rm old resnext
kaiyangzhou
parent
97ee4b423b
commit
fd88366d22
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@ -1,238 +0,0 @@
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from __future__ import absolute_import
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from __future__ import division
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__all__ = ['resnext50_32x4d', 'resnext50_32x4d_fc512']
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import math
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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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import torch.utils.model_zoo as model_zoo
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model_urls = {
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# top1 = 76.3
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'resnext50_32x4d': 'http://www.eecs.qmul.ac.uk/~kz303/deep-person-reid/model-zoo/imagenet-pretrained/resnext50_32x4d-453b60f8.pth',
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}
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class ResNeXtBottleneck(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, groups=32, base_width=4, stride=1, downsample=None):
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super(ResNeXtBottleneck, self).__init__()
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width = int(math.floor(planes * (base_width / 64.)) * groups)
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self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False, stride=1)
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self.bn1 = nn.BatchNorm2d(width)
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self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride, padding=1, groups=groups, bias=False)
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self.bn2 = nn.BatchNorm2d(width)
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self.conv3 = nn.Conv2d(width, planes * self.expansion, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(planes * self.expansion)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x):
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residual = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample is not None:
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residual = self.downsample(x)
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out += residual
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out = self.relu(out)
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return out
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class ResNeXt(nn.Module):
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"""ResNeXt.
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Reference:
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Xie et al. Aggregated Residual Transformations for Deep
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Neural Networks. CVPR 2017.
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Public keys:
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- ``resnext50_32x4d``: ResNeXt50 (groups=32, width=4).
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- ``resnext50_32x4d_fc512`` ResNeXt50 (groups=32, width=4) + FC.
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"""
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def __init__(self, num_classes, loss, block, layers,
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groups=32,
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base_width=4,
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last_stride=2,
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fc_dims=None,
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dropout_p=None,
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**kwargs):
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self.inplanes = 64
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super(ResNeXt, self).__init__()
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self.loss = loss
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self.feature_dim = 512 * block.expansion
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# backbone network
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
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self.bn1 = nn.BatchNorm2d(64)
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self.relu = nn.ReLU(inplace=True)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.layer1 = self._make_layer(block, 64, layers[0], groups, base_width)
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self.layer2 = self._make_layer(block, 128, layers[1], groups, base_width, stride=2)
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self.layer3 = self._make_layer(block, 256, layers[2], groups, base_width, stride=2)
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self.layer4 = self._make_layer(block, 512, layers[3], groups, base_width, stride=last_stride)
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self.global_avgpool = nn.AdaptiveAvgPool2d(1)
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self.fc = self._construct_fc_layer(fc_dims, 512 * block.expansion, dropout_p)
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self.classifier = nn.Linear(self.feature_dim, num_classes)
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self._init_params()
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def _make_layer(self, block, planes, blocks, groups, base_width, stride=1):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.Conv2d(self.inplanes, planes * block.expansion,
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kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, groups, base_width, stride, downsample))
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self.inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(block(self.inplanes, planes, groups, base_width))
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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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"""Constructs fully connected layer.
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Args:
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fc_dims (list or tuple): dimensions of fc layers, if None, no fc layers are constructed
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input_dim (int): input dimension
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dropout_p (float): dropout probability, if None, dropout is unused
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"""
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if fc_dims is None:
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self.feature_dim = input_dim
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return None
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assert isinstance(fc_dims, (list, tuple)), 'fc_dims must be either list or tuple, but got {}'.format(type(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.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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return x
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def forward(self, x):
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f = self.featuremaps(x)
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v = self.global_avgpool(f)
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v = v.view(v.size(0), -1)
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if self.fc is not None:
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v = self.fc(v)
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if not self.training:
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return v
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y = self.classifier(v)
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if self.loss == 'softmax':
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return y
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elif self.loss == 'triplet':
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return y, v
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else:
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raise KeyError("Unsupported loss: {}".format(self.loss))
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def init_pretrained_weights(model, model_url):
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"""Initializes model with pretrained weights.
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Layers that don't match with pretrained layers in name or size are kept unchanged.
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"""
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pretrain_dict = model_zoo.load_url(model_url)
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model_dict = model.state_dict()
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pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()}
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model_dict.update(pretrain_dict)
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model.load_state_dict(model_dict)
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def resnext50_32x4d(num_classes, loss='softmax', pretrained=True, **kwargs):
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model = ResNeXt(
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num_classes=num_classes,
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loss=loss,
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block=ResNeXtBottleneck,
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layers=[3, 4, 6, 3],
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groups=32,
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base_width=4,
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last_stride=2,
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fc_dims=None,
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dropout_p=None,
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**kwargs
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)
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if pretrained:
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init_pretrained_weights(model, model_urls['resnext50_32x4d'])
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return model
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def resnext50_32x4d_fc512(num_classes, loss='softmax', pretrained=True, **kwargs):
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model = ResNeXt(
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num_classes=num_classes,
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loss=loss,
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block=ResNeXtBottleneck,
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layers=[3, 4, 6, 3],
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groups=32,
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base_width=4,
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last_stride=1,
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fc_dims=[512],
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dropout_p=None,
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**kwargs
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)
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if pretrained:
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init_pretrained_weights(model, model_urls['resnext50_32x4d'])
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return model
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