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.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+deep_sort_pytorch/deep_sort/deep/checkpoint/ckpt.t7 filter=lfs diff=lfs merge=lfs -text

deep_sort_pytorch/.gitignore

@@ -0,0 +1,13 @@
+# Folders
+__pycache__/
+build/
+*.egg-info
+
+
+# Files
+*.weights
+*.t7
+*.mp4
+*.avi
+*.so
+*.txt

deep_sort_pytorch/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2020 Ziqiang
+
+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.

deep_sort_pytorch/README.md

@@ -0,0 +1,137 @@
+# Deep Sort with PyTorch
+
+![](demo/demo.gif)
+
+## Update(1-1-2020)
+Changes
+- fix bugs
+- refactor code
+- accerate detection by adding nms on gpu
+
+## Latest Update(07-22)
+Changes
+- bug fix (Thanks @JieChen91 and @yingsen1 for bug reporting).  
+- using batch for feature extracting for each frame, which lead to a small speed up.  
+- code improvement.
+
+Futher improvement direction  
+- Train detector on specific dataset rather than the official one.
+- Retrain REID model on pedestrain dataset for better performance.
+- Replace YOLOv3 detector with advanced ones.
+
+**Any contributions to this repository is welcome!**
+
+
+## Introduction
+This is an implement of MOT tracking algorithm deep sort. Deep sort is basicly the same with sort but added a CNN model to extract features in image of human part bounded by a detector. This CNN model is indeed a RE-ID model and the detector used in [PAPER](https://arxiv.org/abs/1703.07402) is FasterRCNN , and the original source code is [HERE](https://github.com/nwojke/deep_sort).  
+However in original code, the CNN model is implemented with tensorflow, which I'm not familier with. SO I re-implemented the CNN feature extraction model with PyTorch, and changed the CNN model a little bit. Also, I use **YOLOv3** to generate bboxes instead of FasterRCNN.
+
+## Dependencies
+- python 3 (python2 not sure)
+- numpy
+- scipy
+- opencv-python
+- sklearn
+- torch >= 0.4
+- torchvision >= 0.1
+- pillow
+- vizer
+- edict
+
+## Quick Start
+0. Check all dependencies installed
+```bash
+pip install -r requirements.txt
+```
+for user in china, you can specify pypi source to accelerate install like:
+```bash
+pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
+```
+
+1. Clone this repository
+```
+git clone git@github.com:ZQPei/deep_sort_pytorch.git
+```
+
+2. Download YOLOv3 parameters
+```
+cd detector/YOLOv3/weight/
+wget https://pjreddie.com/media/files/yolov3.weights
+wget https://pjreddie.com/media/files/yolov3-tiny.weights
+cd ../../../
+```
+
+3. Download deepsort parameters ckpt.t7
+```
+cd deep_sort/deep/checkpoint
+# download ckpt.t7 from
+https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6 to this folder
+cd ../../../
+```  
+
+4. Compile nms module
+```bash
+cd detector/YOLOv3/nms
+sh build.sh
+cd ../../..
+```
+
+Notice:
+If compiling failed, the simplist way is to **Upgrade your pytorch >= 1.1 and torchvision >= 0.3" and you can avoid the troublesome compiling problems which are most likely caused by either `gcc version too low` or `libraries missing`.
+
+5. Run demo
+```
+usage: python yolov3_deepsort.py VIDEO_PATH
+                                [--help]
+                                [--frame_interval FRAME_INTERVAL]
+                                [--config_detection CONFIG_DETECTION]
+                                [--config_deepsort CONFIG_DEEPSORT]
+                                [--display]
+                                [--display_width DISPLAY_WIDTH]
+                                [--display_height DISPLAY_HEIGHT]
+                                [--save_path SAVE_PATH]          
+                                [--cpu]          
+
+# yolov3 + deepsort
+python yolov3_deepsort.py [VIDEO_PATH]
+
+# yolov3_tiny + deepsort
+python yolov3_deepsort.py [VIDEO_PATH] --config_detection ./configs/yolov3_tiny.yaml
+
+# yolov3 + deepsort on webcam
+python3 yolov3_deepsort.py /dev/video0 --camera 0
+
+# yolov3_tiny + deepsort on webcam
+python3 yolov3_deepsort.py /dev/video0 --config_detection ./configs/yolov3_tiny.yaml --camera 0
+```
+Use `--display` to enable display.  
+Results will be saved to `./output/results.avi` and `./output/results.txt`.
+
+All files above can also be accessed from BaiduDisk!  
+linker：[BaiduDisk](https://pan.baidu.com/s/1YJ1iPpdFTlUyLFoonYvozg)
+passwd：fbuw
+
+## Training the RE-ID model
+The original model used in paper is in original_model.py, and its parameter here [original_ckpt.t7](https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6).  
+
+To train the model, first you need download [Market1501](http://www.liangzheng.com.cn/Project/project_reid.html) dataset or [Mars](http://www.liangzheng.com.cn/Project/project_mars.html) dataset.  
+
+Then you can try [train.py](deep_sort/deep/train.py) to train your own parameter and evaluate it using [test.py](deep_sort/deep/test.py) and [evaluate.py](deep_sort/deep/evalute.py).
+![train.jpg](deep_sort/deep/train.jpg)
+
+## Demo videos and images
+[demo.avi](https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6)
+[demo2.avi](https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6)
+
+![1.jpg](demo/1.jpg)
+![2.jpg](demo/2.jpg)
+
+
+## References
+- paper: [Simple Online and Realtime Tracking with a Deep Association Metric](https://arxiv.org/abs/1703.07402)
+
+- code: [nwojke/deep_sort](https://github.com/nwojke/deep_sort)
+
+- paper: [YOLOv3](https://pjreddie.com/media/files/papers/YOLOv3.pdf)
+
+- code: [Joseph Redmon/yolov3](https://pjreddie.com/darknet/yolo/)

deep_sort_pytorch/configs/deep_sort.yaml

@@ -0,0 +1,10 @@
+DEEPSORT:
+  REID_CKPT: "deep_sort_pytorch/deep_sort/deep/checkpoint/ckpt.t7"
+  MAX_DIST: 0.2
+  MIN_CONFIDENCE: 0.3
+  NMS_MAX_OVERLAP: 0.5
+  MAX_IOU_DISTANCE: 0.7
+  MAX_AGE: 70
+  N_INIT: 3
+  NN_BUDGET: 100
+  

deep_sort_pytorch/deep_sort/README.md

@@ -0,0 +1,3 @@
+# Deep Sort 
+
+This is the implemention of deep sort with pytorch.

deep_sort_pytorch/deep_sort/__init__.py

@@ -0,0 +1,21 @@
+from .deep_sort import DeepSort
+
+
+__all__ = ['DeepSort', 'build_tracker']
+
+
+def build_tracker(cfg, use_cuda):
+    return DeepSort(cfg.DEEPSORT.REID_CKPT, 
+                max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE, 
+                nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE, 
+                max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET, use_cuda=use_cuda)
+    
+
+
+
+
+
+
+
+
+

deep_sort_pytorch/deep_sort/deep/checkpoint/ckpt.t7

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:df75ddef42c3d1bda67bc94b093e7ce61de7f75a89f36a8f868a428462198316
+size 46034619

deep_sort_pytorch/deep_sort/deep/evaluate.py

@@ -0,0 +1,13 @@
+import torch
+
+features = torch.load("features.pth")
+qf = features["qf"]
+ql = features["ql"]
+gf = features["gf"]
+gl = features["gl"]
+
+scores = qf.mm(gf.t())
+res = scores.topk(5, dim=1)[1][:, 0]
+top1correct = gl[res].eq(ql).sum().item()
+
+print("Acc top1:{:.3f}".format(top1correct / ql.size(0)))

deep_sort_pytorch/deep_sort/deep/feature_extractor.py

@@ -0,0 +1,54 @@
+import torch
+import torchvision.transforms as transforms
+import numpy as np
+import cv2
+import logging
+
+from .model import Net
+
+
+class Extractor(object):
+    def __init__(self, model_path, use_cuda=True):
+        self.net = Net(reid=True)
+        self.device = "cuda" if torch.cuda.is_available() and use_cuda else "cpu"
+        state_dict = torch.load(model_path, map_location=torch.device(self.device))[
+            'net_dict']
+        self.net.load_state_dict(state_dict)
+        logger = logging.getLogger("root.tracker")
+        logger.info("Loading weights from {}... Done!".format(model_path))
+        self.net.to(self.device)
+        self.size = (64, 128)
+        self.norm = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        ])
+
+    def _preprocess(self, im_crops):
+        """
+        TODO:
+            1. to float with scale from 0 to 1
+            2. resize to (64, 128) as Market1501 dataset did
+            3. concatenate to a numpy array
+            3. to torch Tensor
+            4. normalize
+        """
+        def _resize(im, size):
+            return cv2.resize(im.astype(np.float32)/255., size)
+
+        im_batch = torch.cat([self.norm(_resize(im, self.size)).unsqueeze(
+            0) for im in im_crops], dim=0).float()
+        return im_batch
+
+    def __call__(self, im_crops):
+        im_batch = self._preprocess(im_crops)
+        with torch.no_grad():
+            im_batch = im_batch.to(self.device)
+            features = self.net(im_batch)
+        return features.cpu().numpy()
+
+
+if __name__ == '__main__':
+    img = cv2.imread("demo.jpg")[:, :, (2, 1, 0)]
+    extr = Extractor("checkpoint/ckpt.t7")
+    feature = extr(img)
+    print(feature.shape)

deep_sort_pytorch/deep_sort/deep/model.py

@@ -0,0 +1,109 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class BasicBlock(nn.Module):
+    def __init__(self, c_in, c_out, is_downsample=False):
+        super(BasicBlock, self).__init__()
+        self.is_downsample = is_downsample
+        if is_downsample:
+            self.conv1 = nn.Conv2d(
+                c_in, c_out, 3, stride=2, padding=1, bias=False)
+        else:
+            self.conv1 = nn.Conv2d(
+                c_in, c_out, 3, stride=1, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(c_out)
+        self.relu = nn.ReLU(True)
+        self.conv2 = nn.Conv2d(c_out, c_out, 3, stride=1,
+                               padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(c_out)
+        if is_downsample:
+            self.downsample = nn.Sequential(
+                nn.Conv2d(c_in, c_out, 1, stride=2, bias=False),
+                nn.BatchNorm2d(c_out)
+            )
+        elif c_in != c_out:
+            self.downsample = nn.Sequential(
+                nn.Conv2d(c_in, c_out, 1, stride=1, bias=False),
+                nn.BatchNorm2d(c_out)
+            )
+            self.is_downsample = True
+
+    def forward(self, x):
+        y = self.conv1(x)
+        y = self.bn1(y)
+        y = self.relu(y)
+        y = self.conv2(y)
+        y = self.bn2(y)
+        if self.is_downsample:
+            x = self.downsample(x)
+        return F.relu(x.add(y), True)
+
+
+def make_layers(c_in, c_out, repeat_times, is_downsample=False):
+    blocks = []
+    for i in range(repeat_times):
+        if i == 0:
+            blocks += [BasicBlock(c_in, c_out, is_downsample=is_downsample), ]
+        else:
+            blocks += [BasicBlock(c_out, c_out), ]
+    return nn.Sequential(*blocks)
+
+
+class Net(nn.Module):
+    def __init__(self, num_classes=751, reid=False):
+        super(Net, self).__init__()
+        # 3 128 64
+        self.conv = nn.Sequential(
+            nn.Conv2d(3, 64, 3, stride=1, padding=1),
+            nn.BatchNorm2d(64),
+            nn.ReLU(inplace=True),
+            # nn.Conv2d(32,32,3,stride=1,padding=1),
+            # nn.BatchNorm2d(32),
+            # nn.ReLU(inplace=True),
+            nn.MaxPool2d(3, 2, padding=1),
+        )
+        # 32 64 32
+        self.layer1 = make_layers(64, 64, 2, False)
+        # 32 64 32
+        self.layer2 = make_layers(64, 128, 2, True)
+        # 64 32 16
+        self.layer3 = make_layers(128, 256, 2, True)
+        # 128 16 8
+        self.layer4 = make_layers(256, 512, 2, True)
+        # 256 8 4
+        self.avgpool = nn.AvgPool2d((8, 4), 1)
+        # 256 1 1
+        self.reid = reid
+        self.classifier = nn.Sequential(
+            nn.Linear(512, 256),
+            nn.BatchNorm1d(256),
+            nn.ReLU(inplace=True),
+            nn.Dropout(),
+            nn.Linear(256, num_classes),
+        )
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        # B x 128
+        if self.reid:
+            x = x.div(x.norm(p=2, dim=1, keepdim=True))
+            return x
+        # classifier
+        x = self.classifier(x)
+        return x
+
+
+if __name__ == '__main__':
+    net = Net()
+    x = torch.randn(4, 3, 128, 64)
+    y = net(x)
+    import ipdb
+    ipdb.set_trace()

deep_sort_pytorch/deep_sort/deep/original_model.py

@@ -0,0 +1,111 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class BasicBlock(nn.Module):
+    def __init__(self, c_in, c_out, is_downsample=False):
+        super(BasicBlock, self).__init__()
+        self.is_downsample = is_downsample
+        if is_downsample:
+            self.conv1 = nn.Conv2d(
+                c_in, c_out, 3, stride=2, padding=1, bias=False)
+        else:
+            self.conv1 = nn.Conv2d(
+                c_in, c_out, 3, stride=1, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(c_out)
+        self.relu = nn.ReLU(True)
+        self.conv2 = nn.Conv2d(c_out, c_out, 3, stride=1,
+                               padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(c_out)
+        if is_downsample:
+            self.downsample = nn.Sequential(
+                nn.Conv2d(c_in, c_out, 1, stride=2, bias=False),
+                nn.BatchNorm2d(c_out)
+            )
+        elif c_in != c_out:
+            self.downsample = nn.Sequential(
+                nn.Conv2d(c_in, c_out, 1, stride=1, bias=False),
+                nn.BatchNorm2d(c_out)
+            )
+            self.is_downsample = True
+
+    def forward(self, x):
+        y = self.conv1(x)
+        y = self.bn1(y)
+        y = self.relu(y)
+        y = self.conv2(y)
+        y = self.bn2(y)
+        if self.is_downsample:
+            x = self.downsample(x)
+        return F.relu(x.add(y), True)
+
+
+def make_layers(c_in, c_out, repeat_times, is_downsample=False):
+    blocks = []
+    for i in range(repeat_times):
+        if i == 0:
+            blocks += [BasicBlock(c_in, c_out, is_downsample=is_downsample), ]
+        else:
+            blocks += [BasicBlock(c_out, c_out), ]
+    return nn.Sequential(*blocks)
+
+
+class Net(nn.Module):
+    def __init__(self, num_classes=625, reid=False):
+        super(Net, self).__init__()
+        # 3 128 64
+        self.conv = nn.Sequential(
+            nn.Conv2d(3, 32, 3, stride=1, padding=1),
+            nn.BatchNorm2d(32),
+            nn.ELU(inplace=True),
+            nn.Conv2d(32, 32, 3, stride=1, padding=1),
+            nn.BatchNorm2d(32),
+            nn.ELU(inplace=True),
+            nn.MaxPool2d(3, 2, padding=1),
+        )
+        # 32 64 32
+        self.layer1 = make_layers(32, 32, 2, False)
+        # 32 64 32
+        self.layer2 = make_layers(32, 64, 2, True)
+        # 64 32 16
+        self.layer3 = make_layers(64, 128, 2, True)
+        # 128 16 8
+        self.dense = nn.Sequential(
+            nn.Dropout(p=0.6),
+            nn.Linear(128*16*8, 128),
+            nn.BatchNorm1d(128),
+            nn.ELU(inplace=True)
+        )
+        # 256 1 1
+        self.reid = reid
+        self.batch_norm = nn.BatchNorm1d(128)
+        self.classifier = nn.Sequential(
+            nn.Linear(128, num_classes),
+        )
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+
+        x = x.view(x.size(0), -1)
+        if self.reid:
+            x = self.dense[0](x)
+            x = self.dense[1](x)
+            x = x.div(x.norm(p=2, dim=1, keepdim=True))
+            return x
+        x = self.dense(x)
+        # B x 128
+        # classifier
+        x = self.classifier(x)
+        return x
+
+
+if __name__ == '__main__':
+    net = Net(reid=True)
+    x = torch.randn(4, 3, 128, 64)
+    y = net(x)
+    import ipdb
+    ipdb.set_trace()

deep_sort_pytorch/deep_sort/deep/test.py

@@ -0,0 +1,80 @@
+import torch
+import torch.backends.cudnn as cudnn
+import torchvision
+
+import argparse
+import os
+
+from model import Net
+
+parser = argparse.ArgumentParser(description="Train on market1501")
+parser.add_argument("--data-dir", default='data', type=str)
+parser.add_argument("--no-cuda", action="store_true")
+parser.add_argument("--gpu-id", default=0, type=int)
+args = parser.parse_args()
+
+# device
+device = "cuda:{}".format(
+    args.gpu_id) if torch.cuda.is_available() and not args.no_cuda else "cpu"
+if torch.cuda.is_available() and not args.no_cuda:
+    cudnn.benchmark = True
+
+# data loader
+root = args.data_dir
+query_dir = os.path.join(root, "query")
+gallery_dir = os.path.join(root, "gallery")
+transform = torchvision.transforms.Compose([
+    torchvision.transforms.Resize((128, 64)),
+    torchvision.transforms.ToTensor(),
+    torchvision.transforms.Normalize(
+        [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+queryloader = torch.utils.data.DataLoader(
+    torchvision.datasets.ImageFolder(query_dir, transform=transform),
+    batch_size=64, shuffle=False
+)
+galleryloader = torch.utils.data.DataLoader(
+    torchvision.datasets.ImageFolder(gallery_dir, transform=transform),
+    batch_size=64, shuffle=False
+)
+
+# net definition
+net = Net(reid=True)
+assert os.path.isfile(
+    "./checkpoint/ckpt.t7"), "Error: no checkpoint file found!"
+print('Loading from checkpoint/ckpt.t7')
+checkpoint = torch.load("./checkpoint/ckpt.t7")
+net_dict = checkpoint['net_dict']
+net.load_state_dict(net_dict, strict=False)
+net.eval()
+net.to(device)
+
+# compute features
+query_features = torch.tensor([]).float()
+query_labels = torch.tensor([]).long()
+gallery_features = torch.tensor([]).float()
+gallery_labels = torch.tensor([]).long()
+
+with torch.no_grad():
+    for idx, (inputs, labels) in enumerate(queryloader):
+        inputs = inputs.to(device)
+        features = net(inputs).cpu()
+        query_features = torch.cat((query_features, features), dim=0)
+        query_labels = torch.cat((query_labels, labels))
+
+    for idx, (inputs, labels) in enumerate(galleryloader):
+        inputs = inputs.to(device)
+        features = net(inputs).cpu()
+        gallery_features = torch.cat((gallery_features, features), dim=0)
+        gallery_labels = torch.cat((gallery_labels, labels))
+
+gallery_labels -= 2
+
+# save features
+features = {
+    "qf": query_features,
+    "ql": query_labels,
+    "gf": gallery_features,
+    "gl": gallery_labels
+}
+torch.save(features, "features.pth")

deep_sort_pytorch/deep_sort/deep/train.py

@@ -0,0 +1,206 @@
+import argparse
+import os
+import time
+
+import numpy as np
+import matplotlib.pyplot as plt
+import torch
+import torch.backends.cudnn as cudnn
+import torchvision
+
+from model import Net
+
+parser = argparse.ArgumentParser(description="Train on market1501")
+parser.add_argument("--data-dir", default='data', type=str)
+parser.add_argument("--no-cuda", action="store_true")
+parser.add_argument("--gpu-id", default=0, type=int)
+parser.add_argument("--lr", default=0.1, type=float)
+parser.add_argument("--interval", '-i', default=20, type=int)
+parser.add_argument('--resume', '-r', action='store_true')
+args = parser.parse_args()
+
+# device
+device = "cuda:{}".format(
+    args.gpu_id) if torch.cuda.is_available() and not args.no_cuda else "cpu"
+if torch.cuda.is_available() and not args.no_cuda:
+    cudnn.benchmark = True
+
+# data loading
+root = args.data_dir
+train_dir = os.path.join(root, "train")
+test_dir = os.path.join(root, "test")
+transform_train = torchvision.transforms.Compose([
+    torchvision.transforms.RandomCrop((128, 64), padding=4),
+    torchvision.transforms.RandomHorizontalFlip(),
+    torchvision.transforms.ToTensor(),
+    torchvision.transforms.Normalize(
+        [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+transform_test = torchvision.transforms.Compose([
+    torchvision.transforms.Resize((128, 64)),
+    torchvision.transforms.ToTensor(),
+    torchvision.transforms.Normalize(
+        [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+trainloader = torch.utils.data.DataLoader(
+    torchvision.datasets.ImageFolder(train_dir, transform=transform_train),
+    batch_size=64, shuffle=True
+)
+testloader = torch.utils.data.DataLoader(
+    torchvision.datasets.ImageFolder(test_dir, transform=transform_test),
+    batch_size=64, shuffle=True
+)
+num_classes = max(len(trainloader.dataset.classes),
+                  len(testloader.dataset.classes))
+
+# net definition
+start_epoch = 0
+net = Net(num_classes=num_classes)
+if args.resume:
+    assert os.path.isfile(
+        "./checkpoint/ckpt.t7"), "Error: no checkpoint file found!"
+    print('Loading from checkpoint/ckpt.t7')
+    checkpoint = torch.load("./checkpoint/ckpt.t7")
+    # import ipdb; ipdb.set_trace()
+    net_dict = checkpoint['net_dict']
+    net.load_state_dict(net_dict)
+    best_acc = checkpoint['acc']
+    start_epoch = checkpoint['epoch']
+net.to(device)
+
+# loss and optimizer
+criterion = torch.nn.CrossEntropyLoss()
+optimizer = torch.optim.SGD(
+    net.parameters(), args.lr, momentum=0.9, weight_decay=5e-4)
+best_acc = 0.
+
+# train function for each epoch
+
+
+def train(epoch):
+    print("\nEpoch : %d" % (epoch+1))
+    net.train()
+    training_loss = 0.
+    train_loss = 0.
+    correct = 0
+    total = 0
+    interval = args.interval
+    start = time.time()
+    for idx, (inputs, labels) in enumerate(trainloader):
+        # forward
+        inputs, labels = inputs.to(device), labels.to(device)
+        outputs = net(inputs)
+        loss = criterion(outputs, labels)
+
+        # backward
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        # accumurating
+        training_loss += loss.item()
+        train_loss += loss.item()
+        correct += outputs.max(dim=1)[1].eq(labels).sum().item()
+        total += labels.size(0)
+
+        # print
+        if (idx+1) % interval == 0:
+            end = time.time()
+            print("[progress:{:.1f}%]time:{:.2f}s Loss:{:.5f} Correct:{}/{} Acc:{:.3f}%".format(
+                100.*(idx+1)/len(trainloader), end-start, training_loss /
+                interval, correct, total, 100.*correct/total
+            ))
+            training_loss = 0.
+            start = time.time()
+
+    return train_loss/len(trainloader), 1. - correct/total
+
+
+def test(epoch):
+    global best_acc
+    net.eval()
+    test_loss = 0.
+    correct = 0
+    total = 0
+    start = time.time()
+    with torch.no_grad():
+        for idx, (inputs, labels) in enumerate(testloader):
+            inputs, labels = inputs.to(device), labels.to(device)
+            outputs = net(inputs)
+            loss = criterion(outputs, labels)
+
+            test_loss += loss.item()
+            correct += outputs.max(dim=1)[1].eq(labels).sum().item()
+            total += labels.size(0)
+
+        print("Testing ...")
+        end = time.time()
+        print("[progress:{:.1f}%]time:{:.2f}s Loss:{:.5f} Correct:{}/{} Acc:{:.3f}%".format(
+            100.*(idx+1)/len(testloader), end-start, test_loss /
+            len(testloader), correct, total, 100.*correct/total
+        ))
+
+    # saving checkpoint
+    acc = 100.*correct/total
+    if acc > best_acc:
+        best_acc = acc
+        print("Saving parameters to checkpoint/ckpt.t7")
+        checkpoint = {
+            'net_dict': net.state_dict(),
+            'acc': acc,
+            'epoch': epoch,
+        }
+        if not os.path.isdir('checkpoint'):
+            os.mkdir('checkpoint')
+        torch.save(checkpoint, './checkpoint/ckpt.t7')
+
+    return test_loss/len(testloader), 1. - correct/total
+
+
+# plot figure
+x_epoch = []
+record = {'train_loss': [], 'train_err': [], 'test_loss': [], 'test_err': []}
+fig = plt.figure()
+ax0 = fig.add_subplot(121, title="loss")
+ax1 = fig.add_subplot(122, title="top1err")
+
+
+def draw_curve(epoch, train_loss, train_err, test_loss, test_err):
+    global record
+    record['train_loss'].append(train_loss)
+    record['train_err'].append(train_err)
+    record['test_loss'].append(test_loss)
+    record['test_err'].append(test_err)
+
+    x_epoch.append(epoch)
+    ax0.plot(x_epoch, record['train_loss'], 'bo-', label='train')
+    ax0.plot(x_epoch, record['test_loss'], 'ro-', label='val')
+    ax1.plot(x_epoch, record['train_err'], 'bo-', label='train')
+    ax1.plot(x_epoch, record['test_err'], 'ro-', label='val')
+    if epoch == 0:
+        ax0.legend()
+        ax1.legend()
+    fig.savefig("train.jpg")
+
+# lr decay
+
+
+def lr_decay():
+    global optimizer
+    for params in optimizer.param_groups:
+        params['lr'] *= 0.1
+        lr = params['lr']
+        print("Learning rate adjusted to {}".format(lr))
+
+
+def main():
+    for epoch in range(start_epoch, start_epoch+40):
+        train_loss, train_err = train(epoch)
+        test_loss, test_err = test(epoch)
+        draw_curve(epoch, train_loss, train_err, test_loss, test_err)
+        if (epoch+1) % 20 == 0:
+            lr_decay()
+
+
+if __name__ == '__main__':
+    main()

deep_sort_pytorch/deep_sort/deep_sort.py

@@ -0,0 +1,113 @@
+import numpy as np
+import torch
+
+from .deep.feature_extractor import Extractor
+from .sort.nn_matching import NearestNeighborDistanceMetric
+from .sort.detection import Detection
+from .sort.tracker import Tracker
+
+
+__all__ = ['DeepSort']
+
+
+class DeepSort(object):
+    def __init__(self, model_path, max_dist=0.2, min_confidence=0.3, nms_max_overlap=1.0, max_iou_distance=0.7, max_age=70, n_init=3, nn_budget=100, use_cuda=True):
+        self.min_confidence = min_confidence
+        self.nms_max_overlap = nms_max_overlap
+
+        self.extractor = Extractor(model_path, use_cuda=use_cuda)
+
+        max_cosine_distance = max_dist
+        metric = NearestNeighborDistanceMetric(
+            "cosine", max_cosine_distance, nn_budget)
+        self.tracker = Tracker(
+            metric, max_iou_distance=max_iou_distance, max_age=max_age, n_init=n_init)
+
+    def update(self, bbox_xywh, confidences, oids, ori_img):
+        self.height, self.width = ori_img.shape[:2]
+        # generate detections
+        features = self._get_features(bbox_xywh, ori_img)
+        bbox_tlwh = self._xywh_to_tlwh(bbox_xywh)
+        detections = [Detection(bbox_tlwh[i], conf, features[i],oid) for i, (conf,oid) in enumerate(zip(confidences,oids)) if conf > self.min_confidence]
+
+        # run on non-maximum supression
+        boxes = np.array([d.tlwh for d in detections])
+        scores = np.array([d.confidence for d in detections])
+
+        # update tracker
+        self.tracker.predict()
+        self.tracker.update(detections)
+
+        # output bbox identities
+        outputs = []
+        for track in self.tracker.tracks:
+            if not track.is_confirmed() or track.time_since_update > 1:
+                continue
+            box = track.to_tlwh()
+            x1, y1, x2, y2 = self._tlwh_to_xyxy(box)
+            track_id = track.track_id
+            track_oid = track.oid
+            outputs.append(np.array([x1, y1, x2, y2, track_id, track_oid], dtype=np.int))
+        if len(outputs) > 0:
+            outputs = np.stack(outputs, axis=0)
+        return outputs
+
+    """
+    TODO:
+        Convert bbox from xc_yc_w_h to xtl_ytl_w_h
+    Thanks JieChen91@github.com for reporting this bug!
+    """
+    @staticmethod
+    def _xywh_to_tlwh(bbox_xywh):
+        if isinstance(bbox_xywh, np.ndarray):
+            bbox_tlwh = bbox_xywh.copy()
+        elif isinstance(bbox_xywh, torch.Tensor):
+            bbox_tlwh = bbox_xywh.clone()
+        bbox_tlwh[:, 0] = bbox_xywh[:, 0] - bbox_xywh[:, 2] / 2.
+        bbox_tlwh[:, 1] = bbox_xywh[:, 1] - bbox_xywh[:, 3] / 2.
+        return bbox_tlwh
+
+    def _xywh_to_xyxy(self, bbox_xywh):
+        x, y, w, h = bbox_xywh
+        x1 = max(int(x - w / 2), 0)
+        x2 = min(int(x + w / 2), self.width - 1)
+        y1 = max(int(y - h / 2), 0)
+        y2 = min(int(y + h / 2), self.height - 1)
+        return x1, y1, x2, y2
+
+    def _tlwh_to_xyxy(self, bbox_tlwh):
+        """
+        TODO:
+            Convert bbox from xtl_ytl_w_h to xc_yc_w_h
+        Thanks JieChen91@github.com for reporting this bug!
+        """
+        x, y, w, h = bbox_tlwh
+        x1 = max(int(x), 0)
+        x2 = min(int(x+w), self.width - 1)
+        y1 = max(int(y), 0)
+        y2 = min(int(y+h), self.height - 1)
+        return x1, y1, x2, y2
+
+    def increment_ages(self):
+        self.tracker.increment_ages()
+
+    def _xyxy_to_tlwh(self, bbox_xyxy):
+        x1, y1, x2, y2 = bbox_xyxy
+
+        t = x1
+        l = y1
+        w = int(x2 - x1)
+        h = int(y2 - y1)
+        return t, l, w, h
+
+    def _get_features(self, bbox_xywh, ori_img):
+        im_crops = []
+        for box in bbox_xywh:
+            x1, y1, x2, y2 = self._xywh_to_xyxy(box)
+            im = ori_img[y1:y2, x1:x2]
+            im_crops.append(im)
+        if im_crops:
+            features = self.extractor(im_crops)
+        else:
+            features = np.array([])
+        return features