from __future__ import division, absolute_import
import warnings
import torch
from torch import nn
from torch.nn import functional as F
__all__ = [
'osnet_ain_x1_0', 'osnet_ain_x0_75', 'osnet_ain_x0_5', 'osnet_ain_x0_25'
]
pretrained_urls = {
'osnet_ain_x1_0':
'https://drive.google.com/uc?id=1-CaioD9NaqbHK_kzSMW8VE4_3KcsRjEo',
'osnet_ain_x0_75':
'https://drive.google.com/uc?id=1apy0hpsMypqstfencdH-jKIUEFOW4xoM',
'osnet_ain_x0_5':
'https://drive.google.com/uc?id=1KusKvEYyKGDTUBVRxRiz55G31wkihB6l',
'osnet_ain_x0_25':
'https://drive.google.com/uc?id=1SxQt2AvmEcgWNhaRb2xC4rP6ZwVDP0Wt'
}
##########
# 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()
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return self.relu(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()
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return self.relu(x)
class Conv1x1Linear(nn.Module):
"""1x1 convolution + bn (w/o non-linearity)."""
def __init__(self, in_channels, out_channels, stride=1, bn=True):
super(Conv1x1Linear, self).__init__()
self.conv = nn.Conv2d(
in_channels, out_channels, 1, stride=stride, padding=0, bias=False
)
self.bn = None
if bn:
self.bn = nn.BatchNorm2d(out_channels)
def forward(self, x):
x = self.conv(x)
if self.bn is not None:
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()
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return self.relu(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()
def forward(self, x):
x = self.conv1(x)
x = self.conv2(x)
x = self.bn(x)
return self.relu(x)
class LightConvStream(nn.Module):
"""Lightweight convolution stream."""
def __init__(self, in_channels, out_channels, depth):
super(LightConvStream, self).__init__()
assert depth >= 1, 'depth must be equal to or larger than 1, but got {}'.format(
depth
)
layers = []
layers += [LightConv3x3(in_channels, out_channels)]
for i in range(depth - 1):
layers += [LightConv3x3(out_channels, out_channels)]
self.layers = nn.Sequential(*layers)
def forward(self, x):
return self.layers(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()
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()
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, reduction=4, T=4, **kwargs):
super(OSBlock, self).__init__()
assert T >= 1
assert out_channels >= reduction and out_channels % reduction == 0
mid_channels = out_channels // reduction
self.conv1 = Conv1x1(in_channels, mid_channels)
self.conv2 = nn.ModuleList()
for t in range(1, T + 1):
self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
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)
def forward(self, x):
identity = x
x1 = self.conv1(x)
x2 = 0
for conv2_t in self.conv2:
x2_t = conv2_t(x1)
x2 = x2 + self.gate(x2_t)
x3 = self.conv3(x2)
if self.downsample is not None:
identity = self.downsample(identity)
out = x3 + identity
return F.relu(out)
class OSBlockINin(nn.Module):
"""Omni-scale feature learning block with instance normalization."""
def __init__(self, in_channels, out_channels, reduction=4, T=4, **kwargs):
super(OSBlockINin, self).__init__()
assert T >= 1
assert out_channels >= reduction and out_channels % reduction == 0
mid_channels = out_channels // reduction
self.conv1 = Conv1x1(in_channels, mid_channels)
self.conv2 = nn.ModuleList()
for t in range(1, T + 1):
self.conv2 += [LightConvStream(mid_channels, mid_channels, t)]
self.gate = ChannelGate(mid_channels)
self.conv3 = Conv1x1Linear(mid_channels, out_channels, bn=False)
self.downsample = None
if in_channels != out_channels:
self.downsample = Conv1x1Linear(in_channels, out_channels)
self.IN = nn.InstanceNorm2d(out_channels, affine=True)
def forward(self, x):
identity = x
x1 = self.conv1(x)
x2 = 0
for conv2_t in self.conv2:
x2_t = conv2_t(x1)
x2 = x2 + self.gate(x2_t)
x3 = self.conv3(x2)
x3 = self.IN(x3) # IN inside residual
if self.downsample is not None:
identity = self.downsample(identity)
out = x3 + identity
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. ICCV, 2019.
- Zhou et al. Learning Generalisable Omni-Scale Representations
for Person Re-Identification. TPAMI, 2021.
"""
def __init__(
self,
num_classes,
blocks,
layers,
channels,
feature_dim=512,
loss='softmax',
conv1_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
self.feature_dim = feature_dim
# convolutional backbone
self.conv1 = ConvLayer(
3, channels[0], 7, stride=2, padding=3, IN=conv1_IN
)
self.maxpool = nn.MaxPool2d(3, stride=2, padding=1)
self.conv2 = self._make_layer(
blocks[0], layers[0], channels[0], channels[1]
)
self.pool2 = nn.Sequential(
Conv1x1(channels[1], channels[1]), nn.AvgPool2d(2, stride=2)
)
self.conv3 = self._make_layer(
blocks[1], layers[1], channels[1], channels[2]
)
self.pool3 = nn.Sequential(
Conv1x1(channels[2], channels[2]), nn.AvgPool2d(2, stride=2)
)
self.conv4 = self._make_layer(
blocks[2], layers[2], channels[2], channels[3]
)
self.conv5 = Conv1x1(channels[3], channels[3])
self.global_avgpool = nn.AdaptiveAvgPool2d(1)
# fully connected layer
self.fc = self._construct_fc_layer(
self.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, blocks, layer, in_channels, out_channels):
layers = []
layers += [blocks[0](in_channels, out_channels)]
for i in range(1, len(blocks)):
layers += [blocks[i](out_channels, out_channels)]
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())
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.InstanceNorm2d):
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.pool2(x)
x = self.conv3(x)
x = self.pool3(x)
x = self.conv4(x)
x = self.conv5(x)
return x
def forward(self, x, return_featuremaps=False):
x = self.featuremaps(x)
if return_featuremaps:
return 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))
def init_pretrained_weights(model, key=''):
"""Initializes model with pretrained weights.
Layers that don't match with pretrained layers in name or size are kept unchanged.
"""
import os
import errno
import gdown
from collections import OrderedDict
def _get_torch_home():
ENV_TORCH_HOME = 'TORCH_HOME'
ENV_XDG_CACHE_HOME = 'XDG_CACHE_HOME'
DEFAULT_CACHE_DIR = '~/.cache'
torch_home = os.path.expanduser(
os.getenv(
ENV_TORCH_HOME,
os.path.join(
os.getenv(ENV_XDG_CACHE_HOME, DEFAULT_CACHE_DIR), 'torch'
)
)
)
return torch_home
torch_home = _get_torch_home()
model_dir = os.path.join(torch_home, 'checkpoints')
try:
os.makedirs(model_dir)
except OSError as e:
if e.errno == errno.EEXIST:
# Directory already exists, ignore.
pass
else:
# Unexpected OSError, re-raise.
raise
filename = key + '_imagenet.pth'
cached_file = os.path.join(model_dir, filename)
if not os.path.exists(cached_file):
gdown.download(pretrained_urls[key], cached_file, quiet=False)
state_dict = torch.load(cached_file)
model_dict = model.state_dict()
new_state_dict = OrderedDict()
matched_layers, discarded_layers = [], []
for k, v in state_dict.items():
if k.startswith('module.'):
k = k[7:] # discard module.
if k in model_dict and model_dict[k].size() == v.size():
new_state_dict[k] = v
matched_layers.append(k)
else:
discarded_layers.append(k)
model_dict.update(new_state_dict)
model.load_state_dict(model_dict)
if len(matched_layers) == 0:
warnings.warn(
'The pretrained weights from "{}" cannot be loaded, '
'please check the key names manually '
'(** ignored and continue **)'.format(cached_file)
)
else:
print(
'Successfully loaded imagenet pretrained weights from "{}"'.
format(cached_file)
)
if len(discarded_layers) > 0:
print(
'** The following layers are discarded '
'due to unmatched keys or layer size: {}'.
format(discarded_layers)
)
##########
# Instantiation
##########
def osnet_ain_x1_0(
num_classes=1000, pretrained=True, loss='softmax', **kwargs
):
model = OSNet(
num_classes,
blocks=[
[OSBlockINin, OSBlockINin], [OSBlock, OSBlockINin],
[OSBlockINin, OSBlock]
],
layers=[2, 2, 2],
channels=[64, 256, 384, 512],
loss=loss,
conv1_IN=True,
**kwargs
)
if pretrained:
init_pretrained_weights(model, key='osnet_ain_x1_0')
return model
def osnet_ain_x0_75(
num_classes=1000, pretrained=True, loss='softmax', **kwargs
):
model = OSNet(
num_classes,
blocks=[
[OSBlockINin, OSBlockINin], [OSBlock, OSBlockINin],
[OSBlockINin, OSBlock]
],
layers=[2, 2, 2],
channels=[48, 192, 288, 384],
loss=loss,
conv1_IN=True,
**kwargs
)
if pretrained:
init_pretrained_weights(model, key='osnet_ain_x0_75')
return model
def osnet_ain_x0_5(
num_classes=1000, pretrained=True, loss='softmax', **kwargs
):
model = OSNet(
num_classes,
blocks=[
[OSBlockINin, OSBlockINin], [OSBlock, OSBlockINin],
[OSBlockINin, OSBlock]
],
layers=[2, 2, 2],
channels=[32, 128, 192, 256],
loss=loss,
conv1_IN=True,
**kwargs
)
if pretrained:
init_pretrained_weights(model, key='osnet_ain_x0_5')
return model
def osnet_ain_x0_25(
num_classes=1000, pretrained=True, loss='softmax', **kwargs
):
model = OSNet(
num_classes,
blocks=[
[OSBlockINin, OSBlockINin], [OSBlock, OSBlockINin],
[OSBlockINin, OSBlock]
],
layers=[2, 2, 2],
channels=[16, 64, 96, 128],
loss=loss,
conv1_IN=True,
**kwargs
)
if pretrained:
init_pretrained_weights(model, key='osnet_ain_x0_25')
return model