@@ -1,5 +1,3 @@
-# Copyright (c) SenseTime. All Rights Reserved.
-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
@@ -14,6 +12,19 @@ cfg = __C
__C.META_ARC = "siamrpn_r50_l234_dwxcorr"
__C.CUDA = True
+# ------------------------------------------------------------------------ #
+# Path
+# ------------------------------------------------------------------------ #
+__C.OM_PATH = '/home/SiamRPN/SiamRPN.om'
+__C.CROP_PATH = 'home/SiamRPN/crop'
+__C.DEVICE_ID = 1
+__C.DATASET_NAME = 'VOT2016'
+__C.JSON_PATH = '/root/datasets/VOT2016'
+__C.DATA_PATH = '/root/datasets/VOT2016'
+__C.RESULT_PATH = '/root/datasets/VOT2016/results'
+__C.MODEL_PATH ='/home/SiamRPN/pysot/experiments/siamrpn_r50_l234_dwxcorr/model.pth'
+__C.CONFIG_PATH = '/home/SiamRPN/pysot/experiments/siamrpn_r50_l234_dwxcorr/config.yaml'
+
# ------------------------------------------------------------------------ #
# Training options
@@ -171,7 +182,7 @@ __C.BACKBONE = CN()
__C.BACKBONE.TYPE = 'res50'
__C.BACKBONE.KWARGS = CN(new_allowed=True)
-
+# print(__C.BACKBONE.KWARGS,'----' )
# Pretrained backbone weights
__C.BACKBONE.PRETRAINED = ''
@@ -9,9 +9,9 @@ import torch.nn as nn
import torch.nn.functional as F
from pysot.core.config import cfg
-from pysot.models.loss import select_cross_entropy_loss, weight_l1_loss
+
from pysot.models.backbone import get_backbone
-from pysot.models.head import get_rpn_head, get_mask_head, get_refine_head
+from pysot.models.head import get_rpn_head
from pysot.models.neck import get_neck
@@ -32,56 +32,35 @@ class ModelBuilder(nn.Module):
self.rpn_head = get_rpn_head(cfg.RPN.TYPE,
**cfg.RPN.KWARGS)
- # build mask head
- if cfg.MASK.MASK:
- self.mask_head = get_mask_head(cfg.MASK.TYPE,
- **cfg.MASK.KWARGS)
-
- if cfg.REFINE.REFINE:
- self.refine_head = get_refine_head(cfg.REFINE.TYPE)
-
def template(self, z):
zf = self.backbone(z)
- if cfg.MASK.MASK:
- zf = zf[-1]
if cfg.ADJUST.ADJUST:
zf = self.neck(zf)
self.zf = zf
def track(self, x):
xf = self.backbone(x)
- if cfg.MASK.MASK:
- self.xf = xf[:-1]
- xf = xf[-1]
if cfg.ADJUST.ADJUST:
xf = self.neck(xf)
cls, loc = self.rpn_head(self.zf, xf)
- if cfg.MASK.MASK:
- mask, self.mask_corr_feature = self.mask_head(self.zf, xf)
return {
- 'cls': cls,
- 'loc': loc,
- 'mask': mask if cfg.MASK.MASK else None
- }
+ 'cls': cls,
+ 'loc': loc,
+ }
def mask_refine(self, pos):
return self.refine_head(self.xf, self.mask_corr_feature, pos)
def log_softmax(self, cls):
b, a2, h, w = cls.size()
- cls = cls.view(b, 2, a2//2, h, w)
+ cls = cls.view(b, 2, a2 // 2, h, w)
cls = cls.permute(0, 2, 3, 4, 1).contiguous()
cls = F.log_softmax(cls, dim=4)
return cls
def forward(self, data):
- """ only used in training
- """
- template = data['template'].cuda()
- search = data['search'].cuda()
- label_cls = data['label_cls'].cuda()
- label_loc = data['label_loc'].cuda()
- label_loc_weight = data['label_loc_weight'].cuda()
+ template = data['template'].cpu()
+ search = data['search'].cpu()
# get feature
zf = self.backbone(template)
@@ -94,22 +73,6 @@ class ModelBuilder(nn.Module):
zf = self.neck(zf)
xf = self.neck(xf)
cls, loc = self.rpn_head(zf, xf)
+ outputs = {'cls': cls, 'loc': loc}
- # get loss
- cls = self.log_softmax(cls)
- cls_loss = select_cross_entropy_loss(cls, label_cls)
- loc_loss = weight_l1_loss(loc, label_loc, label_loc_weight)
-
- outputs = {}
- outputs['total_loss'] = cfg.TRAIN.CLS_WEIGHT * cls_loss + \
- cfg.TRAIN.LOC_WEIGHT * loc_loss
- outputs['cls_loss'] = cls_loss
- outputs['loc_loss'] = loc_loss
-
- if cfg.MASK.MASK:
- # TODO
- mask, self.mask_corr_feature = self.mask_head(zf, xf)
- mask_loss = None
- outputs['total_loss'] += cfg.TRAIN.MASK_WEIGHT * mask_loss
- outputs['mask_loss'] = mask_loss
return outputs
@@ -90,6 +90,4 @@ class SiameseTracker(BaseTracker):
im_patch = im_patch[np.newaxis, :, :, :]
im_patch = im_patch.astype(np.float32)
im_patch = torch.from_numpy(im_patch)
- if cfg.CUDA:
- im_patch = im_patch.cuda()
return im_patch
@@ -7,14 +7,15 @@ from __future__ import unicode_literals
import numpy as np
import torch.nn.functional as F
-
+import torch
+import sys
from pysot.core.config import cfg
from pysot.utils.anchor import Anchors
from pysot.tracker.base_tracker import SiameseTracker
-
+from ais_bench.infer.interface import InferSession
class SiamRPNTracker(SiameseTracker):
- def __init__(self, model):
+ def __init__(self):
super(SiamRPNTracker, self).__init__()
self.score_size = (cfg.TRACK.INSTANCE_SIZE - cfg.TRACK.EXEMPLAR_SIZE) // \
cfg.ANCHOR.STRIDE + 1 + cfg.TRACK.BASE_SIZE
@@ -23,8 +24,11 @@ class SiamRPNTracker(SiameseTracker):
window = np.outer(hanning, hanning)
self.window = np.tile(window.flatten(), self.anchor_num)
self.anchors = self.generate_anchor(self.score_size)
- self.model = model
- self.model.eval()
+
+ device_id = cfg.DEVICE_ID
+ om_path = cfg.OM_PATH
+
+ self.om_context = InferSession(device_id, om_path)
def generate_anchor(self, score_size):
anchors = Anchors(cfg.ANCHOR.STRIDE,
@@ -88,7 +92,8 @@ class SiamRPNTracker(SiameseTracker):
z_crop = self.get_subwindow(img, self.center_pos,
cfg.TRACK.EXEMPLAR_SIZE,
s_z, self.channel_average)
- self.model.template(z_crop)
+
+ self.z_crop = z_crop.data.cpu().contiguous().numpy()
def track(self, img):
"""
@@ -106,10 +111,17 @@ class SiamRPNTracker(SiameseTracker):
cfg.TRACK.INSTANCE_SIZE,
round(s_x), self.channel_average)
- outputs = self.model.track(x_crop)
-
- score = self._convert_score(outputs['cls'])
- pred_bbox = self._convert_bbox(outputs['loc'], self.anchors)
+ x_crop = x_crop.data.cpu().contiguous().numpy()
+ outputs = self.om_context.infer([self.z_crop, x_crop])
+ if outputs[0].shape == (1, 20, 25, 25):
+ data_cls = torch.from_numpy(outputs[1])
+ data_loc = torch.from_numpy(outputs[0])
+ else:
+ data_cls = torch.from_numpy(outputs[0])
+ data_loc = torch.from_numpy(outputs[1])
+
+ score = self._convert_score(data_cls)
+ pred_bbox = self._convert_bbox(data_loc, self.anchors)
def change(r):
return np.maximum(r, 1. / r)
@@ -17,5 +17,5 @@ TRACKS = {
}
-def build_tracker(model):
- return TRACKS[cfg.TRACK.TYPE](model)
+def build_tracker():
+ return TRACKS[cfg.TRACK.TYPE]()
@@ -46,9 +46,8 @@ def remove_prefix(state_dict, prefix):
def load_pretrain(model, pretrained_path):
logger.info('load pretrained model from {}'.format(pretrained_path))
- device = torch.cuda.current_device()
pretrained_dict = torch.load(pretrained_path,
- map_location=lambda storage, loc: storage.cuda(device))
+ map_location=torch.device('cpu'))
if "state_dict" in pretrained_dict.keys():
pretrained_dict = remove_prefix(pretrained_dict['state_dict'],
'module.')
@@ -15,7 +15,7 @@ class Video(object):
self.gt_traj = gt_rect
self.attr = attr
self.pred_trajs = {}
- self.img_names = [os.path.join(os.path.abspath(root), os.path.abspath(x)) for x in img_names]
+ self.img_names = [os.path.join(root, x) for x in img_names]
self.imgs = None
if load_img:
@@ -40,7 +40,7 @@ class Video(object):
if isinstance(tracker_names, str):
tracker_names = [tracker_names]
for name in tracker_names:
- traj_file = os.path.join(path, name, self.name+'.txt')
+ traj_file = os.path.join(path, name, 'model', 'baseline', self.name+'.txt')
if os.path.exists(traj_file):
with open(traj_file, 'r') as f :
pred_traj = [list(map(float, x.strip().split(',')))
@@ -97,7 +97,9 @@ class VOTDataset(Dataset):
"""
def __init__(self, name, dataset_root, load_img=False):
super(VOTDataset, self).__init__(name, dataset_root)
- with open(os.path.join(dataset_root, name+'.json'), 'r') as f:
+ dataset_root = dataset_root.split('/results')[0]
+ dataset_name = dataset_root.split('/')[-1] + '.json'
+ with open(os.path.join(dataset_root, dataset_name), 'r') as f:
meta_data = json.load(f)
# load videos
@@ -7,7 +7,7 @@ import itertools
import numpy as np
from colorama import Style, Fore
-from ..utils import calculate_failures, calculate_accuracy
+from toolkit.utils.statistics import calculate_failures, calculate_accuracy
class AccuracyRobustnessBenchmark:
"""
@@ -4,7 +4,7 @@ import numpy as np
from glob import glob
-from ..utils import calculate_failures, calculate_accuracy, calculate_expected_overlap
+from toolkit.utils.statistics import calculate_failures, calculate_accuracy, calculate_expected_overlap
class EAOBenchmark:
"""
@@ -9,19 +9,19 @@ import argparse
from glob import glob
from tqdm import tqdm
from multiprocessing import Pool
-from toolkit.datasets import OTBDataset, UAVDataset, LaSOTDataset, \
- VOTDataset, NFSDataset, VOTLTDataset
-from toolkit.evaluation import OPEBenchmark, AccuracyRobustnessBenchmark, \
- EAOBenchmark, F1Benchmark
+from toolkit.datasets.vot import VOTDataset
+from toolkit.evaluation.ar_benchmark import AccuracyRobustnessBenchmark
+from toolkit.evaluation.eao_benchmark import EAOBenchmark
+from pysot.core.config import cfg
parser = argparse.ArgumentParser(description='tracking evaluation')
parser.add_argument('--tracker_path', '-p', type=str,
help='tracker result path')
-parser.add_argument('--dataset', '-d', type=str,
+parser.add_argument('--dataset', '-d', default='VOT2016', type=str,
help='dataset name')
parser.add_argument('--num', '-n', default=1, type=int,
help='number of thread to eval')
-parser.add_argument('--tracker_prefix', '-t', default='',
+parser.add_argument('--tracker_prefix', '-t', default='model',
type=str, help='tracker name')
parser.add_argument('--show_video_level', '-s', dest='show_video_level',
action='store_true')
@@ -30,17 +30,24 @@ args = parser.parse_args()
def main():
- tracker_dir = os.path.join(args.tracker_path, args.dataset)
- trackers = glob(os.path.join(args.tracker_path,
+ # fix config
+ if args.dataset != cfg.DATASET_NAME:
+ j_path = os.path.join(cfg.JSON_PATH.split('VOT')[0], args.dataset)
+ d_path = os.path.join(cfg.DATA_PATH.split('VOT')[0], args.dataset)
+ r_path = os.path.join(cfg.RESULT_PATH.split('VOT')[0], args.dataset, 'results')
+ opts = ['JSON_PATH', j_path, 'DATA_PATH', d_path, 'RESULT_PATH', r_path, 'DATASET_NAME', args.dataset]
+ cfg.merge_from_list(opts)
+
+ tracker_dir = os.path.join(cfg.RESULT_PATH, args.dataset)
+ trackers = glob(os.path.join(cfg.RESULT_PATH,
args.dataset,
- args.tracker_prefix+'*'))
+ args.tracker_prefix + '*'))
trackers = [os.path.basename(x) for x in trackers]
assert len(trackers) > 0
args.num = min(args.num, len(trackers))
- root = os.path.realpath(os.path.join(os.path.dirname(__file__),
- '../testing_dataset'))
+ root = os.path.realpath(os.path.join(cfg.RESULT_PATH))
root = os.path.join(root, args.dataset)
if 'OTB' in args.dataset:
dataset = OTBDataset(args.dataset, root)
@@ -49,15 +56,15 @@ def main():
success_ret = {}
with Pool(processes=args.num) as pool:
for ret in tqdm(pool.imap_unordered(benchmark.eval_success,
- trackers), desc='eval success', total=len(trackers), ncols=100):
+ trackers), desc='eval success', total=len(trackers), ncols=100):
success_ret.update(ret)
precision_ret = {}
with Pool(processes=args.num) as pool:
for ret in tqdm(pool.imap_unordered(benchmark.eval_precision,
- trackers), desc='eval precision', total=len(trackers), ncols=100):
+ trackers), desc='eval precision', total=len(trackers), ncols=100):
precision_ret.update(ret)
benchmark.show_result(success_ret, precision_ret,
- show_video_level=args.show_video_level)
+ show_video_level=args.show_video_level)
elif 'LaSOT' == args.dataset:
dataset = LaSOTDataset(args.dataset, root)
dataset.set_tracker(tracker_dir, trackers)
@@ -65,20 +72,20 @@ def main():
success_ret = {}
with Pool(processes=args.num) as pool:
for ret in tqdm(pool.imap_unordered(benchmark.eval_success,
- trackers), desc='eval success', total=len(trackers), ncols=100):
+ trackers), desc='eval success', total=len(trackers), ncols=100):
success_ret.update(ret)
precision_ret = {}
with Pool(processes=args.num) as pool:
for ret in tqdm(pool.imap_unordered(benchmark.eval_precision,
- trackers), desc='eval precision', total=len(trackers), ncols=100):
+ trackers), desc='eval precision', total=len(trackers), ncols=100):
precision_ret.update(ret)
norm_precision_ret = {}
with Pool(processes=args.num) as pool:
for ret in tqdm(pool.imap_unordered(benchmark.eval_norm_precision,
- trackers), desc='eval norm precision', total=len(trackers), ncols=100):
+ trackers), desc='eval norm precision', total=len(trackers), ncols=100):
norm_precision_ret.update(ret)
benchmark.show_result(success_ret, precision_ret, norm_precision_ret,
- show_video_level=args.show_video_level)
+ show_video_level=args.show_video_level)
elif 'UAV' in args.dataset:
dataset = UAVDataset(args.dataset, root)
dataset.set_tracker(tracker_dir, trackers)
@@ -86,15 +93,15 @@ def main():
success_ret = {}
with Pool(processes=args.num) as pool:
for ret in tqdm(pool.imap_unordered(benchmark.eval_success,
- trackers), desc='eval success', total=len(trackers), ncols=100):
+ trackers), desc='eval success', total=len(trackers), ncols=100):
success_ret.update(ret)
precision_ret = {}
with Pool(processes=args.num) as pool:
for ret in tqdm(pool.imap_unordered(benchmark.eval_precision,
- trackers), desc='eval precision', total=len(trackers), ncols=100):
+ trackers), desc='eval precision', total=len(trackers), ncols=100):
precision_ret.update(ret)
benchmark.show_result(success_ret, precision_ret,
- show_video_level=args.show_video_level)
+ show_video_level=args.show_video_level)
elif 'NFS' in args.dataset:
dataset = NFSDataset(args.dataset, root)
dataset.set_tracker(tracker_dir, trackers)
@@ -102,15 +109,15 @@ def main():
success_ret = {}
with Pool(processes=args.num) as pool:
for ret in tqdm(pool.imap_unordered(benchmark.eval_success,
- trackers), desc='eval success', total=len(trackers), ncols=100):
+ trackers), desc='eval success', total=len(trackers), ncols=100):
success_ret.update(ret)
precision_ret = {}
with Pool(processes=args.num) as pool:
for ret in tqdm(pool.imap_unordered(benchmark.eval_precision,
- trackers), desc='eval precision', total=len(trackers), ncols=100):
+ trackers), desc='eval precision', total=len(trackers), ncols=100):
precision_ret.update(ret)
benchmark.show_result(success_ret, precision_ret,
- show_video_level=args.show_video_level)
+ show_video_level=args.show_video_level)
elif args.dataset in ['VOT2016', 'VOT2017', 'VOT2018', 'VOT2019']:
dataset = VOTDataset(args.dataset, root)
dataset.set_tracker(tracker_dir, trackers)
@@ -118,17 +125,17 @@ def main():
ar_result = {}
with Pool(processes=args.num) as pool:
for ret in tqdm(pool.imap_unordered(ar_benchmark.eval,
- trackers), desc='eval ar', total=len(trackers), ncols=100):
+ trackers), desc='eval ar', total=len(trackers), ncols=100):
ar_result.update(ret)
benchmark = EAOBenchmark(dataset)
eao_result = {}
with Pool(processes=args.num) as pool:
for ret in tqdm(pool.imap_unordered(benchmark.eval,
- trackers), desc='eval eao', total=len(trackers), ncols=100):
+ trackers), desc='eval eao', total=len(trackers), ncols=100):
eao_result.update(ret)
ar_benchmark.show_result(ar_result, eao_result,
- show_video_level=args.show_video_level)
+ show_video_level=args.show_video_level)
elif 'VOT2018-LT' == args.dataset:
dataset = VOTLTDataset(args.dataset, root)
dataset.set_tracker(tracker_dir, trackers)
@@ -136,10 +143,10 @@ def main():
f1_result = {}
with Pool(processes=args.num) as pool:
for ret in tqdm(pool.imap_unordered(benchmark.eval,
- trackers), desc='eval f1', total=len(trackers), ncols=100):
+ trackers), desc='eval f1', total=len(trackers), ncols=100):
f1_result.update(ret)
benchmark.show_result(f1_result,
- show_video_level=args.show_video_level)
+ show_video_level=args.show_video_level)
if __name__ == '__main__':
@@ -22,10 +22,12 @@ from toolkit.utils.region import vot_overlap, vot_float2str
parser = argparse.ArgumentParser(description='siamrpn tracking')
-parser.add_argument('--dataset', type=str,
+parser.add_argument('--dataset', type=str, default='VOT2016',
help='datasets')
parser.add_argument('--config', default='', type=str,
help='config file')
+parser.add_argument('--device', default=0, type=int,
+ help='npu id')
parser.add_argument('--snapshot', default='', type=str,
help='snapshot of models to eval')
parser.add_argument('--video', default='', type=str,
@@ -38,26 +40,33 @@ torch.set_num_threads(1)
def main():
# load config
- cfg.merge_from_file(args.config)
-
+ #cfg.merge_from_file(args.config)
+ # fix config
+ if args.dataset != cfg.DATASET_NAME:
+ j_path = os.path.join(cfg.JSON_PATH.split('VOT')[0], args.dataset)
+ d_path = os.path.join(cfg.DATA_PATH.split('VOT')[0], args.dataset)
+ r_path = os.path.join(cfg.RESULT_PATH.split('VOT')[0], args.dataset, 'results')
+ opts = ['JSON_PATH', j_path, 'DATA_PATH', d_path, 'RESULT_PATH', r_path, 'DATASET_NAME', args.dataset,
+ 'DEVICE_ID', args.device]
+ cfg.merge_from_list(opts)
cur_dir = os.path.dirname(os.path.realpath(__file__))
- dataset_root = os.path.join(cur_dir, '../testing_dataset', args.dataset)
+ dataset_root = os.path.join(cfg.JSON_PATH)
# create model
- model = ModelBuilder()
+ # model = ModelBuilder()
# load model
- model = load_pretrain(model, args.snapshot).cuda().eval()
+ # model = load_pretrain(model, args.snapshot).cuda().eval()
# build tracker
- tracker = build_tracker(model)
+ tracker = build_tracker()
# create dataset
dataset = DatasetFactory.create_dataset(name=args.dataset,
dataset_root=dataset_root,
load_img=False)
- model_name = args.snapshot.split('/')[-1].split('.')[0]
+ model_name = cfg.MODEL_PATH.split('/')[-1].split('.')[0]
total_lost = 0
if args.dataset in ['VOT2016', 'VOT2018', 'VOT2019']:
# restart tracking
@@ -69,6 +78,7 @@ def main():
frame_counter = 0
lost_number = 0
toc = 0
+ avg_speed = 0
pred_bboxes = []
for idx, (img, gt_bbox) in enumerate(video):
if len(gt_bbox) == 4:
@@ -118,7 +128,7 @@ def main():
cv2.waitKey(1)
toc /= cv2.getTickFrequency()
# save results
- video_path = os.path.join('results', args.dataset, model_name,
+ video_path = os.path.join(cfg.RESULT_PATH, args.dataset, model_name,
'baseline', video.name)
if not os.path.isdir(video_path):
os.makedirs(video_path)
@@ -129,10 +139,13 @@ def main():
f.write("{:d}\n".format(x))
else:
f.write(','.join([vot_float2str("%.4f", i) for i in x])+'\n')
+ speed = idx / toc
print('({:3d}) Video: {:12s} Time: {:4.1f}s Speed: {:3.1f}fps Lost: {:d}'.format(
v_idx+1, video.name, toc, idx / toc, lost_number))
total_lost += lost_number
+ avg_speed += speed
print("{:s} total lost: {:d}".format(model_name, total_lost))
+ print('average speed:', avg_speed, 'fps')
else:
# OPE tracking
for v_idx, video in enumerate(dataset):