Awesome
beyond-part-models-gluon
Implementation of <Beyond Part Models: Person Retrieval with Refined Part Pooling>, using gluon(mxnet)
Memo
- Model Implementation
- Metric Coding.
- Data Loading.
- Result Saving.
- Process Control.
- Train Process.
- Test Process.
- Eval Process.
- Show Result.
- Market-1501 Prepare
- Duke Prepare
- ...
Result
The model is based on resnet50. Input images are resized to 384x128. Feature channels are set 256. Batchsize is set 32, which is different from the paper. Here we just show some results.
Market-1501
BS | Network | PCB | PN | RPP | FT | CMC1 | CMC5 | CMC10 | mAP | Note |
---|---|---|---|---|---|---|---|---|---|---|
32 | Resnet50_v2 | w/o | w/o | 89.76 | 96.20 | 97.51 | 75.22 | (512dim) | ||
64 | Resnet50_v2 | w/o | w/o | 85.3 | - | - | - | 68.5 | ||
32 | Resnet50_v2 | w/o | w/o | 91.75 | 96.59 | 98.22 | 77.68 | |||
32 | Resnet50_v1 | w/o | w/o | 88.39 | 94.80 | 96.76 | 71.78 | |||
64 | Resnet50_v2 | w | 6 | w/o | 92.3 | 97.2 | 98.2 | 77.4 | (in paper) | |
32 | Resnet50_v2 | w | 6 | w/o | 92.61 | 96.82 | 97.74 | 77.33 | ||
32 | Resnet50_v1 | w | 6 | w/o | - | - | - | - | ||
32 | Resnet50_v2 | w | 6 | w | no | 91.66 | 96.59 | 97.86 | 76.83 | |
32 | Resnet50_v1 | w | 6 | w | no | - | - | - | - | |
64 | Resnet50_v2 | w | 6 | w | yes | 93.8 | 97.5 | 98.5 | 81.6 | (in paper) |
32 | Resnet50_v2 | w | 6 | w | yes | 92.64 | 96.97 | 98.01 | 77.09 | (processing) |
32 | Resnet50_v1 | w | 6 | w | yes | - | - | - | - |
Usage
Usage:
main.py [options]
main.py --withpcb [options]
main.py --withpcb --withrpp [options]
General Options:
-h, --help Print this message
--logfile=<str> File path for saving log message.
--device_type=<str> Device Type for running the model [default: cpu]
--device_id=<int> Device ID for running the model [default: 0]
Network Options:
--basenet_type=<str> BaseNet type for Model [default: resnet50_v2]
--classes_num=<int> Output classes number of the network [default: 751]
--feature_channels=<int> Feature channels of the network [default: 512]
--partnum=<int> The number of the pcb parts. [default: 6]
--feature_weight_share If the six partnum share weights.
--base_not_pretrained If the base network don't pretrained on ImageNet
--pretrain_path=<str> Path to pretrained model.
Training Setting Options:
--Optim=<str> Optimizer Type [default: sgd]
--LRpolicy=<str> Learning rate policy [default: multistep]
--milestones=<list> Step milestone for multistep policy [default: [40,]]
--gamma=<float> Gamma for multistep policy [default: 0.1]
--max_epochs=<int> Max Train epochs [default: 60]
--val_epochs=<int> Val step stone [default: 5]
--snap_epochs=<int> Snap step stone [default: 5]
--Snap=<str> Model state dict file path [default: saved/]
Data Options:
--resize_size=<tuple> Image resize size tuple (height, width) [default: (384, 128)]
--crop_size=<tuple> Image crop size tuple (height, width) [default: (384, 128)]
--batchsize=<int> Batchsize [default: 32]
Train Data Options:
--trainList=<str> Train files list txt [default: datas/Market1501/train.txt]
--trainIMpath=<str> Train sketch images path prefix [default: datas/Market1501/]
Test Data Options:
--queryList=<str> Query files list txt [default: datas/Market1501/query.txt]
--queryIMpath=<str> Query sketch images path prefix [default: datas/Market1501/]
--galleryList=<str> Gallery files list txt [default: datas/Market1501/gallery.txt]
--galleryIMpath=<str> Gallery sketch images path prefix [default: datas/Market1501/]
Learning Rate Options:
--learning_rate=<float> Learning rate for training process [default: 0.01]
--weight_decay=<float> Weight decay for training process [default: 0.0005]
--momentum=<float> Momentum for the SGD Optimizer [default: 0.9]
--base_not_train If don't train base network.
--base_lr_scale=<float> Learing rate scale rate for the base network [default: 0.1]
--tail_not_train If don't train tail module, when w/o pcb and w/o rpp.
--tail_lr_scale=<float> Learing rate scale rate for the tail module.
--rpp_not_train If don't train the rpp module.
--rpp_lr_scale=<float> Learing rate scale rate for the rpp module.
Note
I will introduce my code in this part.
The experiment
contains all scripts used for experiments.
data
In the data
package, I put the scripts about loading data and save snapshots.
textdataset.py
defines the TextDataset, which define the dataset defined by a text file.transform.py
defines the classes Transformer.ListTransformer
is used for training.Market1501_Transformer
is used for market_1501 and duke testing.
saver.py
defines a object for saving snapshots, only when the result is better.
metric
In the reidmetric.py
, which define a metric class designed for Re-ID.
model
The model
package contains the design of the model structures.
The PCBRPPNet
is the implementation of the beyond part model.
Params withpcb
, withrpp
and feature_shared_weight
are designed for the different situation.
process
I defined two kind of processor to control the processes of the training and testing. We could implement function for our own experiments.
epochprocessor.py
is designed for using epoch to control the number of training timesiterprocessor.py
is designed for using iter to control the number of training times