Retraining reid

experiments/finetune_reid_mot17_dla34.sh

@@ -0,0 +1,3 @@
+cd src
+python train.py mot --exp_id finetune_reid_mot17_dla34 --load_model '../models/fairmot_dla34.pth' --data_cfg '../src/lib/cfg/mot17.json'  --downweight_reid_loss_by_occlusion_percent  --freeze_all_layers_except id
+cd ..

experiments/finetune_reid_mot20_dla34.sh

@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+
+cd src
+python train.py mot --exp_id finetune_reid_mot20_dla34 --load_model '../models/fairmot_dla34.pth' --data_cfg '../src/lib/cfg/mot20.json'  --downweight_reid_loss_by_occlusion_percent  --freeze_all_layers_except id
+cd ..

src/constants.py

@@ -0,0 +1,22 @@
+MOT_SEQUENCE_SPLITS = {
+    "MOT17-train": [
+        "MOT17-02",
+        "MOT17-04",
+        "MOT17-05",
+        "MOT17-09",
+        "MOT17-10",
+        "MOT17-11",
+        "MOT17-13",
+    ],
+    "MOT17-test": [
+        "MOT17-01",
+        "MOT17-03",
+        "MOT17-06",
+        "MOT17-07",
+        "MOT17-08",
+        "MOT17-12",
+        "MOT17-14",
+    ],
+    "MOT20-train": ["MOT20-01", "MOT20-02", "MOT20-03", "MOT20-05"],
+    "MOT20-test": ["MOT20-04", "MOT20-06", "MOT20-07", "MOT20-08"],
+}

src/gen_labels_17.py

@@ -0,0 +1,47 @@
+import os.path as osp
+import os
+import numpy as np
+from tqdm import tqdm
+
+
+def mkdirs(d):
+    print(f"creating directory at {d}")
+    if not osp.exists(d):
+        os.makedirs(d)
+
+
+# seq_root = '/data/yfzhang/MOT/JDE/MOT16/images/train'
+seq_root = "/app/data/MOT-benchmark/MOT17/train"
+label_root = '/app/data/experiments/fairmot_base/labels_with_ids/train'
+mkdirs(label_root)
+seqs = [s for s in os.listdir(seq_root)]
+
+tid_curr = 0
+tid_last = -1
+for i, seq in enumerate(seqs):
+    print(f"Processing {seq} {i + 1} of {len(seqs)}")
+    seq_info = open(osp.join(seq_root, seq, 'seqinfo.ini')).read()
+    seq_width = int(seq_info[seq_info.find('imWidth=') + 8:seq_info.find('\nimHeight')])
+    seq_height = int(seq_info[seq_info.find('imHeight=') + 9:seq_info.find('\nimExt')])
+
+    gt_txt = osp.join(seq_root, seq, 'gt', 'gt.txt')
+    gt = np.loadtxt(gt_txt, dtype=np.float64, delimiter=',')
+
+    seq_label_root = osp.join(label_root, seq, 'img1')
+    mkdirs(seq_label_root)
+
+    for fid, tid, x, y, w, h, mark, label, _ in tqdm(gt):
+        if mark == 0 or not label == 1:
+            continue
+        fid = int(fid)
+        tid = int(tid)
+        if not tid == tid_last:
+            tid_curr += 1
+            tid_last = tid
+        x += w / 2
+        y += h / 2
+        label_fpath = osp.join(seq_label_root, '{:06d}.txt'.format(fid))
+        label_str = '0 {:d} {:.6f} {:.6f} {:.6f} {:.6f}\n'.format(
+            tid_curr, x / seq_width, y / seq_height, w / seq_width, h / seq_height)
+        with open(label_fpath, 'a') as f:
+            f.write(label_str)

src/gen_labels_20.py

@@ -8,6 +8,7 @@ def mkdirs(d):
         os.makedirs(d)
 
 
+# TODO: change these paths
 seq_root = '/data/yfzhang/MOT/JDE/MOT20/images/train'
 label_root = '/data/yfzhang/MOT/JDE/MOT20/labels_with_ids/train'
 mkdirs(label_root)

src/lib/cfg/mot17.json

@@ -1,8 +1,9 @@
 {
-    "root":"/home/zyf/dataset",
+    "root":"/app/data/MOT-benchmark/MOT17/train",
+    "labels_root":"/app/data/experiments/fairmot_base/labels_with_ids/train/",
     "train":
     {
-        "mot17":"./data/mot17.train"
+        "mot17":"./data/mot17_seq_as_base_path.train"
     },
     "test_emb":
     {

src/lib/cfg/mot20.json

@@ -1,15 +1,15 @@
 {
-    "root":"/home/zyf/dataset",
+    "root":"/Users/sean/standard/data/reid-tracking-fix/MOT-benchmark/MOT20",
     "train":
     {
-        "mot20":"./data/mot20.train"
+        "mot20":"./train"
     },
     "test_emb":
     {
         "mot20":"./data/mot20.train"
     },
     "test":
     {
-        "mot20":"./data/mot20.train"
+        "mot20":"./train"
     }
 }

src/lib/datasets/dataset/jde.py

@@ -356,7 +356,7 @@ class JointDataset(LoadImagesAndLabels):  # for training
     std = None
     num_classes = 1
 
-    def __init__(self, opt, root, paths, img_size=(1088, 608), augment=False, transforms=None):
+    def __init__(self, opt, root, labels_root, paths, img_size=(1088, 608), augment=False, transforms=None):
         self.opt = opt
         dataset_names = paths.keys()
         self.img_files = OrderedDict()
@@ -372,9 +372,8 @@ def __init__(self, opt, root, paths, img_size=(1088, 608), augment=False, transf
                 self.img_files[ds] = list(filter(lambda x: len(x) > 0, self.img_files[ds]))
 
             self.label_files[ds] = [
-                x.replace('images', 'labels_with_ids').replace('.png', '.txt').replace('.jpg', '.txt')
+                x.replace(root, labels_root).replace('.png', '.txt').replace('.jpg', '.txt')
                 for x in self.img_files[ds]]
-
         for ds, label_paths in self.label_files.items():
             max_index = -1
             for lp in label_paths:

src/lib/opts.py

@@ -25,7 +25,7 @@ def __init__(self):
                                   'in the exp dir if load_model is empty.') 
 
     # system
-    self.parser.add_argument('--gpus', default='2, 3',
+    self.parser.add_argument('--gpus', default='0, 1, 2, 3',
                              help='-1 for CPU, use comma for multiple gpus')
     self.parser.add_argument('--num_workers', type=int, default=8,
                              help='dataloader threads. 0 for single-thread.')
@@ -86,6 +86,7 @@ def __init__(self):
     self.parser.add_argument('--trainval', action='store_true',
                              help='include validation in training and '
                                   'test on test set')
+    # raise NotImplementedError("Add arg for freezing all layers except reid head")
 
     # test
     self.parser.add_argument('--K', type=int, default=500,
@@ -158,6 +159,7 @@ def __init__(self):
                              help='category specific bounding box size.')
     self.parser.add_argument('--not_reg_offset', action='store_true',
                              help='not regress local offset.')
+    # raise NotImplementedError("add arg for training with ID downweighted by occlusion percentage")
 
   def parse(self, args=''):
     if args == '':

src/lib/trains/mot.py

@@ -19,6 +19,12 @@
 from .base_trainer import BaseTrainer
 
 
+class PrototypicalMotLoss(torch.nn.Module):
+    def __init__(self, opt):
+        super().__init__()
+        raise NotImplementedError
+
+
 class MotLoss(torch.nn.Module):
     def __init__(self, opt):
         super(MotLoss, self).__init__()
@@ -41,6 +47,8 @@ def __init__(self, opt):
         self.emb_scale = math.sqrt(2) * math.log(self.nID - 1)
         self.s_det = nn.Parameter(-1.85 * torch.ones(1))
         self.s_id = nn.Parameter(-1.05 * torch.ones(1))
+        # TODO: add attribute from `opt` to parameterize weigthing based on occlusion
+        # raise NotImplementedError("add arg for training with ID downweighted by occlusion percentage")
 
     def forward(self, outputs, batch):
         opt = self.opt
@@ -75,6 +83,10 @@ def forward(self, outputs, batch):
                                                       alpha=0.25, gamma=2.0, reduction="sum"
                                                       ) / id_output.size(0)
                 else:
+                    # import pdb; pdb.set_trace()
+                    # print("add arg for training with ID downweighted by occlusion percentage")
+                    # print(id_output.shape)
+                    # print(id_target.shape)
                     id_loss += self.IDLoss(id_output, id_target)
 
         det_loss = opt.hm_weight * hm_loss + opt.wh_weight * wh_loss + opt.off_weight * off_loss

src/train.py

@@ -1,3 +1,5 @@
+#  python train.py mot --exp_id finetune_reid_mot17_dla34 --load_model '../models/fairmot_dla34.pth' --data_cfg '../src/lib/cfg/mot17.json' # TODO --downweight_reid_loss_by_occlusion_percent  --freeze_all_layers_except id
+
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -14,6 +16,9 @@
 from models.model import create_model, load_model, save_model
 from models.data_parallel import DataParallel
 from logger import Logger
+
+from src.lib.datasets.dataset.jde import JointDataset as Dataset
+from src.lib.trains.mot import MotTrainer as Trainer
 from datasets.dataset_factory import get_dataset
 from trains.train_factory import train_factory
 
@@ -23,21 +28,22 @@ def main(opt):
     torch.backends.cudnn.benchmark = not opt.not_cuda_benchmark and not opt.test
 
     print('Setting up data...')
-    Dataset = get_dataset(opt.dataset, opt.task)
+    # Dataset = get_dataset(opt.dataset, opt.task)
     f = open(opt.data_cfg)
     data_config = json.load(f)
     trainset_paths = data_config['train']
     dataset_root = data_config['root']
+    labels_root = data_config['labels_root']
     f.close()
     transforms = T.Compose([T.ToTensor()])
-    dataset = Dataset(opt, dataset_root, trainset_paths, (1088, 608), augment=True, transforms=transforms)
+    dataset = Dataset(opt, dataset_root, labels_root, trainset_paths, (1088, 608), augment=True, transforms=transforms)
     opt = opts().update_dataset_info_and_set_heads(opt, dataset)
     print(opt)
 
     logger = Logger(opt)
 
     os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus_str
-    opt.device = torch.device('cuda' if opt.gpus[0] >= 0 else 'cpu')
+    opt.device = torch.device(f'cuda:{opt.gpus[0]}' if len(opt.gpus) >= 0 else 'cpu')
 
     print('Creating model...')
     model = create_model(opt.arch, opt.heads, opt.head_conv)
@@ -56,7 +62,7 @@ def main(opt):
     )
 
     print('Starting training...')
-    Trainer = train_factory[opt.task]
+    # Trainer = train_factory[opt.task]
     trainer = Trainer(opt, model, optimizer)
     trainer.set_device(opt.gpus, opt.chunk_sizes, opt.device)