Add ResNet_IBN_a
shaoniangu
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4bf49c24ff
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618d39b951
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# Experiment all tricks with center loss : 256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on-triplet_centerloss0_0005
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# Dataset 1: market1501
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# imagesize: 256x128
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# batchsize: 16x4
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# warmup_step 10
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# random erase prob 0.5
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# labelsmooth: on
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# last stride 1
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# bnneck on
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# with center loss
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python3 tools/train.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('1')" MODEL.NAME "('resnet50_ibn_a')" MODEL.PRETRAIN_PATH "('/home/haoluo/gu/ibn_a.pth')" DATASETS.NAMES "('market1501')" DATASETS.ROOT_DIR "('/home/haoluo/data')" OUTPUT_DIR "('/home/haoluo/log/gu/reid_baseline_review/Opensource_test/market1501/Experiment-resnet50_ibn_a-all-tricks-tri_center-256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on-triplet_centerloss0_0005')"
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# Experiment all tricks with center loss : 256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on-triplet_centerloss0_0005
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# Dataset 2: dukemtmc
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# imagesize: 256x128
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# batchsize: 16x4
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# warmup_step 10
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# random erase prob 0.5
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# labelsmooth: on
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# last stride 1
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# bnneck on
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# with center loss
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python3 tools/train.py --config_file='configs/softmax_triplet_with_center.yml' MODEL.DEVICE_ID "('1')" MODEL.NAME "('se_resnext50')" MODEL.PRETRAIN_PATH "('/home/haoluo/.torch/models/se_resnext50_32x4d-a260b3a4.pth')" DATASETS.NAMES "('dukemtmc')" DATASETS.ROOT_DIR "('/home/haoluo/data')" OUTPUT_DIR "('/home/haoluo/log/gu/reid_baseline_review/Opensource_test/dukemtmc/Experiment-seresnext50-all-tricks-tri_center-256x128-bs16x4-warmup10-erase0_5-labelsmooth_on-laststride1-bnneck_on-triplet_centerloss0_0005')"
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@ -80,6 +80,7 @@ In the future, we will
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| SeResNet101 | 94.6 (87.3) | 87.5 (78.0) |
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| SeResNeXt50 | 94.9 (87.6) | 88.0 (78.3) |
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| SeResNeXt101 | 95.0 (88.0) | 88.4 (79.0) |
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| IBN-Net50-a | 95.0 (88.2) | 90.1 (79.1) |
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[model(Market1501)](https://drive.google.com/open?id=1hn0sXLZ5yJcxtmuY-ItQfYD7hBtHwt7A)
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@ -137,7 +138,7 @@ The designed architecture follows this guide [PyTorch-Project-Template](https://
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5. Prepare pretrained model if you don't have
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（1）Resnet
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（1）ResNet
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```python
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from torchvision import models
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@ -151,8 +152,10 @@ The designed architecture follows this guide [PyTorch-Project-Template](https://
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```
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Then it will automatically download model in `~/.torch/models/`, you should set this path in `config/defaults.py` for all training or set in every single training config file in `configs/` or set in every single command.
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（3）Load your self-trained model
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（3）ResNet50_IBN_a
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Please download from here (Please wait).
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（4）Load your self-trained model
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If you want to continue your train process based on your self-trained model, you can change the configuration `PRETRAIN_CHOICE` from 'imagenet' to 'self' and set the `PRETRAIN_PATH` to your self-trained model. We offer `Experiment-pretrain_choice-all_tricks-tri_center-market.sh` as an example.
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6. If you want to know the detailed configurations and their meaning, please refer to `config/defaults.py`. If you want to set your own parameters, you can follow our method: create a new yml file, then set your own parameters. Add `--config_file='configs/your yml file'` int the commands described below, then our code will merge your configuration. automatically.
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@ -3,7 +3,7 @@
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@author: liaoxingyu
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@contact: sherlockliao01@gmail.com
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"""
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from .cuhk03 import CUHK03
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# from .cuhk03 import CUHK03
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from .dukemtmcreid import DukeMTMCreID
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from .market1501 import Market1501
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from .msmt17 import MSMT17
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@ -12,7 +12,7 @@ from .dataset_loader import ImageDataset
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__factory = {
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'market1501': Market1501,
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'cuhk03': CUHK03,
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# 'cuhk03': CUHK03,
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'dukemtmc': DukeMTMCreID,
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'msmt17': MSMT17,
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'veri': VeRi,
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import torch
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import torch.nn as nn
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import math
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import torch.utils.model_zoo as model_zoo
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__all__ = ['ResNet_IBN', 'resnet50_ibn_a', 'resnet101_ibn_a',
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'resnet152_ibn_a']
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model_urls = {
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'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
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'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
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'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
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}
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class IBN(nn.Module):
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def __init__(self, planes):
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super(IBN, self).__init__()
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half1 = int(planes/2)
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self.half = half1
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half2 = planes - half1
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self.IN = nn.InstanceNorm2d(half1, affine=True)
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self.BN = nn.BatchNorm2d(half2)
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def forward(self, x):
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split = torch.split(x, self.half, 1)
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out1 = self.IN(split[0].contiguous())
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out2 = self.BN(split[1].contiguous())
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out = torch.cat((out1, out2), 1)
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return out
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class Bottleneck_IBN(nn.Module):
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expansion = 4
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def __init__(self, inplanes, planes, ibn=False, stride=1, downsample=None):
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super(Bottleneck_IBN, self).__init__()
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self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
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if ibn:
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self.bn1 = IBN(planes)
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else:
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
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padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.conv3 = nn.Conv2d(planes, 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 ResNet_IBN(nn.Module):
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def __init__(self, last_stride, block, layers, num_classes=1000):
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scale = 64
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self.inplanes = scale
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super(ResNet_IBN, self).__init__()
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self.conv1 = nn.Conv2d(3, scale, kernel_size=7, stride=2, padding=3,
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bias=False)
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self.bn1 = nn.BatchNorm2d(scale)
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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, scale, layers[0])
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self.layer2 = self._make_layer(block, scale*2, layers[1], stride=2)
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self.layer3 = self._make_layer(block, scale*4, layers[2], stride=2)
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self.layer4 = self._make_layer(block, scale*8, layers[3], stride=last_stride)
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self.avgpool = nn.AvgPool2d(7)
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self.fc = nn.Linear(scale * 8 * block.expansion, num_classes)
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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m.weight.data.normal_(0, math.sqrt(2. / n))
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elif isinstance(m, nn.BatchNorm2d):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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elif isinstance(m, nn.InstanceNorm2d):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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def _make_layer(self, block, planes, blocks, 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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ibn = True
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if planes == 512:
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ibn = False
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layers.append(block(self.inplanes, planes, ibn, 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, ibn))
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return nn.Sequential(*layers)
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def forward(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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# x = self.avgpool(x)
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# x = x.view(x.size(0), -1)
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# x = self.fc(x)
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return x
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def load_param(self, model_path):
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param_dict = torch.load(model_path)
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for i in param_dict:
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if 'fc' in i:
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continue
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self.state_dict()[i].copy_(param_dict[i])
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def resnet50_ibn_a(last_stride, pretrained=False, **kwargs):
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"""Constructs a ResNet-50 model.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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"""
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model = ResNet_IBN(last_stride, Bottleneck_IBN, [3, 4, 6, 3], **kwargs)
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if pretrained:
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model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
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return model
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def resnet101_ibn_a(last_stride, pretrained=False, **kwargs):
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"""Constructs a ResNet-101 model.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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"""
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model = ResNet_IBN(last_stride, Bottleneck_IBN, [3, 4, 23, 3], **kwargs)
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if pretrained:
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model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
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return model
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def resnet152_ibn_a(last_stride, pretrained=False, **kwargs):
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"""Constructs a ResNet-152 model.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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"""
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model = ResNet_IBN(last_stride, Bottleneck_IBN, [3, 8, 36, 3], **kwargs)
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if pretrained:
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model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
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return model
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@ -9,6 +9,7 @@ from torch import nn
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from .backbones.resnet import ResNet, BasicBlock, Bottleneck
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from .backbones.senet import SENet, SEResNetBottleneck, SEBottleneck, SEResNeXtBottleneck
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from .backbones.resnet_ibn_a import resnet50_ibn_a
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def weights_init_kaiming(m):
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@ -124,6 +125,8 @@ class Baseline(nn.Module):
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reduction=16,
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dropout_p=0.2,
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last_stride=last_stride)
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elif model_name == 'resnet50_ibn_a':
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self.base = resnet50_ibn_a(last_stride)
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if pretrain_choice == 'imagenet':
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self.base.load_param(model_path)
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