|
inception_c = InceptionC(self.inception_c_list[0][i],
|
2021-05-26 19:43:27 +08:00
|
|
|
self.inception_c_list[1][i])
|
2021-05-26 15:22:01 +08:00
|
|
|
self.inception_block_list.append(inception_c)
|
|
|
|
|
|
|
|
|
|
for i in range(len(self.inception_d_list)):
|
2021-05-26 19:43:27 +08:00
|
|
|
inception_d = InceptionD(self.inception_d_list[i])
|
2021-05-26 15:22:01 +08:00
|
|
|
self.inception_block_list.append(inception_d)
|
|
|
|
|
|
|
|
|
|
for i in range(len(self.inception_e_list)):
|
2021-05-26 19:43:27 +08:00
|
|
|
inception_e = InceptionE(self.inception_e_list[i])
|
2021-05-26 15:22:01 +08:00
|
|
|
self.inception_block_list.append(inception_e)
|
|
|
|
|
|
|
|
|
|
self.gap = AdaptiveAvgPool2D(1)
|
|
|
|
|
self.drop = Dropout(p=0.2, mode="downscale_in_infer")
|
|
|
|
|
stdv = 1.0 / math.sqrt(2048 * 1.0)
|
|
|
|
|
self.out = Linear(
|
|
|
|
|
2048,
|
|
|
|
|
class_num,
|
|
|
|
|
weight_attr=ParamAttr(
|
2021-05-26 19:43:27 +08:00
|
|
|
initializer=Uniform(-stdv, stdv)),
|
|
|
|
|
bias_attr=ParamAttr())
|
2021-05-26 15:22:01 +08:00
|
|
|
|
2021-05-27 14:42:45 +08:00
|
|
|
if pretrained is not None:
|
|
|
|
|
load_dygraph_pretrain(self, pretrained)
|
|
|
|
|
|
2021-05-26 15:22:01 +08:00
|
|
|
def forward(self, x):
|
|
|
|
|
y = self.inception_stem(x)
|
|
|
|
|
for inception_block in self.inception_block_list:
|
|
|
|
|
y = inception_block(y)
|
|
|
|
|
y = self.gap(y)
|
|
|
|
|
y = paddle.reshape(y, shape=[-1, 2048])
|
|
|
|
|
y = self.drop(y)
|
|
|
|
|
y = self.out(y)
|
|
|
|
|
return y
|
|
|
|
|
|
|
|
|
|
|
2021-05-27 14:42:45 +08:00
|
|
|
|
|
|
|
|
|
|
|
|
|
def InceptionV3(**kwargs):
|
|
|
|
|
"""
|
|
|
|
|
InceptionV3
|
|
|
|
|
Args:
|
|
|
|
|
kwargs:
|
|
|
|
|
class_num: int=1000. Output dim of last fc layer.
|
2021-05-27 14:54:30 +08:00
|
|
|
pretrained: str, pretrained model file
|
2021-05-27 14:42:45 +08:00
|
|
|
Returns:
|
|
|
|
|
model: nn.Layer. Specific `InceptionV3` model
|
|
|
|
|
"""
|
|
|
|
|
model = Inception_V3(NET_CONFIG, **kwargs)
|
|
|
|
|
return model
|
|
|
|
|
|
