345 lines
9.5 KiB
Python
345 lines
9.5 KiB
Python
from __future__ import division, absolute_import
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.model_zoo as model_zoo
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__all__ = ['xception']
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pretrained_settings = {
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'xception': {
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'imagenet': {
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'url':
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'http://data.lip6.fr/cadene/pretrainedmodels/xception-43020ad28.pth',
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'input_space': 'RGB',
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'input_size': [3, 299, 299],
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'input_range': [0, 1],
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'mean': [0.5, 0.5, 0.5],
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'std': [0.5, 0.5, 0.5],
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'num_classes': 1000,
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'scale':
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0.8975 # The resize parameter of the validation transform should be 333, and make sure to center crop at 299x299
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}
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}
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}
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class SeparableConv2d(nn.Module):
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def __init__(
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self,
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in_channels,
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out_channels,
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kernel_size=1,
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stride=1,
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padding=0,
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dilation=1,
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bias=False
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):
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super(SeparableConv2d, self).__init__()
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self.conv1 = nn.Conv2d(
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in_channels,
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in_channels,
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kernel_size,
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stride,
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padding,
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dilation,
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groups=in_channels,
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bias=bias
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)
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self.pointwise = nn.Conv2d(
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in_channels, out_channels, 1, 1, 0, 1, 1, bias=bias
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)
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def forward(self, x):
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x = self.conv1(x)
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x = self.pointwise(x)
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return x
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class Block(nn.Module):
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def __init__(
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self,
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in_filters,
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out_filters,
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reps,
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strides=1,
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start_with_relu=True,
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grow_first=True
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):
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super(Block, self).__init__()
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if out_filters != in_filters or strides != 1:
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self.skip = nn.Conv2d(
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in_filters, out_filters, 1, stride=strides, bias=False
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)
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self.skipbn = nn.BatchNorm2d(out_filters)
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else:
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self.skip = None
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self.relu = nn.ReLU(inplace=True)
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rep = []
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filters = in_filters
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if grow_first:
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rep.append(self.relu)
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rep.append(
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SeparableConv2d(
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in_filters,
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out_filters,
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3,
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stride=1,
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padding=1,
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bias=False
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)
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)
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rep.append(nn.BatchNorm2d(out_filters))
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filters = out_filters
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for i in range(reps - 1):
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rep.append(self.relu)
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rep.append(
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SeparableConv2d(
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filters, filters, 3, stride=1, padding=1, bias=False
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)
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)
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rep.append(nn.BatchNorm2d(filters))
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if not grow_first:
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rep.append(self.relu)
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rep.append(
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SeparableConv2d(
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in_filters,
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out_filters,
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3,
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stride=1,
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padding=1,
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bias=False
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)
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)
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rep.append(nn.BatchNorm2d(out_filters))
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if not start_with_relu:
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rep = rep[1:]
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else:
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rep[0] = nn.ReLU(inplace=False)
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if strides != 1:
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rep.append(nn.MaxPool2d(3, strides, 1))
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self.rep = nn.Sequential(*rep)
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def forward(self, inp):
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x = self.rep(inp)
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if self.skip is not None:
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skip = self.skip(inp)
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skip = self.skipbn(skip)
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else:
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skip = inp
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x += skip
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return x
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class Xception(nn.Module):
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"""Xception.
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Reference:
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Chollet. Xception: Deep Learning with Depthwise
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Separable Convolutions. CVPR 2017.
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Public keys:
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- ``xception``: Xception.
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"""
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def __init__(
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self, num_classes, loss, fc_dims=None, dropout_p=None, **kwargs
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):
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super(Xception, self).__init__()
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self.loss = loss
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self.conv1 = nn.Conv2d(3, 32, 3, 2, 0, bias=False)
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self.bn1 = nn.BatchNorm2d(32)
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self.conv2 = nn.Conv2d(32, 64, 3, bias=False)
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self.bn2 = nn.BatchNorm2d(64)
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self.block1 = Block(
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64, 128, 2, 2, start_with_relu=False, grow_first=True
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)
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self.block2 = Block(
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128, 256, 2, 2, start_with_relu=True, grow_first=True
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)
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self.block3 = Block(
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256, 728, 2, 2, start_with_relu=True, grow_first=True
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)
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self.block4 = Block(
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728, 728, 3, 1, start_with_relu=True, grow_first=True
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)
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self.block5 = Block(
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728, 728, 3, 1, start_with_relu=True, grow_first=True
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)
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self.block6 = Block(
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728, 728, 3, 1, start_with_relu=True, grow_first=True
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)
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self.block7 = Block(
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728, 728, 3, 1, start_with_relu=True, grow_first=True
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)
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self.block8 = Block(
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728, 728, 3, 1, start_with_relu=True, grow_first=True
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)
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self.block9 = Block(
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728, 728, 3, 1, start_with_relu=True, grow_first=True
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)
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self.block10 = Block(
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728, 728, 3, 1, start_with_relu=True, grow_first=True
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)
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self.block11 = Block(
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728, 728, 3, 1, start_with_relu=True, grow_first=True
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)
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self.block12 = Block(
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728, 1024, 2, 2, start_with_relu=True, grow_first=False
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)
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self.conv3 = SeparableConv2d(1024, 1536, 3, 1, 1)
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self.bn3 = nn.BatchNorm2d(1536)
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self.conv4 = SeparableConv2d(1536, 2048, 3, 1, 1)
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self.bn4 = nn.BatchNorm2d(2048)
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self.global_avgpool = nn.AdaptiveAvgPool2d(1)
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self.feature_dim = 2048
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self.fc = self._construct_fc_layer(fc_dims, 2048, dropout_p)
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self.classifier = nn.Linear(self.feature_dim, num_classes)
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self._init_params()
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def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
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"""Constructs fully connected layer.
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Args:
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fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
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input_dim (int): input dimension
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dropout_p (float): dropout probability, if None, dropout is unused
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"""
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if fc_dims is None:
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self.feature_dim = input_dim
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return None
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assert isinstance(
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fc_dims, (list, tuple)
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), 'fc_dims must be either list or tuple, but got {}'.format(
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type(fc_dims)
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)
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layers = []
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for dim in fc_dims:
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layers.append(nn.Linear(input_dim, dim))
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layers.append(nn.BatchNorm1d(dim))
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layers.append(nn.ReLU(inplace=True))
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if dropout_p is not None:
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layers.append(nn.Dropout(p=dropout_p))
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input_dim = dim
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self.feature_dim = fc_dims[-1]
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return nn.Sequential(*layers)
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def _init_params(self):
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(
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m.weight, mode='fan_out', nonlinearity='relu'
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)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.BatchNorm1d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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nn.init.normal_(m.weight, 0, 0.01)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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def featuremaps(self, input):
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x = self.conv1(input)
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x = self.bn1(x)
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x = F.relu(x, inplace=True)
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x = self.conv2(x)
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x = self.bn2(x)
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x = F.relu(x, inplace=True)
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x = self.block1(x)
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x = self.block2(x)
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x = self.block3(x)
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x = self.block4(x)
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x = self.block5(x)
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x = self.block6(x)
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x = self.block7(x)
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x = self.block8(x)
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x = self.block9(x)
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x = self.block10(x)
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x = self.block11(x)
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x = self.block12(x)
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x = self.conv3(x)
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x = self.bn3(x)
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x = F.relu(x, inplace=True)
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x = self.conv4(x)
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x = self.bn4(x)
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x = F.relu(x, inplace=True)
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return x
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def forward(self, x):
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f = self.featuremaps(x)
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v = self.global_avgpool(f)
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v = v.view(v.size(0), -1)
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if self.fc is not None:
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v = self.fc(v)
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if not self.training:
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return v
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y = self.classifier(v)
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if self.loss == 'softmax':
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return y
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elif self.loss == 'triplet':
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return y, v
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else:
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raise KeyError('Unsupported loss: {}'.format(self.loss))
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def init_pretrained_weights(model, model_url):
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"""Initialize models with pretrained weights.
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Layers that don't match with pretrained layers in name or size are kept unchanged.
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"""
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pretrain_dict = model_zoo.load_url(model_url)
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model_dict = model.state_dict()
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pretrain_dict = {
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k: v
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for k, v in pretrain_dict.items()
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if k in model_dict and model_dict[k].size() == v.size()
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}
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model_dict.update(pretrain_dict)
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model.load_state_dict(model_dict)
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def xception(num_classes, loss='softmax', pretrained=True, **kwargs):
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model = Xception(num_classes, loss, fc_dims=None, dropout_p=None, **kwargs)
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if pretrained:
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model_url = pretrained_settings['xception']['imagenet']['url']
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init_pretrained_weights(model, model_url)
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return model
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