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# code was heavily based on https://github.com/pytorch/vision/blob/main/torchvision/models/inception.py
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# BSD 3-Clause License
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# Copyright (c) Soumith Chintala 2016
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import math
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import paddle
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import paddle.nn as nn
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from paddle.nn import Conv2D, AvgPool2D, MaxPool2D, BatchNorm, Linear, AdaptiveAvgPool2D
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__all__ = ['InceptionV3']
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class InceptionV3(nn.Layer):
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DEFAULT_BLOCK_INDEX = 3
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BLOCK_INDEX_BY_DIM = {
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64: 0, # First max pooling features
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192: 1, # Second max pooling featurs
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768: 2, # Pre-aux classifier features
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2048: 3 # Final average pooling features
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}
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def __init__(self,
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output_blocks=[DEFAULT_BLOCK_INDEX],
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class_dim=1000,
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aux_logits=False,
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resize_input=True,
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normalize_input=True):
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super(InceptionV3, self).__init__()
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self.resize_input = resize_input
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self.normalize_input = normalize_input
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self.output_blocks = sorted(output_blocks)
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self.last_needed_block = max(output_blocks)
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self.class_dim = class_dim
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self.aux_logits = aux_logits
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assert self.last_needed_block <= 3, 'Last possible output block index is 3'
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self.blocks = []
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self.Conv2d_1a_3x3 = ConvBNLayer(
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3, 32, 3, stride=2, name='Conv2d_1a_3x3')
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self.Conv2d_2a_3x3 = ConvBNLayer(32, 32, 3, name='Conv2d_2a_3x3')
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self.Conv2d_2b_3x3 = ConvBNLayer(
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32, 64, 3, padding=1, name='Conv2d_2b_3x3')
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self.maxpool1 = MaxPool2D(kernel_size=3, stride=2)
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block0 = [
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self.Conv2d_1a_3x3, self.Conv2d_2a_3x3, self.Conv2d_2b_3x3,
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self.maxpool1
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]
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self.blocks.append(nn.Sequential(*block0))
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### block1
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if self.last_needed_block >= 1:
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self.Conv2d_3b_1x1 = ConvBNLayer(64, 80, 1, name='Conv2d_3b_1x1')
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self.Conv2d_4a_3x3 = ConvBNLayer(80, 192, 3, name='Conv2d_4a_3x3')
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self.maxpool2 = MaxPool2D(kernel_size=3, stride=2)
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block1 = [self.Conv2d_3b_1x1, self.Conv2d_4a_3x3, self.maxpool2]
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self.blocks.append(nn.Sequential(*block1))
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### block2
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### Mixed_5b 5c 5d
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if self.last_needed_block >= 2:
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self.Mixed_5b = Fid_inceptionA(
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192, pool_features=32, name='Mixed_5b')
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self.Mixed_5c = Fid_inceptionA(
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256, pool_features=64, name='Mixed_5c')
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self.Mixed_5d = Fid_inceptionA(
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288, pool_features=64, name='Mixed_5d')
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### Mixed_6
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self.Mixed_6a = InceptionB(288, name='Mixed_6a')
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self.Mixed_6b = Fid_inceptionC(768, c7=128, name='Mixed_6b')
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self.Mixed_6c = Fid_inceptionC(768, c7=160, name='Mixed_6c')
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self.Mixed_6d = Fid_inceptionC(768, c7=160, name='Mixed_6d')
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self.Mixed_6e = Fid_inceptionC(768, c7=192, name='Mixed_6e')
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block2 = [
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self.Mixed_5b, self.Mixed_5c, self.Mixed_5d, self.Mixed_6a,
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self.Mixed_6b, self.Mixed_6c, self.Mixed_6d, self.Mixed_6e
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]
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self.blocks.append(nn.Sequential(*block2))
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if self.aux_logits:
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self.AuxLogits = InceptionAux(768, self.class_dim, name='AuxLogits')
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### block3
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### Mixed_7
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if self.last_needed_block >= 3:
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self.Mixed_7a = InceptionD(768, name='Mixed_7a')
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self.Mixed_7b = Fid_inceptionE_1(1280, name='Mixed_7b')
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self.Mixed_7c = Fid_inceptionE_2(2048, name='Mixed_7c')
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self.avgpool = AdaptiveAvgPool2D(output_size=1)
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block3 = [self.Mixed_7a, self.Mixed_7b, self.Mixed_7c, self.avgpool]
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self.blocks.append(nn.Sequential(*block3))
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def forward(self, x):
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out = []
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aux = None
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if self.resize_input:
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x = nn.functional.interpolate(
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x,
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size=[299, 299],
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mode='bilinear',
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align_corners=False,
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align_mode=0)
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if self.normalize_input:
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x = x * 2 - 1
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for idx, block in enumerate(self.blocks):
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x = block(x)
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if self.aux_logits and (idx == 2):
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aux = self.AuxLogits(x)
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if idx in self.output_blocks:
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out.append(x)
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if idx == self.last_needed_block:
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break
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return out, aux
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class InceptionA(nn.Layer):
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def __init__(self, in_channels, pool_features, name=None):
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super(InceptionA, self).__init__()
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self.branch1x1 = ConvBNLayer(
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in_channels, 64, 1, name=name + '.branch1x1')
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self.branch5x5_1 = ConvBNLayer(
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in_channels, 48, 1, name=name + '.branch5x5_1')
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self.branch5x5_2 = ConvBNLayer(
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48, 64, 5, padding=2, name=name + '.branch5x5_2')
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self.branch3x3dbl_1 = ConvBNLayer(
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in_channels, 64, 1, name=name + '.branch3x3dbl_1')
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self.branch3x3dbl_2 = ConvBNLayer(
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64, 96, 3, padding=1, name=name + '.branch3x3dbl_2')
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self.branch3x3dbl_3 = ConvBNLayer(
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96, 96, 3, padding=1, name=name + '.branch3x3dbl_3')
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self.branch_pool0 = AvgPool2D(
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kernel_size=3, stride=1, padding=1, exclusive=True)
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self.branch_pool = ConvBNLayer(
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in_channels, pool_features, 1, name=name + '.branch_pool')
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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_pool0(x)
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branch_pool = self.branch_pool(branch_pool)
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return paddle.concat(
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[branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=1)
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class InceptionB(nn.Layer):
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def __init__(self, in_channels, name=None):
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super(InceptionB, self).__init__()
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self.branch3x3 = ConvBNLayer(
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in_channels, 384, 3, stride=2, name=name + '.branch3x3')
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self.branch3x3dbl_1 = ConvBNLayer(
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in_channels, 64, 1, name=name + '.branch3x3dbl_1')
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self.branch3x3dbl_2 = ConvBNLayer(
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64, 96, 3, padding=1, name=name + '.branch3x3dbl_2')
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self.branch3x3dbl_3 = ConvBNLayer(
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96, 96, 3, stride=2, name=name + '.branch3x3dbl_3')
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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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return paddle.concat([branch3x3, branch3x3dbl, branch_pool], axis=1)
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class InceptionC(nn.Layer):
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def __init__(self, in_channels, c7, name=None):
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super(InceptionC, self).__init__()
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self.branch1x1 = ConvBNLayer(
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in_channels, 192, 1, name=name + '.branch1x1')
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self.branch7x7_1 = ConvBNLayer(
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in_channels, c7, 1, name=name + '.branch7x7_1')
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self.branch7x7_2 = ConvBNLayer(
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c7, c7, (1, 7), padding=(0, 3), name=name + '.branch7x7_2')
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self.branch7x7_3 = ConvBNLayer(
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c7, 192, (7, 1), padding=(3, 0), name=name + '.branch7x7_3')
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self.branch7x7dbl_1 = ConvBNLayer(
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in_channels, c7, 1, name=name + '.branch7x7dbl_1')
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self.branch7x7dbl_2 = ConvBNLayer(
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c7, c7, (7, 1), padding=(3, 0), name=name + '.branch7x7dbl_2')
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self.branch7x7dbl_3 = ConvBNLayer(
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c7, c7, (1, 7), padding=(0, 3), name=name + '.branch7x7dbl_3')
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self.branch7x7dbl_4 = ConvBNLayer(
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c7, c7, (7, 1), padding=(3, 0), name=name + '.branch7x7dbl_4')
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self.branch7x7dbl_5 = ConvBNLayer(
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c7, 192, (1, 7), padding=(0, 3), name=name + '.branch7x7dbl_5')
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self.branch_pool0 = AvgPool2D(
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kernel_size=3, stride=1, padding=1, exclusive=True)
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self.branch_pool = ConvBNLayer(
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in_channels, 192, 1, name=name + '.branch_pool')
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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_pool0(x)
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branch_pool = self.branch_pool(branch_pool)
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return paddle.concat(
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[branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1)
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class InceptionD(nn.Layer):
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def __init__(self, in_channels, name=None):
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super(InceptionD, self).__init__()
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self.branch3x3_1 = ConvBNLayer(
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in_channels, 192, 1, name=name + '.branch3x3_1')
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self.branch3x3_2 = ConvBNLayer(
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192, 320, 3, stride=2, name=name + '.branch3x3_2')
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self.branch7x7x3_1 = ConvBNLayer(
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in_channels, 192, 1, name=name + '.branch7x7x3_1')
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self.branch7x7x3_2 = ConvBNLayer(
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192, 192, (1, 7), padding=(0, 3), name=name + '.branch7x7x3_2')
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self.branch7x7x3_3 = ConvBNLayer(
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192, 192, (7, 1), padding=(3, 0), name=name + '.branch7x7x3_3')
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self.branch7x7x3_4 = ConvBNLayer(
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192, 192, 3, stride=2, name=name + '.branch7x7x3_4')
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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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return paddle.concat([branch3x3, branch7x7x3, branch_pool], axis=1)
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class InceptionE(nn.Layer):
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def __init__(self, in_channels, name=None):
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super(InceptionE, self).__init__()
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self.branch1x1 = ConvBNLayer(
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in_channels, 320, 1, name=name + '.branch1x1')
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self.branch3x3_1 = ConvBNLayer(
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in_channels, 384, 1, name=name + '.branch3x3_1')
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self.branch3x3_2a = ConvBNLayer(
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384, 384, (1, 3), padding=(0, 1), name=name + '.branch3x3_2a')
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self.branch3x3_2b = ConvBNLayer(
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384, 384, (3, 1), padding=(1, 0), name=name + '.branch3x3_2b')
|
|
|
|
|
|
|
|
|
|
self.branch3x3dbl_1 = ConvBNLayer(
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|
|
|
|
in_channels, 448, 1, name=name + '.branch3x3dbl_1')
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|
|
|
|
self.branch3x3dbl_2 = ConvBNLayer(
|
|
|
|
|
448, 384, 3, padding=1, name=name + '.branch3x3dbl_2')
|
|
|
|
|
self.branch3x3dbl_3a = ConvBNLayer(
|
|
|
|
|
384, 384, (1, 3), padding=(0, 1), name=name + '.branch3x3dbl_3a')
|
|
|
|
|
self.branch3x3dbl_3b = ConvBNLayer(
|
|
|
|
|
384, 384, (3, 1), padding=(1, 0), name=name + '.branch3x3dbl_3b')
|
|
|
|
|
|
|
|
|
|
self.branch_pool0 = AvgPool2D(
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|
kernel_size=3, stride=1, padding=1, exclusive=True)
|
|
|
|
|
self.branch_pool = ConvBNLayer(
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|
|
|
in_channels, 192, 1, name=name + '.branch_pool')
|
|
|
|
|
|
|
|
|
|
def forward(self, x):
|
|
|
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|
branch1x1 = self.branch1x1(x)
|
|
|
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|
branch3x3_1 = self.branch3x3_1(x)
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|
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|
branch3x3_2a = self.branch3x3_2a(branch3x3_1)
|
|
|
|
|
branch3x3_2b = self.branch3x3_2b(branch3x3_1)
|
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|
branch3x3 = paddle.concat([branch3x3_2a, branch3x3_2b], axis=1)
|
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|
|
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|
branch3x3dbl = self.branch3x3dbl_1(x)
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|
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|
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
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branch3x3dbl_3a = self.branch3x3dbl_3a(branch3x3dbl)
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|
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|
branch3x3dbl_3b = self.branch3x3dbl_3b(branch3x3dbl)
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|
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|
branch3x3dbl = paddle.concat([branch3x3dbl_3a, branch3x3dbl_3b], axis=1)
|
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branch_pool = self.branch_pool0(x)
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|
branch_pool = self.branch_pool(branch_pool)
|
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|
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|
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|
|
|
return paddle.concat(
|
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|
|
|
[branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1)
|
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|
class InceptionAux(nn.Layer):
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|
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|
def __init__(self, in_channels, num_classes, name=None):
|
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|
|
super(InceptionAux, self).__init__()
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|
|
|
self.num_classes = num_classes
|
|
|
|
|
self.pool0 = AvgPool2D(kernel_size=5, stride=3)
|
|
|
|
|
self.conv0 = ConvBNLayer(in_channels, 128, 1, name=name + '.conv0')
|
|
|
|
|
self.conv1 = ConvBNLayer(128, 768, 5, name=name + '.conv1')
|
|
|
|
|
self.pool1 = AvgPool2D(global_pooling=True)
|
|
|
|
|
|
|
|
|
|
def forward(self, x):
|
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|
|
x = self.pool0(x)
|
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|
|
x = self.conv0(x)
|
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|
|
|
x = self.conv1(x)
|
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|
|
x = self.pool1(x)
|
|
|
|
|
x = paddle.flatten(x, axis=1)
|
|
|
|
|
x = paddle.static.nn.fc(x, size=self.num_classes)
|
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|
|
|
return x
|
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|
class Fid_inceptionA(nn.Layer):
|
|
|
|
|
""" FID block in inception v3
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
def __init__(self, in_channels, pool_features, name=None):
|
|
|
|
|
super(Fid_inceptionA, self).__init__()
|
|
|
|
|
self.branch1x1 = ConvBNLayer(
|
|
|
|
|
in_channels, 64, 1, name=name + '.branch1x1')
|
|
|
|
|
|
|
|
|
|
self.branch5x5_1 = ConvBNLayer(
|
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|
|
|
in_channels, 48, 1, name=name + '.branch5x5_1')
|
|
|
|
|
self.branch5x5_2 = ConvBNLayer(
|
|
|
|
|
48, 64, 5, padding=2, name=name + '.branch5x5_2')
|
|
|
|
|
|
|
|
|
|
self.branch3x3dbl_1 = ConvBNLayer(
|
|
|
|
|
in_channels, 64, 1, name=name + '.branch3x3dbl_1')
|
|
|
|
|
self.branch3x3dbl_2 = ConvBNLayer(
|
|
|
|
|
64, 96, 3, padding=1, name=name + '.branch3x3dbl_2')
|
|
|
|
|
self.branch3x3dbl_3 = ConvBNLayer(
|
|
|
|
|
96, 96, 3, padding=1, name=name + '.branch3x3dbl_3')
|
|
|
|
|
|
|
|
|
|
self.branch_pool0 = AvgPool2D(
|
|
|
|
|
kernel_size=3, stride=1, padding=1, exclusive=True)
|
|
|
|
|
self.branch_pool = ConvBNLayer(
|
|
|
|
|
in_channels, pool_features, 1, name=name + '.branch_pool')
|
|
|
|
|
|
|
|
|
|
def forward(self, x):
|
|
|
|
|
branch1x1 = self.branch1x1(x)
|
|
|
|
|
|
|
|
|
|
branch5x5 = self.branch5x5_1(x)
|
|
|
|
|
branch5x5 = self.branch5x5_2(branch5x5)
|
|
|
|
|
|
|
|
|
|
branch3x3dbl = self.branch3x3dbl_1(x)
|
|
|
|
|
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
|
|
|
|
|
branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
|
|
|
|
|
|
|
|
|
|
branch_pool = self.branch_pool0(x)
|
|
|
|
|
branch_pool = self.branch_pool(branch_pool)
|
|
|
|
|
return paddle.concat(
|
|
|
|
|
[branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=1)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class Fid_inceptionC(nn.Layer):
|
|
|
|
|
""" FID block in inception v3
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
def __init__(self, in_channels, c7, name=None):
|
|
|
|
|
super(Fid_inceptionC, self).__init__()
|
|
|
|
|
self.branch1x1 = ConvBNLayer(
|
|
|
|
|
in_channels, 192, 1, name=name + '.branch1x1')
|
|
|
|
|
|
|
|
|
|
self.branch7x7_1 = ConvBNLayer(
|
|
|
|
|
in_channels, c7, 1, name=name + '.branch7x7_1')
|
|
|
|
|
self.branch7x7_2 = ConvBNLayer(
|
|
|
|
|
c7, c7, (1, 7), padding=(0, 3), name=name + '.branch7x7_2')
|
|
|
|
|
self.branch7x7_3 = ConvBNLayer(
|
|
|
|
|
c7, 192, (7, 1), padding=(3, 0), name=name + '.branch7x7_3')
|
|
|
|
|
|
|
|
|
|
self.branch7x7dbl_1 = ConvBNLayer(
|
|
|
|
|
in_channels, c7, 1, name=name + '.branch7x7dbl_1')
|
|
|
|
|
self.branch7x7dbl_2 = ConvBNLayer(
|
|
|
|
|
c7, c7, (7, 1), padding=(3, 0), name=name + '.branch7x7dbl_2')
|
|
|
|
|
self.branch7x7dbl_3 = ConvBNLayer(
|
|
|
|
|
c7, c7, (1, 7), padding=(0, 3), name=name + '.branch7x7dbl_3')
|
|
|
|
|
self.branch7x7dbl_4 = ConvBNLayer(
|
|
|
|
|
c7, c7, (7, 1), padding=(3, 0), name=name + '.branch7x7dbl_4')
|
|
|
|
|
self.branch7x7dbl_5 = ConvBNLayer(
|
|
|
|
|
c7, 192, (1, 7), padding=(0, 3), name=name + '.branch7x7dbl_5')
|
|
|
|
|
|
|
|
|
|
self.branch_pool0 = AvgPool2D(
|
|
|
|
|
kernel_size=3, stride=1, padding=1, exclusive=True)
|
|
|
|
|
self.branch_pool = ConvBNLayer(
|
|
|
|
|
in_channels, 192, 1, name=name + '.branch_pool')
|
|
|
|
|
|
|
|
|
|
def forward(self, x):
|
|
|
|
|
branch1x1 = self.branch1x1(x)
|
|
|
|
|
|
|
|
|
|
branch7x7 = self.branch7x7_1(x)
|
|
|
|
|
branch7x7 = self.branch7x7_2(branch7x7)
|
|
|
|
|
branch7x7 = self.branch7x7_3(branch7x7)
|
|
|
|
|
|
|
|
|
|
branch7x7dbl = self.branch7x7dbl_1(x)
|
|
|
|
|
branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
|
|
|
|
|
branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
|
|
|
|
|
branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
|
|
|
|
|
branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
|
|
|
|
|
|
|
|
|
|
branch_pool = self.branch_pool0(x)
|
|
|
|
|
branch_pool = self.branch_pool(branch_pool)
|
|
|
|
|
|
|
|
|
|
return paddle.concat(
|
|
|
|
|
[branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class Fid_inceptionE_1(nn.Layer):
|
|
|
|
|
""" FID block in inception v3
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
def __init__(self, in_channels, name=None):
|
|
|
|
|
super(Fid_inceptionE_1, self).__init__()
|
|
|
|
|
self.branch1x1 = ConvBNLayer(
|
|
|
|
|
in_channels, 320, 1, name=name + '.branch1x1')
|
|
|
|
|
|
|
|
|
|
self.branch3x3_1 = ConvBNLayer(
|
|
|
|
|
in_channels, 384, 1, name=name + '.branch3x3_1')
|
|
|
|
|
self.branch3x3_2a = ConvBNLayer(
|
|
|
|
|
384, 384, (1, 3), padding=(0, 1), name=name + '.branch3x3_2a')
|
|
|
|
|
self.branch3x3_2b = ConvBNLayer(
|
|
|
|
|
384, 384, (3, 1), padding=(1, 0), name=name + '.branch3x3_2b')
|
|
|
|
|
|
|
|
|
|
self.branch3x3dbl_1 = ConvBNLayer(
|
|
|
|
|
in_channels, 448, 1, name=name + '.branch3x3dbl_1')
|
|
|
|
|
self.branch3x3dbl_2 = ConvBNLayer(
|
|
|
|
|
448, 384, 3, padding=1, name=name + '.branch3x3dbl_2')
|
|
|
|
|
self.branch3x3dbl_3a = ConvBNLayer(
|
|
|
|
|
384, 384, (1, 3), padding=(0, 1), name=name + '.branch3x3dbl_3a')
|
|
|
|
|
self.branch3x3dbl_3b = ConvBNLayer(
|
|
|
|
|
384, 384, (3, 1), padding=(1, 0), name=name + '.branch3x3dbl_3b')
|
|
|
|
|
|
|
|
|
|
self.branch_pool0 = AvgPool2D(
|
|
|
|
|
kernel_size=3, stride=1, padding=1, exclusive=True)
|
|
|
|
|
self.branch_pool = ConvBNLayer(
|
|
|
|
|
in_channels, 192, 1, name=name + '.branch_pool')
|
|
|
|
|
|
|
|
|
|
def forward(self, x):
|
|
|
|
|
branch1x1 = self.branch1x1(x)
|
|
|
|
|
branch3x3_1 = self.branch3x3_1(x)
|
|
|
|
|
branch3x3_2a = self.branch3x3_2a(branch3x3_1)
|
|
|
|
|
branch3x3_2b = self.branch3x3_2b(branch3x3_1)
|
|
|
|
|
branch3x3 = paddle.concat([branch3x3_2a, branch3x3_2b], axis=1)
|
|
|
|
|
|
|
|
|
|
branch3x3dbl = self.branch3x3dbl_1(x)
|
|
|
|
|
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
|
|
|
|
|
branch3x3dbl_3a = self.branch3x3dbl_3a(branch3x3dbl)
|
|
|
|
|
branch3x3dbl_3b = self.branch3x3dbl_3b(branch3x3dbl)
|
|
|
|
|
branch3x3dbl = paddle.concat([branch3x3dbl_3a, branch3x3dbl_3b], axis=1)
|
|
|
|
|
|
|
|
|
|
branch_pool = self.branch_pool0(x)
|
|
|
|
|
branch_pool = self.branch_pool(branch_pool)
|
|
|
|
|
|
|
|
|
|
return paddle.concat(
|
|
|
|
|
[branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class Fid_inceptionE_2(nn.Layer):
|
|
|
|
|
""" FID block in inception v3
|
|
|
|
|
"""
|
|
|
|
|
|
|
|
|
|
def __init__(self, in_channels, name=None):
|
|
|
|
|
super(Fid_inceptionE_2, self).__init__()
|
|
|
|
|
self.branch1x1 = ConvBNLayer(
|
|
|
|
|
in_channels, 320, 1, name=name + '.branch1x1')
|
|
|
|
|
|
|
|
|
|
self.branch3x3_1 = ConvBNLayer(
|
|
|
|
|
in_channels, 384, 1, name=name + '.branch3x3_1')
|
|
|
|
|
self.branch3x3_2a = ConvBNLayer(
|
|
|
|
|
384, 384, (1, 3), padding=(0, 1), name=name + '.branch3x3_2a')
|
|
|
|
|
self.branch3x3_2b = ConvBNLayer(
|
|
|
|
|
384, 384, (3, 1), padding=(1, 0), name=name + '.branch3x3_2b')
|
|
|
|
|
|
|
|
|
|
self.branch3x3dbl_1 = ConvBNLayer(
|
|
|
|
|
in_channels, 448, 1, name=name + '.branch3x3dbl_1')
|
|
|
|
|
self.branch3x3dbl_2 = ConvBNLayer(
|
|
|
|
|
448, 384, 3, padding=1, name=name + '.branch3x3dbl_2')
|
|
|
|
|
self.branch3x3dbl_3a = ConvBNLayer(
|
|
|
|
|
384, 384, (1, 3), padding=(0, 1), name=name + '.branch3x3dbl_3a')
|
|
|
|
|
self.branch3x3dbl_3b = ConvBNLayer(
|
|
|
|
|
384, 384, (3, 1), padding=(1, 0), name=name + '.branch3x3dbl_3b')
|
|
|
|
|
### same with paper
|
|
|
|
|
self.branch_pool0 = MaxPool2D(kernel_size=3, stride=1, padding=1)
|
|
|
|
|
self.branch_pool = ConvBNLayer(
|
|
|
|
|
in_channels, 192, 1, name=name + '.branch_pool')
|
|
|
|
|
|
|
|
|
|
def forward(self, x):
|
|
|
|
|
branch1x1 = self.branch1x1(x)
|
|
|
|
|
branch3x3_1 = self.branch3x3_1(x)
|
|
|
|
|
branch3x3_2a = self.branch3x3_2a(branch3x3_1)
|
|
|
|
|
branch3x3_2b = self.branch3x3_2b(branch3x3_1)
|
|
|
|
|
branch3x3 = paddle.concat([branch3x3_2a, branch3x3_2b], axis=1)
|
|
|
|
|
|
|
|
|
|
branch3x3dbl = self.branch3x3dbl_1(x)
|
|
|
|
|
branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
|
|
|
|
|
branch3x3dbl_3a = self.branch3x3dbl_3a(branch3x3dbl)
|
|
|
|
|
branch3x3dbl_3b = self.branch3x3dbl_3b(branch3x3dbl)
|
|
|
|
|
branch3x3dbl = paddle.concat([branch3x3dbl_3a, branch3x3dbl_3b], axis=1)
|
|
|
|
|
|
|
|
|
|
branch_pool = self.branch_pool0(x)
|
|
|
|
|
branch_pool = self.branch_pool(branch_pool)
|
|
|
|
|
|
|
|
|
|
return paddle.concat(
|
|
|
|
|
[branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class ConvBNLayer(nn.Layer):
|
|
|
|
|
def __init__(self,
|
|
|
|
|
in_channels,
|
|
|
|
|
num_filters,
|
|
|
|
|
filter_size,
|
|
|
|
|
stride=1,
|
|
|
|
|
padding=0,
|
|
|
|
|
groups=1,
|
|
|
|
|
act='relu',
|
|
|
|
|
name=None):
|
|
|
|
|
super(ConvBNLayer, self).__init__()
|
|
|
|
|
self.conv = Conv2D(
|
|
|
|
|
in_channels=in_channels,
|
|
|
|
|
out_channels=num_filters,
|
|
|
|
|
kernel_size=filter_size,
|
|
|
|
|
stride=stride,
|
|
|
|
|
padding=padding,
|
|
|
|
|
groups=groups,
|
|
|
|
|
weight_attr=paddle.ParamAttr(name=name + ".conv.weight"),
|
|
|
|
|
bias_attr=False)
|
|
|
|
|
self.bn = BatchNorm(
|
|
|
|
|
num_filters,
|
|
|
|
|
act=act,
|
|
|
|
|
epsilon=0.001,
|
|
|
|
|
param_attr=paddle.ParamAttr(name=name + ".bn.weight"),
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bias_attr=paddle.ParamAttr(name=name + ".bn.bias"),
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moving_mean_name=name + '.bn.running_mean',
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moving_variance_name=name + '.bn.running_var')
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def forward(self, inputs):
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y = self.conv(inputs)
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y = self.bn(y)
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return y
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