Source code for torchreid.models.densenet
-from __future__ import absolute_import
+"""
+Code source: https://github.com/pytorch/vision
+"""
+from __future__ import absolute_import
from __future__ import division
__all__ = ['densenet121', 'densenet169', 'densenet201', 'densenet161', 'densenet121_fc512']
diff --git a/_modules/torchreid/models/hacnn.html b/_modules/torchreid/models/hacnn.html
index fd21a28..511a4b1 100644
--- a/_modules/torchreid/models/hacnn.html
+++ b/_modules/torchreid/models/hacnn.html
@@ -8,7 +8,7 @@
- torchreid.models.hacnn — torchreid 0.7.6 documentation
+ torchreid.models.hacnn — torchreid 0.7.7 documentation
@@ -59,7 +59,7 @@
- 0.7.6
+ 0.7.7
diff --git a/_modules/torchreid/models/inceptionresnetv2.html b/_modules/torchreid/models/inceptionresnetv2.html
index d0a4fe1..a19f22f 100644
--- a/_modules/torchreid/models/inceptionresnetv2.html
+++ b/_modules/torchreid/models/inceptionresnetv2.html
@@ -8,7 +8,7 @@
- torchreid.models.inceptionresnetv2 — torchreid 0.7.6 documentation
+ torchreid.models.inceptionresnetv2 — torchreid 0.7.7 documentation
@@ -59,7 +59,7 @@
- 0.7.6
+ 0.7.7
diff --git a/_modules/torchreid/models/inceptionv4.html b/_modules/torchreid/models/inceptionv4.html
index 3b185f4..5612e02 100644
--- a/_modules/torchreid/models/inceptionv4.html
+++ b/_modules/torchreid/models/inceptionv4.html
@@ -8,7 +8,7 @@
- torchreid.models.inceptionv4 — torchreid 0.7.6 documentation
+ torchreid.models.inceptionv4 — torchreid 0.7.7 documentation
@@ -59,7 +59,7 @@
- 0.7.6
+ 0.7.7
diff --git a/_modules/torchreid/models/mlfn.html b/_modules/torchreid/models/mlfn.html
index 345b606..edad60e 100644
--- a/_modules/torchreid/models/mlfn.html
+++ b/_modules/torchreid/models/mlfn.html
@@ -8,7 +8,7 @@
- torchreid.models.mlfn — torchreid 0.7.6 documentation
+ torchreid.models.mlfn — torchreid 0.7.7 documentation
@@ -59,7 +59,7 @@
- 0.7.6
+ 0.7.7
diff --git a/_modules/torchreid/models/mobilenetv2.html b/_modules/torchreid/models/mobilenetv2.html
index 4283987..67ea4a1 100644
--- a/_modules/torchreid/models/mobilenetv2.html
+++ b/_modules/torchreid/models/mobilenetv2.html
@@ -8,7 +8,7 @@
- torchreid.models.mobilenetv2 — torchreid 0.7.6 documentation
+ torchreid.models.mobilenetv2 — torchreid 0.7.7 documentation
@@ -59,7 +59,7 @@
- 0.7.6
+ 0.7.7
diff --git a/_modules/torchreid/models/mudeep.html b/_modules/torchreid/models/mudeep.html
index 64555b8..78ebb2e 100644
--- a/_modules/torchreid/models/mudeep.html
+++ b/_modules/torchreid/models/mudeep.html
@@ -8,7 +8,7 @@
- torchreid.models.mudeep — torchreid 0.7.6 documentation
+ torchreid.models.mudeep — torchreid 0.7.7 documentation
@@ -59,7 +59,7 @@
- 0.7.6
+ 0.7.7
diff --git a/_modules/torchreid/models/nasnet.html b/_modules/torchreid/models/nasnet.html
index c6e3af3..44edac2 100644
--- a/_modules/torchreid/models/nasnet.html
+++ b/_modules/torchreid/models/nasnet.html
@@ -8,7 +8,7 @@
- torchreid.models.nasnet — torchreid 0.7.6 documentation
+ torchreid.models.nasnet — torchreid 0.7.7 documentation
@@ -59,7 +59,7 @@
- 0.7.6
+ 0.7.7
diff --git a/_modules/torchreid/models/pcb.html b/_modules/torchreid/models/pcb.html
index 3ef2582..3073829 100644
--- a/_modules/torchreid/models/pcb.html
+++ b/_modules/torchreid/models/pcb.html
@@ -8,7 +8,7 @@
- torchreid.models.pcb — torchreid 0.7.6 documentation
+ torchreid.models.pcb — torchreid 0.7.7 documentation
@@ -59,7 +59,7 @@
- 0.7.6
+ 0.7.7
diff --git a/_modules/torchreid/models/resnet.html b/_modules/torchreid/models/resnet.html
index 99e3706..45418de 100644
--- a/_modules/torchreid/models/resnet.html
+++ b/_modules/torchreid/models/resnet.html
@@ -8,7 +8,7 @@
- torchreid.models.resnet — torchreid 0.7.6 documentation
+ torchreid.models.resnet — torchreid 0.7.7 documentation
@@ -59,7 +59,7 @@
- 0.7.6
+ 0.7.7
@@ -165,10 +165,14 @@
Source code for torchreid.models.resnet
-from __future__ import absolute_import
+"""
+Code source: https://github.com/pytorch/vision
+"""
+from __future__ import absolute_import
from __future__ import division
-__all__ = ['resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnet50_fc512']
+__all__ = ['resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnext50_32x4d',
+ 'resnext101_32x8d', 'resnet50_fc512']
import torch
from torch import nn
@@ -183,30 +187,45 @@
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+ 'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
+ 'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
}
-def conv3x3(in_planes, out_planes, stride=1):
+def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
+ padding=dilation, groups=groups, bias=False, dilation=dilation)
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+ """1x1 convolution"""
+ return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
class BasicBlock(nn.Module):
expansion = 1
- def __init__(self, inplanes, planes, stride=1, downsample=None):
+ def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
+ base_width=64, dilation=1, norm_layer=None):
super(BasicBlock, self).__init__()
+ if norm_layer is None:
+ norm_layer = nn.BatchNorm2d
+ if groups != 1 or base_width != 64:
+ raise ValueError('BasicBlock only supports groups=1 and base_width=64')
+ if dilation > 1:
+ raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
+ # Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv3x3(inplanes, planes, stride)
- self.bn1 = nn.BatchNorm2d(planes)
+ self.bn1 = norm_layer(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
- self.bn2 = nn.BatchNorm2d(planes)
+ self.bn2 = norm_layer(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
- residual = x
+ identity = x
out = self.conv1(x)
out = self.bn1(out)
@@ -216,9 +235,9 @@
out = self.bn2(out)
if self.downsample is not None:
- residual = self.downsample(x)
+ identity = self.downsample(x)
- out += residual
+ out += identity
out = self.relu(out)
return out
@@ -227,21 +246,25 @@
class Bottleneck(nn.Module):
expansion = 4
- def __init__(self, inplanes, planes, stride=1, downsample=None):
+ def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
+ base_width=64, dilation=1, norm_layer=None):
super(Bottleneck, self).__init__()
- self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
- self.bn1 = nn.BatchNorm2d(planes)
- self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
- padding=1, bias=False)
- self.bn2 = nn.BatchNorm2d(planes)
- self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
- self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+ if norm_layer is None:
+ norm_layer = nn.BatchNorm2d
+ width = int(planes * (base_width / 64.)) * groups
+ # Both self.conv2 and self.downsample layers downsample the input when stride != 1
+ self.conv1 = conv1x1(inplanes, width)
+ self.bn1 = norm_layer(width)
+ self.conv2 = conv3x3(width, width, stride, groups, dilation)
+ self.bn2 = norm_layer(width)
+ self.conv3 = conv1x1(width, planes * self.expansion)
+ self.bn3 = norm_layer(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
- residual = x
+ identity = x
out = self.conv1(x)
out = self.bn1(out)
@@ -255,9 +278,9 @@
out = self.bn3(out)
if self.downsample is not None:
- residual = self.downsample(x)
+ identity = self.downsample(x)
- out += residual
+ out += identity
out = self.relu(out)
return out
@@ -267,7 +290,8 @@
"""Residual network.
Reference:
- He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
+ - He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
+ - Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.
Public keys:
- ``resnet18``: ResNet18.
@@ -275,49 +299,80 @@
- ``resnet50``: ResNet50.
- ``resnet101``: ResNet101.
- ``resnet152``: ResNet152.
+ - ``resnext50_32x4d``: ResNeXt50.
+ - ``resnext101_32x8d``: ResNeXt101.
- ``resnet50_fc512``: ResNet50 + FC.
"""
-
- def __init__(self, num_classes, loss, block, layers,
- last_stride=2,
- fc_dims=None,
- dropout_p=None,
- **kwargs):
- self.inplanes = 64
+
+ def __init__(self, num_classes, loss, block, layers, zero_init_residual=False,
+ groups=1, width_per_group=64, replace_stride_with_dilation=None,
+ norm_layer=None, last_stride=2, fc_dims=None, dropout_p=None, **kwargs):
super(ResNet, self).__init__()
+ if norm_layer is None:
+ norm_layer = nn.BatchNorm2d
+ self._norm_layer = norm_layer
self.loss = loss
self.feature_dim = 512 * block.expansion
-
- # backbone network
- self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
- self.bn1 = nn.BatchNorm2d(64)
+ self.inplanes = 64
+ self.dilation = 1
+ if replace_stride_with_dilation is None:
+ # each element in the tuple indicates if we should replace
+ # the 2x2 stride with a dilated convolution instead
+ replace_stride_with_dilation = [False, False, False]
+ if len(replace_stride_with_dilation) != 3:
+ raise ValueError("replace_stride_with_dilation should be None "
+ "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
+ self.groups = groups
+ self.base_width = width_per_group
+ self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
+ bias=False)
+ self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
- self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
- self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
- self.layer4 = self._make_layer(block, 512, layers[3], stride=last_stride)
-
- self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+ self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
+ dilate=replace_stride_with_dilation[0])
+ self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
+ dilate=replace_stride_with_dilation[1])
+ self.layer4 = self._make_layer(block, 512, layers[3], stride=last_stride,
+ dilate=replace_stride_with_dilation[2])
+ self.global_avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = self._construct_fc_layer(fc_dims, 512 * block.expansion, dropout_p)
self.classifier = nn.Linear(self.feature_dim, num_classes)
self._init_params()
- def _make_layer(self, block, planes, blocks, stride=1):
+ # Zero-initialize the last BN in each residual branch,
+ # so that the residual branch starts with zeros, and each residual block behaves like an identity.
+ # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
+ if zero_init_residual:
+ for m in self.modules():
+ if isinstance(m, Bottleneck):
+ nn.init.constant_(m.bn3.weight, 0)
+ elif isinstance(m, BasicBlock):
+ nn.init.constant_(m.bn2.weight, 0)
+
+ def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
+ norm_layer = self._norm_layer
downsample = None
+ previous_dilation = self.dilation
+ if dilate:
+ self.dilation *= stride
+ stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
- nn.Conv2d(self.inplanes, planes * block.expansion,
- kernel_size=1, stride=stride, bias=False),
- nn.BatchNorm2d(planes * block.expansion),
+ conv1x1(self.inplanes, planes * block.expansion, stride),
+ norm_layer(planes * block.expansion),
)
layers = []
- layers.append(block(self.inplanes, planes, stride, downsample))
+ layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
+ self.base_width, previous_dilation, norm_layer))
self.inplanes = planes * block.expansion
- for i in range(1, blocks):
- layers.append(block(self.inplanes, planes))
+ for _ in range(1, blocks):
+ layers.append(block(self.inplanes, planes, groups=self.groups,
+ base_width=self.base_width, dilation=self.dilation,
+ norm_layer=norm_layer))
return nn.Sequential(*layers)
@@ -409,17 +464,7 @@
model.load_state_dict(model_dict)
-"""
-Residual network configurations:
---
-resnet18: block=BasicBlock, layers=[2, 2, 2, 2]
-resnet34: block=BasicBlock, layers=[3, 4, 6, 3]
-resnet50: block=Bottleneck, layers=[3, 4, 6, 3]
-resnet101: block=Bottleneck, layers=[3, 4, 23, 3]
-resnet152: block=Bottleneck, layers=[3, 8, 36, 3]
-"""
-
-
+"""ResNet"""
def resnet18(num_classes, loss='softmax', pretrained=True, **kwargs):
model = ResNet(
num_classes=num_classes,
@@ -500,8 +545,45 @@
return model
+"""ResNeXt"""
+def resnext50_32x4d(num_classes, loss='softmax', pretrained=True, **kwargs):
+ model = ResNet(
+ num_classes=num_classes,
+ loss=loss,
+ block=Bottleneck,
+ layers=[3, 4, 6, 3],
+ last_stride=2,
+ fc_dims=None,
+ dropout_p=None,
+ groups=32,
+ width_per_group=4,
+ **kwargs
+ )
+ if pretrained:
+ init_pretrained_weights(model, model_urls['resnext50_32x4d'])
+ return model
+
+
+def resnext101_32x8d(num_classes, loss='softmax', pretrained=True, **kwargs):
+ model = ResNet(
+ num_classes=num_classes,
+ loss=loss,
+ block=Bottleneck,
+ layers=[3, 4, 23, 3],
+ last_stride=2,
+ fc_dims=None,
+ dropout_p=None,
+ groups=32,
+ width_per_group=8,
+ **kwargs
+ )
+ if pretrained:
+ init_pretrained_weights(model, model_urls['resnext101_32x8d'])
+ return model
+
+
"""
-resnet + fc
+ResNet + FC
"""
def resnet50_fc512(num_classes, loss='softmax', pretrained=True, **kwargs):
model = ResNet(
diff --git a/_modules/torchreid/models/resnetmid.html b/_modules/torchreid/models/resnetmid.html
index 0e60e6f..6412fb4 100644
--- a/_modules/torchreid/models/resnetmid.html
+++ b/_modules/torchreid/models/resnetmid.html
@@ -8,7 +8,7 @@
- torchreid.models.resnetmid — torchreid 0.7.6 documentation
+ torchreid.models.resnetmid — torchreid 0.7.7 documentation
@@ -59,7 +59,7 @@
- 0.7.6
+ 0.7.7
diff --git a/_modules/torchreid/models/resnext.html b/_modules/torchreid/models/resnext.html
deleted file mode 100644
index 336955c..0000000
--- a/_modules/torchreid/models/resnext.html
+++ /dev/null
@@ -1,447 +0,0 @@
-
-
-
-
-
-
-
-
-
-
- torchreid.models.resnext — torchreid 0.7.6 documentation
-
-
-
-
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-
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-
- Source code for torchreid.models.resnext
-from __future__ import absolute_import
-from __future__ import division
-
-__all__ = ['resnext50_32x4d', 'resnext50_32x4d_fc512']
-
-import math
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-import torchvision
-import torch.utils.model_zoo as model_zoo
-
-
-model_urls = {
- # top1 = 76.3
- 'resnext50_32x4d': 'http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/imagenet-pretrained/resnext50_32x4d-453b60f8.pth',
-}
-
-
-class ResNeXtBottleneck(nn.Module):
- expansion = 4
-
- def __init__(self, inplanes, planes, groups=32, base_width=4, stride=1, downsample=None):
- super(ResNeXtBottleneck, self).__init__()
- width = int(math.floor(planes * (base_width / 64.)) * groups)
- self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False, stride=1)
- self.bn1 = nn.BatchNorm2d(width)
- self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False)
- self.bn2 = nn.BatchNorm2d(width)
- self.conv3 = nn.Conv2d(width, planes * self.expansion, kernel_size=1, bias=False)
- self.bn3 = nn.BatchNorm2d(planes * self.expansion)
- self.relu = nn.ReLU(inplace=True)
- self.downsample = downsample
- self.stride = stride
-
- def forward(self, x):
- residual = x
-
- out = self.conv1(x)
- out = self.bn1(out)
- out = self.relu(out)
-
- out = self.conv2(out)
- out = self.bn2(out)
- out = self.relu(out)
-
- out = self.conv3(out)
- out = self.bn3(out)
-
- if self.downsample is not None:
- residual = self.downsample(x)
-
- out += residual
- out = self.relu(out)
-
- return out
-
-
-[docs]class ResNeXt(nn.Module):
- """ResNeXt.
-
- Reference:
- Xie et al. Aggregated Residual Transformations for Deep
- Neural Networks. CVPR 2017.
-
- Public keys:
- - ``resnext50_32x4d``: ResNeXt50 (groups=32, width=4).
- - ``resnext50_32x4d_fc512`` ResNeXt50 (groups=32, width=4) + FC.
- """
-
- def __init__(self, num_classes, loss, block, layers,
- groups=32,
- base_width=4,
- last_stride=2,
- fc_dims=None,
- dropout_p=None,
- **kwargs):
- self.inplanes = 64
- super(ResNeXt, self).__init__()
- self.loss = loss
- self.feature_dim = 512 * block.expansion
-
- # backbone network
- self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
- self.bn1 = nn.BatchNorm2d(64)
- self.relu = nn.ReLU(inplace=True)
- self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
- self.layer1 = self._make_layer(block, 64, layers[0], groups, base_width)
- self.layer2 = self._make_layer(block, 128, layers[1], groups, base_width, stride=2)
- self.layer3 = self._make_layer(block, 256, layers[2], groups, base_width, stride=2)
- self.layer4 = self._make_layer(block, 512, layers[3], groups, base_width, stride=last_stride)
-
- self.global_avgpool = nn.AdaptiveAvgPool2d(1)
- self.fc = self._construct_fc_layer(fc_dims, 512 * block.expansion, dropout_p)
- self.classifier = nn.Linear(self.feature_dim, num_classes)
-
- self._init_params()
-
- def _make_layer(self, block, planes, blocks, groups, base_width, stride=1):
- downsample = None
- if stride != 1 or self.inplanes != planes * block.expansion:
- downsample = nn.Sequential(
- nn.Conv2d(self.inplanes, planes * block.expansion,
- kernel_size=1, stride=stride, bias=False),
- nn.BatchNorm2d(planes * block.expansion),
- )
-
- layers = []
- layers.append(block(self.inplanes, planes, groups, base_width, stride, downsample))
- self.inplanes = planes * block.expansion
- for i in range(1, blocks):
- layers.append(block(self.inplanes, planes, groups, base_width))
-
- return nn.Sequential(*layers)
-
- def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
- """Constructs fully connected layer.
-
- Args:
- fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
- input_dim (int): input dimension
- dropout_p (float): dropout probability, if None, dropout is unused
- """
- if fc_dims is None:
- self.feature_dim = input_dim
- return None
-
- assert isinstance(fc_dims, (list, tuple)), 'fc_dims must be either list or tuple, but got {}'.format(type(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.bn1(x)
- x = self.relu(x)
- x = self.maxpool(x)
- x = self.layer1(x)
- x = self.layer2(x)
- x = self.layer3(x)
- x = self.layer4(x)
- return x
-
- def forward(self, x):
- f = self.featuremaps(x)
- v = self.global_avgpool(f)
- 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, model_url):
- """Initializes model with pretrained weights.
-
- Layers that don't match with pretrained layers in name or size are kept unchanged.
- """
- pretrain_dict = model_zoo.load_url(model_url)
- model_dict = model.state_dict()
- pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()}
- model_dict.update(pretrain_dict)
- model.load_state_dict(model_dict)
-
-
-def resnext50_32x4d(num_classes, loss='softmax', pretrained=True, **kwargs):
- model = ResNeXt(
- num_classes=num_classes,
- loss=loss,
- block=ResNeXtBottleneck,
- layers=[3, 4, 6, 3],
- groups=32,
- base_width=4,
- last_stride=2,
- fc_dims=None,
- dropout_p=None,
- **kwargs
- )
- if pretrained:
- init_pretrained_weights(model, model_urls['resnext50_32x4d'])
- return model
-
-
-def resnext50_32x4d_fc512(num_classes, loss='softmax', pretrained=True, **kwargs):
- model = ResNeXt(
- num_classes=num_classes,
- loss=loss,
- block=ResNeXtBottleneck,
- layers=[3, 4, 6, 3],
- groups=32,
- base_width=4,
- last_stride=1,
- fc_dims=[512],
- dropout_p=None,
- **kwargs
- )
- if pretrained:
- init_pretrained_weights(model, model_urls['resnext50_32x4d'])
- return model
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/_modules/torchreid/models/senet.html b/_modules/torchreid/models/senet.html
index 03a414e..65de3e9 100644
--- a/_modules/torchreid/models/senet.html
+++ b/_modules/torchreid/models/senet.html
@@ -8,7 +8,7 @@
- torchreid.models.senet — torchreid 0.7.6 documentation
+ torchreid.models.senet — torchreid 0.7.7 documentation
@@ -59,7 +59,7 @@
- 0.7.6
+ 0.7.7
diff --git a/_modules/torchreid/models/shufflenet.html b/_modules/torchreid/models/shufflenet.html
index b644bca..bf63502 100644
--- a/_modules/torchreid/models/shufflenet.html
+++ b/_modules/torchreid/models/shufflenet.html
@@ -8,7 +8,7 @@
- torchreid.models.shufflenet — torchreid 0.7.6 documentation
+ torchreid.models.shufflenet — torchreid 0.7.7 documentation
@@ -59,7 +59,7 @@
- 0.7.6
+ 0.7.7
diff --git a/_modules/torchreid/models/shufflenetv2.html b/_modules/torchreid/models/shufflenetv2.html
new file mode 100644
index 0000000..3ad9e5b
--- /dev/null
+++ b/_modules/torchreid/models/shufflenetv2.html
@@ -0,0 +1,419 @@
+
+
+
+
+
+
+
+
+
+
+ torchreid.models.shufflenetv2 — torchreid 0.7.7 documentation
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Source code for torchreid.models.shufflenetv2
+"""
+Code source: https://github.com/pytorch/vision
+"""
+from __future__ import absolute_import
+from __future__ import division
+
+__all__ = ['shufflenet_v2_x0_5', 'shufflenet_v2_x1_0', 'shufflenet_v2_x1_5', 'shufflenet_v2_x2_0']
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+import torchvision
+import torch.utils.model_zoo as model_zoo
+
+
+model_urls = {
+ 'shufflenetv2_x0.5': 'https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth',
+ 'shufflenetv2_x1.0': 'https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth',
+ 'shufflenetv2_x1.5': None,
+ 'shufflenetv2_x2.0': None,
+}
+
+
+def channel_shuffle(x, groups):
+ batchsize, num_channels, height, width = x.data.size()
+ channels_per_group = num_channels // groups
+
+ # reshape
+ x = x.view(batchsize, groups,
+ channels_per_group, height, width)
+
+ x = torch.transpose(x, 1, 2).contiguous()
+
+ # flatten
+ x = x.view(batchsize, -1, height, width)
+
+ return x
+
+
+class InvertedResidual(nn.Module):
+
+ def __init__(self, inp, oup, stride):
+ super(InvertedResidual, self).__init__()
+
+ if not (1 <= stride <= 3):
+ raise ValueError('illegal stride value')
+ self.stride = stride
+
+ branch_features = oup // 2
+ assert (self.stride != 1) or (inp == branch_features << 1)
+
+ if self.stride > 1:
+ self.branch1 = nn.Sequential(
+ self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1),
+ nn.BatchNorm2d(inp),
+ nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
+ nn.BatchNorm2d(branch_features),
+ nn.ReLU(inplace=True),
+ )
+
+ self.branch2 = nn.Sequential(
+ nn.Conv2d(inp if (self.stride > 1) else branch_features,
+ branch_features, kernel_size=1, stride=1, padding=0, bias=False),
+ nn.BatchNorm2d(branch_features),
+ nn.ReLU(inplace=True),
+ self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1),
+ nn.BatchNorm2d(branch_features),
+ nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
+ nn.BatchNorm2d(branch_features),
+ nn.ReLU(inplace=True),
+ )
+
+ @staticmethod
+ def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
+ return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i)
+
+ def forward(self, x):
+ if self.stride == 1:
+ x1, x2 = x.chunk(2, dim=1)
+ out = torch.cat((x1, self.branch2(x2)), dim=1)
+ else:
+ out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
+
+ out = channel_shuffle(out, 2)
+
+ return out
+
+
+[docs]class ShuffleNetV2(nn.Module):
+ """ShuffleNetV2.
+
+ Reference:
+ Ma et al. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. ECCV 2018.
+
+ Public keys:
+ - ``shufflenet_v2_x0_5``: ShuffleNetV2 x0.5.
+ - ``shufflenet_v2_x1_0``: ShuffleNetV2 x1.0.
+ - ``shufflenet_v2_x1_5``: ShuffleNetV2 x1.5.
+ - ``shufflenet_v2_x2_0``: ShuffleNetV2 x2.0.
+ """
+
+ def __init__(self, num_classes, loss, stages_repeats, stages_out_channels, **kwargs):
+ super(ShuffleNetV2, self).__init__()
+ self.loss = loss
+
+ if len(stages_repeats) != 3:
+ raise ValueError('expected stages_repeats as list of 3 positive ints')
+ if len(stages_out_channels) != 5:
+ raise ValueError('expected stages_out_channels as list of 5 positive ints')
+ self._stage_out_channels = stages_out_channels
+
+ input_channels = 3
+ output_channels = self._stage_out_channels[0]
+ self.conv1 = nn.Sequential(
+ nn.Conv2d(input_channels, output_channels, 3, 2, 1, bias=False),
+ nn.BatchNorm2d(output_channels),
+ nn.ReLU(inplace=True),
+ )
+ input_channels = output_channels
+
+ self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+ stage_names = ['stage{}'.format(i) for i in [2, 3, 4]]
+ for name, repeats, output_channels in zip(
+ stage_names, stages_repeats, self._stage_out_channels[1:]):
+ seq = [InvertedResidual(input_channels, output_channels, 2)]
+ for i in range(repeats - 1):
+ seq.append(InvertedResidual(output_channels, output_channels, 1))
+ setattr(self, name, nn.Sequential(*seq))
+ input_channels = output_channels
+
+ output_channels = self._stage_out_channels[-1]
+ self.conv5 = nn.Sequential(
+ nn.Conv2d(input_channels, output_channels, 1, 1, 0, bias=False),
+ nn.BatchNorm2d(output_channels),
+ nn.ReLU(inplace=True),
+ )
+ self.global_avgpool = nn.AdaptiveAvgPool2d((1, 1))
+
+ self.classifier = nn.Linear(output_channels, num_classes)
+
+ def featuremaps(self, x):
+ x = self.conv1(x)
+ x = self.maxpool(x)
+ x = self.stage2(x)
+ x = self.stage3(x)
+ x = self.stage4(x)
+ x = self.conv5(x)
+ return x
+
+ def forward(self, x):
+ f = self.featuremaps(x)
+ v = self.global_avgpool(f)
+ v = v.view(v.size(0), -1)
+
+ 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, model_url):
+ """Initializes model with pretrained weights.
+
+ Layers that don't match with pretrained layers in name or size are kept unchanged.
+ """
+ if model_url is None:
+ import warnings
+ warnings.warn('ImageNet pretrained weights are unavailable for this model')
+ return
+ pretrain_dict = model_zoo.load_url(model_url)
+ model_dict = model.state_dict()
+ pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()}
+ model_dict.update(pretrain_dict)
+ model.load_state_dict(model_dict)
+
+
+def shufflenet_v2_x0_5(num_classes, loss='softmax', pretrained=True, **kwargs):
+ model = ShuffleNetV2(num_classes, loss, [4, 8, 4], [24, 48, 96, 192, 1024], **kwargs)
+ if pretrained:
+ init_pretrained_weights(model, model_urls['shufflenetv2_x0.5'])
+ return model
+
+
+def shufflenet_v2_x1_0(num_classes, loss='softmax', pretrained=True, **kwargs):
+ model = ShuffleNetV2(num_classes, loss, [4, 8, 4], [24, 116, 232, 464, 1024], **kwargs)
+ if pretrained:
+ init_pretrained_weights(model, model_urls['shufflenetv2_x1.0'])
+ return model
+
+
+def shufflenet_v2_x1_5(num_classes, loss='softmax', pretrained=True, **kwargs):
+ model = ShuffleNetV2(num_classes, loss, [4, 8, 4], [24, 176, 352, 704, 1024], **kwargs)
+ if pretrained:
+ init_pretrained_weights(model, model_urls['shufflenetv2_x1.5'])
+ return model
+
+
+def shufflenet_v2_x2_0(num_classes, loss='softmax', pretrained=True, **kwargs):
+ model = ShuffleNetV2(num_classes, loss, [4, 8, 4], [24, 244, 488, 976, 2048], **kwargs)
+ if pretrained:
+ init_pretrained_weights(model, model_urls['shufflenetv2_x2.0'])
+ return model
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/_modules/torchreid/models/squeezenet.html b/_modules/torchreid/models/squeezenet.html
index a7a5a1e..5b93d03 100644
--- a/_modules/torchreid/models/squeezenet.html
+++ b/_modules/torchreid/models/squeezenet.html
@@ -8,7 +8,7 @@
- torchreid.models.squeezenet — torchreid 0.7.6 documentation
+ torchreid.models.squeezenet — torchreid 0.7.7 documentation
@@ -59,7 +59,7 @@
- 0.7.6
+ 0.7.7
@@ -165,7 +165,10 @@
Source code for torchreid.models.squeezenet
-from __future__ import absolute_import
+"""
+Code source: https://github.com/pytorch/vision
+"""
+from __future__ import absolute_import
from __future__ import division
__all__ = [
diff --git a/_modules/torchreid/models/xception.html b/_modules/torchreid/models/xception.html
index 5d08f36..7f85a86 100644
--- a/_modules/torchreid/models/xception.html
+++ b/_modules/torchreid/models/xception.html
@@ -8,7 +8,7 @@
- torchreid.models.xception — torchreid 0.7.6 documentation
+ torchreid.models.xception — torchreid 0.7.7 documentation
@@ -59,7 +59,7 @@
- 0.7.6
+ 0.7.7
diff --git a/_modules/torchreid/optim/lr_scheduler.html b/_modules/torchreid/optim/lr_scheduler.html
index 9bdf9f0..a1d8705 100644
--- a/_modules/torchreid/optim/lr_scheduler.html
+++ b/_modules/torchreid/optim/lr_scheduler.html
@@ -8,7 +8,7 @@
- torchreid.optim.lr_scheduler — torchreid 0.7.6 documentation
+ torchreid.optim.lr_scheduler — torchreid 0.7.7 documentation
@@ -59,7 +59,7 @@
- 0.7.6
+ 0.7.7
diff --git a/_modules/torchreid/optim/optimizer.html b/_modules/torchreid/optim/optimizer.html
index d9173b2..8dcb348 100644
--- a/_modules/torchreid/optim/optimizer.html
+++ b/_modules/torchreid/optim/optimizer.html
@@ -8,7 +8,7 @@
- torchreid.optim.optimizer — torchreid 0.7.6 documentation
+ torchreid.optim.optimizer — torchreid 0.7.7 documentation
@@ -59,7 +59,7 @@
- 0.7.6
+ 0.7.7
diff --git a/_modules/torchreid/utils/avgmeter.html b/_modules/torchreid/utils/avgmeter.html
index 774eade..6829733 100644
--- a/_modules/torchreid/utils/avgmeter.html
+++ b/_modules/torchreid/utils/avgmeter.html
@@ -8,7 +8,7 @@
- torchreid.utils.avgmeter — torchreid 0.7.6 documentation
+ torchreid.utils.avgmeter — torchreid 0.7.7 documentation
@@ -59,7 +59,7 @@
- 0.7.6
+ 0.7.7
diff --git a/_modules/torchreid/utils/loggers.html b/_modules/torchreid/utils/loggers.html
index 39ef183..3c14be2 100644
--- a/_modules/torchreid/utils/loggers.html
+++ b/_modules/torchreid/utils/loggers.html
@@ -8,7 +8,7 @@
- torchreid.utils.loggers — torchreid 0.7.6 documentation
+ torchreid.utils.loggers — torchreid 0.7.7 documentation
@@ -59,7 +59,7 @@
- 0.7.6
+ 0.7.7
diff --git a/_modules/torchreid/utils/model_complexity.html b/_modules/torchreid/utils/model_complexity.html
index ee93c6f..6042abb 100644
--- a/_modules/torchreid/utils/model_complexity.html
+++ b/_modules/torchreid/utils/model_complexity.html
@@ -8,7 +8,7 @@
- torchreid.utils.model_complexity — torchreid 0.7.6 documentation
+ torchreid.utils.model_complexity — torchreid 0.7.7 documentation
@@ -59,7 +59,7 @@
- 0.7.6
+ 0.7.7
diff --git a/_modules/torchreid/utils/reidtools.html b/_modules/torchreid/utils/reidtools.html
index 23b9b8d..bd6c701 100644
--- a/_modules/torchreid/utils/reidtools.html
+++ b/_modules/torchreid/utils/reidtools.html
@@ -8,7 +8,7 @@
- torchreid.utils.reidtools — torchreid 0.7.6 documentation
+ torchreid.utils.reidtools — torchreid 0.7.7 documentation
@@ -59,7 +59,7 @@
- 0.7.6
+ 0.7.7
diff --git a/_modules/torchreid/utils/tools.html b/_modules/torchreid/utils/tools.html
index c44220b..ea4211c 100644
--- a/_modules/torchreid/utils/tools.html
+++ b/_modules/torchreid/utils/tools.html
@@ -8,7 +8,7 @@
- torchreid.utils.tools — torchreid 0.7.6 documentation
+ torchreid.utils.tools — torchreid 0.7.7 documentation
@@ -59,7 +59,7 @@
- 0.7.6
+ 0.7.7
@@ -170,7 +170,7 @@
from __future__ import division
__all__ = ['mkdir_if_missing', 'check_isfile', 'read_json', 'write_json',
- 'set_random_seed', 'download_url', 'read_image']
+ 'set_random_seed', 'download_url', 'read_image', 'collect_env_info']
import sys
import os
@@ -182,6 +182,7 @@
import warnings
import random
import numpy as np
+import PIL
from PIL import Image
import torch
@@ -281,6 +282,17 @@
print('IOError incurred when reading "{}". Will redo. Don\'t worry. Just chill.'.format(img_path))
pass
return img
+
+
+[docs]def collect_env_info():
+ """Returns env info as a string.
+
+ Code source: github.com/facebookresearch/maskrcnn-benchmark
+ """
+ from torch.utils.collect_env import get_pretty_env_info
+ env_str = get_pretty_env_info()
+ env_str += '\n Pillow ({})'.format(PIL.__version__)
+ return env_str
diff --git a/_modules/torchreid/utils/torchtools.html b/_modules/torchreid/utils/torchtools.html
index 28dd621..0e7fad9 100644
--- a/_modules/torchreid/utils/torchtools.html
+++ b/_modules/torchreid/utils/torchtools.html
@@ -8,7 +8,7 @@
- torchreid.utils.torchtools — torchreid 0.7.6 documentation
+ torchreid.utils.torchtools — torchreid 0.7.7 documentation
@@ -59,7 +59,7 @@
- 0.7.6
+ 0.7.7
diff --git a/_sources/pkg/models.rst.txt b/_sources/pkg/models.rst.txt
index 81be2bb..c8110ea 100644
--- a/_sources/pkg/models.rst.txt
+++ b/_sources/pkg/models.rst.txt
@@ -14,7 +14,6 @@ ImageNet Classification Models
-------------------------------
.. autoclass:: torchreid.models.resnet.ResNet
-.. autoclass:: torchreid.models.resnext.ResNeXt
.. autoclass:: torchreid.models.senet.SENet
.. autoclass:: torchreid.models.densenet.DenseNet
.. autoclass:: torchreid.models.inceptionresnetv2.InceptionResNetV2
@@ -29,6 +28,7 @@ Lightweight Models
.. autoclass:: torchreid.models.mobilenetv2.MobileNetV2
.. autoclass:: torchreid.models.shufflenet.ShuffleNet
.. autoclass:: torchreid.models.squeezenet.SqueezeNet
+.. autoclass:: torchreid.models.shufflenetv2.ShuffleNetV2
ReID-specific Models
diff --git a/_static/documentation_options.js b/_static/documentation_options.js
index 9fb6716..bb72704 100644
--- a/_static/documentation_options.js
+++ b/_static/documentation_options.js
@@ -1,6 +1,6 @@
var DOCUMENTATION_OPTIONS = {
URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
- VERSION: '0.7.6',
+ VERSION: '0.7.7',
LANGUAGE: 'None',
COLLAPSE_INDEX: false,
FILE_SUFFIX: '.html',
diff --git a/datasets.html b/datasets.html
index e81a1a7..e00191d 100644
--- a/datasets.html
+++ b/datasets.html
@@ -8,7 +8,7 @@
- Datasets — torchreid 0.7.6 documentation
+ Datasets — torchreid 0.7.7 documentation
@@ -61,7 +61,7 @@
- 0.7.6
+ 0.7.7
diff --git a/evaluation.html b/evaluation.html
index ea9eed7..3a560bb 100644
--- a/evaluation.html
+++ b/evaluation.html
@@ -8,7 +8,7 @@
- Evaluation — torchreid 0.7.6 documentation
+ Evaluation — torchreid 0.7.7 documentation
@@ -61,7 +61,7 @@
- 0.7.6
+ 0.7.7
diff --git a/genindex.html b/genindex.html
index e16da2a..b1b5a71 100644
--- a/genindex.html
+++ b/genindex.html
@@ -9,7 +9,7 @@
- Index — torchreid 0.7.6 documentation
+ Index — torchreid 0.7.7 documentation
@@ -60,7 +60,7 @@
- 0.7.6
+ 0.7.7
@@ -227,6 +227,8 @@
check_before_run() (torchreid.data.datasets.dataset.Dataset method)
check_isfile() (in module torchreid.utils.tools)
+
+ collect_env_info() (in module torchreid.utils.tools)
ColorAugmentation (class in torchreid.data.transforms)
@@ -236,10 +238,10 @@
(torchreid.data.datasets.video.mars.Mars method)
- compute_distance_matrix() (in module torchreid.metrics.distance)
-
+ - compute_distance_matrix() (in module torchreid.metrics.distance)
+
- compute_model_complexity() (in module torchreid.utils.model_complexity)
- cosine_distance() (in module torchreid.metrics.distance)
@@ -457,8 +459,6 @@
- ResNet (class in torchreid.models.resnet)
- ResNetMid (class in torchreid.models.resnetmid)
-
- - ResNeXt (class in torchreid.models.resnext)
- resume_from_checkpoint() (in module torchreid.utils.torchtools)
@@ -495,6 +495,8 @@
- ShuffleNet (class in torchreid.models.shufflenet)
+
+ - ShuffleNetV2 (class in torchreid.models.shufflenetv2)
- SqueezeNet (class in torchreid.models.squeezenet)
diff --git a/index.html b/index.html
index 4581576..8a6d9d6 100644
--- a/index.html
+++ b/index.html
@@ -8,7 +8,7 @@
- Torchreid — torchreid 0.7.6 documentation
+ Torchreid — torchreid 0.7.7 documentation
@@ -60,7 +60,7 @@
- 0.7.6
+ 0.7.7
diff --git a/objects.inv b/objects.inv
index 430262e..17f8025 100644
Binary files a/objects.inv and b/objects.inv differ
diff --git a/pkg/data.html b/pkg/data.html
index 8942fac..bb7c628 100644
--- a/pkg/data.html
+++ b/pkg/data.html
@@ -8,7 +8,7 @@
- torchreid.data — torchreid 0.7.6 documentation
+ torchreid.data — torchreid 0.7.7 documentation
@@ -61,7 +61,7 @@
- 0.7.6
+ 0.7.7
diff --git a/pkg/engine.html b/pkg/engine.html
index fad8fd3..27d9f45 100644
--- a/pkg/engine.html
+++ b/pkg/engine.html
@@ -8,7 +8,7 @@
- torchreid.engine — torchreid 0.7.6 documentation
+ torchreid.engine — torchreid 0.7.7 documentation
@@ -61,7 +61,7 @@
- 0.7.6
+ 0.7.7
@@ -210,7 +210,7 @@ or torchreid.data.V
- max_epoch (int) – maximum epoch.
- start_epoch (int, optional) – starting epoch. Default is 0.
- fixbase_epoch (int, optional) – number of epochs to train
open_layers (new layers)
-while keeping base layers frozen. Default is 0. fixbase_epoch is not counted
+while keeping base layers frozen. Default is 0. fixbase_epoch is counted
in max_epoch.
- open_layers (str or list, optional) – layers (attribute names) open for training.
- start_eval (int, optional) – from which epoch to start evaluation. Default is 0.
@@ -363,23 +363,17 @@ or torchreid.data.V
-
-
train(epoch, trainloader, fixbase=False, open_layers=None, print_freq=10)[source]¶
-Trains the model for one epoch on source datasets using softmax loss.
-
-Parameters:
-- epoch (int) – current epoch.
-- trainloader (Dataloader) – training dataloader.
-- fixbase (bool, optional) – whether to fix base layers. Default is False.
-- open_layers (str or list, optional) – layers open for training.
-- print_freq (int, optional) – print frequency. Default is 10.
-
-
+train(epoch, max_epoch, trainloader, fixbase_epoch=0, open_layers=None, print_freq=10)[source]¶
+Performs training on source datasets for one epoch.
+This will be called every epoch in run(), e.g.
+for epoch in range(start_epoch, max_epoch):
+ self.train(some_arguments)
+
+
+
+Note
+This needs to be implemented in subclasses.
+
@@ -448,23 +442,17 @@ or torchreid.data.V
-
-
train(epoch, trainloader, fixbase=False, open_layers=None, print_freq=10)[source]¶
-Trains the model for one epoch on source datasets using hard mining triplet loss.
-
-Parameters:
-- epoch (int) – current epoch.
-- trainloader (Dataloader) – training dataloader.
-- fixbase (bool, optional) – whether to fix base layers. Default is False.
-- open_layers (str or list, optional) – layers open for training.
-- print_freq (int, optional) – print frequency. Default is 10.
-
-
+train(epoch, max_epoch, trainloader, fixbase_epoch=0, open_layers=None, print_freq=10)[source]¶
+Performs training on source datasets for one epoch.
+This will be called every epoch in run(), e.g.
+for epoch in range(start_epoch, max_epoch):
+ self.train(some_arguments)
+
+
+
+Note
+This needs to be implemented in subclasses.
+
diff --git a/pkg/losses.html b/pkg/losses.html
index 5aaafee..b374f38 100644
--- a/pkg/losses.html
+++ b/pkg/losses.html
@@ -8,7 +8,7 @@
- torchreid.losses — torchreid 0.7.6 documentation
+ torchreid.losses — torchreid 0.7.7 documentation
@@ -61,7 +61,7 @@
- 0.7.6
+ 0.7.7
diff --git a/pkg/metrics.html b/pkg/metrics.html
index 4a1d3e6..eac95e9 100644
--- a/pkg/metrics.html
+++ b/pkg/metrics.html
@@ -8,7 +8,7 @@
- torchreid.metrics — torchreid 0.7.6 documentation
+ torchreid.metrics — torchreid 0.7.7 documentation
@@ -61,7 +61,7 @@
- 0.7.6
+ 0.7.7
diff --git a/pkg/models.html b/pkg/models.html
index 73c9c30..55d2542 100644
--- a/pkg/models.html
+++ b/pkg/models.html
@@ -8,7 +8,7 @@
- torchreid.models — torchreid 0.7.6 documentation
+ torchreid.models — torchreid 0.7.7 documentation
@@ -61,7 +61,7 @@
- 0.7.6
+ 0.7.7
@@ -231,11 +231,15 @@ Default is True.
ImageNet Classification Models¶
-
-class
torchreid.models.resnet.ResNet(num_classes, loss, block, layers, last_stride=2, fc_dims=None, dropout_p=None, **kwargs)[source]¶
+class torchreid.models.resnet.ResNet(num_classes, loss, block, layers, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None, last_stride=2, fc_dims=None, dropout_p=None, **kwargs)[source]¶
Residual network.
- Reference:
-- He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
+
+- He et al. Deep Residual Learning for Image Recognition. CVPR 2016.
+- Xie et al. Aggregated Residual Transformations for Deep Neural Networks. CVPR 2017.
+
+- Public keys:
resnet18: ResNet18.resnet50: ResNet50.resnet101: ResNet101.resnet152: ResNet152.resnext50_32x4d: ResNeXt50.resnext101_32x8d: ResNeXt101.resnet50_fc512: ResNet50 + FC.
-
--
-class
torchreid.models.resnext.ResNeXt(num_classes, loss, block, layers, groups=32, base_width=4, last_stride=2, fc_dims=None, dropout_p=None, **kwargs)[source]¶
-ResNeXt.
-
-- Reference:
-- Xie et al. Aggregated Residual Transformations for Deep
-Neural Networks. CVPR 2017.
-- Public keys:
-
-resnext50_32x4d: ResNeXt50 (groups=32, width=4).resnext50_32x4d_fc512 ResNeXt50 (groups=32, width=4) + FC.
-
-
-
-
class
torchreid.models.senet.SENet(num_classes, loss, block, layers, groups, reduction, dropout_p=0.2, inplanes=128, input_3x3=True, downsample_kernel_size=3, downsample_padding=1, last_stride=2, fc_dims=None, **kwargs)[source]¶
@@ -424,6 +413,24 @@ and< 0.5 MB model size. arXiv:1602.07360.
+
+-
+class
torchreid.models.shufflenetv2.ShuffleNetV2(num_classes, loss, stages_repeats, stages_out_channels, **kwargs)[source]¶
+ShuffleNetV2.
+
+- Reference:
+- Ma et al. ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design. ECCV 2018.
+- Public keys:
+
+shufflenet_v2_x0_5: ShuffleNetV2 x0.5.shufflenet_v2_x1_0: ShuffleNetV2 x1.0.shufflenet_v2_x1_5: ShuffleNetV2 x1.5.shufflenet_v2_x2_0: ShuffleNetV2 x2.0.
+
+
+
ReID-specific Models¶
diff --git a/pkg/optim.html b/pkg/optim.html
index 9bbff75..f9e22ba 100644
--- a/pkg/optim.html
+++ b/pkg/optim.html
@@ -8,7 +8,7 @@
- torchreid.optim — torchreid 0.7.6 documentation
+ torchreid.optim — torchreid 0.7.7 documentation
@@ -61,7 +61,7 @@
- 0.7.6
+ 0.7.7
diff --git a/pkg/utils.html b/pkg/utils.html
index d6a6327..1a44f9a 100644
--- a/pkg/utils.html
+++ b/pkg/utils.html
@@ -8,7 +8,7 @@
- torchreid.utils — torchreid 0.7.6 documentation
+ torchreid.utils — torchreid 0.7.7 documentation
@@ -61,7 +61,7 @@
- 0.7.6
+ 0.7.7
@@ -382,6 +382,13 @@ to show the summarized results, which are convenient for analysis.
+
+
ReID Tools¶
diff --git a/py-modindex.html b/py-modindex.html
index 84f9a87..c0a7133 100644
--- a/py-modindex.html
+++ b/py-modindex.html
@@ -8,7 +8,7 @@
- Python Module Index — torchreid 0.7.6 documentation
+ Python Module Index — torchreid 0.7.7 documentation
@@ -62,7 +62,7 @@
- 0.7.6
+ 0.7.7
diff --git a/search.html b/search.html
index 1aaeefb..278e33f 100644
--- a/search.html
+++ b/search.html
@@ -8,7 +8,7 @@
- Search — torchreid 0.7.6 documentation
+ Search — torchreid 0.7.7 documentation
@@ -60,7 +60,7 @@
- 0.7.6
+ 0.7.7
diff --git a/searchindex.js b/searchindex.js
index 462d226..aa303bf 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({docnames:["AWESOME_REID","MODEL_ZOO","datasets","evaluation","index","pkg/data","pkg/engine","pkg/losses","pkg/metrics","pkg/models","pkg/optim","pkg/utils","user_guide"],envversion:{"sphinx.domains.c":1,"sphinx.domains.changeset":1,"sphinx.domains.cpp":1,"sphinx.domains.javascript":1,"sphinx.domains.math":2,"sphinx.domains.python":1,"sphinx.domains.rst":1,"sphinx.domains.std":1,"sphinx.ext.viewcode":1,sphinx:54},filenames:["AWESOME_REID.md","MODEL_ZOO.md","datasets.rst","evaluation.rst","index.rst","pkg/data.rst","pkg/engine.rst","pkg/losses.rst","pkg/metrics.rst","pkg/models.rst","pkg/optim.rst","pkg/utils.rst","user_guide.rst"],objects:{"torchreid.data":{datamanager:[5,0,0,"-"],sampler:[5,0,0,"-"],transforms:[5,0,0,"-"]},"torchreid.data.datamanager":{DataManager:[5,1,1,""],ImageDataManager:[5,1,1,""],VideoDataManager:[5,1,1,""]},"torchreid.data.datamanager.DataManager":{num_train_cams:[5,2,1,""],num_train_pids:[5,2,1,""],return_dataloaders:[5,3,1,""],return_testdataset_by_name:[5,3,1,""]},"torchreid.data.datasets":{__init__:[5,0,0,"-"],dataset:[5,0,0,"-"]},"torchreid.data.datasets.__init__":{init_image_dataset:[5,4,1,""],init_video_dataset:[5,4,1,""],register_image_dataset:[5,4,1,""],register_video_dataset:[5,4,1,""]},"torchreid.data.datasets.dataset":{Dataset:[5,1,1,""],ImageDataset:[5,1,1,""],VideoDataset:[5,1,1,""]},"torchreid.data.datasets.dataset.Dataset":{check_before_run:[5,3,1,""],combine_all:[5,3,1,""],download_dataset:[5,3,1,""],get_num_cams:[5,3,1,""],get_num_pids:[5,3,1,""],parse_data:[5,3,1,""],show_summary:[5,3,1,""]},"torchreid.data.datasets.dataset.ImageDataset":{show_summary:[5,3,1,""]},"torchreid.data.datasets.dataset.VideoDataset":{show_summary:[5,3,1,""]},"torchreid.data.datasets.image":{cuhk01:[5,0,0,"-"],cuhk03:[5,0,0,"-"],dukemtmcreid:[5,0,0,"-"],grid:[5,0,0,"-"],ilids:[5,0,0,"-"],market1501:[5,0,0,"-"],msmt17:[5,0,0,"-"],prid:[5,0,0,"-"],sensereid:[5,0,0,"-"],viper:[5,0,0,"-"]},"torchreid.data.datasets.image.cuhk01":{CUHK01:[5,1,1,""]},"torchreid.data.datasets.image.cuhk01.CUHK01":{prepare_split:[5,3,1,""]},"torchreid.data.datasets.image.cuhk03":{CUHK03:[5,1,1,""]},"torchreid.data.datasets.image.dukemtmcreid":{DukeMTMCreID:[5,1,1,""]},"torchreid.data.datasets.image.grid":{GRID:[5,1,1,""]},"torchreid.data.datasets.image.ilids":{iLIDS:[5,1,1,""]},"torchreid.data.datasets.image.market1501":{Market1501:[5,1,1,""]},"torchreid.data.datasets.image.msmt17":{MSMT17:[5,1,1,""]},"torchreid.data.datasets.image.prid":{PRID:[5,1,1,""]},"torchreid.data.datasets.image.sensereid":{SenseReID:[5,1,1,""]},"torchreid.data.datasets.image.viper":{VIPeR:[5,1,1,""]},"torchreid.data.datasets.video":{dukemtmcvidreid:[5,0,0,"-"],ilidsvid:[5,0,0,"-"],mars:[5,0,0,"-"],prid2011:[5,0,0,"-"]},"torchreid.data.datasets.video.dukemtmcvidreid":{DukeMTMCVidReID:[5,1,1,""]},"torchreid.data.datasets.video.ilidsvid":{iLIDSVID:[5,1,1,""]},"torchreid.data.datasets.video.mars":{Mars:[5,1,1,""]},"torchreid.data.datasets.video.mars.Mars":{combine_all:[5,3,1,""]},"torchreid.data.datasets.video.prid2011":{PRID2011:[5,1,1,""]},"torchreid.data.sampler":{RandomIdentitySampler:[5,1,1,""],build_train_sampler:[5,4,1,""]},"torchreid.data.transforms":{ColorAugmentation:[5,1,1,""],Random2DTranslation:[5,1,1,""],RandomErasing:[5,1,1,""],build_transforms:[5,4,1,""]},"torchreid.engine.engine":{Engine:[6,1,1,""]},"torchreid.engine.engine.Engine":{run:[6,3,1,""],test:[6,3,1,""],train:[6,3,1,""]},"torchreid.engine.image.softmax":{ImageSoftmaxEngine:[6,1,1,""]},"torchreid.engine.image.softmax.ImageSoftmaxEngine":{train:[6,3,1,""]},"torchreid.engine.image.triplet":{ImageTripletEngine:[6,1,1,""]},"torchreid.engine.image.triplet.ImageTripletEngine":{train:[6,3,1,""]},"torchreid.engine.video.softmax":{VideoSoftmaxEngine:[6,1,1,""]},"torchreid.engine.video.triplet":{VideoTripletEngine:[6,1,1,""]},"torchreid.losses":{cross_entropy_loss:[7,0,0,"-"],hard_mine_triplet_loss:[7,0,0,"-"]},"torchreid.losses.cross_entropy_loss":{CrossEntropyLoss:[7,1,1,""]},"torchreid.losses.cross_entropy_loss.CrossEntropyLoss":{forward:[7,3,1,""]},"torchreid.losses.hard_mine_triplet_loss":{TripletLoss:[7,1,1,""]},"torchreid.losses.hard_mine_triplet_loss.TripletLoss":{forward:[7,3,1,""]},"torchreid.metrics":{accuracy:[8,0,0,"-"],distance:[8,0,0,"-"],rank:[8,0,0,"-"]},"torchreid.metrics.accuracy":{accuracy:[8,4,1,""]},"torchreid.metrics.distance":{compute_distance_matrix:[8,4,1,""],cosine_distance:[8,4,1,""],euclidean_squared_distance:[8,4,1,""]},"torchreid.metrics.rank":{evaluate_rank:[8,4,1,""]},"torchreid.models":{__init__:[9,0,0,"-"]},"torchreid.models.__init__":{build_model:[9,4,1,""],show_avai_models:[9,4,1,""]},"torchreid.models.densenet":{DenseNet:[9,1,1,""]},"torchreid.models.hacnn":{HACNN:[9,1,1,""]},"torchreid.models.inceptionresnetv2":{InceptionResNetV2:[9,1,1,""]},"torchreid.models.inceptionv4":{InceptionV4:[9,1,1,""]},"torchreid.models.mlfn":{MLFN:[9,1,1,""]},"torchreid.models.mobilenetv2":{MobileNetV2:[9,1,1,""]},"torchreid.models.mudeep":{Mu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diff --git a/user_guide.html b/user_guide.html
index 14dd10d..8f818ea 100644
--- a/user_guide.html
+++ b/user_guide.html
@@ -8,7 +8,7 @@
- How-to — torchreid 0.7.6 documentation
+ How-to — torchreid 0.7.7 documentation
@@ -61,7 +61,7 @@
- 0.7.6
+ 0.7.7