Initial commit

.gitignore

@@ -0,0 +1,54 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Project Specific
+models/*.weights
+models/*.pth
+data/*
+data/*.csv

LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020 Torben Teepe
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -3,3 +3,27 @@ This repository contains the PyTorch code for:
 GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition
 [Torben Teepe](https://github.com/tteepe), Ali Khan, Johannes Gilg, [Fabian Herzog](https://github.com/fubel)
 
+![Pipeline](images/pipeline.png)
+
+## Quick Start
+Quick Start & models coming soon!
+
+## Main Results
+Top-1 Accuracy per probe angle excluding identical-view cases for the provided models on 
+[CASIA-B](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp) dataset.
+
+|        |    0 |   18 |   36 |   54 |   72 |   90 |   108 |   126 |   144 |   162 |   180 |   mean |
+|:-------|-----:|-----:|-----:|-----:|-----:|-----:|------:|------:|------:|------:|------:|-------:|
+| NM#5-6 | 85.3 | 88.5 | 91   | 92.5 | 87.2 | 86.5 |  88.4 |  89.2 |  87.9 |  85.9 |  81.9 |   87.7 |
+| BG#1-2 | 75.8 | 76.7 | 75.9 | 76.1 | 71.4 | 73.9 |  78   |  74.7 |  75.4 |  75.4 |  69.2 |   74.8 |
+| CL#1-2 | 69.6 | 66.1 | 68.8 | 67.2 | 64.5 | 62   |  69.5 |  65.6 |  65.7 |  66.1 |  64.3 |   66.3 |
+
+## Licence & Acknowledgement
+GaitPose itself is released under the MIT License (see LICENSE).
+
+The following parts of the code are borrowed from other projects. Thanks for their wonderful work!
+- Object Detector: [eriklindernoren/PyTorch-YOLOv3](https://github.com/eriklindernoren/PyTorch-YOLOv3)
+- Pose Estimator: [HRNet/HRNet-Human-Pose-Estimation](https://github.com/HRNet/HRNet-Human-Pose-Estimation)
+- ST-GCN Model: [yysijie/st-gcn](https://github.com/yysijie/st-gcn)
+- ResGCNv1 Model: [yfsong0709/ResGCNv1](https://github.com/yfsong0709/ResGCNv1)
+- SupCon Loss: [HobbitLong/SupContrast](https://github.com/HobbitLong/SupContrast)

models/download_weights.sh

@@ -0,0 +1,14 @@
+#!/bin/bash
+# Download weights for vanilla YOLOv3
+wget -c https://pjreddie.com/media/files/yolov3.weights
+# Download weights for tiny YOLOv3
+wget -c https://pjreddie.com/media/files/yolov3-tiny.weights
+## Download weights for backbone network
+#wget -c https://pjreddie.com/media/files/darknet53.conv.74
+
+print "#############################################################"
+print "######## Weights for HRNet Pose Estimation need to ##########"
+print "######## be downloaded manually from here:         ##########"
+print "######## https://drive.google.com/drive/folders/1nzM_OBV9LbAEA7HClC0chEyf_7ECDXYA"
+print "######## Files: pose_hrnet_*.pth                   ##########"
+print "#############################################################"

requirements.txt

@@ -0,0 +1,13 @@
+yacs==0.1.8
+numpy==1.19.5
+torch==1.7.1
+torchvision==0.8.2
+matplotlib==3.3.3
+tabulate==0.8.7
+tensorflow==2.4.0
+tensorboard==2.4.0
+pillow==8.1.0
+tqdm==4.56.0
+opencv-python~=4.5
+jupyter==1.0.0
+pandas==1.1.0

src/common.py

@@ -0,0 +1,171 @@
+import os
+import argparse
+import torch
+from models.st_gcn.st_gcn import STGCNEmbedding
+import models.ResGCNv1
+
+
+def parse_option():
+    parser = argparse.ArgumentParser(description="Training model on gait sequence")
+    parser.add_argument("dataset", choices=["casia-b", "outdoor-gait", "tum-gaid"])
+    parser.add_argument("train_data_path", help="Path to train data CSV")
+    parser.add_argument("--valid_data_path", help="Path to validation data CSV")
+    parser.add_argument("--valid_split", type=float, default=0.2)
+
+    parser.add_argument("--checkpoint_path", help="Path to checkpoint to resume")
+    parser.add_argument("--weight_path", help="Path to weights for model")
+
+    # Optionals
+    parser.add_argument("--num_workers", type=int, default=8)
+    parser.add_argument(
+        "--gpus", default="0", help="-1 for CPU, use comma for multiple gpus"
+    )
+    parser.add_argument("--batch_size", type=int, default=64)
+    parser.add_argument("--batch_size_validation", type=int, default=64)
+    parser.add_argument("--epochs", type=int, default=500)
+    parser.add_argument("--start_epoch", type=int, default=1)
+    parser.add_argument("--log_interval", type=int, default=10)
+    parser.add_argument("--save_interval", type=int, default=50, help="save frequency")
+    parser.add_argument(
+        "--save_best_start", type=float, default=0.3, help="save frequency"
+    )
+    parser.add_argument("--use_amp", action="store_true")
+    parser.add_argument("--tune", action="store_true")
+    parser.add_argument("--shuffle", action="store_true")
+    parser.add_argument("--exp_name", help="Name of the experiment")
+
+    parser.add_argument("--network_name", default="resgcn-n39-r4")
+    parser.add_argument("--sequence_length", type=int, default=60)
+    parser.add_argument("--embedding_layer_size", type=int, default=256)
+    parser.add_argument("--temporal_kernel_size", type=int, default=9)
+    parser.add_argument("--dropout", type=float, default=0.4)
+    parser.add_argument("--learning_rate", type=float, default=1e-3)
+    parser.add_argument(
+        "--lr_decay_rate", type=float, default=0.1, help="decay rate for learning rate"
+    )
+    parser.add_argument("--point_noise_std", type=float, default=0.05)
+    parser.add_argument("--joint_noise_std", type=float, default=0.1)
+    parser.add_argument("--flip_probability", type=float, default=0.5)
+    parser.add_argument("--mirror_probability", type=float, default=0.5)
+    parser.add_argument("--weight_decay", type=float, default=1e-5)
+    parser.add_argument("--use_multi_branch", action="store_true")
+    parser.add_argument(
+        "--temp", type=float, default=0.07, help="temperature for loss function"
+    )
+    opt = parser.parse_args()
+
+    # Sanitize opts
+    opt.gpus_str = opt.gpus
+    opt.gpus = [int(gpu) for gpu in opt.gpus.split(",")]
+
+    return opt
+
+
+def log_hyperparameter(writer, opt, accuracy, loss):
+    writer.add_hparams(
+        {
+            "batch_size": opt.batch_size,
+            "sequence_length": opt.sequence_length,
+            "embedding_layer_size": opt.embedding_layer_size,
+            "dropout": opt.dropout,
+            "learning_rate": opt.learning_rate,
+            "lr_decay_rate": opt.lr_decay_rate,
+            "point_noise_std": opt.point_noise_std,
+            "weight_decay": opt.weight_decay,
+            "temp": opt.temp,
+        },
+        {
+            "hparam/accuracy": accuracy,
+            "hparam/loss": loss,
+        },
+    )
+
+
+def setup_environment(opt):
+    # HACK: Fix tensorboard
+    import tensorflow as tf
+    import tensorboard as tb
+
+    tf.io.gfile = tb.compat.tensorflow_stub.io.gfile
+
+    os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpus_str
+    opt.cuda = opt.gpus[0] >= 0
+    torch.device("cuda" if opt.cuda else "cpu")
+
+    return opt
+
+
+def get_model_stgcn(opt):
+    # Model
+    input_channels = 3
+    edge_importance_weighting = True
+    graph_args = {"strategy": "spatial"}
+
+    embedding_net = STGCNEmbedding(
+        input_channels,
+        graph_args,
+        edge_importance_weighting=edge_importance_weighting,
+        embedding_layer_size=opt.embedding_layer_size,
+        temporal_kernel_size=opt.temporal_kernel_size,
+        dropout=opt.dropout,
+    )
+
+    return embedding_net
+
+
+def get_model_resgcn(graph, opt):
+    model_args = {
+        "A": torch.tensor(graph.A, dtype=torch.float32, requires_grad=False),
+        "num_class": opt.embedding_layer_size,
+        "num_input": 1 if not opt.use_multi_branch else 3,
+        "num_channel": 3 if not opt.use_multi_branch else 6,
+        "parts": graph.parts,
+    }
+    return models.ResGCNv1.create(opt.network_name, **model_args)
+
+
+def get_trainer(model, opt, steps_per_epoch):
+    optimizer = torch.optim.Adam(
+        model.parameters(), lr=opt.learning_rate, weight_decay=opt.weight_decay
+    )
+    scheduler = torch.optim.lr_scheduler.OneCycleLR(
+        optimizer, opt.learning_rate, epochs=opt.epochs, steps_per_epoch=steps_per_epoch
+    )
+    scaler = torch.cuda.amp.GradScaler(enabled=opt.use_amp)
+
+    return optimizer, scheduler, scaler
+
+
+def load_checkpoint(model, optimizer, scheduler, scaler, opt):
+    if opt.checkpoint_path is not None:
+        checkpoint = torch.load(opt.checkpoint_path)
+        model.load_state_dict(checkpoint["model"])
+        optimizer.load_state_dict(checkpoint["optimizer"])
+        scheduler.load_state_dict(checkpoint["scheduler"])
+        scaler.load_state_dict(checkpoint["scaler"])
+        opt.start_epoch = checkpoint["epoch"]
+
+    if opt.weight_path is not None:
+        checkpoint = torch.load(opt.weight_path)
+        model.load_state_dict(checkpoint["model"], strict=False)
+
+
+def save_model(model, optimizer, scheduler, scaler, opt, epoch, save_file):
+    print("==> Saving...")
+    state = {
+        "opt": opt,
+        "model": model.state_dict(),
+        "optimizer": optimizer.state_dict(),
+        "scheduler": scheduler.state_dict(),
+        "scaler": scaler.state_dict(),
+        "epoch": epoch,
+    }
+    torch.save(state, save_file)
+    del state
+
+
+def count_parameters(model):
+    """
+    Useful function to compute number of parameters in a model.
+    """
+    return sum(p.numel() for p in model.parameters() if p.requires_grad)

src/datasets/__init__.py

@@ -0,0 +1,11 @@
+from .preparation import DatasetSimple, DatasetDetections
+from .gait import (
+    CasiaBPose,
+)
+
+
+def dataset_factory(name):
+    if name == "casia-b":
+        return CasiaBPose
+
+    raise ValueError()