from __future__ import absolute_import
from __future__ import division
__all__ = ['osnet_x1_0', 'osnet_x0_75', 'osnet_x0_5', 'osnet_x0_25', 'osnet_ibn_x1_0']
import torch
from torch import nn
from torch.nn import functional as F
import torchvision
##########
# Basic layers
##########
class ConvLayer(nn.Module):
"""Convolution layer (conv + bn + relu)."""
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, groups=1, IN=False):
super(ConvLayer, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride,
padding=padding, bias=False, groups=groups)
if IN:
self.bn = nn.InstanceNorm2d(out_channels, affine=True)
else:
self.bn = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
class Conv1x1(nn.Module):
"""1x1 convolution + bn + relu."""
def __init__(self, in_channels, out_channels, stride=1, groups=1):
super(Conv1x1, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, 1, stride=stride, padding=0,
bias=False, groups=groups)
self.bn = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
class Conv1x1Linear(nn.Module):
"""1x1 convolution + bn (w/o non-linearity)."""
def __init__(self, in_channels, out_channels, stride=1):
super(Conv1x1Linear, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, 1, stride=stride, padding=0, bias=False)
self.bn = nn.BatchNorm2d(out_channels)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return x
class Conv3x3(nn.Module):
"""3x3 convolution + bn + relu."""
def __init__(self, in_channels, out_channels, stride=1, groups=1):
super(Conv3x3, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, 3, stride=stride, padding=1,
bias=False, groups=groups)
self.bn = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.relu(x)
return x
class LightConv3x3(nn.Module):
"""Lightweight 3x3 convolution.
1x1 (linear) + dw 3x3 (nonlinear).
"""
def __init__(self, in_channels, out_channels):
super(LightConv3x3, self).__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, 1, stride=1, padding=0, bias=False)
self.conv2 = nn.Conv2d(out_channels, out_channels, 3, stride=1, padding=1, bias=False, groups=out_channels)
self.bn = nn.BatchNorm2d(out_channels)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.bn(x)
x = self.relu(x)
return x
##########
# Building blocks for omni-scale feature learning
##########
class ChannelGate(nn.Module):
"""A mini-network that generates channel-wise gates conditioned on input tensor."""
def __init__(self, in_channels, num_gates=None, return_gates=False,
gate_activation='sigmoid', reduction=16, layer_norm=False):
super(ChannelGate, self).__init__()
if num_gates is None:
num_gates = in_channels
self.return_gates = return_gates
self.global_avgpool = nn.AdaptiveAvgPool2d(1)
self.fc1 = nn.Conv2d(in_channels, in_channels//reduction, kernel_size=1, bias=True, padding=0)
self.norm1 = None
if layer_norm:
self.norm1 = nn.LayerNorm((in_channels//reduction, 1, 1))
self.relu = nn.ReLU(inplace=True)
self.fc2 = nn.Conv2d(in_channels//reduction, num_gates, kernel_size=1, bias=True, padding=0)
if gate_activation == 'sigmoid':
self.gate_activation = nn.Sigmoid()
elif gate_activation == 'relu':
self.gate_activation = nn.ReLU(inplace=True)
elif gate_activation == 'linear':
self.gate_activation = None
else:
raise RuntimeError("Unknown gate activation: {}".format(gate_activation))
def forward(self, x):
input = x
x = self.global_avgpool(x)
x = self.fc1(x)
if self.norm1 is not None:
x = self.norm1(x)
x = self.relu(x)
x = self.fc2(x)
if self.gate_activation is not None:
x = self.gate_activation(x)
if self.return_gates:
return x
return input * x
class OSBlock(nn.Module):
"""Omni-scale feature learning block."""
def __init__(self, in_channels, out_channels, IN=False, bottleneck_reduction=4, **kwargs):
super(OSBlock, self).__init__()
mid_channels = out_channels // bottleneck_reduction
self.conv1 = Conv1x1(in_channels, mid_channels)
self.conv2a = LightConv3x3(mid_channels, mid_channels)
self.conv2b = nn.Sequential(
LightConv3x3(mid_channels, mid_channels),
LightConv3x3(mid_channels, mid_channels),
)
self.conv2c = nn.Sequential(
LightConv3x3(mid_channels, mid_channels),
LightConv3x3(mid_channels, mid_channels),
LightConv3x3(mid_channels, mid_channels),
)
self.conv2d = nn.Sequential(
LightConv3x3(mid_channels, mid_channels),
LightConv3x3(mid_channels, mid_channels),
LightConv3x3(mid_channels, mid_channels),
LightConv3x3(mid_channels, mid_channels),
)
self.gate = ChannelGate(mid_channels)
self.conv3 = Conv1x1Linear(mid_channels, out_channels)
self.downsample = None
if in_channels != out_channels:
self.downsample = Conv1x1Linear(in_channels, out_channels)
self.IN = None
if IN:
self.IN = nn.InstanceNorm2d(out_channels, affine=True)
def forward(self, x):
residual = x
x1 = self.conv1(x)
x2a = self.conv2a(x1)
x2b = self.conv2b(x1)
x2c = self.conv2c(x1)
x2d = self.conv2d(x1)
x2 = self.gate(x2a) + self.gate(x2b) + self.gate(x2c) + self.gate(x2d)
x3 = self.conv3(x2)
if self.downsample is not None:
residual = self.downsample(residual)
out = x3 + residual
if self.IN is not None:
out = self.IN(out)
return F.relu(out)
##########
# Network architecture
##########
[docs]class OSNet(nn.Module):
"""Omni-Scale Network.
Reference:
- Zhou et al. Omni-Scale Feature Learning for Person Re-Identification. ArXiv preprint, 2019.
https://arxiv.org/abs/1905.00953
"""
def __init__(self, num_classes, blocks, layers, channels, feature_dim=512, loss='softmax', IN=False, **kwargs):
super(OSNet, self).__init__()
num_blocks = len(blocks)
assert num_blocks == len(layers)
assert num_blocks == len(channels) - 1
self.loss = loss
# convolutional backbone
self.conv1 = ConvLayer(3, channels[0], 7, stride=2, padding=3, IN=IN)
self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
self.conv2 = self._make_layer(blocks[0], layers[0], channels[0], channels[1], reduce_spatial_size=True, IN=IN)
self.conv3 = self._make_layer(blocks[1], layers[1], channels[1], channels[2], reduce_spatial_size=True)
self.conv4 = self._make_layer(blocks[2], layers[2], channels[2], channels[3], reduce_spatial_size=False)
self.conv5 = Conv1x1(channels[3], channels[3])
self.global_avgpool = nn.AdaptiveAvgPool2d(1)
# fully connected layer
self.fc = self._construct_fc_layer(feature_dim, channels[3], dropout_p=None)
# identity classification layer
self.classifier = nn.Linear(self.feature_dim, num_classes)
self._init_params()
def _make_layer(self, block, layer, in_channels, out_channels, reduce_spatial_size, IN=False):
layers = []
layers.append(block(in_channels, out_channels, IN=IN))
for i in range(1, layer):
layers.append(block(out_channels, out_channels, IN=IN))
if reduce_spatial_size:
layers.append(
nn.Sequential(
Conv1x1(out_channels, out_channels),
nn.AvgPool2d(2, stride=2)
)
)
return nn.Sequential(*layers)
def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
if fc_dims is None or fc_dims<0:
self.feature_dim = input_dim
return None
if isinstance(fc_dims, int):
fc_dims = [fc_dims]
layers = []
for dim in fc_dims:
layers.append(nn.Linear(input_dim, dim))
layers.append(nn.BatchNorm1d(dim))
layers.append(nn.ReLU(inplace=True))
if dropout_p is not None:
layers.append(nn.Dropout(p=dropout_p))
input_dim = dim
self.feature_dim = fc_dims[-1]
return nn.Sequential(*layers)
def _init_params(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm1d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def featuremaps(self, x):
x = self.conv1(x)
x = self.maxpool(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
x = self.conv5(x)
return x
def forward(self, x):
x = self.featuremaps(x)
v = self.global_avgpool(x)
v = v.view(v.size(0), -1)
if self.fc is not None:
v = self.fc(v)
if not self.training:
return v
y = self.classifier(v)
if self.loss == 'softmax':
return y
elif self.loss == 'triplet':
return y, v
else:
raise KeyError("Unsupported loss: {}".format(self.loss))
##########
# Instantiation
##########
def osnet_x1_0(num_classes=1000, loss='softmax', **kwargs):
# standard size (width x1.0)
return OSNet(num_classes, blocks=[OSBlock, OSBlock, OSBlock], layers=[2, 2, 2],
channels=[64, 256, 384, 512], loss=loss, **kwargs)
def osnet_x0_75(num_classes=1000, loss='softmax', **kwargs):
# medium size (width x0.75)
return OSNet(num_classes, blocks=[OSBlock, OSBlock, OSBlock], layers=[2, 2, 2],
channels=[48, 192, 288, 384], loss=loss, **kwargs)
def osnet_x0_5(num_classes=1000, loss='softmax', **kwargs):
# tiny size (width x0.5)
return OSNet(num_classes, blocks=[OSBlock, OSBlock, OSBlock], layers=[2, 2, 2],
channels=[32, 128, 192, 256], loss=loss, **kwargs)
def osnet_x0_25(num_classes=1000, loss='softmax', **kwargs):
# very tiny size (width x0.25)
return OSNet(num_classes, blocks=[OSBlock, OSBlock, OSBlock], layers=[2, 2, 2],
channels=[16, 64, 96, 128], loss=loss, **kwargs)
def osnet_ibn_x1_0(num_classes=1000, loss='softmax', **kwargs):
# standard size (width x1.0) + IBN layer
# Ref: Pan et al. Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net. ECCV, 2018.
return OSNet(num_classes, blocks=[OSBlock, OSBlock, OSBlock], layers=[2, 2, 2],
channels=[64, 256, 384, 512], loss=loss, IN=True, **kwargs)