345 lines
12 KiB
Python
345 lines
12 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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__all__ = ['osnet_x1_0', 'osnet_x0_75', 'osnet_x0_5', 'osnet_x0_25', 'osnet_ibn_x1_0']
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import torch
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from torch import nn
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from torch.nn import functional as F
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import torchvision
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##########
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# Basic layers
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##########
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class ConvLayer(nn.Module):
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"""Convolution layer (conv + bn + relu)."""
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def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, groups=1, IN=False):
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super(ConvLayer, self).__init__()
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self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride,
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padding=padding, bias=False, groups=groups)
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if IN:
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self.bn = nn.InstanceNorm2d(out_channels, affine=True)
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else:
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self.bn = nn.BatchNorm2d(out_channels)
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self.relu = nn.ReLU(inplace=True)
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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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x = self.relu(x)
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return x
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class Conv1x1(nn.Module):
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"""1x1 convolution + bn + relu."""
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def __init__(self, in_channels, out_channels, stride=1, groups=1):
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super(Conv1x1, self).__init__()
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self.conv = nn.Conv2d(in_channels, out_channels, 1, stride=stride, padding=0,
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bias=False, groups=groups)
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self.bn = nn.BatchNorm2d(out_channels)
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self.relu = nn.ReLU(inplace=True)
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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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x = self.relu(x)
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return x
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class Conv1x1Linear(nn.Module):
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"""1x1 convolution + bn (w/o non-linearity)."""
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def __init__(self, in_channels, out_channels, stride=1):
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super(Conv1x1Linear, self).__init__()
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self.conv = nn.Conv2d(in_channels, out_channels, 1, stride=stride, padding=0, bias=False)
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self.bn = nn.BatchNorm2d(out_channels)
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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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return x
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class Conv3x3(nn.Module):
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"""3x3 convolution + bn + relu."""
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def __init__(self, in_channels, out_channels, stride=1, groups=1):
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super(Conv3x3, self).__init__()
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self.conv = nn.Conv2d(in_channels, out_channels, 3, stride=stride, padding=1,
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bias=False, groups=groups)
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self.bn = nn.BatchNorm2d(out_channels)
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self.relu = nn.ReLU(inplace=True)
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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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x = self.relu(x)
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return x
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class LightConv3x3(nn.Module):
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"""Lightweight 3x3 convolution.
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1x1 (linear) + dw 3x3 (nonlinear).
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"""
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def __init__(self, in_channels, out_channels):
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super(LightConv3x3, self).__init__()
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self.conv1 = nn.Conv2d(in_channels, out_channels, 1, stride=1, padding=0, bias=False)
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self.conv2 = nn.Conv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=False, groups=out_channels)
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self.bn = nn.BatchNorm2d(out_channels)
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self.relu = nn.ReLU(inplace=True)
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def forward(self, x):
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x = self.conv1(x)
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x = self.conv2(x)
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x = self.bn(x)
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x = self.relu(x)
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return x
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##########
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# Building blocks for omni-scale feature learning
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##########
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class ChannelGate(nn.Module):
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"""A mini-network that generates channel-wise gates conditioned on input tensor."""
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def __init__(self, in_channels, num_gates=None, return_gates=False,
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gate_activation='sigmoid', reduction=16, layer_norm=False):
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super(ChannelGate, self).__init__()
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if num_gates is None:
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num_gates = in_channels
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self.return_gates = return_gates
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self.global_avgpool = nn.AdaptiveAvgPool2d(1)
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self.fc1 = nn.Conv2d(in_channels, in_channels//reduction, kernel_size=1, bias=True, padding=0)
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self.norm1 = None
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if layer_norm:
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self.norm1 = nn.LayerNorm((in_channels//reduction, 1, 1))
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self.relu = nn.ReLU(inplace=True)
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self.fc2 = nn.Conv2d(in_channels//reduction, num_gates, kernel_size=1, bias=True, padding=0)
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if gate_activation == 'sigmoid':
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self.gate_activation = nn.Sigmoid()
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elif gate_activation == 'relu':
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self.gate_activation = nn.ReLU(inplace=True)
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elif gate_activation == 'linear':
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self.gate_activation = None
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else:
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raise RuntimeError("Unknown gate activation: {}".format(gate_activation))
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def forward(self, x):
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input = x
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x = self.global_avgpool(x)
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x = self.fc1(x)
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if self.norm1 is not None:
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x = self.norm1(x)
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x = self.relu(x)
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x = self.fc2(x)
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if self.gate_activation is not None:
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x = self.gate_activation(x)
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if self.return_gates:
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return x
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return input * x
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class OSBlock(nn.Module):
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"""Omni-scale feature learning block."""
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def __init__(self, in_channels, out_channels, IN=False, bottleneck_reduction=4, **kwargs):
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super(OSBlock, self).__init__()
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mid_channels = out_channels // bottleneck_reduction
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self.conv1 = Conv1x1(in_channels, mid_channels)
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self.conv2a = LightConv3x3(mid_channels, mid_channels)
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self.conv2b = nn.Sequential(
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LightConv3x3(mid_channels, mid_channels),
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LightConv3x3(mid_channels, mid_channels),
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)
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self.conv2c = nn.Sequential(
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LightConv3x3(mid_channels, mid_channels),
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LightConv3x3(mid_channels, mid_channels),
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LightConv3x3(mid_channels, mid_channels),
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)
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self.conv2d = nn.Sequential(
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LightConv3x3(mid_channels, mid_channels),
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LightConv3x3(mid_channels, mid_channels),
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LightConv3x3(mid_channels, mid_channels),
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LightConv3x3(mid_channels, mid_channels),
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)
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self.gate = ChannelGate(mid_channels)
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self.conv3 = Conv1x1Linear(mid_channels, out_channels)
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self.downsample = None
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if in_channels != out_channels:
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self.downsample = Conv1x1Linear(in_channels, out_channels)
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self.IN = None
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if IN:
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self.IN = nn.InstanceNorm2d(out_channels, affine=True)
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def forward(self, x):
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residual = x
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x1 = self.conv1(x)
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x2a = self.conv2a(x1)
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x2b = self.conv2b(x1)
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x2c = self.conv2c(x1)
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x2d = self.conv2d(x1)
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x2 = self.gate(x2a) + self.gate(x2b) + self.gate(x2c) + self.gate(x2d)
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x3 = self.conv3(x2)
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if self.downsample is not None:
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residual = self.downsample(residual)
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out = x3 + residual
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if self.IN is not None:
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out = self.IN(out)
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return F.relu(out)
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##########
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# Network architecture
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##########
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class OSNet(nn.Module):
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"""Omni-Scale Network.
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Reference:
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- Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ArXiv preprint, 2019.
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https://arxiv.org/abs/1905.00953
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"""
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def __init__(self, num_classes, blocks, layers, channels, feature_dim=512, loss='softmax', IN=False, **kwargs):
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super(OSNet, self).__init__()
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num_blocks = len(blocks)
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assert num_blocks == len(layers)
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assert num_blocks == len(channels) - 1
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self.loss = loss
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# convolutional backbone
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self.conv1 = ConvLayer(3, channels[0], 7, stride=2, padding=3, IN=IN)
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self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
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self.conv2 = self._make_layer(blocks[0], layers[0], channels[0], channels[1], reduce_spatial_size=True, IN=IN)
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self.conv3 = self._make_layer(blocks[1], layers[1], channels[1], channels[2], reduce_spatial_size=True)
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self.conv4 = self._make_layer(blocks[2], layers[2], channels[2], channels[3], reduce_spatial_size=False)
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self.conv5 = Conv1x1(channels[3], channels[3])
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self.global_avgpool = nn.AdaptiveAvgPool2d(1)
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# fully connected layer
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self.fc = self._construct_fc_layer(feature_dim, channels[3], dropout_p=None)
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# identity classification layer
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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 _make_layer(self, block, layer, in_channels, out_channels, reduce_spatial_size, IN=False):
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layers = []
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layers.append(block(in_channels, out_channels, IN=IN))
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for i in range(1, layer):
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layers.append(block(out_channels, out_channels, IN=IN))
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if reduce_spatial_size:
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layers.append(
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nn.Sequential(
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Conv1x1(out_channels, out_channels),
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nn.AvgPool2d(2, stride=2)
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)
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)
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return nn.Sequential(*layers)
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def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
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if fc_dims is None or fc_dims<0:
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self.feature_dim = input_dim
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return None
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if isinstance(fc_dims, int):
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fc_dims = [fc_dims]
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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_(m.weight, mode='fan_out', nonlinearity='relu')
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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, x):
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x = self.conv1(x)
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x = self.maxpool(x)
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x = self.conv2(x)
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x = self.conv3(x)
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x = self.conv4(x)
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x = self.conv5(x)
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return x
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def forward(self, x):
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x = self.featuremaps(x)
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v = self.global_avgpool(x)
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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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##########
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# Instantiation
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##########
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def osnet_x1_0(num_classes=1000, loss='softmax', **kwargs):
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# standard size (width x1.0)
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return OSNet(num_classes, blocks=[OSBlock, OSBlock, OSBlock], layers=[2, 2, 2],
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channels=[64, 256, 384, 512], loss=loss, **kwargs)
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def osnet_x0_75(num_classes=1000, loss='softmax', **kwargs):
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# medium size (width x0.75)
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return OSNet(num_classes, blocks=[OSBlock, OSBlock, OSBlock], layers=[2, 2, 2],
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channels=[48, 192, 288, 384], loss=loss, **kwargs)
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def osnet_x0_5(num_classes=1000, loss='softmax', **kwargs):
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# tiny size (width x0.5)
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return OSNet(num_classes, blocks=[OSBlock, OSBlock, OSBlock], layers=[2, 2, 2],
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channels=[32, 128, 192, 256], loss=loss, **kwargs)
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def osnet_x0_25(num_classes=1000, loss='softmax', **kwargs):
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# very tiny size (width x0.25)
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return OSNet(num_classes, blocks=[OSBlock, OSBlock, OSBlock], layers=[2, 2, 2],
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channels=[16, 64, 96, 128], loss=loss, **kwargs)
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def osnet_ibn_x1_0(num_classes=1000, loss='softmax', **kwargs):
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# standard size (width x1.0) + IBN layer
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# Ref: Pan et al. Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net. ECCV, 2018.
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return OSNet(num_classes, blocks=[OSBlock, OSBlock, OSBlock], layers=[2, 2, 2],
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channels=[64, 256, 384, 512], loss=loss, IN=True, **kwargs)
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