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Add VOT models (#4257) 

* First version for VOT models.
* Include SiamFC and ATOM.
* A unified architecture for ATOM and Siames series models.
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[submodule "pytracking/pysot-toolkit"]
path = pytracking/pysot-toolkit
url = https://github.com/StrangerZhang/pysot-toolkit.git
PaddleCV/tracking/LICENSE 0 → 100644
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<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.
PaddleCV/tracking/README.md 0 → 100644
浏览文件 @ 7ec3ec11
# tracking 单目标跟踪框架
## 目标跟踪介绍
tracking 是基于百度深度学习框架Paddle研发的视频单目标跟踪（Visual Object Tracking, VOT）库, 整体框架参考 [pytracking](https://github.com/visionml/pytracking)，其优秀的设计使得我们能够方便地将其他跟踪器如SiamFC，SiamRPN，SiamMask等融合到一个框架中，方便后续统一的实验和比较。
当前tracking涵盖当前目标跟踪的主流模型，包括SiamFC, SiamRPN, SiamMask, ATOM。tracking旨在给开发者提供一系列基于PaddlePaddle的便捷、高效的目标跟踪深度学习算法，后续会不断的扩展模型的丰富度。
## 目标跟踪库的代码目录结构
```
ltr 包含模型训练代码
└─ actors 输入数据，输出优化目标
└─ admin 管理数据路径等
└─ data 多线程数据读取和预处理
└─ dataset 训练数据集读取
└─ models 模型定义
└─ train_settings 训练配置
└─ trainers 模型训练器
└─ run_training.py 模型训练入口程序
pytracking 包含跟踪代码
└─ admin 管理数据路径，模型位置等
└─ features 特征提取
└─ libs 跟踪常用操作
└─ parameter 跟踪器参数设置
└─ tracker 跟踪器
└─ utils 画图等
└─ pysot-toolkit 评估数据集载入和指标计算
└─ eval_benchmark.py 评估跟踪器入口程序
└─ visualize_results_on_benchmark.ipynb 可视化跟踪结果
```
## 开始使用
### 数据准备
目标跟踪的训练集和测试集是不同的，目前最好的模型往往是使用多个训练集进行训练。常用的数据集如下:
主流的训练数据集有：
- [VID](http://bvisionweb1.cs.unc.edu/ilsvrc2015/ILSVRC2015_VID.tar.gz)
- [Microsoft COCO 2014](http://cocodataset.org/#download)
- [LaSOT](https://drive.google.com/file/d/1O2DLxPP8M4Pn4-XCttCJUW3A29tDIeNa/view)
下载并解压后的数据集的组织方式为：
```
/Datasets/
└─ ILSVRC2015_VID/
└─ train2014/
└─ LaSOTBenchmark/
```
注：数据集较大，请预留足够的磁盘空间。训练Siamfc时，只需要下载VID数据集，训练ATOM需要全部下载上述三个数据集。
## 快速开始
tracking的工作环境：
- python3
- PaddlePaddle1.7
### 安装依赖
1. 安装paddle，需要安装1.7版本的Paddle，如低于这个版本，请升级到Paddle 1.7.
```bash
pip install paddlepaddle==1.7.0
```
2. 安装第三方库，建议使用anaconda
```bash
# (可选) 0. 强烈建议新建一个 conda 环境，在安装 anaconda 后执行
# conda create -n paddle1.7-py3.6 python=3.6
# conda activate paddle1.7-py3.6
cd tracking
pip install -r requirements.txt
# (可选) 1. 推荐安装：快速读取 jpeg 文件
apt-get install libturbojpeg
# (可选) 2. 推荐安装：进程控制
apt-get install build-essential libcap-dev
pip install python-prctl
```
### 预训练 backbone 下载
在开始训练前，先准备SiamRPN、SiamMask、ATOM模型的Backbone预训练模型。
我们提供 ATOM ResNet18 和 ResNet50 的 backbone模型。可从[这里](https://paddlemodels.bj.bcebos.com/paddle_track/vot/pretrained_models.tar)下载所有预训练模型的压缩包。
压缩包解压后的文件夹为 `pretrained_models`. 文件的目录结构如下：
```
/pretrained_models/
└─ atom
└─ atom_resnet18.pdparams
└─ atom_resnet50.pdparams
└─ backbone
└─ ResNet18.pdparams
└─ ResNet50.pdparams
```
其中/pretrained_models/backbone/文件夹包含，ResNet18、ResNet50在Imagenet上的预训练模型。
### 设置训练参数
在启动训练前，需要设置tracking使用的数据集路径，以及训练模型保存的路径，这些参数在ltr/admin/local.py中设置。
首先，需要先生成local.py文件。
```bash
# 到代码库根目录
cd tracking
# 生成 local.py 文件，再次训练时不需要重新生成
python -c "from ltr.admin.environment import create_default_local_file; create_default_local_file()"
```
其次，设置训练模型文件保存路径：workspace_dir，backbone模型路径：backbone_dir，数据集路径等等，对于没有用到的数据集，可以不用设置其路径。
```
# 用你常用的编辑器编辑 ltr/admin/local.py
# 比方说，vim ltr/admin/local.py
# 其中，
# workspace_dir = './checkpoints' # 要保存训练模型的位置
# backbone_dir = Your BACKBONE_PATH # 训练SiamFC时不需要设置
# 并依次设定需要使用的训练数据集如 VID, LaSOT, COCO 等，比如：
# imagenet_dir = '/Datasets/ILSVRC2015/' # 设置训练集VID的路径
```
训练SiamFC时需要只需要配置 workspace_dir和 imagenet_dir即可，如下：
```bash
self.workspace_dir = './checkpoints'
self.imagenet_dir = '/Datasets/ILSVRC2015/'
```
训练ATOM时，除了 workspace_dir和 imagenet_dir外，还需要指定coco和lasot的数据集路径，参考如下：
```bash
self.workspace_dir = './checkpoints'
self.lasot_dir = '/Datasets/LaSOTBenchmark/'
self.coco_dir = '/Datasets/train2014/'
self.imagenet_dir = '/Datasets/ILSVRC2015/'
```
另外，训练ATOM时，需要准备got10k和lasot的数据集划分文件，方式如下：
```bash
cd ltr/data_specs/
wget https://paddlemodels.cdn.bcebos.com/paddle_track/vot/got10k_lasot_split.tar
tar xvf got10k_lasot_split.tar
```
### 启动训练
```bash
# 到训练代码目录
cd ltr
# 训练 ATOM ResNet18
python run_training.py bbreg atom_res18_vid_lasot_coco
# 训练 ATOM ResNet50
python run_training.py bbreg atom_res50_vid_lasot_coco
# 训练 SiamFC
python run_training.py siamfc siamfc_alexnet_vid
```
## 模型评估
评估训练后的模型使用[pysot-toolkit](https://github.com/StrangerZhang/pysot-toolkit)工具包，其提供了多个单目标跟踪数据集的评估API。测试数据集建议从pysot-toolkit 提供的链接中下载。
准备好测试数据后，使用如下命令，克隆跟踪评估pysot-toolkit的代码模块，运行如下命令：
```bash
cd pytracking
git clone https://github.com/StrangerZhang/pysot-toolkit.git
mv pysot-toolkit pysot_toolkit
cd pysot_toolkit
pip install -r requirements.txt
cd pysot/utils/
python setup.py build_ext --inplace
```
### 测试数据集准备
按照pysot-toolkit的方式准备数据集VOT2018，放到/Datasets 文件夹下。
### 设置模型评估环境
接下来开始设置评估环境：
```bash
# 生成 local.py 文件，在local.py文件中设置测试数据集、待测试模型、以及测试结果的保存路径
python -c "from pytracking.admin.environment import create_default_local_file; create_default_local_file()"
# 用你常用的编辑器编辑 pytracking/pysot_toolkit/local.py
# 比方说，vim pytracking/pysot_toolkit/local.py
# 其中 settings.dataset_path 和 settings.network_path 分别设置为测试集的路径和模型训练参数的路径
```
### 准备测试数据和模型
按照pysot-toolkit的方式准备数据集VOT2018，放到settings.dataset_path指定文件夹中，或者自行设置settings.dataset_path指向测试数据集。
将自己训练的模型拷贝到 `NETWORK_PATH`，或者建立软链接，如
```bash
ln -s tracking/ltr/Logs/checkpoints/ltr/bbreg/atom_res18_vid_lasot_coco $NETWORK_PATH/bbreg
```
### 开始测试：
测试ATOM模型：
```bash
# 在VOT2018上评测ATOM模型
# -d VOT2018 表示使用VOT2018数据集进行评测
# -tr bbreg.atom_res18_vid_lasot_coco 表示要评测的模型，和训练保持一致
# -te atom.default_vot 表示加载定义超参数的文件pytracking/parameter/atom/default_vot.py
# -e 40 表示使用第40个epoch的模型进行评测，也可以设置为'range(1, 50, 1)' 表示测试从第1个epoch到第50个epoch模型
python eval_benchmark.py -d VOT2018 -tr bbreg.atom_res18_vid_lasot_coco -te atom.default_vot -e 40
```
测试SiamFC
```
# 在VOT2018上测试SiamFC
python eval_benchmark.py -d VOT2018 -tr siamfc.siamfc_alexnet_vid -te siamfc.default -e 'range(1, 50, 1)'
```
## 跟踪结果可视化
在数据集上评测完后，可以通过可视化跟踪器的结果来定位问题。我们提供下面的方法来可视化跟踪结果：
```bash
cd pytracking
# 开启 jupyter notebook，请留意终端是否输出 token
jupyter notebook --ip 0.0.0.0 --port 8888
```
在你的浏览器中输入服务器的 IP 地址加上端口号，若是在本地执行则打开
`http://localhost:8888`。若需要输入 token 请查看执行 `jupyter notebook --ip 0.0.0.0 --port 8888` 命令时的终端输出。
打开网页之后，打开 `visualize_results_on_benchmark.ipynb` 来可视化结果。
## 指标结果
| 数据集 | 模型 | Backbone | 论文结果 | 训练结果 | 模型|
| :-------: | :-------: | :---: | :---: | :---------: |:---------: |
|VOT2018| ATOM | Res18 | EAO: 0.401 | 0.399 | [model]() |
|VOT2018| ATOM | AlexNet | EAO: 0.188 | 0.211 | [model]() |
## 引用与参考
SiamFC **[[Paper]](https://arxiv.org/pdf/1811.07628.pdf) [[Code]](https://www.robots.ox.ac.uk/~luca/siamese-fc.html)**
@inproceedings{bertinetto2016fully,
title={Fully-convolutional siamese networks for object tracking},
author={Bertinetto, Luca and Valmadre, Jack and Henriques, Joao F and Vedaldi, Andrea and Torr, Philip HS},
booktitle={European conference on computer vision},
pages={850--865},
year={2016},
organization={Springer}
}
ATOM **[[Paper]](https://arxiv.org/pdf/1811.07628.pdf) [[Raw results]](https://drive.google.com/drive/folders/1MdJtsgr34iJesAgL7Y_VelP8RvQm_IG_) [[Models]](https://drive.google.com/open?id=1EsNSQr25qfXHYLqjZaVZElbGdUg-nyzd) [[Training Code]](https://github.com/visionml/pytracking/blob/master/ltr/README.md#ATOM) [[Tracker Code]](https://github.com/visionml/pytracking/blob/master/pytracking/README.md#ATOM)**
@inproceedings{danelljan2019atom,
title={Atom: Accurate tracking by overlap maximization},
author={Danelljan, Martin and Bhat, Goutam and Khan, Fahad Shahbaz and Felsberg, Michael},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={4660--4669},
year={2019}
}
DiMP **[[Paper]](https://arxiv.org/pdf/1904.07220v1.pdf) [[Raw results]](https://drive.google.com/drive/folders/15mpUAJmzxemnOC6gmvMTCDJ-0v6hxJ7y) [[Models]](https://drive.google.com/open?id=1YEJySjhFokyQ6zgQg6vFAnzEFi1Onq7G) [[Training Code]](https://github.com/visionml/pytracking/blob/master/ltr/README.md#DiMP) [[Tracker Code]](https://github.com/visionml/pytracking/blob/master/pytracking/README.md#DiMP)**
@inproceedings{bhat2019learning,
title={Learning discriminative model prediction for tracking},
author={Bhat, Goutam and Danelljan, Martin and Gool, Luc Van and Timofte, Radu},
booktitle={Proceedings of the IEEE International Conference on Computer Vision},
pages={6182--6191},
year={2019}
}
ECO **[[Paper]](https://arxiv.org/pdf/1611.09224.pdf) [[Models]](https://drive.google.com/open?id=1aWC4waLv_te-BULoy0k-n_zS-ONms21S) [[Tracker Code]](https://github.com/visionml/pytracking/blob/master/pytracking/README.md#ECO)**
@inproceedings{danelljan2017eco,
title={Eco: Efficient convolution operators for tracking},
author={Danelljan, Martin and Bhat, Goutam and Shahbaz Khan, Fahad and Felsberg, Michael},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={6638--6646},
year={2017}
}
PaddleCV/tracking/ltr/actors/__init__.py 0 → 100644
浏览文件 @ 7ec3ec11
from .base_actor import BaseActor
from .bbreg import AtomActor
from .siamfc import SiamFCActor
PaddleCV/tracking/ltr/actors/base_actor.py 0 → 100644
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from pytracking.libs import TensorDict
class BaseActor:
""" Base class for actor. The actor class handles the passing of the data through the network
and calculation the loss"""
def __init__(self, net, objective):
"""
args:
net - The network to train
objective - The loss function
"""
self.net = net
self.objective = objective
def train(self):
""" Set whether the network is in train mode.
args:
mode (True) - Bool specifying whether in training mode.
"""
self.net.train()
def eval(self):
""" Set network to eval mode"""
self.net.eval()
PaddleCV/tracking/ltr/actors/bbreg.py 0 → 100644
浏览文件 @ 7ec3ec11
from . import BaseActor
import paddle.fluid as fluid
class AtomActor(BaseActor):
""" Actor for training the IoU-Net in ATOM"""
def __call__(self, data):
"""
args:
data - The input data, should contain the fields 'train_images', 'test_images', 'train_anno',
'test_proposals' and 'proposal_iou'.
returns:
loss - the training loss
states - dict containing detailed losses
"""
# Run network to obtain IoU prediction for each proposal in 'test_proposals'
iou_pred = self.net(data['train_images'], data['test_images'],
data['train_anno'], data['test_proposals'])
iou_pred = fluid.layers.reshape(iou_pred, [-1, iou_pred.shape[2]])
iou_gt = fluid.layers.reshape(data['proposal_iou'],
[-1, data['proposal_iou'].shape[2]])
# Compute loss
loss = self.objective(iou_pred, iou_gt)
loss = fluid.layers.mean(loss)
# Use scale loss if exists
scale_loss = getattr(self.net, "scale_loss", None)
if callable(scale_loss):
loss = scale_loss(loss)
# Return training stats
stats = {'Loss/total': loss.numpy(), 'Loss/iou': loss.numpy()}
return loss, stats
PaddleCV/tracking/ltr/actors/siamfc.py 0 → 100644
浏览文件 @ 7ec3ec11
import numpy as np
import paddle.fluid as fluid
from . import BaseActor
class SiamFCActor(BaseActor):
""" Actor for training the IoU-Net in ATOM"""
def __init__(self, net, objective, batch_size, shape, radius, stride):
super().__init__(net, objective)
self.label_mask, self.label_weights = self._creat_gt_mask(
batch_size, shape, radius, stride)
def _creat_gt_mask(self, batch_size, shape, radius, stride):
h, w = shape
y = np.arange(h, dtype=np.float32) - (h - 1) / 2.
x = np.arange(w, dtype=np.float32) - (w - 1) / 2.
y, x = np.meshgrid(y, x)
dist = np.sqrt(x**2 + y**2)
mask = np.zeros((h, w))
mask[dist <= radius / stride] = 1
mask = mask[np.newaxis, :, :]
weights = np.ones_like(mask)
weights[mask == 1] = 0.5 / np.sum(mask == 1)
weights[mask == 0] = 0.5 / np.sum(mask == 0)
mask = np.repeat(mask, batch_size, axis=0)[:, np.newaxis, :, :]
weights = np.repeat(weights, batch_size, axis=0)[:, np.newaxis, :, :]
weights = fluid.dygraph.to_variable(weights.astype(np.float32))
mask = fluid.dygraph.to_variable(mask.astype(np.float32))
return mask, weights
def __call__(self, data):
# Run network to obtain IoU prediction for each proposal in 'test_proposals'
target_estimations = self.net(data['train_images'], data['test_images'])
# weighted loss
loss_mat = fluid.layers.sigmoid_cross_entropy_with_logits(
target_estimations, self.label_mask, normalize=False)
loss = fluid.layers.elementwise_mul(loss_mat, self.label_weights)
loss = fluid.layers.reduce_sum(loss) / loss.shape[0]
# Return training stats
stats = {'Loss/total': loss.numpy(), 'Loss/center': loss.numpy()}
return loss, stats
PaddleCV/tracking/ltr/admin/environment.py 0 → 100644
浏览文件 @ 7ec3ec11
import importlib
import os
from collections import OrderedDict
def create_default_local_file():
path = os.path.join(os.path.dirname(__file__), 'local.py')
empty_str = '\'\''
default_settings = OrderedDict({
'workspace_dir': empty_str,
'tensorboard_dir': 'self.workspace_dir + \'/tensorboard/\'',
'backbone_dir': empty_str,
'lasot_dir': empty_str,
'got10k_dir': empty_str,
'trackingnet_dir': empty_str,
'coco_dir': empty_str,
'imagenet_dir': empty_str,
'imagenetdet_dir': empty_str
})
comment = {
'workspace_dir': 'Base directory for saving network checkpoints.',
'tensorboard_dir': 'Directory for tensorboard files.'
}
with open(path, 'w') as f:
f.write('class EnvironmentSettings:\n')
f.write(' def __init__(self):\n')
for attr, attr_val in default_settings.items():
comment_str = None
if attr in comment:
comment_str = comment[attr]
if comment_str is None:
f.write(' self.{} = {}\n'.format(attr, attr_val))
else:
f.write(' self.{} = {} # {}\n'.format(attr, attr_val,
comment_str))
def env_settings():
env_module_name = 'ltr.admin.local'
try:
env_module = importlib.import_module(env_module_name)
return env_module.EnvironmentSettings()
except:
env_file = os.path.join(os.path.dirname(__file__), 'local.py')
create_default_local_file()
raise RuntimeError(
'YOU HAVE NOT SETUP YOUR local.py!!!\n Go to "{}" and set all the paths you need. Then try to run again.'.
format(env_file))
PaddleCV/tracking/ltr/admin/model_constructor.py 0 → 100644
浏览文件 @ 7ec3ec11
from functools import wraps
import importlib
def model_constructor(f):
""" Wraps the function 'f' which returns the network. An extra field 'constructor' is added to the network returned
by 'f'. This field contains an instance of the 'NetConstructor' class, which contains the information needed to
re-construct the network, such as the name of the function 'f', the function arguments etc. Thus, the network can
be easily constructed from a saved checkpoint by calling NetConstructor.get() function.
"""
@wraps(f)
def f_wrapper(*args, **kwds):
net_constr = NetConstructor(f.__name__, f.__module__, args, kwds)
output = f(*args, **kwds)
if isinstance(output, (tuple, list)):
# Assume first argument is the network
output[0].constructor = net_constr
else:
output.constructor = net_constr
return output
return f_wrapper
class NetConstructor:
""" Class to construct networks. Takes as input the function name (e.g. atom_resnet18), the name of the module
which contains the network function (e.g. ltr.models.bbreg.atom) and the arguments for the network
function. The class object can then be stored along with the network weights to re-construct the network."""
def __init__(self, fun_name, fun_module, args, kwds):
"""
args:
fun_name - The function which returns the network
fun_module - the module which contains the network function
args - arguments which are passed to the network function
kwds - arguments which are passed to the network function
"""
self.fun_name = fun_name
self.fun_module = fun_module
self.args = args
self.kwds = kwds
def get(self):
""" Rebuild the network by calling the network function with the correct arguments. """
net_module = importlib.import_module(self.fun_module)
net_fun = getattr(net_module, self.fun_name)
return net_fun(*self.args, **self.kwds)
PaddleCV/tracking/ltr/admin/settings.py 0 → 100644
浏览文件 @ 7ec3ec11
from ltr.admin.environment import env_settings
class Settings:
""" Training settings, e.g. the paths to datasets and networks."""
def __init__(self):
self.set_default()
def set_default(self):
self.env = env_settings()
self.use_gpu = True
PaddleCV/tracking/ltr/admin/stats.py 0 → 100644
浏览文件 @ 7ec3ec11
class StatValue:
def __init__(self):
self.clear()
def reset(self):
self.val = 0
def clear(self):
self.reset()
self.history = []
def update(self, val):
self.val = val
self.history.append(self.val)
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.clear()
self.has_new_data = False
def reset(self):
self.avg = 0
self.val = 0
self.sum = 0
self.count = 0
def clear(self):
self.reset()
self.history = []
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def new_epoch(self):
if self.count > 0:
self.history.append(self.avg)
self.reset()
self.has_new_data = True
else:
self.has_new_data = False
def topk_accuracy(output, target, topk=(1, )):
"""Computes the precision@k for the specified values of k"""
single_input = not isinstance(topk, (tuple, list))
if single_input:
topk = (topk, )
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)[0]
res.append(correct_k * 100.0 / batch_size)
if single_input:
return res[0]
return res
PaddleCV/tracking/ltr/admin/tensorboard.py 0 → 100644
浏览文件 @ 7ec3ec11
import os
from collections import OrderedDict
from tensorboardX import SummaryWriter
class TensorboardWriter:
def __init__(self, directory, loader_names):
self.directory = directory
self.writer = OrderedDict({
name: SummaryWriter(os.path.join(self.directory, name))
for name in loader_names
})
def write_info(self, module_name, script_name, description):
tb_info_writer = SummaryWriter(os.path.join(self.directory, 'info'))
tb_info_writer.add_text('Modulet_name', module_name)
tb_info_writer.add_text('Script_name', script_name)
tb_info_writer.add_text('Description', description)
tb_info_writer.close()
def write_epoch(self, stats: OrderedDict, epoch: int, ind=-1):
for loader_name, loader_stats in stats.items():
if loader_stats is None:
continue
for var_name, val in loader_stats.items():
if hasattr(val, 'history') and getattr(val, 'has_new_data',
True):
self.writer[loader_name].add_scalar(var_name,
val.history[ind], epoch)
PaddleCV/tracking/ltr/data/__init__.py 0 → 100644
浏览文件 @ 7ec3ec11
from .loader import LTRLoader
PaddleCV/tracking/ltr/data/image_loader.py 0 → 100644
浏览文件 @ 7ec3ec11
import jpeg4py
import cv2 as cv
import lmdb
import numpy as np
def default_image_loader(path):
"""The default image loader, reads the image from the given path. It first tries to use the jpeg4py_loader,
but reverts to the opencv_loader if the former is not available."""
if default_image_loader.use_jpeg4py is None:
# Try using jpeg4py
im = jpeg4py_loader(path)
if im is None:
default_image_loader.use_jpeg4py = False
print('Using opencv_loader instead.')
else:
default_image_loader.use_jpeg4py = True
return im
if default_image_loader.use_jpeg4py:
return jpeg4py_loader(path)
return opencv_loader(path)
default_image_loader.use_jpeg4py = None
def jpeg4py_loader(path):
""" Image reading using jpeg4py (https://github.com/ajkxyz/jpeg4py)"""
try:
return jpeg4py.JPEG(path).decode()
except Exception as e:
print('ERROR: Could not read image "{}"'.format(path))
print(e)
return None
def opencv_loader(path):
""" Read image using opencv's imread function and returns it in rgb format"""
try:
im = cv.imread(path, cv.IMREAD_COLOR)
# convert to rgb and return
return cv.cvtColor(im, cv.COLOR_BGR2RGB)
except Exception as e:
print('ERROR: Could not read image "{}"'.format(path))
print(e)
return None
def lmdb_loader(path, lmdb_path=None):
try:
if lmdb_loader.txn is None:
db = lmdb.open(lmdb_path, readonly=True, map_size=int(300e9))
lmdb_loader.txn = db.begin(write=False)
img_buffer = lmdb_loader.txn.get(path.encode())
img_buffer = np.frombuffer(img_buffer, np.uint8)
return cv.imdecode(img_buffer, cv.IMREAD_COLOR)
except Exception as e:
print('ERROR: Could not read image "{}"'.format(path))
print(e)
return None
lmdb_loader.txn = None
PaddleCV/tracking/ltr/data/loader.py 0 → 100644
浏览文件 @ 7ec3ec11
import os
import signal
import sys
import dataflow as df
import numpy as np
# handle terminate reader process, do not print stack frame
def _reader_quit(signum, frame):
print("Reader process exit.")
sys.exit()
def _term_group(sig_num, frame):
print('pid {} terminated, terminate group '
'{}...'.format(os.getpid(), os.getpgrp()))
os.killpg(os.getpgid(os.getpid()), signal.SIGKILL)
signal.signal(signal.SIGTERM, _reader_quit)
signal.signal(signal.SIGINT, _term_group)
class LTRLoader(df.DataFlow):
"""
Data loader. Combines a dataset and a sampler, and provides
single- or multi-process iterators over the dataset.
Note: an additional option stack_dim is available to
select along which dimension the data should be stacked to form a batch.
Arguments:
dataset (Dataset): dataset from which to load the data.
batch_size (int, optional): how many samples per batch to load
(default: 1).
shuffle (bool, optional): set to ``True`` to have the data reshuffled
at every epoch (default: False).
sampler (Sampler, optional): defines the strategy to draw samples from
the dataset. If specified, ``shuffle`` must be False.
batch_sampler (Sampler, optional): like sampler, but returns a batch of
indices at a time. Mutually exclusive with batch_size, shuffle,
sampler, and drop_last.
num_workers (int, optional): how many subprocesses to use for data
loading. 0 means that the data will be loaded in the main process.
(default: 0)
collate_fn (callable, optional): merges a list of samples to form a mini-batch.
stack_dim (int): Dimension along which to stack to form the batch. (default: 0)
pin_memory (bool, optional): If ``True``, the data loader will copy tensors
into CUDA pinned memory before returning them.
drop_last (bool, optional): set to ``True`` to drop the last incomplete batch,
if the dataset size is not divisible by the batch size. If ``False`` and
the size of dataset is not divisible by the batch size, then the last batch
will be smaller. (default: False)
timeout (numeric, optional): if positive, the timeout value for collecting a batch
from workers. Should always be non-negative. (default: 0)
worker_init_fn (callable, optional): If not None, this will be called on each
worker subprocess with the worker id (an int in ``[0, num_workers - 1]``) as
input, after seeding and before data loading. (default: None)
.. warning:: If ``spawn`` start method is used, :attr:`worker_init_fn` cannot be an
unpicklable object, e.g., a lambda function.
"""
__initialized = False
def __init__(self,
name,
dataset,
training=True,
batch_size=1,
shuffle=False,
sampler=None,
batch_sampler=None,
num_workers=0,
epoch_interval=1,
collate_fn=None,
stack_dim=0,
pin_memory=False,
drop_last=False,
timeout=0,
worker_init_fn=None):
super().__init__()
ds = df.RepeatedData(dataset, -1)
ds = df.MultiProcessRunnerZMQ(ds, num_proc=num_workers, hwm=300)
# ds = df.MultiThreadRunner(lambda: ds, num_prefetch=1024, num_thread=num_workers)
ds = df.BatchData(ds, batch_size)
self.ds = ds
self.name = name
self.training = training
self.epoch_interval = epoch_interval
self.stack_dim = stack_dim
self.batches_per_epoch = len(dataset) // batch_size
def __len__(self):
return self.batches_per_epoch
def __iter__(self):
if not self.__initialized:
self.reset_state()
self.__initialized = True
for d in self.ds:
if self.stack_dim > 0:
for k, v in d.items():
if len(v.shape) >= self.stack_dim + 1:
d[k] = np.swapaxes(v, 0, self.stack_dim)
yield d
def reset_state(self):
self.ds.reset_state()
PaddleCV/tracking/ltr/data/processing.py 0 → 100644
浏览文件 @ 7ec3ec11
import numpy as np
from ltr.data import transforms
import ltr.data.processing_utils as prutils
from pytracking.libs import TensorDict
class BaseProcessing:
""" Base class for Processing. Processing class is used to process the data returned by a dataset, before passing it
through the network. For example, it can be used to crop a search region around the object, apply various data
augmentations, etc."""
def __init__(self,
transform=transforms.ToArray(),
train_transform=None,
test_transform=None,
joint_transform=None):
"""
args:
transform - The set of transformations to be applied on the images. Used only if train_transform or
test_transform is None.
train_transform - The set of transformations to be applied on the train images. If None, the 'transform'
argument is used instead.
test_transform - The set of transformations to be applied on the test images. If None, the 'transform'
argument is used instead.
joint_transform - The set of transformations to be applied 'jointly' on the train and test images. For
example, it can be used to convert both test and train images to grayscale.
"""
self.transform = {
'train': transform if train_transform is None else train_transform,
'test': transform if test_transform is None else test_transform,
'joint': joint_transform
}
def __call__(self, data: TensorDict):
raise NotImplementedError
class SiamFCProcessing(BaseProcessing):
def __init__(self,
search_area_factor,
output_sz,
center_jitter_factor,
scale_jitter_factor,
mode='pair',
scale_type='context',
border_type='meanpad',
*args,
**kwargs):
super().__init__(*args, **kwargs)
self.search_area_factor = search_area_factor
self.output_sz = output_sz
self.center_jitter_factor = center_jitter_factor
self.scale_jitter_factor = scale_jitter_factor
self.mode = mode
self.scale_type = scale_type
self.border_type = border_type
def _get_jittered_box(self, box, mode, rng):
jittered_size = box[2:4] * np.exp(
rng.randn(2) * self.scale_jitter_factor[mode])
max_offset = (np.sqrt(jittered_size.prod()) *
self.center_jitter_factor[mode])
jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (rng.rand(2)
- 0.5)
return np.concatenate(
(jittered_center - 0.5 * jittered_size, jittered_size), axis=0)
def __call__(self, data: TensorDict, rng=None):
# Apply joint transforms
if self.transform['joint'] is not None:
num_train_images = len(data['train_images'])
all_images = data['train_images'] + data['test_images']
all_images_trans = self.transform['joint'](*all_images)
data['train_images'] = all_images_trans[:num_train_images]
data['test_images'] = all_images_trans[num_train_images:]
for s in ['train', 'test']:
assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \
"In pair mode, num train/test frames must be 1"
# Add a uniform noise to the center pos
jittered_anno = [
self._get_jittered_box(a, s, rng) for a in data[s + '_anno']
]
# Crop image region centered at jittered_anno box
try:
crops, boxes = prutils.jittered_center_crop(
data[s + '_images'],
jittered_anno,
data[s + '_anno'],
self.search_area_factor[s],
self.output_sz[s],
scale_type=self.scale_type,
border_type=self.border_type)
except Exception as e:
print('{}, anno: {}'.format(data['dataset'], data[s + '_anno']))
raise e
# Apply transforms
data[s + '_images'] = [self.transform[s](x) for x in crops]
data[s + '_anno'] = boxes
# Prepare output
if self.mode == 'sequence':
data = data.apply(prutils.stack_tensors)
else:
data = data.apply(lambda x: x[0] if isinstance(x, list) else x)
return data
class ATOMProcessing(BaseProcessing):
""" The processing class used for training ATOM. The images are processed in the following way.
First, the target bounding box is jittered by adding some noise. Next, a square region (called search region )
centered at the jittered target center, and of area search_area_factor^2 times the area of the jittered box is
cropped from the image. The reason for jittering the target box is to avoid learning the bias that the target is
always at the center of the search region. The search region is then resized to a fixed size given by the
argument output_sz. A set of proposals are then generated for the test images by jittering the ground truth box.
"""
def __init__(self,
search_area_factor,
output_sz,
center_jitter_factor,
scale_jitter_factor,
proposal_params,
mode='pair',
*args,
**kwargs):
"""
args:
search_area_factor - The size of the search region relative to the target size.
output_sz - An integer, denoting the size to which the search region is resized. The search region is always
square.
center_jitter_factor - A dict containing the amount of jittering to be applied to the target center before
extracting the search region. See _get_jittered_box for how the jittering is done.
scale_jitter_factor - A dict containing the amount of jittering to be applied to the target size before
extracting the search region. See _get_jittered_box for how the jittering is done.
proposal_params - Arguments for the proposal generation process. See _generate_proposals for details.
mode - Either 'pair' or 'sequence'. If mode='sequence', then output has an extra dimension for frames
"""
super().__init__(*args, **kwargs)
self.search_area_factor = search_area_factor
self.output_sz = output_sz
self.center_jitter_factor = center_jitter_factor
self.scale_jitter_factor = scale_jitter_factor
self.proposal_params = proposal_params
self.mode = mode
def _get_jittered_box(self, box, mode, rng):
""" Jitter the input box
args:
box - input bounding box
mode - string 'train' or 'test' indicating train or test data
returns:
Variable - jittered box
"""
jittered_size = box[2:4] * np.exp(
rng.randn(2) * self.scale_jitter_factor[mode])
max_offset = (np.sqrt(jittered_size.prod()) *
self.center_jitter_factor[mode])
jittered_center = box[0:2] + 0.5 * box[2:4] + max_offset * (rng.rand(2)
- 0.5)
return np.concatenate(
(jittered_center - 0.5 * jittered_size, jittered_size), axis=0)
def _generate_proposals(self, box, rng):
""" Generates proposals by adding noise to the input box
args:
box - input box
returns:
array - Array of shape (num_proposals, 4) containing proposals
array - Array of shape (num_proposals,) containing IoU overlap of each proposal with the input box. The
IoU is mapped to [-1, 1]
"""
# Generate proposals
num_proposals = self.proposal_params['boxes_per_frame']
proposals = np.zeros((num_proposals, 4))
gt_iou = np.zeros(num_proposals)
for i in range(num_proposals):
proposals[i, :], gt_iou[i] = prutils.perturb_box(
box,
min_iou=self.proposal_params['min_iou'],
sigma_factor=self.proposal_params['sigma_factor'],
rng=rng)
# Map to [-1, 1]
gt_iou = gt_iou * 2 - 1
return proposals, gt_iou
def __call__(self, data: TensorDict, rng=None):
"""
args:
data - The input data, should contain the following fields:
'train_images' -
'test_images' -
'train_anno' -
'test_anno' -
returns:
TensorDict - output data block with following fields:
'train_images' -
'test_images' -
'train_anno' -
'test_anno' -
'test_proposals'-
'proposal_iou' -
"""
# Apply joint transforms
if self.transform['joint'] is not None:
num_train_images = len(data['train_images'])
all_images = data['train_images'] + data['test_images']
all_images_trans = self.transform['joint'](*all_images)
data['train_images'] = all_images_trans[:num_train_images]
data['test_images'] = all_images_trans[num_train_images:]
for s in ['train', 'test']:
assert self.mode == 'sequence' or len(data[s + '_images']) == 1, \
"In pair mode, num train/test frames must be 1"
# Add a uniform noise to the center pos
jittered_anno = [
self._get_jittered_box(a, s, rng) for a in data[s + '_anno']
]
# Crop image region centered at jittered_anno box
try:
crops, boxes = prutils.jittered_center_crop(
data[s + '_images'], jittered_anno, data[s + '_anno'],
self.search_area_factor, self.output_sz)
except Exception as e:
print('{}, anno: {}'.format(data['dataset'], data[s + '_anno']))
raise e
# Apply transforms
data[s + '_images'] = [self.transform[s](x) for x in crops]
data[s + '_anno'] = boxes
# Generate proposals
frame2_proposals, gt_iou = zip(
* [self._generate_proposals(a, rng) for a in data['test_anno']])
data['test_proposals'] = list(frame2_proposals)
data['proposal_iou'] = list(gt_iou)
# Prepare output
if self.mode == 'sequence':
data = data.apply(prutils.stack_tensors)
else:
data = data.apply(lambda x: x[0] if isinstance(x, list) else x)
return data
PaddleCV/tracking/ltr/data/processing_utils.py 0 → 100644
浏览文件 @ 7ec3ec11
import math
import numpy as np
import cv2 as cv
def stack_tensors(x):
if isinstance(x, list) and isinstance(x[0], np.ndarray):
return np.stack(x)
return x
def sample_target(im,
target_bb,
search_area_factor,
output_sz=None,
scale_type='original',
border_type='replicate'):
""" Extracts a square crop centered at target_bb box, of area search_area_factor^2 times target_bb area
args:
im - cv image
target_bb - target box [x, y, w, h]
search_area_factor - Ratio of crop size to target size
output_sz - (float) Size to which the extracted crop is resized (always square). If None, no resizing is done.
returns:
cv image - extracted crop
float - the factor by which the crop has been resized to make the crop size equal output_size
"""
x, y, w, h = target_bb.tolist()
# Crop image
if scale_type == 'original':
crop_sz = math.ceil(math.sqrt(w * h) * search_area_factor)
elif scale_type == 'context':
# some context is added into the target_size
# now, the search factor is respect to the "target + context"
# when search_factor = 1, output_size = 127
# when search_factor = 2, output_size = 255
context = (w + h) / 2
base_size = math.sqrt(
(w + context) * (h + context)) # corresponds to 127 in crop
crop_sz = math.ceil(search_area_factor * base_size)
else:
raise NotImplementedError
if crop_sz < 1:
raise Exception('Too small bounding box. w: {}, h: {}'.format(w, h))
x1 = round(x + 0.5 * w - crop_sz * 0.5)
x2 = x1 + crop_sz
y1 = round(y + 0.5 * h - crop_sz * 0.5)
y2 = y1 + crop_sz
x1_pad = max(0, -x1)
x2_pad = max(x2 - im.shape[1] + 1, 0)
y1_pad = max(0, -y1)
y2_pad = max(y2 - im.shape[0] + 1, 0)
# Crop target
im_crop = im[y1 + y1_pad:y2 - y2_pad, x1 + x1_pad:x2 - x2_pad, :]
# Pad
if border_type == 'replicate':
im_crop_padded = cv.copyMakeBorder(im_crop, y1_pad, y2_pad, x1_pad,
x2_pad, cv.BORDER_REPLICATE)
elif border_type == 'zeropad':
im_crop_padded = cv.copyMakeBorder(im_crop, y1_pad, y2_pad, x1_pad,
x2_pad, cv.BORDER_CONSTANT)
elif border_type == 'meanpad':
avg_chans = np.array(
[np.mean(im[:, :, 0]), np.mean(im[:, :, 1]), np.mean(im[:, :, 2])])
im_crop_padded = cv.copyMakeBorder(
im_crop,
y1_pad,
y2_pad,
x1_pad,
x2_pad,
cv.BORDER_CONSTANT,
value=avg_chans)
else:
raise NotImplementedError
if output_sz is not None:
resize_factor = output_sz / crop_sz
return cv.resize(im_crop_padded, (output_sz, output_sz)), resize_factor
else:
return im_crop_padded, 1.0
def transform_image_to_crop(box_in: np.ndarray,
box_extract: np.ndarray,
resize_factor: float,
crop_sz: np.ndarray) -> np.ndarray:
""" Transform the box co-ordinates from the original image co-ordinates to the co-ordinates of the cropped image
args:
box_in - the box for which the co-ordinates are to be transformed
box_extract - the box about which the image crop has been extracted.
resize_factor - the ratio between the original image scale and the scale of the image crop
crop_sz - size of the cropped image
returns:
array - transformed co-ordinates of box_in
"""
box_extract_center = box_extract[0:2] + 0.5 * box_extract[2:4]
box_in_center = box_in[0:2] + 0.5 * box_in[2:4]
box_out_center = (crop_sz - 1) / 2 + (box_in_center - box_extract_center
) * resize_factor
box_out_wh = box_in[2:4] * resize_factor
box_out = np.concatenate((box_out_center - 0.5 * box_out_wh, box_out_wh))
return box_out
def centered_crop(frames, anno, area_factor, output_sz):
crops_resize_factors = [
sample_target(f, a, area_factor, output_sz)
for f, a in zip(frames, anno)
]
frames_crop, resize_factors = zip(*crops_resize_factors)
crop_sz = np.array([output_sz, output_sz], 'int')
# find the bb location in the crop
anno_crop = [
transform_image_to_crop(a, a, rf, crop_sz)
for a, rf in zip(anno, resize_factors)
]
return frames_crop, anno_crop
def jittered_center_crop(frames,
box_extract,
box_gt,
search_area_factor,
output_sz,
scale_type='original',
border_type='replicate'):
""" For each frame in frames, extracts a square crop centered at box_extract, of area search_area_factor^2
times box_extract area. The extracted crops are then resized to output_sz. Further, the co-ordinates of the box
box_gt are transformed to the image crop co-ordinates
args:
frames - list of frames
box_extract - list of boxes of same length as frames. The crops are extracted using anno_extract
box_gt - list of boxes of same length as frames. The co-ordinates of these boxes are transformed from
image co-ordinates to the crop co-ordinates
search_area_factor - The area of the extracted crop is search_area_factor^2 times box_extract area
output_sz - The size to which the extracted crops are resized
returns:
list - list of image crops
list - box_gt location in the crop co-ordinates
"""
crops_resize_factors = [
sample_target(
f,
a,
search_area_factor,
output_sz,
scale_type=scale_type,
border_type=border_type) for f, a in zip(frames, box_extract)
]
frames_crop, resize_factors = zip(*crops_resize_factors)
crop_sz = np.array([output_sz, output_sz], 'int')
# find the bb location in the crop
box_crop = [
transform_image_to_crop(a_gt, a_ex, rf, crop_sz)
for a_gt, a_ex, rf in zip(box_gt, box_extract, resize_factors)
]
return frames_crop, box_crop
def iou(reference, proposals):
"""Compute the IoU between a reference box with multiple proposal boxes.
args:
reference - Tensor of shape (1, 4).
proposals - Tensor of shape (num_proposals, 4)
returns:
array - shape (num_proposals,) containing IoU of reference box with each proposal box.
"""
# Intersection box
tl = np.maximum(reference[:, :2], proposals[:, :2])
br = np.minimum(reference[:, :2] + reference[:, 2:],
proposals[:, :2] + proposals[:, 2:])
sz = np.clip(br - tl, 0, np.inf)
# Area
intersection = np.prod(sz, axis=1)
union = np.prod(
reference[:, 2:], axis=1) + np.prod(
proposals[:, 2:], axis=1) - intersection
return intersection / union
def rand_uniform(a, b, rng=None, shape=1):
""" sample numbers uniformly between a and b.
args:
a - lower bound
b - upper bound
shape - shape of the output tensor
returns:
array
"""
rand = np.random.rand if rng is None else rng.rand
return (b - a) * rand(shape) + a
def perturb_box(box, min_iou=0.5, sigma_factor=0.1, rng=None):
""" Perturb the input box by adding gaussian noise to the co-ordinates
args:
box - input box
min_iou - minimum IoU overlap between input box and the perturbed box
sigma_factor - amount of perturbation, relative to the box size. Can be either a single element, or a list of
sigma_factors, in which case one of them will be uniformly sampled. Further, each of the
sigma_factor element can be either a float, or a tensor
of shape (4,) specifying the sigma_factor per co-ordinate
returns:
array - the perturbed box
"""
if rng is None:
rng = np.random
if isinstance(sigma_factor, list):
# If list, sample one sigma_factor as current sigma factor
c_sigma_factor = rng.choice(sigma_factor)
else:
c_sigma_factor = sigma_factor
if not isinstance(c_sigma_factor, np.ndarray):
c_sigma_factor = c_sigma_factor * np.ones(4)
perturb_factor = np.sqrt(box[2] * box[3]) * c_sigma_factor
# multiple tries to ensure that the perturbed box has iou > min_iou with the input box
for i_ in range(100):
c_x = box[0] + 0.5 * box[2]
c_y = box[1] + 0.5 * box[3]
c_x_per = rng.normal(c_x, perturb_factor[0])
c_y_per = rng.normal(c_y, perturb_factor[1])
w_per = rng.normal(box[2], perturb_factor[2])
h_per = rng.normal(box[3], perturb_factor[3])
if w_per <= 1:
w_per = box[2] * rand_uniform(0.15, 0.5, rng)[0]
if h_per <= 1:
h_per = box[3] * rand_uniform(0.15, 0.5, rng)[0]
box_per = np.round(
np.array(
[c_x_per - 0.5 * w_per, c_y_per - 0.5 * h_per, w_per, h_per]))
if box_per[2] <= 1:
box_per[2] = box[2] * rand_uniform(0.15, 0.5, rng)
if box_per[3] <= 1:
box_per[3] = box[3] * rand_uniform(0.15, 0.5, rng)
box_iou = iou(np.reshape(box, (1, 4)), np.reshape(box_per, (1, 4)))
# if there is sufficient overlap, return
if box_iou > min_iou:
return box_per, box_iou
# else reduce the perturb factor
perturb_factor *= 0.9
return box_per, box_iou
PaddleCV/tracking/ltr/data/sampler.py 0 → 100644
浏览文件 @ 7ec3ec11
import numpy as np
import dataflow as df
from pytracking.libs import TensorDict
def no_processing(data, rng=None):
return data
class ATOMSampler(df.RNGDataFlow):
""" Class responsible for sampling frames from training sequences to form batches. Each training sample is a
tuple consisting of i) a train frame, used to obtain the modulation vector, and ii) a set of test frames on which
the IoU prediction loss is calculated.
The sampling is done in the following ways. First a dataset is selected at random. Next, a sequence is selected
from that dataset. A 'train frame' is then sampled randomly from the sequence. Next, depending on the
frame_sample_mode, the required number of test frames are sampled randomly, either from the range
[train_frame_id - max_gap, train_frame_id + max_gap] in the 'default' mode, or from [train_frame_id, train_frame_id + max_gap]
in the 'causal' mode. Only the frames in which the target is visible are sampled, and if enough visible frames are
not found, the 'max_gap' is incremented.
The sampled frames are then passed through the input 'processing' function for the necessary processing-
"""
def __init__(self,
datasets,
p_datasets,
samples_per_epoch,
max_gap,
num_test_frames=1,
processing=no_processing,
frame_sample_mode='default'):
"""
args:
datasets - List of datasets to be used for training
p_datasets - List containing the probabilities by which each dataset will be sampled
samples_per_epoch - Number of training samples per epoch
max_gap - Maximum gap, in frame numbers, between the train (reference) frame and the test frames.
num_test_frames - Number of test frames used for calculating the IoU prediction loss.
processing - An instance of Processing class which performs the necessary processing of the data.
frame_sample_mode - Either 'default' or 'causal'. If 'causal', then the test frames are sampled in a causal
manner.
"""
self.datasets = datasets
# If p not provided, sample uniformly from all videos
if p_datasets is None:
p_datasets = [1 for d in self.datasets]
# Normalize
p_total = sum(p_datasets)
self.p_datasets = [x / p_total for x in p_datasets]
self.samples_per_epoch = samples_per_epoch
self.max_gap = max_gap
self.num_test_frames = num_test_frames
self.num_train_frames = 1 # Only a single train frame allowed
self.processing = processing
self.frame_sample_mode = frame_sample_mode
def __len__(self):
return self.samples_per_epoch
def _sample_visible_ids(self, visible, num_ids=1, min_id=None, max_id=None):
""" Samples num_ids frames between min_id and max_id for which target is visible
args:
visible - 1d Tensor indicating whether target is visible for each frame
num_ids - number of frames to be samples
min_id - Minimum allowed frame number
max_id - Maximum allowed frame number
returns:
list - List of sampled frame numbers. None if not sufficient visible frames could be found.
"""
if min_id is None or min_id < 0:
min_id = 0
if max_id is None or max_id > len(visible):
max_id = len(visible)
valid_ids = [i for i in range(min_id, max_id) if visible[i]]
# No visible ids
if len(valid_ids) == 0:
return None
inds = self.rng.choice(
range(len(valid_ids)), size=num_ids, replace=True)
ids = [valid_ids[ii] for ii in inds]
# return random.choices(valid_ids, k=num_ids)
return ids
def __iter__(self):
"""
args:
index (int): Index (Ignored since we sample randomly)
returns:
TensorDict - dict containing all the data blocks
"""
# Select a dataset
# dataset = self.rng.choices(self.datasets, self.p_datasets)[0]
dataset_idx = self.rng.choice(
range(len(self.datasets)), p=self.p_datasets, replace=False)
dataset = self.datasets[dataset_idx]
is_video_dataset = dataset.is_video_sequence()
min_visible_frames = 2 * (self.num_test_frames + self.num_train_frames)
enough_visible_frames = False
# Sample a sequence with enough visible frames and get anno for the same
while not enough_visible_frames:
seq_id = self.rng.randint(0, dataset.get_num_sequences() - 1)
anno, visible = dataset.get_sequence_info(seq_id)
num_visible = np.sum(visible.astype('int64'))
enough_visible_frames = not is_video_dataset or (
num_visible > min_visible_frames and len(visible) >= 20)
if is_video_dataset:
train_frame_ids = None
test_frame_ids = None
gap_increase = 0
if self.frame_sample_mode == 'default':
# Sample frame numbers
while test_frame_ids is None:
train_frame_ids = self._sample_visible_ids(
visible, num_ids=self.num_train_frames)
test_frame_ids = self._sample_visible_ids(
visible,
min_id=train_frame_ids[0] - self.max_gap - gap_increase,
max_id=train_frame_ids[0] + self.max_gap + gap_increase,
num_ids=self.num_test_frames)
gap_increase += 5 # Increase gap until a frame is found
elif self.frame_sample_mode == 'causal':
# Sample frame numbers in a causal manner, i.e. test_frame_ids > train_frame_ids
while test_frame_ids is None:
base_frame_id = self._sample_visible_ids(
visible,
num_ids=1,
min_id=self.num_train_frames - 1,
max_id=len(visible) - self.num_test_frames)
prev_frame_ids = self._sample_visible_ids(
visible,
num_ids=self.num_train_frames - 1,
min_id=base_frame_id[0] - self.max_gap - gap_increase,
max_id=base_frame_id[0])
if prev_frame_ids is None:
gap_increase += 5
continue
train_frame_ids = base_frame_id + prev_frame_ids
test_frame_ids = self._sample_visible_ids(
visible,
min_id=train_frame_ids[0] + 1,
max_id=train_frame_ids[0] + self.max_gap + gap_increase,
num_ids=self.num_test_frames)
gap_increase += 5 # Increase gap until a frame is found
else:
raise ValueError('Unknown frame_sample_mode.')
else:
train_frame_ids = [1] * self.num_train_frames
test_frame_ids = [1] * self.num_test_frames
# Get frames
train_frames, train_anno, _ = dataset.get_frames(seq_id,
train_frame_ids, anno)
test_frames, test_anno, _ = dataset.get_frames(seq_id, test_frame_ids,
anno)
# Prepare data
data = TensorDict({
'train_images': train_frames,
'train_anno': train_anno,
'test_images': test_frames,
'test_anno': test_anno,
'dataset': dataset.get_name()
})
# Send for processing
yield self.processing(data, rng=self.rng)
PaddleCV/tracking/ltr/data/transforms.py 0 → 100644
浏览文件 @ 7ec3ec11
import random
import numpy as np
import math
import cv2 as cv
from paddle.fluid import layers
from pytracking.libs.paddle_utils import PTensor
class Transform:
""" Class for applying various image transformations."""
def __call__(self, *args):
rand_params = self.roll()
if rand_params is None:
rand_params = ()
elif not isinstance(rand_params, tuple):
rand_params = (rand_params, )
output = [self.transform(img, *rand_params) for img in args]
if len(output) == 1:
return output[0]
return output
def roll(self):
return None
def transform(self, img, *args):
"""Must be deterministic"""
raise NotImplementedError
class Compose:
"""Composes several transforms together.
Args:
transforms (list of ``Transform`` objects): list of transforms to compose.
"""
def __init__(self, transforms):
self.transforms = transforms
def __call__(self, *args):
for t in self.transforms:
if not isinstance(args, tuple):
args = (args, )
args = t(*args)
return args
def __repr__(self):
format_string = self.__class__.__name__ + '('
for t in self.transforms:
format_string += '\n'
format_string += ' {0}'.format(t)
format_string += '\n)'
return format_string
class Normalize(object):
"""Normalize an tensor image with mean and standard deviation.
Given mean: ``(M1,...,Mn)`` and std: ``(S1,..,Sn)`` for ``n`` channels, this transform
will normalize each channel of the input i.e.
``input[channel] = (input[channel] - mean[channel]) / std[channel]``
Args:
mean (sequence): Sequence of means for each channel.
std (sequence): Sequence of standard deviations for each channel.
"""
def __init__(self, mean, std):
self.mean = np.reshape(mean, [-1, 1, 1])
self.std = np.reshape(std, [-1, 1, 1])
def __call__(self, tensor):
"""
Args:
tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
Returns:
Tensor: Normalized Tensor image.
"""
return (tensor - self.mean) / self.std
class ToArray(Transform):
""" Transpose image and jitter brightness"""
def __init__(self, brightness_jitter=0.0):
self.brightness_jitter = brightness_jitter
def __call__(self, img):
img = img.transpose((2, 0, 1))
return img.astype('float32') / 255.
class ToArrayAndJitter(Transform):
""" Transpose image and jitter brightness"""
def __init__(self, brightness_jitter=0.0):
self.brightness_jitter = brightness_jitter
def roll(self):
return np.random.uniform(
max(0, 1 - self.brightness_jitter), 1 + self.brightness_jitter)
def transform(self, img, brightness_factor):
# handle numpy array
img = img.transpose((2, 0, 1))
# backward compatibility
return np.clip(
img.astype('float32') * brightness_factor / 255.0, 0.0, 1.0)
class ToGrayscale(Transform):
"""Converts image to grayscale with probability"""
def __init__(self, probability=0.5):
self.probability = probability
self.color_weights = np.array(
[0.2989, 0.5870, 0.1140], dtype=np.float32)
def roll(self):
return random.random() < self.probability
def transform(self, img, do_grayscale):
if do_grayscale:
if isinstance(img, PTensor):
raise NotImplementedError('Implement paddle variant.')
img_gray = cv.cvtColor(img, cv.COLOR_RGB2GRAY)
return np.stack([img_gray, img_gray, img_gray], axis=2)
# return np.repeat(np.sum(img * self.color_weights, axis=2, keepdims=True).astype(np.uint8), 3, axis=2)
return img
class RandomHorizontalFlip(Transform):
"""Horizontally flip the given NumPy Image randomly with a probability p."""
def __init__(self, probability=0.5):
self.probability = probability
def roll(self):
return random.random() < self.probability
def transform(self, img, do_flip):
if do_flip:
if isinstance(img, PTensor):
return layers.reverse(img, 2)
return np.fliplr(img).copy()
return img
PaddleCV/tracking/ltr/dataset/__init__.py 0 → 100644
浏览文件 @ 7ec3ec11
from .lasot import Lasot
from .got10k import Got10k
from .tracking_net import TrackingNet
from .imagenetvid import ImagenetVID
from .coco_seq import MSCOCOSeq
from .vot import VOT
from .youtube_vos import VOS
from .youtube_bb import YoutubeBB
PaddleCV/tracking/ltr/dataset/base_dataset.py 0 → 100644
浏览文件 @ 7ec3ec11
from ltr.data.image_loader import default_image_loader
class BaseDataset(object):
""" Base class for datasets """
def __init__(self, root, image_loader=default_image_loader):
"""
args:
root - The root path to the dataset
image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)
is used by default.
"""
if root == '':
raise Exception(
'The dataset path is not setup. Check your "ltr/admin/local.py".'
)
self.root = root
self.image_loader = image_loader
self.sequence_list = [] # Contains the list of sequences.
def __len__(self):
""" Returns size of the dataset
returns:
int - number of samples in the dataset
"""
return self.get_num_sequences()
def __getitem__(self, index):
""" Not to be used! Check get_frames() instead.
"""
return None
def is_video_sequence(self):
""" Returns whether the dataset is a video dataset or an image dataset
returns:
bool - True if a video dataset
"""
return True
def get_name(self):
""" Name of the dataset
returns:
string - Name of the dataset
"""
raise NotImplementedError
def get_num_sequences(self):
""" Number of sequences in a dataset
returns:
int - number of sequences in the dataset."""
return len(self.sequence_list)
def get_sequence_info(self, seq_id):
""" Returns information about a particular sequences,
args:
seq_id - index of the sequence
returns:
Tensor - Annotation for the sequence. A 2d tensor of shape (num_frames, 4).
Format [top_left_x, top_left_y, width, height]
Tensor - 1d Tensor specifying whether target is present (=1 )for each frame. shape (num_frames,)
"""
raise NotImplementedError
def get_frames(self, seq_id, frame_ids, anno=None):
""" Get a set of frames from a particular sequence
args:
seq_id - index of sequence
frame_ids - a list of frame numbers
anno(None) - The annotation for the sequence (see get_sequence_info). If None, they will be loaded.
returns:
list - List of frames corresponding to frame_ids
list - List of annotations (tensor of shape (4,)) for each frame
dict - A dict containing meta information about the sequence, e.g. class of the target object.
"""
raise NotImplementedError
PaddleCV/tracking/ltr/dataset/coco_seq.py 0 → 100644
浏览文件 @ 7ec3ec11
import os
from .base_dataset import BaseDataset
from ltr.data.image_loader import default_image_loader
from pycocotools.coco import COCO
from collections import OrderedDict
from ltr.admin.environment import env_settings
import numpy as np
class MSCOCOSeq(BaseDataset):
""" The COCO dataset. COCO is an image dataset. Thus, we treat each image as a sequence of length 1.
Publication:
Microsoft COCO: Common Objects in Context.
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona,
Deva Ramanan, Piotr Dollar and C. Lawrence Zitnick
ECCV, 2014
https://arxiv.org/pdf/1405.0312.pdf
Download the images along with annotations from http://cocodataset.org/#download. The root folder should be
organized as follows.
- coco_root
- annotations
- instances_train2014.json
- images
- train2014
Note: You also have to install the coco pythonAPI from https://github.com/cocodataset/cocoapi.
"""
def __init__(self,
root=None,
filter=None,
image_loader=default_image_loader):
root = env_settings().coco_dir if root is None else root
super().__init__(root, image_loader)
self.filter = filter
# self.img_pth = os.path.join(root, 'train2014/')
self.img_pth = os.path.join(root, 'train2017/')
# self.anno_path = os.path.join(root, 'annotations/instances_train2014.json')
self.anno_path = os.path.join(root,
'annotations/instances_train2017.json')
# Load the COCO set.
self.coco_set = COCO(self.anno_path)
self.cats = self.coco_set.cats
self.sequence_list = self._get_sequence_list()
def _get_sequence_list(self):
ann_list = list(self.coco_set.anns.keys())
seq_list = []
print('COCO before: {}'.format(len(ann_list)))
for a in ann_list:
if self.coco_set.anns[a]['iscrowd'] == 0:
box = self.coco_set.anns[a]['bbox']
box = np.reshape(np.array(box), (1, 4))
target_visible = (box[:, 2] > 0) & (box[:, 3] > 0)
if self.filter:
target_large = (box[:, 2] * box[:, 3] > 30 * 30)
ratio = box[:, 2] / box[:, 3]
target_reasonable_ratio = (10 > ratio) & (ratio > 0.1)
target_visible = target_visible & target_large & target_reasonable_ratio
if target_visible:
seq_list.append(a)
print('COCO after: {}'.format(len(seq_list)))
return seq_list
def is_video_sequence(self):
return False
def get_name(self):
return 'coco'
def get_num_sequences(self):
return len(self.sequence_list)
def get_sequence_info(self, seq_id):
anno = self._get_anno(seq_id)
target_visible = (anno[:, 2] > 0) & (anno[:, 3] > 0)
return anno, target_visible
def _get_anno(self, seq_id):
anno = self.coco_set.anns[self.sequence_list[seq_id]]['bbox']
return np.reshape(np.array(anno), (1, 4))
def _get_frames(self, seq_id):
path = self.coco_set.loadImgs(
[self.coco_set.anns[self.sequence_list[seq_id]]['image_id']])[0][
'file_name']
img = self.image_loader(os.path.join(self.img_pth, path))
return img
def get_meta_info(self, seq_id):
try:
cat_dict_current = self.cats[self.coco_set.anns[self.sequence_list[
seq_id]]['category_id']]
object_meta = OrderedDict({
'object_class': cat_dict_current['name'],
'motion_class': None,
'major_class': cat_dict_current['supercategory'],
'root_class': None,
'motion_adverb': None
})
except:
object_meta = OrderedDict({
'object_class': None,
'motion_class': None,
'major_class': None,
'root_class': None,
'motion_adverb': None
})
return object_meta
def get_frames(self, seq_id=None, frame_ids=None, anno=None):
# COCO is an image dataset. Thus we replicate the image denoted by seq_id len(frame_ids) times, and return a
# list containing these replicated images.
frame = self._get_frames(seq_id)
frame_list = [frame.copy() for _ in frame_ids]
if anno is None:
anno = self._get_anno(seq_id)
anno_frames = [anno.copy()[0, :] for _ in frame_ids]
object_meta = self.get_meta_info(seq_id)
return frame_list, anno_frames, object_meta
PaddleCV/tracking/ltr/dataset/got10k.py 0 → 100644
浏览文件 @ 7ec3ec11
import os
import os.path
import numpy as np
import csv
import pandas
from collections import OrderedDict
from .base_dataset import BaseDataset
from ltr.data.image_loader import default_image_loader
from ltr.admin.environment import env_settings
class Got10k(BaseDataset):
""" GOT-10k dataset.
Publication:
GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild
Lianghua Huang, Xin Zhao, and Kaiqi Huang
arXiv:1810.11981, 2018
https://arxiv.org/pdf/1810.11981.pdf
Download dataset from http://got-10k.aitestunion.com/downloads
"""
def __init__(self,
root=None,
filter=None,
image_loader=default_image_loader,
split=None,
seq_ids=None):
"""
args:
root - path to the got-10k training data. Note: This should point to the 'train' folder inside GOT-10k
image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)
is used by default.
split - 'train' or 'val'. Note: The validation split here is a subset of the official got-10k train split,
not NOT the official got-10k validation split. To use the official validation split, provide that as
the root folder instead.
seq_ids - List containing the ids of the videos to be used for training. Note: Only one of 'split' or 'seq_ids'
options can be used at the same time.
"""
root = env_settings().got10k_dir if root is None else root
super().__init__(root, image_loader)
# all folders inside the root
self.sequence_list = self._get_sequence_list()
if split == 'vot-train':
ltr_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)), '..')
with open(
os.path.join(ltr_path, 'data_specs',
'got10k_prohibited_for_VOT.txt')) as f:
prohibited = [l.strip() for l in f.readlines()]
print('GOT10K before: {}'.format(len(self.sequence_list)))
self.sequence_list = [
x for x in self.sequence_list if x not in prohibited
]
print('GOT10K after: {}'.format(len(self.sequence_list)))
else:
# seq_id is the index of the folder inside the got10k root path
if split is not None:
if seq_ids is not None:
raise ValueError('Cannot set both split_name and seq_ids.')
ltr_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)), '..')
if split == 'train':
file_path = os.path.join(ltr_path, 'data_specs',
'got10k_train_split.txt')
elif split == 'val':
file_path = os.path.join(ltr_path, 'data_specs',
'got10k_val_split.txt')
else:
raise ValueError('Unknown split name.')
seq_ids = pandas.read_csv(
file_path, header=None, squeeze=True,
dtype=np.int64).values.tolist()
elif seq_ids is None:
seq_ids = list(range(0, len(self.sequence_list)))
# self.seq_ids = seq_ids
self.sequence_list = [self.sequence_list[i] for i in seq_ids]
self.sequence_meta_info = self._load_meta_info()
self.filter = filter
def get_name(self):
return 'got10k'
def _load_meta_info(self):
sequence_meta_info = {
s: self._read_meta(os.path.join(self.root, s))
for s in self.sequence_list
}
return sequence_meta_info
def _read_meta(self, seq_path):
try:
with open(os.path.join(seq_path, 'meta_info.ini')) as f:
meta_info = f.readlines()
object_meta = OrderedDict({
'object_class': meta_info[5].split(': ')[-1][:-1],
'motion_class': meta_info[6].split(': ')[-1][:-1],
'major_class': meta_info[7].split(': ')[-1][:-1],
'root_class': meta_info[8].split(': ')[-1][:-1],
'motion_adverb': meta_info[9].split(': ')[-1][:-1]
})
except:
object_meta = OrderedDict({
'object_class': None,
'motion_class': None,
'major_class': None,
'root_class': None,
'motion_adverb': None
})
return object_meta
def _get_sequence_list(self):
with open(os.path.join(self.root, 'list.txt')) as f:
# dir_names = f.readlines()
dir_list = list(csv.reader(f))
dir_list = [dir_name[0] for dir_name in dir_list]
return dir_list
def _read_anno(self, seq_path):
anno_file = os.path.join(seq_path, "groundtruth.txt")
gt = pandas.read_csv(
anno_file,
delimiter=',',
header=None,
dtype=np.float32,
na_filter=False,
low_memory=False).values
return np.array(gt)
def _read_target_visible(self, seq_path, anno):
# Read full occlusion and out_of_view
occlusion_file = os.path.join(seq_path, "absence.label")
cover_file = os.path.join(seq_path, "cover.label")
with open(occlusion_file, 'r', newline='') as f:
occlusion = np.array([int(v[0]) for v in csv.reader(f)], 'byte')
with open(cover_file, 'r', newline='') as f:
cover = np.array([int(v[0]) for v in csv.reader(f)], 'byte')
target_visible = ~occlusion & (cover > 0) & (anno[:, 2] > 0) & (
anno[:, 3] > 0)
return target_visible
def _get_sequence_path(self, seq_id):
return os.path.join(self.root, self.sequence_list[seq_id])
def get_sequence_info(self, seq_id):
seq_path = self._get_sequence_path(seq_id)
anno = self._read_anno(seq_path)
target_visible = self._read_target_visible(seq_path, anno)
if self.filter:
target_large = (anno[:, 2] * anno[:, 3] > 30 * 30)
ratio = anno[:, 2] / anno[:, 3]
target_reasonable_ratio = (10 > ratio) & (ratio > 0.1)
target_visible = target_visible & target_large & target_reasonable_ratio
return anno, target_visible
def _get_frame_path(self, seq_path, frame_id):
return os.path.join(
seq_path, '{:08}.jpg'.format(frame_id + 1)) # frames start from 1
def _get_frame(self, seq_path, frame_id):
return self.image_loader(self._get_frame_path(seq_path, frame_id))
def get_frames(self, seq_id, frame_ids, anno=None):
seq_path = self._get_sequence_path(seq_id)
obj_meta = self.sequence_meta_info[self.sequence_list[seq_id]]
frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids]
if anno is None:
anno = self._read_anno(seq_path)
# Return as list of tensors
anno_frames = [anno[f_id, :] for f_id in frame_ids]
return frame_list, anno_frames, obj_meta
PaddleCV/tracking/ltr/dataset/imagenetvid.py 0 → 100644
浏览文件 @ 7ec3ec11
import os
import numpy as np
from .base_dataset import BaseDataset
from ltr.data.image_loader import default_image_loader
import xml.etree.ElementTree as ET
import json
from collections import OrderedDict
import nltk
from nltk.corpus import wordnet
from ltr.admin.environment import env_settings
def get_target_to_image_ratio(seq):
anno = np.array(seq['anno'])
img_sz = np.array(seq['image_size'])
return np.sqrt(anno[0, 2:4].prod() / (img_sz.prod()))
class ImagenetVID(BaseDataset):
""" Imagenet VID dataset.
Publication:
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy,
Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei
IJCV, 2015
https://arxiv.org/pdf/1409.0575.pdf
Download the dataset from http://image-net.org/
"""
def __init__(self,
root=None,
filter=None,
image_loader=default_image_loader,
min_length=0,
max_target_area=1):
"""
args:
root - path to the imagenet vid dataset.
image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)
is used by default.
min_length - Minimum allowed sequence length.
max_target_area - max allowed ratio between target area and image area. Can be used to filter out targets
which cover complete image.
"""
root = env_settings().imagenet_dir if root is None else root
super().__init__(root, image_loader)
cache_file = os.path.join(root, 'cache.json')
if os.path.isfile(cache_file):
# If available, load the pre-processed cache file containing meta-info for each sequence
with open(cache_file, 'r') as f:
sequence_list_dict = json.load(f)
self.sequence_list = sequence_list_dict
else:
# Else process the imagenet annotations and generate the cache file
self.sequence_list = self._process_anno(root)
with open(cache_file, 'w') as f:
json.dump(self.sequence_list, f)
# Filter the sequences based on min_length and max_target_area in the first frame
self.sequence_list = [
x for x in self.sequence_list
if len(x['anno']) >= min_length and get_target_to_image_ratio(x) <
max_target_area
]
self.filter = filter
def get_name(self):
return 'imagenetvid'
def get_num_sequences(self):
return len(self.sequence_list)
def get_sequence_info(self, seq_id):
anno = np.array(self.sequence_list[seq_id]['anno'])
target_visible = np.array(self.sequence_list[seq_id]['target_visible'],
'bool')
target_visible = target_visible & (anno[:, 2] > 0) & (anno[:, 3] > 0)
if self.filter is not None:
target_large = (anno[:, 2] * anno[:, 3] > 30 * 30)
ratio = anno[:, 2] / anno[:, 3]
target_reasonable_ratio = (10 > ratio) & (ratio > 0.1)
target_visible = target_visible & target_reasonable_ratio & target_large
return anno, target_visible
def _get_frame(self, sequence, frame_id):
set_name = 'ILSVRC2015_VID_train_{:04d}'.format(sequence['set_id'])
vid_name = 'ILSVRC2015_train_{:08d}'.format(sequence['vid_id'])
frame_number = frame_id + sequence['start_frame']
frame_path = os.path.join(self.root, 'Data', 'VID', 'train', set_name,
vid_name, '{:06d}.JPEG'.format(frame_number))
# frame_path = os.path.join(self.root, 'Data', 'VID', 'train', vid_name,
# '{:06d}.jpg'.format(frame_number))
return self.image_loader(frame_path)
def get_frames(self, seq_id, frame_ids, anno=None):
sequence = self.sequence_list[seq_id]
frame_list = [self._get_frame(sequence, f) for f in frame_ids]
if anno is None:
anno = sequence['anno']
# Return as list of tensors
anno_frames = [anno[f_id, :] for f_id in frame_ids]
# added the class info to the meta info
object_meta = OrderedDict({
'object_class': sequence['class_name'],
'motion_class': None,
'major_class': None,
'root_class': None,
'motion_adverb': None
})
return frame_list, anno_frames, object_meta
def _process_anno(self, root):
# Builds individual tracklets
base_vid_anno_path = os.path.join(root, 'Annotations', 'VID', 'train')
all_sequences = []
# for set in sorted(os.listdir(base_vid_anno_path)):
for set in sorted([
'ILSVRC2015_VID_train_0000', 'ILSVRC2015_VID_train_0001',
'ILSVRC2015_VID_train_0002', 'ILSVRC2015_VID_train_0003'
]):
set_id = int(set.split('_')[-1])
for vid in sorted(
os.listdir(os.path.join(base_vid_anno_path, set))):
vid_id = int(vid.split('_')[-1])
anno_files = sorted(
os.listdir(os.path.join(base_vid_anno_path, set, vid)))
frame1_anno = ET.parse(
os.path.join(base_vid_anno_path, set, vid, anno_files[0]))
image_size = [
int(frame1_anno.find('size/width').text),
int(frame1_anno.find('size/height').text)
]
objects = [
ET.ElementTree(file=os.path.join(base_vid_anno_path, set,
vid, f)).findall('object')
for f in anno_files
]
tracklets = {}
# Find all tracklets along with start frame
for f_id, all_targets in enumerate(objects):
for target in all_targets:
tracklet_id = target.find('trackid').text
if tracklet_id not in tracklets:
tracklets[tracklet_id] = f_id
for tracklet_id, tracklet_start in tracklets.items():
tracklet_anno = []
target_visible = []
class_name = None
for f_id in range(tracklet_start, len(objects)):
found = False
for target in objects[f_id]:
if target.find('trackid').text == tracklet_id:
if not class_name:
class_name_id = target.find('name').text
class_name = class_name_id
# class_name = self._get_class_name_from_id(class_name_id)
x1 = int(target.find('bndbox/xmin').text)
y1 = int(target.find('bndbox/ymin').text)
x2 = int(target.find('bndbox/xmax').text)
y2 = int(target.find('bndbox/ymax').text)
tracklet_anno.append([x1, y1, x2 - x1, y2 - y1])
target_visible.append(
target.find('occluded').text == '0')
found = True
break
if not found:
break
new_sequence = {
'set_id': set_id,
'vid_id': vid_id,
'class_name': class_name,
'start_frame': tracklet_start,
'anno': tracklet_anno,
'target_visible': target_visible,
'image_size': image_size
}
all_sequences.append(new_sequence)
return all_sequences
PaddleCV/tracking/ltr/dataset/lasot.py 0 → 100644
浏览文件 @ 7ec3ec11
import os
import os.path
import numpy as np
import pandas
import csv
from collections import OrderedDict
from .base_dataset import BaseDataset
from ltr.data.image_loader import default_image_loader
from ltr.admin.environment import env_settings
class Lasot(BaseDataset):
""" LaSOT dataset.
Publication:
LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking
Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Hexin Bai, Yong Xu, Chunyuan Liao and Haibin Ling
CVPR, 2019
https://arxiv.org/pdf/1809.07845.pdf
Download the dataset from https://cis.temple.edu/lasot/download.html
"""
def __init__(self,
root=None,
filter=None,
image_loader=default_image_loader,
vid_ids=None,
split=None):
"""
args:
root - path to the lasot dataset.
image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)
is used by default.
vid_ids - List containing the ids of the videos (1 - 20) used for training. If vid_ids = [1, 3, 5], then the
videos with subscripts -1, -3, and -5 from each class will be used for training.
split - If split='train', the official train split (protocol-II) is used for training. Note: Only one of
vid_ids or split option can be used at a time.
"""
root = env_settings().lasot_dir if root is None else root
super().__init__(root, image_loader)
self.sequence_list = self._build_sequence_list(vid_ids, split)
self.filter = filter
def _build_sequence_list(self, vid_ids=None, split=None):
if split is not None:
if vid_ids is not None:
raise ValueError('Cannot set both split_name and vid_ids.')
ltr_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)), '..')
if split == 'train':
file_path = os.path.join(ltr_path, 'data_specs',
'lasot_train_split.txt')
else:
raise ValueError('Unknown split name.')
sequence_list = pandas.read_csv(
file_path, header=None, squeeze=True).values.tolist()
elif vid_ids is not None:
sequence_list = [
c + '-' + str(v) for c in self.class_list for v in vid_ids
]
else:
raise ValueError('Set either split_name or vid_ids.')
return sequence_list
def get_name(self):
return 'lasot'
def get_num_sequences(self):
return len(self.sequence_list)
def _read_anno(self, seq_path):
anno_file = os.path.join(seq_path, "groundtruth.txt")
gt = pandas.read_csv(
anno_file,
delimiter=',',
header=None,
dtype=np.float32,
na_filter=False,
low_memory=False).values
return np.array(gt)
def _read_target_visible(self, seq_path, anno):
# Read full occlusion and out_of_view
occlusion_file = os.path.join(seq_path, "full_occlusion.txt")
out_of_view_file = os.path.join(seq_path, "out_of_view.txt")
with open(occlusion_file, 'r', newline='') as f:
occlusion = np.array([int(v) for v in list(csv.reader(f))[0]],
'byte')
with open(out_of_view_file, 'r') as f:
out_of_view = np.array([int(v) for v in list(csv.reader(f))[0]],
'byte')
target_visible = ~occlusion & ~out_of_view & (anno[:, 2] > 0) & (
anno[:, 3] > 0)
return target_visible
def _get_sequence_path(self, seq_id):
seq_name = self.sequence_list[seq_id]
class_name = seq_name.split('-')[0]
vid_id = seq_name.split('-')[1]
return os.path.join(self.root, class_name, class_name + '-' + vid_id)
def get_sequence_info(self, seq_id):
seq_path = self._get_sequence_path(seq_id)
anno = self._read_anno(seq_path)
target_visible = self._read_target_visible(seq_path, anno)
if self.filter is not None:
target_large = (anno[:, 2] * anno[:, 3] > 30 * 30)
ratio = anno[:, 2] / anno[:, 3]
target_reasonable_ratio = (10 > ratio) & (ratio > 0.1)
target_visible = target_visible & target_reasonable_ratio & target_large
return anno, target_visible
def _get_frame_path(self, seq_path, frame_id):
return os.path.join(
seq_path, 'img',
'{:08}.jpg'.format(frame_id + 1)) # frames start from 1
def _get_frame(self, seq_path, frame_id):
return self.image_loader(self._get_frame_path(seq_path, frame_id))
def _get_class(self, seq_path):
obj_class = seq_path.split('/')[-2]
return obj_class
def get_frames(self, seq_id, frame_ids, anno=None):
seq_path = self._get_sequence_path(seq_id)
obj_class = self._get_class(seq_path)
frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids]
if anno is None:
anno = self._read_anno(seq_path)
# Return as list of tensors
anno_frames = [anno[f_id, :] for f_id in frame_ids]
object_meta = OrderedDict({
'object_class': obj_class,
'motion_class': None,
'major_class': None,
'root_class': None,
'motion_adverb': None
})
return frame_list, anno_frames, object_meta
PaddleCV/tracking/ltr/dataset/tracking_net.py 0 → 100644
浏览文件 @ 7ec3ec11
import os
import os.path
import numpy as np
import pandas
from collections import OrderedDict
from ltr.data.image_loader import default_image_loader
from .base_dataset import BaseDataset
from ltr.admin.environment import env_settings
def list_sequences(root, set_ids):
""" Lists all the videos in the input set_ids. Returns a list of tuples (set_id, video_name)
args:
root: Root directory to TrackingNet
set_ids: Sets (0-11) which are to be used
returns:
list - list of tuples (set_id, video_name) containing the set_id and video_name for each sequence
"""
sequence_list = []
for s in set_ids:
anno_dir = os.path.join(root, "TRAIN_" + str(s), "anno")
sequences_cur_set = [(s, os.path.splitext(f)[0])
for f in os.listdir(anno_dir)
if f.endswith('.txt')]
sequence_list += sequences_cur_set
return sequence_list
class TrackingNet(BaseDataset):
""" TrackingNet dataset.
Publication:
TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild.
Matthias Mueller,Adel Bibi, Silvio Giancola, Salman Al-Subaihi and Bernard Ghanem
ECCV, 2018
https://ivul.kaust.edu.sa/Documents/Publications/2018/TrackingNet%20A%20Large%20Scale%20Dataset%20and%20Benchmark%20for%20Object%20Tracking%20in%20the%20Wild.pdf
Download the dataset using the toolkit https://github.com/SilvioGiancola/TrackingNet-devkit.
"""
def __init__(self,
root=None,
image_loader=default_image_loader,
set_ids=None):
"""
args:
root - The path to the TrackingNet folder, containing the training sets.
image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)
is used by default.
set_ids (None) - List containing the ids of the TrackingNet sets to be used for training. If None, all the
sets (0 - 11) will be used.
"""
root = env_settings().trackingnet_dir if root is None else root
super().__init__(root, image_loader)
if set_ids is None:
set_ids = [i for i in range(12)]
self.set_ids = set_ids
# Keep a list of all videos. Sequence list is a list of tuples (set_id, video_name) containing the set_id and
# video_name for each sequence
self.sequence_list = list_sequences(self.root, self.set_ids)
def get_name(self):
return 'trackingnet'
def _read_anno(self, seq_id):
set_id = self.sequence_list[seq_id][0]
vid_name = self.sequence_list[seq_id][1]
anno_file = os.path.join(self.root, "TRAIN_" + str(set_id), "anno",
vid_name + ".txt")
gt = pandas.read_csv(
anno_file,
delimiter=',',
header=None,
dtype=np.float32,
na_filter=False,
low_memory=False).values
return np.array(gt)
def get_sequence_info(self, seq_id):
anno = self._read_anno(seq_id)
target_visible = (anno[:, 2] > 0) & (anno[:, 3] > 0)
return anno, target_visible
def _get_frame(self, seq_id, frame_id):
set_id = self.sequence_list[seq_id][0]
vid_name = self.sequence_list[seq_id][1]
frame_path = os.path.join(self.root, "TRAIN_" + str(set_id), "frames",
vid_name, str(frame_id) + ".jpg")
return self.image_loader(frame_path)
def get_frames(self, seq_id, frame_ids, anno=None):
frame_list = [self._get_frame(seq_id, f) for f in frame_ids]
if anno is None:
anno = self._read_anno(seq_id)
# Return as list of tensors
anno_frames = [anno[f_id, :] for f_id in frame_ids]
object_meta = OrderedDict({
'object_class': None,
'motion_class': None,
'major_class': None,
'root_class': None,
'motion_adverb': None
})
return frame_list, anno_frames, object_meta
PaddleCV/tracking/ltr/dataset/vot.py 0 → 100644
浏览文件 @ 7ec3ec11
import os
from .base_dataset import BaseDataset
from ltr.data.image_loader import default_image_loader
import numpy as np
import cv2 as cv
from collections import OrderedDict
from ltr.admin.environment import env_settings
def get_axis_aligned_bbox(region):
region = np.array(region)
if len(region.shape) == 3:
# region (1,4,2)
region = np.array([
region[0][0][0], region[0][0][1], region[0][1][0], region[0][1][1],
region[0][2][0], region[0][2][1], region[0][3][0], region[0][3][1]
])
cx = np.mean(region[0::2])
cy = np.mean(region[1::2])
x1 = min(region[0::2])
x2 = max(region[0::2])
y1 = min(region[1::2])
y2 = max(region[1::2])
A1 = np.linalg.norm(region[0:2] - region[2:4]) * np.linalg.norm(region[
2:4] - region[4:6])
A2 = (x2 - x1) * (y2 - y1)
s = np.sqrt(A1 / A2)
w = s * (x2 - x1) + 1
h = s * (y2 - y1) + 1
x11 = cx - w // 2
y11 = cy - h // 2
return x11, y11, w, h
class VOT(BaseDataset):
def __init__(self, root=None, image_loader=default_image_loader):
# root = env_settings().vot_dir if root is None else root
assert root is not None
super().__init__(root, image_loader)
self.sequence_list = self._get_sequence_list()
self.ann = self._get_annotations()
def _get_sequence_list(self):
seq_list = []
for d in os.listdir(self.root):
if os.path.isdir(os.path.join(self.root, d)):
seq_list.append(d)
return sorted(seq_list)
def _get_annotations(self):
ann = {}
for seq in self.sequence_list:
ann[seq] = {'bbox': [], 'rbb': []}
with open(os.path.join(self.root, seq, 'groundtruth.txt')) as f:
lines = [l.strip().split(',') for l in f.readlines()]
for l in lines:
vs = [float(v) for v in l]
if len(vs) == 4:
polys = [
vs[0], vs[1] + vs[3] - 1, vs[0], vs[1],
vs[0] + vs[2] - 1, vs[1], vs[0] + vs[2] - 1,
vs[1] + vs[3] - 1
]
else:
polys = vs
box = get_axis_aligned_bbox(polys)
rbb = cv.minAreaRect(
np.int0(np.array(polys).reshape((-1, 2))))
# assume small rotation angle, switch height, width
if rbb[2] < -45:
angle = rbb[2] + 90
height = rbb[1][0]
width = rbb[1][1]
else:
angle = rbb[2]
height = rbb[1][1]
width = rbb[1][0]
rbb = [rbb[0][0], rbb[0][1], width, height, angle]
ann[seq]['bbox'].append(box)
ann[seq]['rbb'].append(rbb)
return ann
def is_video_sequence(self):
return True
def get_name(self):
return 'vot'
def get_num_sequences(self):
return len(self.sequence_list)
def get_sequence_info(self, seq_id):
anno = self._get_anno(seq_id)
target_visible = (anno[:, 2] > 0) & (anno[:, 3] > 0)
return anno, target_visible
def _get_anno(self, seq_id):
anno = self.ann[self.sequence_list[seq_id]]['bbox']
return np.reshape(np.array(anno), (-1, 4))
def get_meta_info(self, seq_id):
object_meta = OrderedDict({
'object_class': None,
'motion_class': None,
'major_class': None,
'root_class': None,
'motion_adverb': None
})
return object_meta
def _get_sequence_path(self, seq_id):
return os.path.join(self.root, self.sequence_list[seq_id])
def _get_frame_path(self, seq_path, frame_id):
return os.path.join(
seq_path, 'color',
'{:08}.jpg'.format(frame_id + 1)) # frames start from 1
def _get_frame(self, seq_path, frame_id):
return self.image_loader(self._get_frame_path(seq_path, frame_id))
def get_frames(self, seq_id=None, frame_ids=None, anno=None):
seq_path = self._get_sequence_path(seq_id)
frame_list = [self._get_frame(seq_path, f_id) for f_id in frame_ids]
if anno is None:
anno = self._get_anno(seq_id)
anno_frames = [anno[f_id, :] for f_id in frame_ids]
object_meta = self.get_meta_info(seq_id)
return frame_list, anno_frames, object_meta
PaddleCV/tracking/ltr/dataset/youtube_bb.py 0 → 100644
浏览文件 @ 7ec3ec11
import os
from .base_dataset import BaseDataset
from ltr.data.image_loader import default_image_loader
import xml.etree.ElementTree as ET
import json
import pickle
from collections import OrderedDict
import numpy as np
import nltk
from nltk.corpus import wordnet
from ltr.admin.environment import env_settings
def get_target_to_image_ratio(seq):
anno = np.array(seq['anno'])
img_sz = np.array(seq['image_size'])
return np.sqrt(anno[0, 2:4].prod() / (img_sz.prod()))
class YoutubeBB(BaseDataset):
""" YoutubeBB dataset.
Publication:
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy,
Aditya Khosla, Michael Bernstein, Alexander C. Berg and Li Fei-Fei
IJCV, 2015
https://arxiv.org/pdf/1409.0575.pdf
Download the dataset from http://image-net.org/
"""
def __init__(self,
root=None,
filter=None,
image_loader=default_image_loader,
min_length=0,
max_target_area=1):
"""
args:
root - path to the imagenet vid dataset.
image_loader (jpeg4py_loader) - The function to read the images. jpeg4py (https://github.com/ajkxyz/jpeg4py)
is used by default.
min_length - Minimum allowed sequence length.
max_target_area - max allowed ratio between target area and image area. Can be used to filter out targets
which cover complete image.
"""
super().__init__(root, image_loader)
meta_file = os.path.join(root, 'ytb_meta.pickle')
with open(meta_file, 'rb') as f:
meta = pickle.load(f)
sequence_list = []
for video_name, video_info in meta:
if 'ILSVRC' not in video_name:
seq_info = {}
for trkid in video_info:
if len(video_info[trkid]['img']) > 2:
seq_info['video_name'] = video_name
seq_info['anno'] = video_info[trkid]['box']
seq_info['img_paths'] = video_info[trkid]['img']
sequence_list.append(seq_info)
print('num_sequences: {}'.format(len(sequence_list)))
self.sequence_list = sequence_list
# Filter the sequences based on min_length and max_target_area in the first frame
# self.sequence_list = [x for x in self.sequence_list if len(x['anno']) >= min_length and
# get_target_to_image_ratio(x) < max_target_area]
self.filter = filter
def get_name(self):
return 'youtubebb'
def get_num_sequences(self):
return len(self.sequence_list)
def get_sequence_info(self, seq_id):
anno = np.array(self.sequence_list[seq_id]['anno'])
target_visible = (anno[:, 2] > 0) & (anno[:, 3] > 0)
if self.filter is not None:
target_large = (anno[:, 2] * anno[:, 3] > 30 * 30)
target_resonable = (anno[:, 2] * anno[:, 3] < 500 * 500)
ratio = anno[:, 2] / anno[:, 3]
target_reasonable_ratio = (10 > ratio) & (ratio > 0.1)
target_visible = target_visible & target_reasonable_ratio & target_large & target_resonable
return anno, target_visible
def _get_frame(self, sequence, frame_id):
frame_path = os.path.join(self.root, sequence['video_name'],
sequence['img_paths'][frame_id] + '.jpg')
return self.image_loader(frame_path)
def get_frames(self, seq_id, frame_ids, anno=None):
sequence = self.sequence_list[seq_id]
frame_list = [self._get_frame(sequence, f) for f in frame_ids]
if anno is None:
anno = sequence['anno']
# Return as list of tensors
anno_frames = [anno[f_id, :] for f_id in frame_ids]
# added the class info to the meta info
object_meta = OrderedDict({
'object_class': None,
'motion_class': None,
'major_class': None,
'root_class': None,
'motion_adverb': None
})
return frame_list, anno_frames, object_meta
PaddleCV/tracking/ltr/dataset/youtube_vos.py 0 → 100644
浏览文件 @ 7ec3ec11
import os
from .base_dataset import BaseDataset
from ltr.data.image_loader import default_image_loader
import numpy as np
import cv2 as cv
import json
from collections import OrderedDict
from ltr.admin.environment import env_settings
def get_axis_aligned_bbox(region):
region = np.array(region)
if len(region.shape) == 3:
# region (1,4,2)
region = np.array([
region[0][0][0], region[0][0][1], region[0][1][0], region[0][1][1],
region[0][2][0], region[0][2][1], region[0][3][0], region[0][3][1]
])
cx = np.mean(region[0::2])
cy = np.mean(region[1::2])
x1 = min(region[0::2])
x2 = max(region[0::2])
y1 = min(region[1::2])
y2 = max(region[1::2])
A1 = np.linalg.norm(region[0:2] - region[2:4]) * np.linalg.norm(region[
2:4] - region[4:6])
A2 = (x2 - x1) * (y2 - y1)
s = np.sqrt(A1 / A2)
if s is np.nan:
x11, y11, w, h = 0, 0, 0, 0
else:
w = s * (x2 - x1) + 1
h = s * (y2 - y1) + 1
x11 = cx - w // 2
y11 = cy - h // 2
return x11, y11, w, h
class VOS(BaseDataset):
def __init__(self, root=None, image_loader=default_image_loader):
# root = env_settings().vot_dir if root is None else root
assert root is not None
super().__init__(root, image_loader)
with open(os.path.join(self.root, 'meta.json')) as f:
self.meta = json.load(f)['videos']
self.sequence_list = self._get_sequence_list()
self.ann = self._get_annotations()
def _get_sequence_list(self):
seq_list = []
videos = self.meta.keys()
for v in videos:
objs = self.meta[v]['objects'].keys()
for o in objs:
if "rotate_box" in self.meta[v]['objects'][o]:
seq_list.append((v, o))
assert len(seq_list) > 0
return seq_list
def _get_annotations(self):
ann = {}
for seq in self.sequence_list:
ann[seq] = {'bbox': [], 'rbb': []}
polygons = self.meta[seq[0]]['objects'][seq[1]]['rotate_box']
for vs in polygons:
if len(vs) == 4:
polys = [
vs[0], vs[1] + vs[3] - 1, vs[0], vs[1],
vs[0] + vs[2] - 1, vs[1], vs[0] + vs[2] - 1,
vs[1] + vs[3] - 1
]
else:
polys = vs
if not np.all(polys == 0):
box = get_axis_aligned_bbox(polys)
rbb = cv.minAreaRect(
np.int0(np.array(polys).reshape((-1, 2))))
else:
box = np.array([0, 0, 0, 0])
rbb = ((0, 0), (0, 0), 0)
if box[2] * box[3] > 500 * 500:
print(box)
# assume small rotation angle, switch height, width
if rbb[2] < -45:
angle = rbb[2] + 90
height = rbb[1][0]
width = rbb[1][1]
else:
angle = rbb[2]
height = rbb[1][1]
width = rbb[1][0]
rbb = [rbb[0][0], rbb[0][1], width, height, angle]
ann[seq]['bbox'].append(box)
ann[seq]['rbb'].append(rbb)
return ann
def is_video_sequence(self):
return True
def get_name(self):
return 'vot'
def get_num_sequences(self):
return len(self.sequence_list)
def get_sequence_info(self, seq_id):
anno = self._get_anno(seq_id)
target_visible = (anno[:, 2] > 0) & (anno[:, 3] > 0)
target_large = (anno[:, 2] * anno[:, 3] > 30 * 30)
target_resonable = (anno[:, 2] * anno[:, 3] < 500 * 500)
return anno, target_visible & target_large & target_resonable
def _get_anno(self, seq_id):
anno = self.ann[self.sequence_list[seq_id]]['bbox']
return np.reshape(np.array(anno), (-1, 4))
def get_meta_info(self, seq_id):
object_meta = OrderedDict({
'object_class': None,
'motion_class': None,
'major_class': None,
'root_class': None,
'motion_adverb': None
})
return object_meta
def _get_frame_path(self, seq_id, frame_id):
v, o = self.sequence_list[seq_id]
frame_name = self.meta[v]['objects'][o]['frames'][frame_id]
return os.path.join(self.root, 'JPEGImages', v,
'{}.jpg'.format(frame_name)) # frames start from 1
def _get_frame(self, seq_id, frame_id):
return self.image_loader(self._get_frame_path(seq_id, frame_id))
def get_frames(self, seq_id=None, frame_ids=None, anno=None):
frame_list = [self._get_frame(seq_id, f_id) for f_id in frame_ids]
if anno is None:
anno = self._get_anno(seq_id)
anno_frames = [anno[f_id, :] for f_id in frame_ids]
object_meta = self.get_meta_info(seq_id)
return frame_list, anno_frames, object_meta
PaddleCV/tracking/ltr/models/backbone/resnet.py 0 → 100644
浏览文件 @ 7ec3ec11
import os
import paddle.fluid as fluid
import paddle.fluid.dygraph.nn as nn
from ltr.admin.environment import env_settings
CURRENT_DIR = os.path.dirname(__file__)
def weight_init():
init = fluid.initializer.MSRAInitializer(uniform=False)
param = fluid.ParamAttr(initializer=init)
return param
def norm_weight_init(constant=1.0):
init = fluid.initializer.ConstantInitializer(constant)
param = fluid.ParamAttr(initializer=init)
return param
def norm_bias_init():
init = fluid.initializer.ConstantInitializer(value=0.)
param = fluid.ParamAttr(initializer=init)
return param
class ConvBNLayer(fluid.dygraph.Layer):
def __init__(self,
in_channels,
out_channels,
filter_size,
stride=1,
groups=1,
bn_init_constant=1.0,
is_test=False):
super(ConvBNLayer, self).__init__()
self.conv = nn.Conv2D(
num_channels=in_channels,
filter_size=filter_size,
num_filters=out_channels,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
bias_attr=False,
param_attr=weight_init())
self.bn = nn.BatchNorm(
out_channels,
param_attr=norm_weight_init(bn_init_constant),
bias_attr=norm_bias_init(),
act=None,
momentum=0.9,
use_global_stats=is_test)
def forward(self, inputs):
res = self.conv(inputs)
self.conv_res = res
res = self.bn(res)
return res
class BasicBlock(fluid.dygraph.Layer):
expansion = 1
def __init__(self,
in_channels,
out_channels,
stride=1,
is_downsample=None,
is_test=False):
super(BasicBlock, self).__init__()
self.expansion = 1
self.conv_bn1 = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
filter_size=3,
stride=stride,
groups=1,
is_test=is_test)
self.conv_bn2 = ConvBNLayer(
in_channels=out_channels,
out_channels=out_channels,
filter_size=3,
stride=1,
groups=1,
is_test=is_test)
self.is_downsample = is_downsample
if self.is_downsample:
self.downsample = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
filter_size=1,
stride=stride,
is_test=is_test)
self.stride = stride
def forward(self, inputs):
identity = inputs
res = self.conv_bn1(inputs)
res = fluid.layers.relu(res)
res = self.conv_bn2(res)
if self.is_downsample:
identity = self.downsample(identity)
res += identity
res = fluid.layers.relu(res)
return res
class Bottleneck(fluid.dygraph.Layer):
expansion = 4
def __init__(self,
in_channels,
out_channels,
stride=1,
is_downsample=None,
base_width=64,
dilation=1,
groups=1,
is_test=False):
super(Bottleneck, self).__init__()
width = int(out_channels * (base_width / 64.)) * groups
self.conv_bn1 = ConvBNLayer(
in_channels=in_channels,
filter_size=1,
out_channels=width,
groups=1,
is_test=is_test)
self.conv_bn2 = ConvBNLayer(
in_channels=width,
filter_size=3,
out_channels=width,
stride=stride,
groups=groups,
is_test=is_test)
self.conv_bn3 = ConvBNLayer(
in_channels=width,
filter_size=1,
out_channels=out_channels * self.expansion,
bn_init_constant=0.,
is_test=is_test)
self.is_downsample = is_downsample
if self.is_downsample:
self.downsample = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels * self.expansion,
filter_size=1,
stride=stride,
is_test=is_test)
self.stride = stride
def forward(self, inputs):
identify = inputs
out = self.conv_bn1(inputs)
out = fluid.layers.relu(out)
out = self.conv_bn2(out)
out = fluid.layers.relu(out)
out = self.conv_bn3(out)
if self.is_downsample:
identify = self.downsample(inputs)
out += identify
out = fluid.layers.relu(out)
return out
class ResNet(fluid.dygraph.Layer):
def __init__(self,
name,
Block,
layers,
num_classes=1000,
groups=1,
is_test=False):
"""
:param name: str, namescope
:param layers: int, the layer of defined network
:param num_classes: int, the dimension of final output
:param groups: int, default is 1
"""
super(ResNet, self).__init__(name_scope=name)
support_layers = [18, 34, 50, 101, 152]
assert layers in support_layers, \
"support layer can only be one of [18, 34, 50, 101, 152]"
self.layers = layers
if layers == 18:
depths = [2, 2, 2, 2]
elif layers == 50 or layers == 34:
depths = [3, 4, 6, 3]
elif layers == 101:
depths = [3, 4, 23, 3]
elif layers == 152:
depths = [3, 8, 36, 3]
strides = [1, 2, 2, 2]
num_filters = [64, 128, 256, 512]
self.in_channels = 64
self.dilation = 1
self.groups = groups
self.conv_bn_init = ConvBNLayer(
3,
out_channels=self.in_channels,
filter_size=7,
stride=2,
is_test=is_test)
block_collect = []
downsample = None
for i in range(len(depths)):
# collect layers in each block
_block = []
stride = strides[i]
out_channel = num_filters[i]
if stride != 1 or self.in_channels != num_filters[
i] * Block.expansion:
downsample = True
bottleneck_block = self.add_sublayer(
"block{}_0".format(i),
Block(
self.in_channels,
out_channel,
stride=stride,
is_downsample=downsample,
is_test=is_test))
downsample = False
_block.append(bottleneck_block)
self.in_channels = num_filters[i] * Block.expansion
for j in range(1, depths[i]):
bottleneck_block = self.add_sublayer(
"block{}_{}".format(i, j),
Block(
self.in_channels, out_channel, is_test=is_test))
_block.append(bottleneck_block)
# collect blocks
block_collect.append(_block)
self.block_collect = block_collect
self.maxpool = nn.Pool2D(
pool_size=3, pool_stride=2, pool_padding=1, pool_type="max")
self.global_pool = nn.Pool2D(pool_type='avg', global_pooling=True)
self.fc = nn.Linear(
input_dim=512 * Block.expansion, output_dim=num_classes)
def _add_output_and_check(self, name, x, outputs, output_layers):
if name in output_layers:
outputs[name] = x
return len(output_layers) == len(outputs)
def forward(self, inputs, feat_layers):
out = {}
res = self.conv_bn_init(inputs)
res = fluid.layers.relu(res)
res = self.maxpool(res)
# out['conv_init'] = res
for i in range(len(self.block_collect)):
for layer in self.block_collect[i]:
res = layer(res)
name = 'block{}'.format(i)
if name in feat_layers:
out[name] = res
if len(out) == len(feat_layers):
return out
res = self.global_pool(res)
B, C, _, _ = res.shape
res = fluid.layers.reshape(res, [B, C])
res = self.fc(res)
out['fc'] = res
return out
def resnet18(name, is_test=False, pretrained=False):
net = ResNet(name, Block=BasicBlock, layers=18, is_test=is_test)
if pretrained:
params_path = os.path.join(env_settings().backbone_dir, 'ResNet18')
print("=> loading backbone model from '{}'".format(params_path))
params, _ = fluid.load_dygraph(params_path)
net.load_dict(params)
print("Done")
return net
def resnet50(name, is_test=False, pretrained=False):
net = ResNet(name, Block=Bottleneck, layers=50, is_test=is_test)
if pretrained:
params_path = os.path.join(env_settings().backbone_dir, 'ResNet50')
print("=> loading backbone model from '{}'".format(params_path))
params, _ = fluid.load_dygraph(params_path)
net.load_dict(params)
print("Done")
return net
PaddleCV/tracking/ltr/models/backbone/sfc_alexnet.py 0 → 100644
浏览文件 @ 7ec3ec11
from collections import OrderedDict
from paddle import fluid
from paddle.fluid.dygraph import nn
class SFC_AlexNet(fluid.dygraph.Layer):
def __init__(self, name, is_test):
super(SFC_AlexNet, self).__init__()
self.is_test = is_test
self.layer_init()
def layer_init(self):
# for conv1
self.conv1 = nn.Conv2D(
num_channels=3,
num_filters=96,
filter_size=11,
stride=2,
padding=0,
groups=1,
param_attr=self.weight_init(),
bias_attr=self.bias_init())
self.bn1 = nn.BatchNorm(
num_channels=96,
is_test=self.is_test,
param_attr=self.norm_weight_init(),
bias_attr=self.bias_init(),
use_global_stats=self.is_test)
self.pool1 = nn.Pool2D(
pool_size=3, pool_type="max", pool_stride=2, pool_padding=0)
# for conv2
self.conv2 = nn.Conv2D(
num_channels=96,
num_filters=256,
filter_size=5,
stride=1,
padding=0,
groups=2,
param_attr=self.weight_init(),
bias_attr=self.bias_init())
self.bn2 = nn.BatchNorm(
num_channels=256,
is_test=self.is_test,
param_attr=self.norm_weight_init(),
bias_attr=self.bias_init(),
use_global_stats=self.is_test)
self.pool2 = nn.Pool2D(
pool_size=3, pool_type="max", pool_stride=2, pool_padding=0)
# for conv3
self.conv3 = nn.Conv2D(
num_channels=256,
num_filters=384,
filter_size=3,
stride=1,
padding=0,
groups=1,
param_attr=self.weight_init(),
bias_attr=self.bias_init())
self.bn3 = nn.BatchNorm(
num_channels=384,
is_test=self.is_test,
param_attr=self.norm_weight_init(),
bias_attr=self.bias_init(),
use_global_stats=self.is_test)
# for conv4
self.conv4 = nn.Conv2D(
num_channels=384,
num_filters=384,
filter_size=3,
stride=1,
padding=0,
groups=2,
param_attr=self.weight_init(),
bias_attr=self.bias_init())
self.bn4 = nn.BatchNorm(
num_channels=384,
is_test=self.is_test,
param_attr=self.norm_weight_init(),
bias_attr=self.bias_init(),
use_global_stats=self.is_test)
# for conv5
self.conv5 = nn.Conv2D(
num_channels=384,
num_filters=256,
filter_size=3,
stride=1,
padding=0,
groups=2,
param_attr=self.weight_init(),
bias_attr=self.bias_init())
def _add_output_and_check(self, name, x, outputs, output_layers):
if name in output_layers:
outputs[name] = x
return len(output_layers) == len(outputs)
def forward(self, inputs, output_layers):
outputs = OrderedDict()
out1 = self.conv1(inputs)
out1 = self.bn1(out1)
out1 = fluid.layers.relu(out1)
if self._add_output_and_check('conv1', out1, outputs, output_layers):
return outputs
out1 = self.pool1(out1)
out2 = self.conv2(out1)
out2 = self.bn2(out2)
out2 = fluid.layers.relu(out2)
if self._add_output_and_check('conv2', out2, outputs, output_layers):
return outputs
out2 = self.pool2(out2)
out3 = self.conv3(out2)
out3 = self.bn3(out3)
out3 = fluid.layers.relu(out3)
if self._add_output_and_check('conv3', out3, outputs, output_layers):
return outputs
out4 = self.conv4(out3)
out4 = self.bn4(out4)
out4 = fluid.layers.relu(out4)
if self._add_output_and_check('conv4', out4, outputs, output_layers):
return outputs
out5 = self.conv5(out4)
if self._add_output_and_check('conv5', out5, outputs, output_layers):
return outputs
return outputs
def norm_weight_init(self):
init = fluid.initializer.ConstantInitializer(1.0)
param = fluid.ParamAttr(initializer=init)
return param
def weight_init(self):
init = fluid.initializer.MSRAInitializer(uniform=False)
param = fluid.ParamAttr(initializer=init)
return param
def bias_init(self):
init = fluid.initializer.ConstantInitializer(value=0.)
param = fluid.ParamAttr(initializer=init)
return param
PaddleCV/tracking/ltr/models/bbreg/__init__.py 0 → 100644
浏览文件 @ 7ec3ec11
from .atom_iou_net import AtomIouNet
PaddleCV/tracking/ltr/models/bbreg/atom.py 0 → 100644
浏览文件 @ 7ec3ec11
import paddle
import paddle.fluid as fluid
import paddle.fluid.dygraph as dygraph
import os.path as osp
import sys
CURRENT_DIR = osp.dirname(__file__)
sys.path.append(osp.join(CURRENT_DIR, '..', '..', '..'))
from ltr.models.backbone.resnet import resnet50, resnet18
from ltr.models.bbreg.atom_iou_net import AtomIouNet
class ATOMnet(dygraph.layers.Layer):
def __init__(self,
name,
feature_extractor,
bb_regressor,
bb_regressor_layer,
extractor_grad=True):
"""
:param feature_extractor: backbone
:param bb_regressor: IOUnet
:param bb_regressor_layer: list, which layer is used in IOUnet,
:param extractor_grad: default is True
"""
super(ATOMnet, self).__init__(name)
self.feature_extractor = feature_extractor
self.bb_regressor = bb_regressor
self.bb_regressor_layer = bb_regressor_layer
layers_gt = ['block0', 'block1', 'block2', 'block3', 'fc']
if bb_regressor_layer is not None:
for key in bb_regressor_layer:
assert key in layers_gt
else:
raise ValueError("bb_regressor_layer can only be one of :",
layers_gt)
def forward(self, train_imgs, test_imgs, train_bb, test_proposals):
num_sequences = train_imgs.shape[-4]
num_train_images = train_imgs.shape[0] if len(
train_imgs.shape) == 5 else 1
num_test_images = test_imgs.shape[0] if len(test_imgs.shape) == 5 else 1
if len(train_imgs.shape) == 5:
train_imgs = fluid.layers.reshape(
train_imgs, [-1, *list(train_imgs.shape)[-3:]])
test_imgs = fluid.layers.reshape(test_imgs,
[-1, *list(test_imgs.shape)[-3:]])
train_feat = self.extract_backbone_features(train_imgs)
test_feat = self.extract_backbone_features(test_imgs)
# For clarity, send the features to bb_regressor in sequenceform, i.e. [sequence, batch, feature, row, col]
train_feat_iou = [
fluid.layers.reshape(feat, (num_train_images, num_sequences,
*feat.shape[-3:]))
for feat in train_feat.values()
]
test_feat_iou = [
fluid.layers.reshape(feat, (num_test_images, num_sequences,
*feat.shape[-3:]))
for feat in test_feat.values()
]
# Obtain iou prediction
iou_pred = self.bb_regressor(train_feat_iou, test_feat_iou, train_bb,
test_proposals)
return iou_pred
def extract_backbone_features(self, im, layers=None):
if layers is None:
layers = self.bb_regressor_layer
return self.feature_extractor(im, layers)
def extract_features(self, im, layers):
return self.feature_extractor(im, layers)
def atom_resnet18(iou_input_dim=(256, 256),
iou_inter_dim=(256, 256),
backbone_pretrained=True,
backbone_is_test=False,
iounet_is_test=False):
backbone = resnet18(
'ResNet18', is_test=backbone_is_test, pretrained=backbone_pretrained)
iou_predictor = AtomIouNet(
'IOUnet',
pred_input_dim=iou_input_dim,
pred_inter_dim=iou_inter_dim,
is_test=iounet_is_test)
model = ATOMnet(
'ATOM',
feature_extractor=backbone,
bb_regressor=iou_predictor,
bb_regressor_layer=['block1', 'block2'],
extractor_grad=False)
return model
def atom_resnet50(iou_input_dim=(256, 256),
iou_inter_dim=(256, 256),
backbone_pretrained=True,
backbone_is_test=False,
iounet_is_test=False):
backbone = resnet50(
'ResNet50', is_test=backbone_is_test, pretrained=backbone_pretrained)
iou_predictor = AtomIouNet(
'IOUnet',
input_dim=(512, 1024),
pred_input_dim=iou_input_dim,
pred_inter_dim=iou_inter_dim,
is_test=iounet_is_test)
model = ATOMnet(
'ATOM',
feature_extractor=backbone,
bb_regressor=iou_predictor,
bb_regressor_layer=['block1', 'block2'],
extractor_grad=False)
return model
if __name__ == '__main__':
import numpy as np
a = np.random.uniform(-1, 1, [1, 3, 144, 144]).astype(np.float32)
b = np.random.uniform(-1, 1, [1, 3, 144, 144]).astype(np.float32)
bbox = [[3, 4, 10, 11]]
proposal_bbox = [[4, 5, 11, 12] * 16]
bbox = np.reshape(np.array(bbox), [1, 1, 4]).astype(np.float32)
proposal_bbox = np.reshape(np.array(proposal_bbox),
[1, 16, 4]).astype(np.float32)
with fluid.dygraph.guard():
a_pd = fluid.dygraph.to_variable(a)
b_pd = fluid.dygraph.to_variable(b)
bbox_pd = fluid.dygraph.to_variable(bbox)
proposal_bbox_pd = fluid.dygraph.to_variable(proposal_bbox)
model = atom_resnet50()
res = model(a_pd, b_pd, bbox_pd, proposal_bbox_pd)
params = model.state_dict()
for v in params:
print(v)
PaddleCV/tracking/ltr/models/bbreg/atom_iou_net.py 0 → 100644
浏览文件 @ 7ec3ec11
"""
the implementation of ATOM iou net
"""
import paddle
import paddle.fluid as fluid
import paddle.fluid.dygraph.nn as nn
import numpy as np
import os.path as osp
import sys
CURRENT_DIR = osp.dirname(__file__)
sys.path.append(osp.join(CURRENT_DIR, '..', '..', '..'))
def weight_init():
init = fluid.initializer.MSRAInitializer(uniform=False)
param = fluid.ParamAttr(initializer=init)
return param
def bias_init():
init = fluid.initializer.ConstantInitializer(value=0.)
param = fluid.ParamAttr(initializer=init)
return param
def norm_weight_init():
# init = fluid.initializer.ConstantInitializer(1.0)
init = fluid.initializer.Uniform(low=0., high=1.)
param = fluid.ParamAttr(initializer=init)
return param
def norm_bias_init():
init = fluid.initializer.ConstantInitializer(value=0.)
param = fluid.ParamAttr(initializer=init)
return param
class ConvBNReluLayer(fluid.dygraph.Layer):
def __init__(self,
in_channels,
out_channels,
filter_size,
stride=1,
groups=1,
padding=1,
is_test=False):
super(ConvBNReluLayer, self).__init__()
self.conv = nn.Conv2D(
num_channels=in_channels,
filter_size=filter_size,
num_filters=out_channels,
stride=stride,
padding=padding,
groups=groups,
bias_attr=bias_init(),
param_attr=weight_init())
self.bn = nn.BatchNorm(
out_channels,
param_attr=norm_weight_init(),
bias_attr=norm_bias_init(),
act=None,
momentum=0.9,
use_global_stats=is_test)
def forward(self, inputs):
res = self.conv(inputs)
self.conv_res = res
res = self.bn(res)
res = fluid.layers.relu(res)
return res
class FCBNReluLayer(fluid.dygraph.Layer):
def __init__(self,
in_channels,
out_channels,
in_size,
is_bias=True,
is_bn=True,
is_relu=True,
is_test=False):
super(FCBNReluLayer, self).__init__()
self.is_bn = is_bn
self.is_relu = is_relu
if is_bias:
bias_init = fluid.ParamAttr(
initializer=fluid.initializer.ConstantInitializer(0.))
else:
bias_init = False
self.linear = nn.Linear(
in_channels * in_size * in_size, out_channels, bias_attr=bias_init)
self.bn = nn.BatchNorm(
out_channels,
param_attr=norm_weight_init(),
bias_attr=norm_bias_init(),
act=None,
momentum=0.9,
use_global_stats=is_test)
def forward(self, x):
x = fluid.layers.reshape(x, [x.shape[0], -1])
x = self.linear(x)
if self.is_bn:
x = self.bn(x)
if self.is_relu:
x = fluid.layers.relu(x)
return x
class AtomIouNet(fluid.dygraph.Layer):
def __init__(self,
name,
input_dim=(128, 256),
pred_input_dim=(256, 256),
pred_inter_dim=(256, 256),
is_test=False):
super(AtomIouNet, self).__init__(name)
self.name = self.full_name()
self.conv3_1r = ConvBNReluLayer(
input_dim[0], 128, filter_size=3, stride=1, is_test=is_test)
self.conv3_1t = ConvBNReluLayer(
input_dim[0], 256, filter_size=3, stride=1, is_test=is_test)
self.conv3_2t = ConvBNReluLayer(
256, pred_input_dim[0], filter_size=3, stride=1, is_test=is_test)
self.fc3_1r = ConvBNReluLayer(
128, 256, filter_size=3, stride=1, padding=0, is_test=is_test)
self.conv4_1r = ConvBNReluLayer(
input_dim[1], 256, filter_size=3, stride=1, is_test=is_test)
self.conv4_1t = ConvBNReluLayer(
input_dim[1], 256, filter_size=3, stride=1, is_test=is_test)
self.conv4_2t = ConvBNReluLayer(
256, pred_input_dim[1], filter_size=3, stride=1, is_test=is_test)
self.fc34_3r = ConvBNReluLayer(
512,
pred_input_dim[0],
filter_size=1,
stride=1,
padding=0,
is_test=is_test)
self.fc34_4r = ConvBNReluLayer(
512,
pred_input_dim[1],
filter_size=1,
stride=1,
padding=0,
is_test=is_test)
self.fc3_rt = FCBNReluLayer(
pred_input_dim[0], pred_inter_dim[0], in_size=5, is_test=is_test)
self.fc4_rt = FCBNReluLayer(
pred_input_dim[1], pred_inter_dim[1], in_size=3, is_test=is_test)
bias_init = fluid.initializer.ConstantInitializer(0.)
self.iou_predictor = nn.Linear(
pred_inter_dim[0] + pred_inter_dim[1], 1, bias_attr=bias_init)
self.outs = {}
def predict_iou(self, filter, feat2, proposals):
"""
predicts IOU for the given proposals
:param modulation: Modulation vectors for the targets. Dims (batch, feature_dim).
:param feat: IoU features (from get_iou_feat) for test images. Dims (batch, feature_dim, H, W).
:param proposals: Proposal boxes for which the IoU will be predicted (batch, num_proposals, 4).
:return:
"""
fc34_3_r, fc34_4_r = filter
c3_t, c4_t = feat2
batch_size = c3_t.shape[0]
# Modulation
c3_t_att = c3_t * fluid.layers.reshape(fc34_3_r, [batch_size, -1, 1, 1])
c4_t_att = c4_t * fluid.layers.reshape(fc34_4_r, [batch_size, -1, 1, 1])
# add batch roi nums
num_proposals_per_batch = proposals.shape[1]
batch_roi_nums = np.array([num_proposals_per_batch] *
batch_size).astype(np.int64)
batch_roi_nums = fluid.dygraph.to_variable(batch_roi_nums)
# input proposals2 is in format xywh, convert it to x0y0x1y1 format
proposals_xyxy = fluid.layers.concat(
[
proposals[:, :, 0:2],
proposals[:, :, 0:2] + proposals[:, :, 2:4]
],
axis=2)
roi2 = fluid.layers.reshape(proposals_xyxy, [-1, 4])
roi2.stop_gradient = False
roi3t = fluid.layers.prroi_pool(
c3_t_att, roi2, 1 / 8., 5, 5, batch_roi_nums=batch_roi_nums)
roi4t = fluid.layers.prroi_pool(
c4_t_att, roi2, 1 / 16., 3, 3, batch_roi_nums=batch_roi_nums)
fc3_rt = self.fc3_rt(roi3t)
fc4_rt = self.fc4_rt(roi4t)
fc34_rt_cat = fluid.layers.concat([fc3_rt, fc4_rt], axis=1)
iou_pred = self.iou_predictor(fc34_rt_cat)
iou_pred = fluid.layers.reshape(iou_pred,
[batch_size, num_proposals_per_batch])
return iou_pred
def forward(self, feat1, feat2, bb1, proposals2):
"""Runs the ATOM IoUNet during training operation.
This forward pass is mainly used for training. Call the individual functions during tracking instead.
args:
feat1: Variable, Features from the reference frames (4 or 5 dims).
feat2: Variable, Features from the test frames (4 or 5 dims).
bb1: Target boxes (x,y,x2,y2) in image coords in the reference samples. Dims (images, sequences, 4).
proposals2: Proposal boxes for which the IoU will be predicted (images, sequences, num_proposals, 4)."""
assert len(feat1[0].shape) == 5, 'Expect 5 dimensional feat1'
num_test_images = feat2[0].shape[0]
batch_size = feat2[0].shape[1]
# Extract first train sample
feat1 = [f[0] for f in feat1]
bb1 = bb1[0]
# Get modulation vector
modulation = self.get_filter(feat1, bb1)
feat2 = [
fluid.layers.reshape(f,
(batch_size * num_test_images, *f.shape[-3:]))
for f in feat2
]
iou_feat = self.get_iou_feat(feat2)
new_modulation = []
for i in range(0, len(modulation)):
tmp = modulation[i]
tmp = fluid.layers.reshape(tmp, [1, batch_size, -1])
tmp = fluid.layers.expand(tmp, [num_test_images, 1, 1])
tmp = fluid.layers.reshape(tmp, [batch_size * num_test_images, -1])
new_modulation.append(tmp)
proposals2 = fluid.layers.reshape(
proposals2, [batch_size * num_test_images, -1, 4])
pred_iou = self.predict_iou(new_modulation, iou_feat, proposals2)
pred_iou = fluid.layers.reshape(pred_iou,
[num_test_images, batch_size, -1])
return pred_iou
def get_filter(self, feat1, bb1):
"""
get modulation feature [feature1, feature2] for the targets
:param feat1: variable, Backbone features from reference images. shapes (batch, feature_dim, H, W).
:param bb1: variable, Target boxes (x,y,w,h) in image coords in the reference samples. shapes (batch, 4).
:return:
"""
feat3_r, feat4_r = feat1
c3_r = self.conv3_1r(feat3_r)
# Add batch_index to rois
batch_size = bb1.shape[0]
batch_roi_nums = np.array([1] * batch_size).astype(np.int64)
batch_roi_nums = fluid.dygraph.to_variable(batch_roi_nums)
# input bb is in format xywh, convert it to x0y0x1y1 format
roi1 = fluid.layers.concat(
[bb1[:, 0:2], bb1[:, 0:2] + bb1[:, 2:4]], axis=1)
roi1.stop_gradient = False
roi3r = fluid.layers.prroi_pool(c3_r, roi1, 1 / 8., 3, 3,
batch_roi_nums)
c4_r = self.conv4_1r(feat4_r)
roi4r = fluid.layers.prroi_pool(c4_r, roi1, 1 / 16., 1, 1,
batch_roi_nums)
fc3_r = self.fc3_1r(roi3r)
# Concatenate
fc34_r = fluid.layers.concat([fc3_r, roi4r], axis=1)
fc34_3_r = self.fc34_3r(fc34_r)
fc34_4_r = self.fc34_4r(fc34_r)
return fc34_3_r, fc34_4_r
def get_iou_feat(self, feat2):
"""
Get IoU prediction features from a 4 or 5 dimensional backbone input.
:param feat2: variable, Backbone features from reference images. [feature1, feature2]
:return: features, variable
"""
feat3_t, feat4_t = feat2
c3_t = self.conv3_2t(self.conv3_1t(feat3_t))
c4_t = self.conv4_2t(self.conv4_1t(feat4_t))
return c3_t, c4_t
def atom_iounet(name,
input_dim=(128, 256),
pred_input_dim=(256, 256),
pred_inter_dim=(256, 256)):
return AtomIouNet(
name,
input_dim=input_dim,
pred_input_dim=pred_input_dim,
pred_inter_dim=pred_inter_dim)
def test_paddle_iounet():
a = np.random.uniform(-1, 1, [1, 1, 512, 18, 18]).astype(np.float32)
b = np.random.uniform(-1, 1, [1, 1, 1024, 9, 9]).astype(np.float32)
bbox = [[3, 4, 10, 11]]
proposal_bbox = [[4, 5, 11, 12] * 16]
bbox = np.reshape(np.array(bbox), [1, 1, 4]).astype(np.float32)
proposal_bbox = np.reshape(np.array(proposal_bbox),
[1, 16, 4]).astype(np.float32)
with fluid.dygraph.guard():
a_pd = fluid.dygraph.to_variable(a)
b_pd = fluid.dygraph.to_variable(b)
bbox_pd = fluid.dygraph.to_variable(bbox)
proposal_bbox_pd = fluid.dygraph.to_variable(proposal_bbox)
feat1 = [a_pd, b_pd]
feat2 = [a_pd, b_pd]
model = AtomIouNet('IOUNet', input_dim=(512, 1024))
res = model(feat1, feat2, bbox_pd, proposal_bbox_pd)
print(res.shape)
params = model.state_dict()
for v in params:
print(v, '\t', params[v].shape)
print(len(params))
if __name__ == '__main__':
test_paddle_iounet()
PaddleCV/tracking/ltr/models/siamese/__init__.py 0 → 100644
浏览文件 @ 7ec3ec11
from .target_estimator_net import SiamFCEstimator
PaddleCV/tracking/ltr/models/siamese/siam.py 0 → 100644
浏览文件 @ 7ec3ec11
from paddle import fluid
from paddle.fluid import dygraph
import ltr.models.siamese.target_estimator_net as tgt_estimator
class SiamNet(dygraph.layers.Layer):
def __init__(self,
name,
feature_extractor,
target_estimator,
target_estimator_layer,
extractor_grad=True):
"""
:param feature_extractor: backbone
:param target_estimator: headers
:param target_estimator_layer: list, which layer is used in header,
:param extractor_grad: default is True
"""
super(SiamNet, self).__init__(name)
self.feature_extractor = feature_extractor
self.target_estimator = target_estimator
self.target_estimator_layer = target_estimator_layer
def forward(self, train_imgs, test_imgs):
# extract backbone features
if len(train_imgs.shape) == 5:
train_imgs = fluid.layers.reshape(
train_imgs, [-1, *list(train_imgs.shape)[-3:]])
test_imgs = fluid.layers.reshape(test_imgs,
[-1, *list(test_imgs.shape)[-3:]])
train_feat = self.extract_backbone_features(train_imgs)
test_feat = self.extract_backbone_features(test_imgs)
train_feat = [feat for feat in train_feat.values()]
test_feat = [feat for feat in test_feat.values()]
# Obtain target estimation
targets = self.target_estimator(train_feat, test_feat)
return targets
def extract_backbone_features(self, im, layers=None):
if layers is None:
layers = self.target_estimator_layer
return self.feature_extractor(im, layers)
def extract_features(self, im, layers):
return self.feature_extractor(im, layers)
def siamfc_alexnet(backbone_pretrained=False,
backbone_is_test=False,
estimator_is_test=False):
from ltr.models.backbone.sfc_alexnet import SFC_AlexNet
backbone_net = SFC_AlexNet('AlexNet', is_test=backbone_is_test)
target_estimator = tgt_estimator.SiamFCEstimator('CenterEstimator')
model = SiamNet(
'SiamFC',
backbone_net,
target_estimator,
['conv5'], )
return model
PaddleCV/tracking/ltr/models/siamese/target_estimator_net.py 0 → 100644
浏览文件 @ 7ec3ec11
from paddle import fluid
from paddle.fluid import dygraph
from paddle.fluid.dygraph import nn
from pytracking.libs.Fconv2d import Conv2D
class SiamFCEstimator(dygraph.layers.Layer):
def __init__(self, name):
super().__init__(name)
init_w = fluid.ParamAttr(
name="a_weight",
initializer=fluid.initializer.ConstantInitializer(0.001),
learning_rate=0.,
trainable=False)
init_b = fluid.ParamAttr(
name="a_bias",
initializer=fluid.initializer.ConstantInitializer(0.),
trainable=True)
self.adjust_conv = nn.Conv2D(
1, 1, 1, 1, 0, param_attr=init_w, bias_attr=init_b)
def forward(self, exemplar, instance):
exemplar_f = self.get_reference(exemplar)
instance_f = self.get_search_feat(instance)
score_map = self.estimate(exemplar_f, instance_f)
return score_map
def get_reference(self, feat):
# remove list warp
return feat[0]
def get_search_feat(self, feat):
# remove list warp
return feat[0]
def estimate(self, exemplar, instance):
shape = instance.shape
instance = fluid.layers.reshape(
instance, shape=[1, -1, shape[2], shape[3]])
cross_conv = Conv2D(stride=1, padding=0, dilation=1, groups=shape[0])
score_map = cross_conv(instance, exemplar)
score_map = fluid.layers.transpose(score_map, [1, 0, 2, 3])
score_map = self.adjust_conv(score_map)
return score_map
PaddleCV/tracking/ltr/run_training.py 0 → 100644
浏览文件 @ 7ec3ec11
import os
import sys
import argparse
import importlib
import multiprocessing
import paddle
import cv2 as cv
env_path = os.path.join(os.path.dirname(__file__), '..')
if env_path not in sys.path:
sys.path.append(env_path)
import ltr.admin.settings as ws_settings
def run_training(train_module, train_name):
"""Run a train scripts in train_settings.
args:
train_module: Name of module in the "train_settings/" folder.
train_name: Name of the train settings file.
"""
# set single threads in opencv
cv.setNumThreads(0)
print('Training: {} {}'.format(train_module, train_name))
settings = ws_settings.Settings()
if settings.env.workspace_dir == '':
raise Exception('Setup your workspace_dir in "ltr/admin/local.py".')
settings.module_name = train_module
settings.script_name = train_name
settings.project_path = 'ltr/{}/{}'.format(train_module, train_name)
expr_module = importlib.import_module('ltr.train_settings.{}.{}'.format(
train_module, train_name))
expr_func = getattr(expr_module, 'run')
expr_func(settings)
def main():
parser = argparse.ArgumentParser(
description='Run a train scripts in train_settings.')
parser.add_argument(
'train_module',
type=str,
help='Name of module in the "train_settings/" folder.')
parser.add_argument(
'train_name', type=str, help='Name of the train settings file.')
args = parser.parse_args()
run_training(args.train_module, args.train_name)
if __name__ == '__main__':
multiprocessing.set_start_method('spawn', force=True)
main()
PaddleCV/tracking/ltr/train_settings/bbreg/atom_res18_vid_lasot_coco.py 0 → 100644
浏览文件 @ 7ec3ec11
import paddle.fluid as fluid
import paddle.fluid.dygraph as dygraph
import ltr.actors as actors
import ltr.data.transforms as dltransforms
from ltr.data import processing, sampler, loader
from ltr.dataset import ImagenetVID, MSCOCOSeq, Lasot, Got10k
from ltr.models.bbreg.atom import atom_resnet50, atom_resnet18
from ltr.trainers import LTRTrainer
def run(settings):
# Most common settings are assigned in the settings struct
settings.description = 'ATOM IoUNet with ResNet18 backbone and trained with vid, lasot, coco.'
settings.print_interval = 1 # How often to print loss and other info
settings.batch_size = 64 # Batch size
settings.num_workers = 4 # Number of workers for image loading
settings.normalize_mean = [0.485, 0.456, 0.406
] # Normalize mean (default ImageNet values)
settings.normalize_std = [0.229, 0.224,
0.225] # Normalize std (default ImageNet values)
settings.search_area_factor = 5.0 # Image patch size relative to target size
settings.feature_sz = 18 # Size of feature map
settings.output_sz = settings.feature_sz * 16 # Size of input image patches
# Settings for the image sample and proposal generation
settings.center_jitter_factor = {'train': 0, 'test': 4.5}
settings.scale_jitter_factor = {'train': 0, 'test': 0.5}
settings.proposal_params = {
'min_iou': 0.1,
'boxes_per_frame': 16,
'sigma_factor': [0.01, 0.05, 0.1, 0.2, 0.3]
}
# Train datasets
vid_train = ImagenetVID()
lasot_train = Lasot(split='train')
coco_train = MSCOCOSeq()
# Validation datasets
got10k_val = Got10k(split='val')
# The joint augmentation transform, that is applied to the pairs jointly
transform_joint = dltransforms.ToGrayscale(probability=0.05)
# The augmentation transform applied to the training set (individually to each image in the pair)
transform_train = dltransforms.Compose([
dltransforms.ToArrayAndJitter(0.2), dltransforms.Normalize(
mean=settings.normalize_mean, std=settings.normalize_std)
])
# The augmentation transform applied to the validation set (individually to each image in the pair)
transform_val = dltransforms.Compose([
dltransforms.ToArray(), dltransforms.Normalize(
mean=settings.normalize_mean, std=settings.normalize_std)
])
# Data processing to do on the training pairs
data_processing_train = processing.ATOMProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
proposal_params=settings.proposal_params,
transform=transform_train,
joint_transform=transform_joint)
# Data processing to do on the validation pairs
data_processing_val = processing.ATOMProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
proposal_params=settings.proposal_params,
transform=transform_val,
joint_transform=transform_joint)
# The sampler for training
dataset_train = sampler.ATOMSampler(
[vid_train, lasot_train, coco_train], [1, 1, 1],
samples_per_epoch=1000 * settings.batch_size,
max_gap=50,
processing=data_processing_train)
# The loader for training
train_loader = loader.LTRLoader(
'train',
dataset_train,
training=True,
batch_size=settings.batch_size,
num_workers=4,
stack_dim=1)
# The sampler for validation
dataset_val = sampler.ATOMSampler(
[got10k_val], [1, ],
samples_per_epoch=500 * settings.batch_size,
max_gap=50,
processing=data_processing_val)
# The loader for validation
val_loader = loader.LTRLoader(
'val',
dataset_val,
training=False,
batch_size=settings.batch_size,
epoch_interval=5,
num_workers=4,
stack_dim=1)
# creat network, set objective, creat optimizer, learning rate scheduler, trainer
with dygraph.guard():
# Create network
net = atom_resnet18(backbone_pretrained=True)
# Freeze backbone
state_dicts = net.state_dict()
for k in state_dicts.keys():
if 'feature_extractor' in k and "running" not in k:
state_dicts[k].stop_gradient = True
# Set objective
objective = fluid.layers.square_error_cost
# Create actor, which wraps network and objective
actor = actors.AtomActor(net=net, objective=objective)
# Set to training mode
actor.train()
# define optimizer and learning rate
gama = 0.2
lr = 1e-3
lr_scheduler = fluid.dygraph.PiecewiseDecay(
[15, 30, 45],
values=[lr, lr * gama, lr * gama * gama],
step=1000,
begin=0)
optimizer = fluid.optimizer.Adam(
parameter_list=net.bb_regressor.parameters(),
learning_rate=lr_scheduler)
trainer = LTRTrainer(actor, [train_loader, val_loader], optimizer,
settings, lr_scheduler)
trainer.train(40, load_latest=False, fail_safe=False)
PaddleCV/tracking/ltr/train_settings/bbreg/atom_res50_vid_lasot_coco.py 0 → 100644
浏览文件 @ 7ec3ec11
import paddle.fluid as fluid
import paddle.fluid.dygraph as dygraph
import ltr.actors as actors
import ltr.data.transforms as dltransforms
from ltr.data import processing, sampler, loader
from ltr.dataset import ImagenetVID, MSCOCOSeq, Lasot, Got10k
from ltr.models.bbreg.atom import atom_resnet50, atom_resnet18
from ltr.trainers import LTRTrainer
def run(settings):
# Most common settings are assigned in the settings struct
settings.description = 'ATOM IoUNet with ResNet50 backbone and trained with vid, lasot, coco.'
settings.print_interval = 1 # How often to print loss and other info
settings.batch_size = 64 # Batch size
settings.num_workers = 4 # Number of workers for image loading
settings.normalize_mean = [0.485, 0.456, 0.406
] # Normalize mean (default ImageNet values)
settings.normalize_std = [0.229, 0.224,
0.225] # Normalize std (default ImageNet values)
settings.search_area_factor = 5.0 # Image patch size relative to target size
settings.feature_sz = 18 # Size of feature map
settings.output_sz = settings.feature_sz * 16 # Size of input image patches
# Settings for the image sample and proposal generation
settings.center_jitter_factor = {'train': 0, 'test': 4.5}
settings.scale_jitter_factor = {'train': 0, 'test': 0.5}
settings.proposal_params = {
'min_iou': 0.1,
'boxes_per_frame': 16,
'sigma_factor': [0.01, 0.05, 0.1, 0.2, 0.3]
}
# Train datasets
vid_train = ImagenetVID()
lasot_train = Lasot(split='train')
coco_train = MSCOCOSeq()
# Validation datasets
got10k_val = Got10k(split='val')
# The joint augmentation transform, that is applied to the pairs jointly
transform_joint = dltransforms.ToGrayscale(probability=0.05)
# The augmentation transform applied to the training set (individually to each image in the pair)
transform_train = dltransforms.Compose([
dltransforms.ToArrayAndJitter(0.2), dltransforms.Normalize(
mean=settings.normalize_mean, std=settings.normalize_std)
])
# The augmentation transform applied to the validation set (individually to each image in the pair)
transform_val = dltransforms.Compose([
dltransforms.ToArray(), dltransforms.Normalize(
mean=settings.normalize_mean, std=settings.normalize_std)
])
# Data processing to do on the training pairs
data_processing_train = processing.ATOMProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
proposal_params=settings.proposal_params,
transform=transform_train,
joint_transform=transform_joint)
# Data processing to do on the validation pairs
data_processing_val = processing.ATOMProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
mode='sequence',
proposal_params=settings.proposal_params,
transform=transform_val,
joint_transform=transform_joint)
# The sampler for training
dataset_train = sampler.ATOMSampler(
[vid_train, lasot_train, coco_train], [1, 1, 1],
samples_per_epoch=1000 * settings.batch_size,
max_gap=50,
processing=data_processing_train)
# The loader for training
train_loader = loader.LTRLoader(
'train',
dataset_train,
training=True,
batch_size=settings.batch_size,
num_workers=4,
stack_dim=1)
# The sampler for validation
dataset_val = sampler.ATOMSampler(
[got10k_val], [1, ],
samples_per_epoch=500 * settings.batch_size,
max_gap=50,
processing=data_processing_val)
# The loader for validation
val_loader = loader.LTRLoader(
'val',
dataset_val,
training=False,
batch_size=settings.batch_size,
num_workers=4,
epoch_interval=5,
stack_dim=1)
# creat network, set objective, creat optimizer, learning rate scheduler, trainer
with dygraph.guard():
# Create network
net = atom_resnet50(backbone_pretrained=True)
# Freeze backbone
state_dicts = net.state_dict()
for k in state_dicts.keys():
if 'feature_extractor' in k and "running" not in k:
state_dicts[k].stop_gradient = True
# Set objective
objective = fluid.layers.square_error_cost
# Create actor, which wraps network and objective
actor = actors.AtomActor(net=net, objective=objective)
# Set to training mode
actor.train()
# define optimizer and learning rate
gama = 0.2
lr = 1e-3
lr_scheduler = fluid.dygraph.PiecewiseDecay(
[15, 30, 45],
values=[lr, lr * gama, lr * gama * gama],
step=1000,
begin=0)
optimizer = fluid.optimizer.Adam(
parameter_list=net.bb_regressor.parameters(),
learning_rate=lr_scheduler)
trainer = LTRTrainer(actor, [train_loader, val_loader], optimizer,
settings, lr_scheduler)
trainer.train(40, load_latest=False, fail_safe=False)
PaddleCV/tracking/ltr/train_settings/siamfc/siamfc_alexnet_vid.py 0 → 100644
浏览文件 @ 7ec3ec11
import paddle.fluid as fluid
import paddle.fluid.dygraph as dygraph
import ltr.actors as actors
import ltr.data.transforms as dltransforms
from ltr.data import processing, sampler, loader
from ltr.dataset import ImagenetVID, Got10k
from ltr.models.siamese.siam import siamfc_alexnet
from ltr.trainers import LTRTrainer
import numpy as np
import cv2 as cv
from PIL import Image, ImageEnhance
class DataAug(dltransforms.Transform):
def __init__(self):
pass
def random_blur(self, img):
k = np.random.choice([3, 5, 7])
return cv.GaussianBlur(img, (k, k), sigmaX=0, sigmaY=0)
def brightness(self, img):
img = Image.fromarray(img.astype('uint8'))
enh_bri = ImageEnhance.Brightness(img)
brightness = np.random.choice(np.linspace(0.5, 1.25, 4))
img_brighted = enh_bri.enhance(brightness)
return np.array(img_brighted)
def contrast(self, img):
img = Image.fromarray(img.astype('uint8'))
enh_con = ImageEnhance.Contrast(img)
contrast = np.random.choice(np.linspace(0.5, 1.25, 4))
image_contrasted = enh_con.enhance(contrast)
return np.array(image_contrasted)
def no_aug(self, img):
return img
def flip(self, img):
return cv.flip(img, 1)
def transform(self, img, *args):
func = np.random.choice(
[self.contrast, self.random_blur, self.brightness, self.flip])
return func(img)
def run(settings):
# Most common settings are assigned in the settings struct
settings.description = 'SiamFC with Alexnet backbone and trained with vid'
settings.print_interval = 100 # How often to print loss and other info
settings.batch_size = 8 # Batch size
settings.num_workers = 8 # Number of workers for image loading
settings.normalize_mean = [0., 0., 0.] # Normalize mean
settings.normalize_std = [1 / 255., 1 / 255., 1 / 255.] # Normalize std
settings.search_area_factor = {
'train': 1.0,
'test': 2.0078740157480315
} # roughly the same as SiamFC
settings.output_sz = {'train': 127, 'test': 255}
settings.scale_type = 'context'
settings.border_type = 'meanpad'
# Settings for the image sample and proposal generation
settings.center_jitter_factor = {'train': 0, 'test': 0}
settings.scale_jitter_factor = {'train': 0, 'test': 0.}
# Train datasets
vid_train = ImagenetVID()
# Validation datasets
got10k_val = vid_train #Got10k(split='val')
# The joint augmentation transform, that is applied to the pairs jointly
transform_joint = dltransforms.ToGrayscale(probability=0.25)
# The augmentation transform applied to the training set (individually to each image in the pair)
transform_exemplar = dltransforms.Compose([
dltransforms.ToArray(), dltransforms.Normalize(
mean=settings.normalize_mean, std=settings.normalize_std)
])
transform_instance = dltransforms.Compose([
DataAug(), dltransforms.ToArray(), dltransforms.Normalize(
mean=settings.normalize_mean, std=settings.normalize_std)
])
# Data processing to do on the training pairs
data_processing_train = processing.SiamFCProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
scale_type=settings.scale_type,
border_type=settings.border_type,
mode='sequence',
train_transform=transform_exemplar,
test_transform=transform_instance,
joint_transform=transform_joint)
# Data processing to do on the validation pairs
data_processing_val = processing.SiamFCProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
scale_type=settings.scale_type,
border_type=settings.border_type,
mode='sequence',
transform=transform_exemplar,
joint_transform=transform_joint)
# The sampler for training
dataset_train = sampler.ATOMSampler(
[vid_train], [1, ],
samples_per_epoch=6650 * settings.batch_size,
max_gap=100,
processing=data_processing_train)
# The loader for training
train_loader = loader.LTRLoader(
'train',
dataset_train,
training=True,
batch_size=settings.batch_size,
num_workers=settings.num_workers,
stack_dim=1)
# The sampler for validation
dataset_val = sampler.ATOMSampler(
[got10k_val], [1, ],
samples_per_epoch=1000 * settings.batch_size,
max_gap=100,
processing=data_processing_val)
# The loader for validation
val_loader = loader.LTRLoader(
'val',
dataset_val,
training=False,
batch_size=settings.batch_size,
num_workers=settings.num_workers,
epoch_interval=5,
stack_dim=1)
# creat network, set objective, creat optimizer, learning rate scheduler, trainer
with dygraph.guard():
# Create network
net = siamfc_alexnet()
# Create actor, which wraps network and objective
actor = actors.SiamFCActor(
net=net,
objective=None,
batch_size=settings.batch_size,
shape=(17, 17),
radius=16,
stride=8)
# Set to training mode
actor.train()
# define optimizer and learning rate
lr_scheduler = fluid.layers.exponential_decay(
learning_rate=0.01,
decay_steps=6650,
decay_rate=0.8685,
staircase=True)
regularizer = fluid.regularizer.L2DecayRegularizer(
regularization_coeff=0.0005)
optimizer = fluid.optimizer.Momentum(
momentum=0.9,
regularization=regularizer,
parameter_list=net.parameters(),
learning_rate=lr_scheduler)
trainer = LTRTrainer(actor, [train_loader], optimizer, settings,
lr_scheduler)
trainer.train(50, load_latest=False, fail_safe=False)
PaddleCV/tracking/ltr/train_settings/siamfc/siamfc_alexnet_vid_replicate.py 0 → 100644
浏览文件 @ 7ec3ec11
import paddle.fluid as fluid
import paddle.fluid.dygraph as dygraph
import ltr.actors as actors
import ltr.data.transforms as dltransforms
from ltr.data import processing, sampler, loader
from ltr.dataset import ImagenetVID, Got10k
from ltr.models.siamese.siam import siamfc_alexnet
from ltr.trainers import LTRTrainer
import numpy as np
import cv2 as cv
from PIL import Image, ImageEnhance
class DataAug(dltransforms.Transform):
def __init__(self):
pass
def random_blur(self, img):
k = np.random.choice([3, 5, 7])
return cv.GaussianBlur(img, (k, k), sigmaX=0, sigmaY=0)
def brightness(self, img):
img = Image.fromarray(img.astype('uint8'))
enh_bri = ImageEnhance.Brightness(img)
brightness = np.random.choice(np.linspace(0.5, 1.25, 4))
img_brighted = enh_bri.enhance(brightness)
return np.array(img_brighted)
def contrast(self, img):
img = Image.fromarray(img.astype('uint8'))
enh_con = ImageEnhance.Contrast(img)
contrast = np.random.choice(np.linspace(0.5, 1.25, 4))
image_contrasted = enh_con.enhance(contrast)
return np.array(image_contrasted)
def no_aug(self, img):
return img
def flip(self, img):
return cv.flip(img, 1)
def transform(self, img, *args):
func = np.random.choice(
[self.contrast, self.random_blur, self.brightness, self.flip])
return func(img)
def run(settings):
# Most common settings are assigned in the settings struct
settings.description = 'SiamFC with Alexnet backbone and trained with vid'
settings.print_interval = 1 # How often to print loss and other info
settings.batch_size = 8 # Batch size
settings.num_workers = 8 # Number of workers for image loading
settings.normalize_mean = [0., 0., 0.] # Normalize mean
settings.normalize_std = [1 / 255., 1 / 255., 1 / 255.] # Normalize std
settings.search_area_factor = {
'train': 1.0,
'test': 2.0078740157480315
} # roughly the same as SiamFC
settings.output_sz = {'train': 127, 'test': 255}
settings.scale_type = 'context'
settings.border_type = 'replicate'
# Settings for the image sample and proposal generation
settings.center_jitter_factor = {'train': 0, 'test': 0}
settings.scale_jitter_factor = {'train': 0, 'test': 0.}
# Train datasets
vid_train = ImagenetVID()
# Validation datasets
got10k_val = Got10k(split='val')
# The joint augmentation transform, that is applied to the pairs jointly
transform_joint = dltransforms.ToGrayscale(probability=0.25)
# The augmentation transform applied to the training set (individually to each image in the pair)
transform_exemplar = dltransforms.Compose([
dltransforms.ToArray(), dltransforms.Normalize(
mean=settings.normalize_mean, std=settings.normalize_std)
])
transform_instance = dltransforms.Compose([
DataAug(), dltransforms.ToArray(), dltransforms.Normalize(
mean=settings.normalize_mean, std=settings.normalize_std)
])
# Data processing to do on the training pairs
data_processing_train = processing.SiamFCProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
scale_type=settings.scale_type,
border_type=settings.border_type,
mode='sequence',
train_transform=transform_exemplar,
test_transform=transform_instance,
joint_transform=transform_joint)
# Data processing to do on the validation pairs
data_processing_val = processing.SiamFCProcessing(
search_area_factor=settings.search_area_factor,
output_sz=settings.output_sz,
center_jitter_factor=settings.center_jitter_factor,
scale_jitter_factor=settings.scale_jitter_factor,
scale_type=settings.scale_type,
border_type=settings.border_type,
mode='sequence',
transform=transform_exemplar,
joint_transform=transform_joint)
# The sampler for training
dataset_train = sampler.ATOMSampler(
[vid_train], [1, ],
samples_per_epoch=6650 * settings.batch_size,
max_gap=100,
processing=data_processing_train)
# The loader for training
train_loader = loader.LTRLoader(
'train',
dataset_train,
training=True,
batch_size=settings.batch_size,
num_workers=settings.num_workers,
stack_dim=1)
# The sampler for validation
dataset_val = sampler.ATOMSampler(
[got10k_val], [1, ],
samples_per_epoch=1000 * settings.batch_size,
max_gap=100,
processing=data_processing_val)
# The loader for validation
val_loader = loader.LTRLoader(
'val',
dataset_val,
training=False,
batch_size=settings.batch_size,
num_workers=settings.num_workers,
epoch_interval=5,
stack_dim=1)
# creat network, set objective, creat optimizer, learning rate scheduler, trainer
with dygraph.guard():
# Create network
net = siamfc_alexnet()
# Create actor, which wraps network and objective
actor = actors.SiamFCActor(
net=net,
objective=None,
batch_size=settings.batch_size,
shape=(17, 17),
radius=16,
stride=8)
# Set to training mode
actor.train()
# define optimizer and learning rate
lr_scheduler = fluid.layers.exponential_decay(
learning_rate=0.01,
decay_steps=6650,
decay_rate=0.8685,
staircase=True)
regularizer = fluid.regularizer.L2DecayRegularizer(
regularization_coeff=0.0005)
optimizer = fluid.optimizer.Momentum(
momentum=0.9,
regularization=regularizer,
parameter_list=net.parameters(),
learning_rate=lr_scheduler)
trainer = LTRTrainer(actor, [train_loader, val_loader], optimizer,
settings, lr_scheduler)
trainer.train(50, load_latest=False, fail_safe=False)
PaddleCV/tracking/ltr/trainers/__init__.py 0 → 100644
浏览文件 @ 7ec3ec11
from .base_trainer import BaseTrainer
from .ltr_trainer import LTRTrainer
PaddleCV/tracking/ltr/trainers/base_trainer.py 0 → 100644
浏览文件 @ 7ec3ec11
import os
import glob
from paddle import fluid
from paddle.fluid import dygraph
import pickle
class BaseTrainer:
"""Base trainer class. Contains functions for training and saving/loading chackpoints.
Trainer classes should inherit from this one and overload the train_epoch function."""
def __init__(self, actor, loaders, optimizer, settings, lr_scheduler=None):
"""
args:
actor - The actor for training the network
loaders - list of dataset loaders, e.g. [train_loader, val_loader]. In each epoch, the trainer runs one
epoch for each loader.
optimizer - The optimizer used for training, e.g. Adam
settings - Training settings
lr_scheduler - Learning rate scheduler
"""
self.actor = actor
self.optimizer = optimizer
self.lr_scheduler = lr_scheduler
self.loaders = loaders
self.update_settings(settings)
self.epoch = 0
self.stats = {}
def update_settings(self, settings=None):
"""Updates the trainer settings. Must be called to update internal settings."""
if settings is not None:
self.settings = settings
if self.settings.env.workspace_dir is not None:
self.settings.env.workspace_dir = os.path.expanduser(
self.settings.env.workspace_dir)
self._checkpoint_dir = os.path.join(self.settings.env.workspace_dir,
'checkpoints')
if not os.path.exists(self._checkpoint_dir):
os.makedirs(self._checkpoint_dir)
else:
self._checkpoint_dir = None
def train(self, max_epochs, load_latest=False, fail_safe=True):
"""Do training for the given number of epochs.
args:
max_epochs - Max number of training epochs,
load_latest - Bool indicating whether to resume from latest epoch.
fail_safe - Bool indicating whether the training to automatically restart in case of any crashes.
"""
num_tries = 10
for i in range(num_tries):
try:
if load_latest:
self.load_checkpoint()
for epoch in range(self.epoch + 1, max_epochs + 1):
self.epoch = epoch
self.train_epoch()
if self._checkpoint_dir:
self.save_checkpoint()
except:
print('Training crashed at epoch {}'.format(self.epoch))
if fail_safe:
load_latest = True
print('Restarting training from last epoch ...')
else:
raise
print('Finished training!')
def train_epoch(self):
raise NotImplementedError
def save_checkpoint(self):
"""Saves a checkpoint of the network and other variables."""
actor_type = type(self.actor).__name__
net_type = type(self.actor.net).__name__
state = {
'epoch': self.epoch,
'actor_type': actor_type,
'net_type': net_type,
'net_info': getattr(self.actor.net, 'info', None),
'constructor': getattr(self.actor.net, 'constructor', None),
'stats': self.stats,
'settings': self.settings
}
directory = '{}/{}/{}_ep{:04d}'.format(self._checkpoint_dir,
self.settings.project_path,
net_type, self.epoch)
if not os.path.exists(directory):
os.makedirs(directory)
fluid.save_dygraph(self.actor.net.state_dict(), directory)
fluid.save_dygraph(self.optimizer.state_dict(), directory)
with open(os.path.join(directory, '_custom_state.pickle'), 'wb') as f:
pickle.dump(state, f)
def load_checkpoint(self, checkpoint=None):
"""Loads a network checkpoint file.
Can be called in three different ways:
load_checkpoint():
Loads the latest epoch from the workspace. Use this to continue training.
load_checkpoint(epoch_num):
Loads the network at the given epoch number (int).
load_checkpoint(path_to_checkpoint):
Loads the file from the given absolute path (str).
"""
net_type = type(self.actor.net).__name__
if checkpoint is None:
# Load most recent checkpoint
checkpoint_list = sorted(
glob.glob('{}/{}/{}_ep*'.format(self._checkpoint_dir,
self.settings.project_path,
net_type)))
if checkpoint_list:
checkpoint_path = checkpoint_list[-1].split('.')[0]
else:
print('No matching checkpoint file found')
return
elif isinstance(checkpoint, int):
# Checkpoint is the epoch number
checkpoint_path = '{}/{}/{}_ep{:04d}'.format(
self._checkpoint_dir, self.settings.project_path, net_type,
checkpoint)
elif isinstance(checkpoint, str):
# checkpoint is the path
checkpoint_path = os.path.expanduser(checkpoint)
else:
raise TypeError
# paddle load network
net_params, opt_params = fluid.load_dygraph(checkpoint_path)
self.actor.net.load_dict(net_params)
self.optimizer.set_dict(opt_params)
# paddle load state
state_path = '{}/{}/custom_state.pickle'.format(
self._checkpoint_dir, self.settings.project_path)
current_state = pickle.load(
open(os.path.join(state_path, 'custom_state.pickle'), 'rb'))
print("\nload checkpoint done !! Current states are as follows:")
for key, value in enumerate(current_state):
print(key, value)
return True
PaddleCV/tracking/ltr/trainers/ltr_trainer.py 0 → 100644
浏览文件 @ 7ec3ec11
import os
from collections import OrderedDict
from ltr.trainers import BaseTrainer
from ltr.admin.stats import AverageMeter, StatValue
from ltr.admin.tensorboard import TensorboardWriter
import paddle
import paddle.fluid as fluid
import paddle.fluid.dygraph as dygraph
import time
import numpy as np
class LTRTrainer(BaseTrainer):
def __init__(self, actor, loaders, optimizer, settings, lr_scheduler=None):
"""
args:
actor - The actor for training the network
loaders - list of dataset loaders, e.g. [train_loader, val_loader]. In each epoch, the trainer runs one
epoch for each loader.
optimizer - The optimizer used for training, e.g. Adam
settings - Training settings
lr_scheduler - Learning rate scheduler
"""
super().__init__(actor, loaders, optimizer, settings, lr_scheduler)
self._set_default_settings()
# Initialize statistics variables
self.stats = OrderedDict({loader.name: None for loader in self.loaders})
# Initialize tensorboard
tensorboard_writer_dir = os.path.join(self.settings.env.tensorboard_dir,
self.settings.project_path)
self.tensorboard_writer = TensorboardWriter(tensorboard_writer_dir,
[l.name for l in loaders])
def _set_default_settings(self):
# Dict of all default values
default = {'print_interval': 10, 'print_stats': None, 'description': ''}
for param, default_value in default.items():
if getattr(self.settings, param, None) is None:
setattr(self.settings, param, default_value)
def cycle_dataset(self, loader):
"""Do a cycle of training or validation."""
if loader.training:
self.actor.train()
else:
self.actor.eval()
self._init_timing()
for i, data in enumerate(loader, 1):
# get inputs
data = self.to_variable(data)
data['epoch'] = self.epoch
data['settings'] = self.settings
# forward pass
loss, stats = self.actor(data)
# backward pass and update weights
if loader.training:
loss.backward()
apply_collective_grads = getattr(self.actor.net,
"apply_collective_grads", None)
if callable(apply_collective_grads):
apply_collective_grads()
self.optimizer.minimize(loss)
self.actor.net.clear_gradients()
# update statistics
batch_size = data['train_images'].shape[loader.stack_dim]
self._update_stats(stats, batch_size, loader)
self._print_stats(i, loader, batch_size)
if i % loader.__len__() == 0:
self.save_checkpoint()
self._stats_new_epoch()
self._write_tensorboard()
return
def to_variable(self, data_dict):
keys = data_dict.keys()
for k in keys:
if k != "dataset":
data_dict[k] = dygraph.to_variable(
np.array(data_dict[k]).astype(np.float32))
return data_dict
def to_array(self, data_dict):
keys = data_dict.keys()
for k in keys:
if k != "dataset":
data_dict[k] = data_dict[k].numpy()
return data_dict
def train_epoch(self):
"""Do one epoch for each loader."""
for loader in self.loaders:
if self.epoch % loader.epoch_interval == 0:
self.cycle_dataset(loader)
self._stats_new_epoch()
self._write_tensorboard()
print('{}th epoch train / eval done!'.format(self.epoch))
def _init_timing(self):
self.num_frames = 0
self.start_time = time.time()
self.prev_time = self.start_time
def _update_stats(self, new_stats: OrderedDict, batch_size, loader):
# Initialize stats if not initialized yet
if loader.name not in self.stats.keys() or self.stats[
loader.name] is None:
self.stats[loader.name] = OrderedDict(
{name: AverageMeter()
for name in new_stats.keys()})
for name, val in new_stats.items():
if name not in self.stats[loader.name].keys():
self.stats[loader.name][name] = AverageMeter()
self.stats[loader.name][name].update(val, batch_size)
def _print_stats(self, i, loader, batch_size):
self.num_frames += batch_size
current_time = time.time()
batch_fps = batch_size / (current_time - self.prev_time)
average_fps = self.num_frames / (current_time - self.start_time)
self.prev_time = current_time
if i % self.settings.print_interval == 0 or i == loader.__len__():
print_str = '[%s: %d, %d / %d] ' % (loader.name, self.epoch, i,
loader.__len__())
print_str += 'FPS: %.1f (%.1f) , ' % (average_fps, batch_fps)
for name, val in self.stats[loader.name].items():
if (self.settings.print_stats is None or
name in self.settings.print_stats) and hasattr(val,
'avg'):
print_str += '%s: %.5f , ' % (name, val.avg)
print_str += '%s: %.5f , ' % ("time", batch_size / batch_fps *
self.settings.print_interval)
print(print_str[:-5])
def _stats_new_epoch(self):
for loader_stats in self.stats.values():
if loader_stats is None:
continue
for stat_value in loader_stats.values():
if hasattr(stat_value, 'new_epoch'):
stat_value.new_epoch()
def _write_tensorboard(self):
if self.epoch == 1:
self.tensorboard_writer.write_info(self.settings.module_name,
self.settings.script_name,
self.settings.description)
self.tensorboard_writer.write_epoch(self.stats, self.epoch)
print('{}/{}'.format(self.settings.module_name,
self.settings.script_name))
PaddleCV/tracking/pytracking/admin/environment.py 0 → 100644
浏览文件 @ 7ec3ec11
import importlib
import os
class EnvSettings:
def __init__(self):
pytracking_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
self.results_path = '{}/tracking_results/'.format(pytracking_path)
self.network_path = '{}/networks/'.format(pytracking_path)
self.dataset_path = '{}/benchmark_datasets/'.format(pytracking_path)
def create_default_local_file():
comment = {'results_path': 'Where to store tracking results',
'dataset_path': 'Where benchmark datasets are stored',
'network_path': 'Where tracking networks are stored.'}
path = os.path.join(os.path.dirname(__file__), 'local.py')
with open(path, 'w') as f:
settings = EnvSettings()
f.write('from pytracking.admin.environment import EnvSettings\n\n')
f.write('def local_env_settings():\n')
f.write(' settings = EnvSettings()\n\n')
f.write(' # Set your local paths here.\n\n')
for attr in dir(settings):
comment_str = None
if attr in comment:
comment_str = comment[attr]
attr_val = getattr(settings, attr)
if not attr.startswith('__') and not callable(attr_val):
if comment_str is None:
f.write(' settings.{} = \'{}\'\n'.format(attr, attr_val))
else:
f.write(' settings.{} = \'{}\' # {}\n'.format(attr, attr_val, comment_str))
f.write('\n return settings\n\n')
def env_settings():
env_module_name = 'pytracking.admin.local'
try:
env_module = importlib.import_module(env_module_name)
return env_module.local_env_settings()
except:
env_file = os.path.join(os.path.dirname(__file__), 'local.py')
# Create a default file
create_default_local_file()
raise RuntimeError('YOU HAVE NOT SETUP YOUR local.py!!!\n Go to "{}" and set all the paths you need. '
'Then try to run again.'.format(env_file))
PaddleCV/tracking/pytracking/eval_benchmark.py 0 → 100644
浏览文件 @ 7ec3ec11
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import argparse
import importlib
import os
import os.path as osp
import pickle
import sys
from glob import glob
import cv2 as cv
import numpy as np
from tqdm import tqdm
CURRENT_DIR = osp.dirname(__file__)
sys.path.append(osp.join(CURRENT_DIR, '..'))
from pytracking.admin.environment import env_settings
from pytracking.pysot_toolkit.datasets import DatasetFactory
from pytracking.pysot_toolkit.evaluation import EAOBenchmark, AccuracyRobustnessBenchmark, OPEBenchmark
from pytracking.pysot_toolkit.utils.region import vot_overlap
parser = argparse.ArgumentParser(description='tracking evaluation')
parser.add_argument('--dataset', '-d', type=str, help='dataset name')
parser.add_argument(
'--training_base_param', '-tr', type=str, help='training base params name')
parser.add_argument('--epoch', '-e', type=str, help='epoch specifications')
parser.add_argument(
'--tracking_base_param', '-te', type=str, help='tracking base params name')
parser.add_argument(
'--num_repeat', '-n', default=1, type=int, help='number of repeat')
parser.add_argument(
'--exp_id', '-ex', default='', type=str, help='experiment id')
args = parser.parse_args()
def read_image(x):
if isinstance(x, str):
img = cv.imread(x)
else:
img = x
return cv.cvtColor(img, cv.COLOR_BGR2RGB)
def get_tracker_params(param_module, params):
tracker_params = param_module.parameters()
tracker_params.debug = 0 # disable debug
# change checkpoint path
tracker_params.features.features[0].net_path = params['checkpoint']
return tracker_params
def create_tracker(params):
base_param = params['tracking_base_param']
base_tracker = base_param.split('.')[0]
param_module = importlib.import_module('pytracking.parameter.{}'.format(
base_param))
tracker_params = get_tracker_params(param_module, params)
tracker_module = importlib.import_module('pytracking.tracker.{}'.format(
base_tracker))
tracker_class = tracker_module.get_tracker_class()
return tracker_class(tracker_params)
def get_axis_aligned_bbox(region):
region = np.array(region)
if len(region.shape) == 3:
# region (1,4,2)
region = np.array([
region[0][0][0], region[0][0][1], region[0][1][0], region[0][1][1],
region[0][2][0], region[0][2][1], region[0][3][0], region[0][3][1]
])
cx = np.mean(region[0::2])
cy = np.mean(region[1::2])
x1 = min(region[0::2])
x2 = max(region[0::2])
y1 = min(region[1::2])
y2 = max(region[1::2])
A1 = np.linalg.norm(region[0:2] - region[2:4]) * np.linalg.norm(region[
2:4] - region[4:6])
A2 = (x2 - x1) * (y2 - y1)
s = np.sqrt(A1 / A2)
w = s * (x2 - x1) + 1
h = s * (y2 - y1) + 1
x11 = cx - w // 2
y11 = cy - h // 2
return x11, y11, w, h
def run_tracker(tracker, video, reset=False):
if reset:
frame_counter = 0
pred_bboxes = []
for idx, (img_p, gt_bbox) in enumerate(video):
if idx == frame_counter:
# init your tracker here
image = read_image(img_p)
if len(gt_bbox) == 8:
init_bbox = get_axis_aligned_bbox(gt_bbox)
else:
init_bbox = gt_bbox
tracker.initialize(image, init_bbox)
pred_bboxes.append(1)
elif idx > frame_counter:
# get tracking result here
image = read_image(img_p)
pred_bbox = tracker.track(image)
overlap = vot_overlap(pred_bbox, gt_bbox,
(image.shape[1], image.shape[0]))
if overlap > 0:
# continue tracking
pred_bboxes.append(pred_bbox)
else:
# lost target, restart
pred_bboxes.append(2)
frame_counter = idx + 5
else:
pred_bboxes.append(0)
else:
pred_bboxes = []
for idx, (img_p, gt_bbox) in enumerate(video):
if idx == 0:
# init your tracker here
image = read_image(img_p)
if len(gt_bbox) == 8:
init_bbox = get_axis_aligned_bbox(gt_bbox)
else:
init_bbox = gt_bbox
tracker.initialize(image, init_bbox)
pred_bboxes.append(init_bbox)
else:
# get tracking result here
image = read_image(img_p)
pred_bbox = tracker.track(image)
pred_bboxes.append(pred_bbox)
return pred_bboxes
def run_one_sequence(video, params, tracker=None):
# idt = multiprocessing.current_process()._identity[0]
# os.environ["CUDA_VISIBLE_DEVICES"] = "{}".format(idt % 4)
save_dir = osp.join(params['result_dir'], params['save_dataset_name'],
params['tracking_base_param'], params['exp_id'])
if tracker is None:
tracker = create_tracker(params)
if 'VOT' in params['dataset_name']:
save_sub_dir = osp.join(save_dir, 'baseline', video.name)
os.makedirs(save_sub_dir, exist_ok=True)
num_repeat = params.get('num_repeat', 1)
for repeat_idx in range(1, num_repeat + 1):
save_path = osp.join(save_sub_dir,
video.name + '_{:03d}.txt'.format(repeat_idx))
if osp.exists(save_path): continue
pred_bboxes = run_tracker(tracker, video, reset=True)
# Save tracking results
with open(save_path, 'w') as f:
outputs = []
for res in pred_bboxes:
if isinstance(res, int):
outputs.append('{}'.format(res))
else:
outputs.append('{},{},{},{}'.format(res[0], res[1], res[
2], res[3]))
f.write('\n'.join(outputs))
else:
os.makedirs(save_dir, exist_ok=True)
save_path = osp.join(save_dir, video.name + '.txt')
if osp.exists(save_path): return
pred_bboxes = run_tracker(tracker, video, reset=False)
# Save tracking results
with open(save_path, 'w') as f:
outputs = []
for res in pred_bboxes:
outputs.append('{},{},{},{}'.format(res[0], res[1], res[2], res[
3]))
f.write('\n'.join(outputs))
def run_one_dataset(dataset, params):
# use the same tracker for all sequences
tracker = create_tracker(params)
# create new tracker for each sequence
# tracker = None
for video in tqdm(list(dataset.videos.values())):
run_one_sequence(video, params, tracker=tracker)
def compute_evaluation_metrics(dataset, params):
result_dir = osp.join(params['result_dir'], params['save_dataset_name'],
params['tracking_base_param'])
tracker_name = params['exp_id']
trackers = [tracker_name]
dataset.set_tracker(result_dir, trackers)
if 'VOT' in params['dataset_name']:
ar_benchmark = AccuracyRobustnessBenchmark(dataset)
ar_result = {}
ar_result.update(ar_benchmark.eval(trackers))
eao_benchmark = EAOBenchmark(dataset)
eao_result = {}
eao_result.update(eao_benchmark.eval(trackers))
ar_benchmark.show_result(ar_result, eao_result)
metrics = {'ar': ar_result, 'eao': eao_result}
else:
benchmark = OPEBenchmark(dataset)
success_result = {}
precision_result = {}
success_result.update(benchmark.eval_success(trackers))
precision_result.update(benchmark.eval_precision(trackers))
benchmark.show_result(success_result, precision_result)
metrics = {'success': success_result, 'precision': precision_result}
return metrics
def save_info(params, metrics):
save_dir = osp.join(params['result_dir'], params['save_dataset_name'],
params['tracking_base_param'], params['exp_id'])
with open(osp.join(save_dir, 'params.pickle'), 'wb') as f:
pickle.dump(params, f)
with open(osp.join(save_dir, 'metrics.txt'), 'w') as f:
f.write('{}'.format(metrics))
def run_tracking_and_evaluate(params):
"""Receive hyperparameters and return the evaluation metric"""
# load dataset
root = os.path.abspath(
osp.join(env_settings().dataset_path, params['save_dataset_name']))
dataset = DatasetFactory.create_dataset(
name=params['dataset_name'], dataset_root=root)
run_one_dataset(dataset, params)
metrics = compute_evaluation_metrics(dataset, params)
return metrics
def get_checkpoint_path(training_base_param, epoch):
model_dir = osp.abspath(
osp.join(env_settings().network_path, *training_base_param.split('.')))
model_names = glob(model_dir + '/*.pdparams')
prefix = '_'.join(model_names[0].split('_')[:-1])
return osp.join(model_dir, '{}_ep{:04d}'.format(prefix, epoch))
def parse_epoch(epoch_str):
epochs = eval(epoch_str)
try:
iterator = iter(epochs)
except:
if isinstance(epochs, int):
iterator = [epochs]
else:
raise NotImplementedError
return iterator
def main():
for epoch in parse_epoch(args.epoch):
# get checkpoint
checkpoint_pth = get_checkpoint_path(args.training_base_param, epoch)
if args.exp_id == '':
exp_id = args.training_base_param + '.epoch{}'.format(epoch)
else:
exp_id = args.exp_id
print('=> Evaluating: {}'.format(exp_id))
if args.dataset in ['CVPR13', 'OTB50', 'OTB100']:
# for OTB datasets, we save results into the same directory
save_dataset_name = 'OTB100'
else:
save_dataset_name = args.dataset
# set up parameters
params = {
'dataset_name': args.dataset,
'checkpoint': checkpoint_pth,
'tracking_base_param': args.tracking_base_param,
'num_repeat': args.num_repeat,
'exp_id': exp_id,
'result_dir': env_settings().results_path,
'save_dataset_name': save_dataset_name,
}
metrics = run_tracking_and_evaluate(params)
save_info(params, metrics)
if __name__ == '__main__':
main()
PaddleCV/tracking/pytracking/features/augmentation.py 0 → 100644
浏览文件 @ 7ec3ec11
import numpy as np
import math
from paddle.fluid import layers
import cv2 as cv
from pytracking.features.preprocessing import numpy_to_paddle, paddle_to_numpy
from pytracking.libs.Fconv2d import Fconv2d
from pytracking.libs.paddle_utils import PTensor, _padding, n2p
class Transform:
"""Base data augmentation transform class."""
def __init__(self, output_sz=None, shift=None):
self.output_sz = output_sz
self.shift = (0, 0) if shift is None else shift
def __call__(self, image):
raise NotImplementedError
def crop_to_output(self, image, shift=None):
if isinstance(image, PTensor):
imsz = image.shape[2:]
else:
imsz = image.shape[:2]
if self.output_sz is None:
pad_h = 0
pad_w = 0
else:
pad_h = (self.output_sz[0] - imsz[0]) / 2
pad_w = (self.output_sz[1] - imsz[1]) / 2
if shift is None:
shift = self.shift
pad_left = math.floor(pad_w) + shift[1]
pad_right = math.ceil(pad_w) - shift[1]
pad_top = math.floor(pad_h) + shift[0]
pad_bottom = math.ceil(pad_h) - shift[0]
if isinstance(image, PTensor):
return _padding(
image, (pad_left, pad_right, pad_top, pad_bottom),
mode='replicate')
else:
return _padding(
image, (0, 0, pad_left, pad_right, pad_top, pad_bottom),
mode='replicate')
class Identity(Transform):
"""Identity transformation."""
def __call__(self, image):
return self.crop_to_output(image)
class FlipHorizontal(Transform):
"""Flip along horizontal axis."""
def __call__(self, image):
if isinstance(image, PTensor):
return self.crop_to_output(layers.reverse(image, 3))
else:
return self.crop_to_output(np.fliplr(image))
class FlipVertical(Transform):
"""Flip along vertical axis."""
def __call__(self, image: PTensor):
if isinstance(image, PTensor):
return self.crop_to_output(layers.reverse(image, 2))
else:
return self.crop_to_output(np.flipud(image))
class Translation(Transform):
"""Translate."""
def __init__(self, translation, output_sz=None, shift=None):
super().__init__(output_sz, shift)
self.shift = (self.shift[0] + translation[0],
self.shift[1] + translation[1])
def __call__(self, image):
return self.crop_to_output(image)
class Scale(Transform):
"""Scale."""
def __init__(self, scale_factor, output_sz=None, shift=None):
super().__init__(output_sz, shift)
self.scale_factor = scale_factor
def __call__(self, image):
# Calculate new size. Ensure that it is even so that crop/pad becomes easier
h_orig, w_orig = image.shape[2:]
if h_orig != w_orig:
raise NotImplementedError
h_new = round(h_orig / self.scale_factor)
h_new += (h_new - h_orig) % 2
w_new = round(w_orig / self.scale_factor)
w_new += (w_new - w_orig) % 2
if isinstance(image, PTensor):
image_resized = layers.resize_bilinear(
image, [h_new, w_new], align_corners=False)
else:
image_resized = cv.resize(
image, (w_new, h_new), interpolation=cv.INTER_LINEAR)
return self.crop_to_output(image_resized)
class Affine(Transform):
"""Affine transformation."""
def __init__(self, transform_matrix, output_sz=None, shift=None):
super().__init__(output_sz, shift)
self.transform_matrix = transform_matrix
def __call__(self, image, crop=True):
if isinstance(image, PTensor):
return self.crop_to_output(
numpy_to_paddle(self(
paddle_to_numpy(image), crop=False)))
else:
warp = cv.warpAffine(
image,
self.transform_matrix,
image.shape[1::-1],
borderMode=cv.BORDER_REPLICATE)
if crop:
return self.crop_to_output(warp)
else:
return warp
class Rotate(Transform):
"""Rotate with given angle."""
def __init__(self, angle, output_sz=None, shift=None):
super().__init__(output_sz, shift)
self.angle = math.pi * angle / 180
def __call__(self, image, crop=True):
if isinstance(image, PTensor):
return self.crop_to_output(
numpy_to_paddle(self(
paddle_to_numpy(image), crop=False)))
else:
c = (np.expand_dims(np.array(image.shape[:2]), 1) - 1) / 2
R = np.array([[math.cos(self.angle), math.sin(self.angle)],
[-math.sin(self.angle), math.cos(self.angle)]])
H = np.concatenate([R, c - R @c], 1)
warp = cv.warpAffine(
image, H, image.shape[1::-1], borderMode=cv.BORDER_REPLICATE)
if crop:
return self.crop_to_output(warp)
else:
return warp
class Blur(Transform):
"""Blur with given sigma (can be axis dependent)."""
def __init__(self, sigma, output_sz=None, shift=None):
super().__init__(output_sz, shift)
if isinstance(sigma, (float, int)):
sigma = (sigma, sigma)
self.sigma = sigma
self.filter_size = [math.ceil(2 * s) for s in self.sigma]
x_coord = [
np.arange(
-sz, sz + 1, 1, dtype='float32') for sz in self.filter_size
]
self.filter_np = [
np.exp(0 - (x * x) / (2 * s**2))
for x, s in zip(x_coord, self.sigma)
]
self.filter_np[0] = np.reshape(
self.filter_np[0], [1, 1, -1, 1]) / np.sum(self.filter_np[0])
self.filter_np[1] = np.reshape(
self.filter_np[1], [1, 1, 1, -1]) / np.sum(self.filter_np[1])
def __call__(self, image):
if isinstance(image, PTensor):
sz = image.shape[2:]
filter = [n2p(f) for f in self.filter_np]
im1 = Fconv2d(
layers.reshape(image, [-1, 1, sz[0], sz[1]]),
filter[0],
padding=(self.filter_size[0], 0))
return self.crop_to_output(
layers.reshape(
Fconv2d(
im1, filter[1], padding=(0, self.filter_size[1])),
[1, -1, sz[0], sz[1]]))
else:
return paddle_to_numpy(self(numpy_to_paddle(image)))
PaddleCV/tracking/pytracking/features/color.py 0 → 100644
浏览文件 @ 7ec3ec11
from paddle.fluid import layers
from pytracking.features.featurebase import FeatureBase
from pytracking.libs.paddle_utils import PTensor
import numpy as np
class RGB(FeatureBase):
"""RGB feature normalized to [-0.5, 0.5]."""
def dim(self):
return 3
def stride(self):
return self.pool_stride
def extract(self, im: np.ndarray):
return im / 255 - 0.5
class Grayscale(FeatureBase):
"""Grayscale feature normalized to [-0.5, 0.5]."""
def dim(self):
return 1
def stride(self):
return self.pool_stride
def extract(self, im: np.ndarray):
return np.mean(im / 255 - 0.5, 1, keepdims=True)
PaddleCV/tracking/pytracking/features/deep.py 0 → 100644
浏览文件 @ 7ec3ec11
import os
import numpy as np
from paddle import fluid
from ltr.models.bbreg.atom import atom_resnet50, atom_resnet18
from ltr.models.siamese.siam import siamfc_alexnet
from pytracking.admin.environment import env_settings
from pytracking.features.featurebase import MultiFeatureBase
from pytracking.libs import TensorList
from pytracking.libs.paddle_utils import n2p
class ResNet18(MultiFeatureBase):
"""ResNet18 feature.
args:
output_layers: List of layers to output.
net_path: Relative or absolute net path (default should be fine).
use_gpu: Use GPU or CPU.
"""
def __init__(self,
output_layers=('block2', ),
net_path='atom_iou',
use_gpu=True,
*args,
**kwargs):
super().__init__(*args, **kwargs)
self.output_layers = list(output_layers)
self.use_gpu = use_gpu
self.net_path = net_path
def initialize(self):
with fluid.dygraph.guard():
if os.path.isabs(self.net_path):
net_path_full = self.net_path
else:
net_path_full = os.path.join(env_settings().network_path,
self.net_path)
self.net = atom_resnet18(
backbone_pretrained=False,
backbone_is_test=True,
iounet_is_test=True)
state_dictsm, _ = fluid.load_dygraph(net_path_full)
self.net.load_dict(state_dictsm)
self.net.train()
self.iou_predictor = self.net.bb_regressor
self.layer_stride = {
'conv0': 2,
'conv1': 2,
'block0': 4,
'block1': 8,
'block2': 16,
'block3': 32,
'classification': 16,
'fc': None
}
self.layer_dim = {
'conv0': 64,
'conv1': 64,
'block0': 64,
'block1': 128,
'block2': 256,
'block3': 512,
'classification': 256,
'fc': None
}
self.iounet_feature_layers = self.net.bb_regressor_layer
if isinstance(self.pool_stride, int) and self.pool_stride == 1:
self.pool_stride = [1] * len(self.output_layers)
self.feature_layers = sorted(
list(set(self.output_layers + self.iounet_feature_layers)))
self.mean = np.reshape([0.485, 0.456, 0.406], [1, -1, 1, 1])
self.std = np.reshape([0.229, 0.224, 0.225], [1, -1, 1, 1])
def free_memory(self):
if hasattr(self, 'net'):
del self.net
if hasattr(self, 'iou_predictor'):
del self.iou_predictor
if hasattr(self, 'iounet_backbone_features'):
del self.iounet_backbone_features
if hasattr(self, 'iounet_features'):
del self.iounet_features
def dim(self):
return TensorList([self.layer_dim[l] for l in self.output_layers])
def stride(self):
return TensorList([
s * self.layer_stride[l]
for l, s in zip(self.output_layers, self.pool_stride)
])
def extract(self, im: np.ndarray, debug_save_name=None):
with fluid.dygraph.guard():
if debug_save_name is not None:
np.savez(debug_save_name, im)
im = im / 255. # don't use im /= 255. since we don't want to alter the input
im -= self.mean
im /= self.std
im = n2p(im)
output_features = self.net.extract_features(im, self.feature_layers)
# Store the raw resnet features which are input to iounet
iounet_backbone_features = TensorList([
output_features[layer] for layer in self.iounet_feature_layers
])
self.iounet_backbone_features = iounet_backbone_features.numpy()
# Store the processed features from iounet, just before pooling
self.iounet_features = TensorList([
f.numpy()
for f in self.iou_predictor.get_iou_feat(
iounet_backbone_features)
])
output = TensorList([
output_features[layer].numpy() for layer in self.output_layers
])
return output
class ResNet50(MultiFeatureBase):
"""ResNet50 feature.
args:
output_layers: List of layers to output.
net_path: Relative or absolute net path (default should be fine).
use_gpu: Use GPU or CPU.
"""
def __init__(self,
output_layers=('block2', ),
net_path='atom_iou',
use_gpu=True,
*args,
**kwargs):
super().__init__(*args, **kwargs)
self.output_layers = list(output_layers)
self.use_gpu = use_gpu
self.net_path = net_path
def initialize(self):
with fluid.dygraph.guard():
if os.path.isabs(self.net_path):
net_path_full = self.net_path
else:
net_path_full = os.path.join(env_settings().network_path,
self.net_path)
self.net = atom_resnet50(
backbone_pretrained=False,
backbone_is_test=True,
iounet_is_test=True)
state_dictsm, _ = fluid.load_dygraph(net_path_full)
self.net.load_dict(state_dictsm)
self.net.train()
self.iou_predictor = self.net.bb_regressor
self.layer_stride = {
'conv0': 2,
'conv1': 2,
'block0': 4,
'block1': 8,
'block2': 16,
'block3': 32,
'classification': 16,
'fc': None
}
self.layer_dim = {
'conv0': 64,
'conv1': 64,
'block0': 256,
'block1': 512,
'block2': 1024,
'block3': 2048,
'classification': 256,
'fc': None
}
self.iounet_feature_layers = self.net.bb_regressor_layer
if isinstance(self.pool_stride, int) and self.pool_stride == 1:
self.pool_stride = [1] * len(self.output_layers)
self.feature_layers = sorted(
list(set(self.output_layers + self.iounet_feature_layers)))
self.mean = np.reshape([0.485, 0.456, 0.406], [1, -1, 1, 1])
self.std = np.reshape([0.229, 0.224, 0.225], [1, -1, 1, 1])
def free_memory(self):
if hasattr(self, 'net'):
del self.net
if hasattr(self, 'iou_predictor'):
del self.iou_predictor
if hasattr(self, 'iounet_backbone_features'):
del self.iounet_backbone_features
if hasattr(self, 'iounet_features'):
del self.iounet_features
def dim(self):
return TensorList([self.layer_dim[l] for l in self.output_layers])
def stride(self):
return TensorList([
s * self.layer_stride[l]
for l, s in zip(self.output_layers, self.pool_stride)
])
def extract(self, im: np.ndarray, debug_save_name=None):
with fluid.dygraph.guard():
if debug_save_name is not None:
np.savez(debug_save_name, im)
im = im / 255. # don't use im /= 255. since we don't want to alter the input
im -= self.mean
im /= self.std
im = n2p(im)
output_features = self.net.extract_features(im, self.feature_layers)
# Store the raw resnet features which are input to iounet
iounet_backbone_features = TensorList([
output_features[layer] for layer in self.iounet_feature_layers
])
self.iounet_backbone_features = iounet_backbone_features.numpy()
# Store the processed features from iounet, just before pooling
self.iounet_features = TensorList([
f.numpy()
for f in self.iou_predictor.get_iou_feat(
iounet_backbone_features)
])
output = TensorList([
output_features[layer].numpy() for layer in self.output_layers
])
return output
class SFCAlexnet(MultiFeatureBase):
"""Alexnet feature.
args:
output_layers: List of layers to output.
net_path: Relative or absolute net path (default should be fine).
use_gpu: Use GPU or CPU.
"""
def __init__(self,
output_layers=('conv5', ),
net_path='estimator',
use_gpu=True,
*args,
**kwargs):
super().__init__(*args, **kwargs)
self.output_layers = list(output_layers)
self.use_gpu = use_gpu
self.net_path = net_path
def initialize(self):
with fluid.dygraph.guard():
if os.path.isabs(self.net_path):
net_path_full = self.net_path
else:
net_path_full = os.path.join(env_settings().network_path,
self.net_path)
self.net = siamfc_alexnet(
backbone_pretrained=False,
backbone_is_test=True,
estimator_is_test=True)
state_dictsm, _ = fluid.load_dygraph(net_path_full)
self.net.load_dict(state_dictsm)
self.net.train()
self.target_estimator = self.net.target_estimator
self.layer_stride = {'conv5': 8}
self.layer_dim = {'conv5': 256}
self.estimator_feature_layers = self.net.target_estimator_layer
if isinstance(self.pool_stride, int) and self.pool_stride == 1:
self.pool_stride = [1] * len(self.output_layers)
self.feature_layers = sorted(
list(set(self.output_layers + self.estimator_feature_layers)))
self.mean = np.reshape([0., 0., 0.], [1, -1, 1, 1])
self.std = np.reshape([1 / 255., 1 / 255., 1 / 255.], [1, -1, 1, 1])
def free_memory(self):
if hasattr(self, 'net'):
del self.net
if hasattr(self, 'target_estimator'):
del self.target_estimator
if hasattr(self, 'estimator_backbone_features'):
del self.estimator_backbone_features
def dim(self):
return TensorList([self.layer_dim[l] for l in self.output_layers])
def stride(self):
return TensorList([
s * self.layer_stride[l]
for l, s in zip(self.output_layers, self.pool_stride)
])
def extract(self, im: np.ndarray, debug_save_name=None):
with fluid.dygraph.guard():
if debug_save_name is not None:
np.savez(debug_save_name, im)
im = im / 255. # don't use im /= 255. since we don't want to alter the input
im -= self.mean
im /= self.std
im = n2p(im)
output_features = self.net.extract_features(im, self.feature_layers)
# Store the raw backbone features which are input to estimator
estimator_backbone_features = TensorList([
output_features[layer]
for layer in self.estimator_feature_layers
])
self.estimator_backbone_features = estimator_backbone_features.numpy(
)
output = TensorList([
output_features[layer].numpy() for layer in self.output_layers
])
return output
PaddleCV/tracking/pytracking/features/extractor.py 0 → 100644
浏览文件 @ 7ec3ec11
import numpy as np
from paddle import fluid
from paddle.fluid import layers
from pytracking.features.preprocessing import sample_patch
from pytracking.libs import TensorList
class ExtractorBase:
"""Base feature extractor class.
args:
features: List of features.
"""
def __init__(self, features):
self.features = features
def initialize(self):
for f in self.features:
f.initialize()
def free_memory(self):
for f in self.features:
f.free_memory()
class SingleResolutionExtractor(ExtractorBase):
"""Single resolution feature extractor.
args:
features: List of features.
"""
def __init__(self, features):
super().__init__(features)
self.feature_stride = self.features[0].stride()
if isinstance(self.feature_stride, (list, TensorList)):
self.feature_stride = self.feature_stride[0]
def stride(self):
return self.feature_stride
def size(self, input_sz):
return input_sz // self.stride()
def extract(self, im, pos, scales, image_sz):
if isinstance(scales, (int, float)):
scales = [scales]
# Get image patches
im_patches = np.stack(
[sample_patch(im, pos, s * image_sz, image_sz) for s in scales])
im_patches = np.transpose(im_patches, (0, 3, 1, 2))
# Compute features
feature_map = layers.concat(
TensorList(
[f.get_feature(im_patches) for f in self.features]).unroll(),
axis=1)
return feature_map
class MultiResolutionExtractor(ExtractorBase):
"""Multi-resolution feature extractor.
args:
features: List of features.
"""
def __init__(self, features):
super().__init__(features)
self.is_color = None
def stride(self):
return TensorList(
[f.stride() for f in self.features
if self._return_feature(f)]).unroll()
def size(self, input_sz):
return TensorList([
f.size(input_sz) for f in self.features if self._return_feature(f)
]).unroll()
def dim(self):
return TensorList(
[f.dim() for f in self.features
if self._return_feature(f)]).unroll()
def get_fparams(self, name: str=None):
if name is None:
return [f.fparams for f in self.features if self._return_feature(f)]
return TensorList([
getattr(f.fparams, name) for f in self.features
if self._return_feature(f)
]).unroll()
def get_attribute(self, name: str, ignore_missing: bool=False):
if ignore_missing:
return TensorList([
getattr(f, name) for f in self.features
if self._return_feature(f) and hasattr(f, name)
])
else:
return TensorList([
getattr(f, name, None) for f in self.features
if self._return_feature(f)
])
def get_unique_attribute(self, name: str):
feat = None
for f in self.features:
if self._return_feature(f) and hasattr(f, name):
if feat is not None:
raise RuntimeError('The attribute was not unique.')
feat = f
if feat is None:
raise RuntimeError('The attribute did not exist')
return getattr(feat, name)
def _return_feature(self, f):
return self.is_color is None or self.is_color and f.use_for_color or not self.is_color and f.use_for_gray
def set_is_color(self, is_color: bool):
self.is_color = is_color
def extract(self, im, pos, scales, image_sz, debug_save_name=None):
"""Extract features.
args:
im: Image.
pos: Center position for extraction.
scales: Image scales to extract features from.
image_sz: Size to resize the image samples to before extraction.
"""
if isinstance(scales, (int, float)):
scales = [scales]
# Get image patches
with fluid.dygraph.guard(fluid.CPUPlace()):
im_patches = np.stack([
sample_patch(im, pos, s * image_sz, image_sz) for s in scales
])
if debug_save_name is not None:
np.save(debug_save_name, im_patches)
im_patches = np.transpose(im_patches, (0, 3, 1, 2))
# Compute features
feature_map = TensorList(
[f.get_feature(im_patches) for f in self.features]).unroll()
return feature_map
def extract_transformed(self,
im,
pos,
scale,
image_sz,
transforms,
debug_save_name=None):
"""Extract features from a set of transformed image samples.
args:
im: Image.
pos: Center position for extraction.
scale: Image scale to extract features from.
image_sz: Size to resize the image samples to before extraction.
transforms: A set of image transforms to apply.
"""
# Get image patche
im_patch = sample_patch(im, pos, scale * image_sz, image_sz)
# Apply transforms
with fluid.dygraph.guard(fluid.CPUPlace()):
im_patches = np.stack([T(im_patch) for T in transforms])
if debug_save_name is not None:
np.save(debug_save_name, im_patches)
im_patches = np.transpose(im_patches, (0, 3, 1, 2))
# Compute features
feature_map = TensorList(
[f.get_feature(im_patches) for f in self.features]).unroll()
return feature_map
PaddleCV/tracking/pytracking/features/featurebase.py 0 → 100644
浏览文件 @ 7ec3ec11
from paddle import fluid
from paddle.fluid import layers
from pytracking.libs import TensorList
from pytracking.libs.paddle_utils import floordiv, n2p, broadcast_op
import numpy as np
class FeatureBase:
"""Base feature class.
args:
fparams: Feature specific parameters.
pool_stride: Amount of average pooling to apply do downsample the feature map.
output_size: Alternatively, specify the output size of the feature map. Adaptive average pooling will be applied.
normalize_power: The power exponent for the normalization. None means no normalization (default).
use_for_color: Use this feature for color images.
use_for_gray: Use this feature for grayscale images.
"""
def __init__(self,
fparams=None,
pool_stride=None,
output_size=None,
normalize_power=None,
use_for_color=True,
use_for_gray=True):
self.fparams = fparams
self.pool_stride = 1 if pool_stride is None else pool_stride
self.output_size = output_size
self.normalize_power = normalize_power
self.use_for_color = use_for_color
self.use_for_gray = use_for_gray
def initialize(self):
pass
def free_memory(self):
pass
def dim(self):
raise NotImplementedError
def stride(self):
raise NotImplementedError
def size(self, im_sz):
if self.output_size is None:
return floordiv(im_sz, self.stride())
return self.output_size
def extract(self, im):
"""Performs feature extraction."""
raise NotImplementedError
def get_feature(self, im: np.ndarray):
"""Get the feature. Generally, call this function.
args:
im: image patch
"""
# Return empty tensor if it should not be used
is_color = im.shape[1] == 3
if is_color and not self.use_for_color or not is_color and not self.use_for_gray:
return np.array([])
# Extract feature
feat = self.extract(im)
# Pool/downsample
with fluid.dygraph.guard():
feat = n2p(feat)
if self.output_size is not None:
feat = layers.adaptive_pool2d(feat, self.output_size, 'avg')
elif self.pool_stride != 1:
feat = layers.pool2d(
feat,
self.pool_stride,
pool_stride=self.pool_stride,
pool_type='avg')
# Normalize
if self.normalize_power is not None:
feat /= (
layers.reduce_sum(
layers.reshape(
layers.abs(feat), [feat.shape[0], 1, 1, -1])**
self.normalize_power,
dim=3,
keep_dim=True) /
(feat.shape[1] * feat.shape[2] * feat.shape[3]) + 1e-10)**(
1 / self.normalize_power)
feat = feat.numpy()
return feat
class MultiFeatureBase(FeatureBase):
"""Base class for features potentially having multiple feature blocks as output (like CNNs).
See FeatureBase for more info.
"""
def size(self, im_sz):
if self.output_size is None:
return TensorList([floordiv(im_sz, s) for s in self.stride()])
if isinstance(im_sz, PTensor):
return TensorList([
floordiv(im_sz, s) if sz is None else np.array([sz[0], sz[1]])
for sz, s in zip(self.output_size, self.stride())
])
def get_feature(self, im: np.ndarray):
"""Get the feature. Generally, call this function.
args:
im: image patch
"""
# Return empty tensor if it should not be used
is_color = im.shape[1] == 3
if is_color and not self.use_for_color or not is_color and not self.use_for_gray:
return np.array([])
feat_list = self.extract(im)
output_sz = [None] * len(
feat_list) if self.output_size is None else self.output_size
# Pool/downsample
with fluid.dygraph.guard():
feat_list = [n2p(f) for f in feat_list]
for i, (sz, s) in enumerate(zip(output_sz, self.pool_stride)):
if sz is not None:
feat_list[i] = layers.adaptive_pool2d(
feat_list[i], sz, pool_type='avg')
elif s != 1:
feat_list[i] = layers.pool2d(
feat_list[i], s, pool_stride=s, pool_type='avg')
# Normalize
if self.normalize_power is not None:
new_feat_list = []
for feat in feat_list:
norm = (layers.reduce_sum(
layers.reshape(
layers.abs(feat), [feat.shape[0], 1, 1, -1])**
self.normalize_power,
dim=3,
keep_dim=True) /
(feat.shape[1] * feat.shape[2] * feat.shape[3]
) + 1e-10)**(1 / self.normalize_power)
feat = broadcast_op(feat, norm, 'div')
new_feat_list.append(feat)
feat_list = new_feat_list
# To numpy
feat_list = TensorList([f.numpy() for f in feat_list])
return feat_list
PaddleCV/tracking/pytracking/features/preprocessing.py 0 → 100644
浏览文件 @ 7ec3ec11
import numpy as np
import cv2 as cv
from paddle.fluid import dygraph
from paddle.fluid import layers
from pytracking.libs.paddle_utils import PTensor, n2p, _padding, squeeze, unsqueeze
def numpy_to_paddle(a: np.ndarray):
return unsqueeze(
layers.transpose(
layers.cast(dygraph.to_variable(a), 'float32'), [2, 0, 1]), [0])
def paddle_to_numpy(a: PTensor):
return layers.transpose(squeeze(a, [0]), [1, 2, 0]).numpy()
def sample_patch(im: np.ndarray,
pos: np.ndarray,
sample_sz: np.ndarray,
output_sz: np.ndarray=None):
"""Sample an image patch.
args:
im: Image
pos: center position of crop
sample_sz: size to crop
output_sz: size to resize to
"""
# copy and convert
posl = pos.astype('long')
# Compute pre-downsampling factor
if output_sz is not None:
resize_factor = np.min(
sample_sz.astype('float32') / output_sz.astype('float32'))
df = int(max(int(resize_factor - 0.1), 1))
else:
df = int(1)
sz = sample_sz.astype('float32') / df # new size
# Do downsampling
if df > 1:
os = posl % df # offset
posl = ((posl - os) / df).astype('long') # new position
im2 = im[os[0]::df, os[1]::df] # downsample
else:
im2 = im
# compute size to crop
szl = np.maximum(
np.round(sz), np.array(
[2., 2.], dtype='float32')).astype('long')
# Extract top and bottom coordinates
tl = posl - (szl - 1) // 2
br = posl + szl // 2
# Get image patch
im_patch = _padding(
im2, (0, 0, -tl[1], br[1] - im2.shape[1] + 1, -tl[0],
br[0] - im2.shape[0] + 1),
mode='replicate')
if output_sz is None or (im_patch.shape[0] == output_sz[0] and
im_patch.shape[1] == output_sz[1]):
return im_patch
# Resample
osz = output_sz.astype('long')
im_patch = cv.resize(
im_patch, (osz[1], osz[0]), interpolation=cv.INTER_LINEAR)
return im_patch
def sample_patch_with_mean_pad(im: np.ndarray,
pos: np.ndarray,
sample_sz: np.ndarray,
output_sz: np.ndarray=None):
"""Sample an image patch.
args:
im: Image
pos: center position of crop
sample_sz: size to crop
output_sz: size to resize to
"""
# copy and convert
# posl = np.round(pos).astype('long') # TODO: maybe we should use round
posl = pos.astype('long')
im2 = im
sz = sample_sz.astype('float32')
# compute size to crop
szl = np.maximum(
np.round(sz), np.array(
[2., 2.], dtype='float32')).astype('long')
# Extract top and bottom coordinates
tl = posl - (szl - 1) // 2
br = posl + szl // 2
# Get image patch
im_patch = _padding(
im2, (0, 0, -tl[1], br[1] - im2.shape[1] + 1, -tl[0],
br[0] - im2.shape[0] + 1),
mode='replicate')
if output_sz is None or (im_patch.shape[0] == output_sz[0] and
im_patch.shape[1] == output_sz[1]):
return im_patch
# Resample
osz = output_sz.astype('long')
im_patch = cv.resize(
im_patch, (osz[1], osz[0]), interpolation=cv.INTER_LINEAR)
return im_patch
PaddleCV/tracking/pytracking/libs/Fconv2d.py 0 → 100644
浏览文件 @ 7ec3ec11
from __future__ import print_function
import paddle
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.dygraph.layer_object_helper import LayerObjectHelper
from paddle.fluid.initializer import Normal, Constant, NumpyArrayInitializer
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.framework import Variable, OpProtoHolder, in_dygraph_mode
from paddle.fluid.layers import utils
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid.initializer import Normal, Constant, NumpyArrayInitializer
from paddle.fluid.dygraph import dygraph_utils
from paddle.fluid.framework import Variable, OpProtoHolder, in_dygraph_mode
from paddle.fluid.layers import utils
def Fconv2d(
input,
filter,
stride=1,
padding=0,
dilation=1,
groups=1,
use_cudnn=True, ):
"""
Similar with conv2d, this is a convolution2D layers. Difference
is filter can be token as input directly instead of setting filter size
and number of fliters. Filter is a 4-D tensor with shape
[num_filter, num_channel, filter_size_h, filter_size_w].
Args:
input (Variable): The input image with [N, C, H, W] format.
filter(Variable): The input filter with [out_channels, in_channels, H, W] format.
stride (int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride. Default: stride = 1.
padding (int|tuple): The padding size. If padding is a tuple, it must
contain two integers, (padding_H, padding_W). Otherwise, the
padding_H = padding_W = padding. Default: padding = 0.
dilation (int|tuple): The dilation size. If dilation is a tuple, it must
contain two integers, (dilation_H, dilation_W). Otherwise, the
dilation_H = dilation_W = dilation. Default: dilation = 1.
bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv2d.
If it is set to False, no bias will be added to the output units.
If it is set to None or one attribute of ParamAttr, conv2d
will create ParamAttr as bias_attr. If the Initializer of the bias_attr
is not set, the bias is initialized zero. Default: None.
use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
act (str): Activation type, if it is set to None, activation is not appended.
Default: None
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically. Default: None
Returns:
Variable: The tensor variable storing the convolution and \
non-linearity activation result.
Raises:
ValueError: If the shapes of input, filter_size, stride, padding and
groups mismatch.
Examples:
.. code-block:: python
data = fluid.layers.data(name='data', shape=[3, 32, 32], \
dtype='float32')
filter = fluid.layers.data(name='filter',shape=[10,3,3,3], \
dtype='float32',append_batch_size=False)
conv2d = fluid.layers.conv2d(input=data,
filter=filter,
act="relu")
"""
conv_with_filter = Conv2D(
stride=stride, padding=padding, dilation=dilation, groups=groups)
return conv_with_filter(input, filter)
class Conv2D(fluid.dygraph.layers.Layer):
"""
This interface is used to construct a callable object of the ``Conv2D`` class.
For more details, refer to code examples.
The convolution2D layer calculates the output based on the input, filter
and strides, paddings, dilations, groups parameters. Input and
Output are in NCHW format, where N is batch size, C is the number of
the feature map, H is the height of the feature map, and W is the width of the feature map.
Filter's shape is [MCHW] , where M is the number of output feature map,
C is the number of input feature map, H is the height of the filter,
and W is the width of the filter. If the groups is greater than 1,
C will equal the number of input feature map divided by the groups.
Please refer to UFLDL's `convolution
<http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/>`_
for more detials.
If bias attribution and activation type are provided, bias is added to the
output of the convolution, and the corresponding activation function is
applied to the final result.
For each input :math:`X`, the equation is:
.. math::
Out = \\sigma (W \\ast X + b)
Where:
* :math:`X`: Input value, a ``Tensor`` with NCHW format.
* :math:`W`: Filter value, a ``Tensor`` with shape [MCHW] .
* :math:`\\ast`: Convolution operation.
* :math:`b`: Bias value, a 2-D ``Tensor`` with shape [M, 1].
* :math:`\\sigma`: Activation function.
* :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
Example:
- Input:
Input shape: :math:`(N, C_{in}, H_{in}, W_{in})`
Filter shape: :math:`(C_{out}, C_{in}, H_f, W_f)`
- Output:
Output shape: :math:`(N, C_{out}, H_{out}, W_{out})`
Where
.. math::
H_{out}&= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\\\
W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1
Parameters:
num_channels(int): The number of channels in the input image.
num_filters(int): The number of filter. It is as same as the output
feature map.
filter_size (int or tuple): The filter size. If filter_size is a tuple,
it must contain two integers, (filter_size_H, filter_size_W).
Otherwise, the filter will be a square.
stride (int or tuple, optional): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride. Default: 1.
padding (int or tuple, optional): The padding size. If padding is a tuple, it must
contain two integers, (padding_H, padding_W). Otherwise, the
padding_H = padding_W = padding. Default: 0.
dilation (int or tuple, optional): The dilation size. If dilation is a tuple, it must
contain two integers, (dilation_H, dilation_W). Otherwise, the
dilation_H = dilation_W = dilation. Default: 1.
groups (int, optional): The groups number of the Conv2d Layer. According to grouped
convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
the first half of the filters is only connected to the first half
of the input channels, while the second half of the filters is only
connected to the second half of the input channels. Default: 1.
param_attr (ParamAttr, optional): The parameter attribute for learnable weights(Parameter)
of conv2d. If it is set to None or one attribute of ParamAttr, conv2d
will create ParamAttr as param_attr. If the Initializer of the param_attr
is not set, the parameter is initialized with :math:`Normal(0.0, std)`,
and the :math:`std` is :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None.
bias_attr (ParamAttr or bool, optional): The attribute for the bias of conv2d.
If it is set to False, no bias will be added to the output units.
If it is set to None or one attribute of ParamAttr, conv2d
will create ParamAttr as bias_attr. If the Initializer of the bias_attr
is not set, the bias is initialized zero. Default: None.
use_cudnn (bool, optional): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True.
act (str, optional): Activation type, if it is set to None, activation is not appended.
Default: None.
dtype (str, optional): Data type, it can be "float32" or "float64". Default: "float32".
Attribute:
**weight** (Parameter): the learnable weights of filter of this layer.
**bias** (Parameter or None): the learnable bias of this layer.
Returns:
None
Raises:
ValueError: if ``use_cudnn`` is not a bool value.
Examples:
.. code-block:: python
from paddle.fluid.dygraph.base import to_variable
import paddle.fluid as fluid
from paddle.fluid.dygraph import Conv2D
import numpy as np
data = np.random.uniform(-1, 1, [10, 3, 32, 32]).astype('float32')
with fluid.dygraph.guard():
conv2d = Conv2D(3, 2, 3)
data = to_variable(data)
conv = conv2d(data)
"""
def __init__(self,
stride=1,
padding=0,
dilation=1,
groups=None,
use_cudnn=True,
act=None,
dtype='float32'):
super(Conv2D, self).__init__()
self._groups = groups
self._stride = utils.convert_to_list(stride, 2, 'stride')
self._padding = utils.convert_to_list(padding, 2, 'padding')
self._dilation = utils.convert_to_list(dilation, 2, 'dilation')
self._act = act
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
self._use_cudnn = use_cudnn
self._dtype = dtype
# TODO: recover the usage of depthwise_conv2d when it's
# kernel fixed https://github.com/PaddlePaddle/Paddle/issues/17098
# if (self._num_channels == self._groups and
# num_filters % self._num_channels == 0 and not self._use_cudnn):
# self._l_type = 'depthwise_conv2d'
# else:
# self._l_type = 'conv2d'
self._l_type = 'conv2d'
def forward(self, input, weight, bias=None):
inputs = {
'Input': [input],
'Filter': [weight],
}
attrs = {
'strides': self._stride,
'paddings': self._padding,
'dilations': self._dilation,
'groups': self._groups if self._groups else 1,
'use_cudnn': self._use_cudnn,
'use_mkldnn': False,
}
if in_dygraph_mode():
outs = core.ops.conv2d(inputs, attrs)
pre_bias = outs['Output'][0]
pre_act = dygraph_utils._append_bias_in_dygraph(pre_bias, bias, 1)
return dygraph_utils._append_activation_in_dygraph(pre_act,
self._act)
pre_bias = self._helper.create_variable_for_type_inference(
dtype=self._dtype)
self._helper.append_op(
type=self._l_type,
inputs={
'Input': input,
'Filter': weight,
},
outputs={"Output": pre_bias},
attrs=attrs)
if bias is not None:
pre_act = self._helper.create_variable_for_type_inference(
dtype=self._dtype)
self._helper.append_op(
type='elementwise_add',
inputs={'X': [pre_bias],
'Y': [bias]},
outputs={'Out': [pre_act]},
attrs={'axis': 1})
else:
pre_act = pre_bias
# Currently, we don't support inplace in dygraph mode
return self._helper.append_activation(pre_act, act=self._act)
PaddleCV/tracking/pytracking/libs/Fconv2d_static.py 0 → 100644
浏览文件 @ 7ec3ec11
from __future__ import print_function
import paddle
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.dygraph.layer_object_helper import LayerObjectHelper
from paddle.fluid.initializer import Normal, Constant, NumpyArrayInitializer
from paddle.fluid.param_attr import ParamAttr
from paddle.fluid.framework import Variable, OpProtoHolder, in_dygraph_mode
from paddle.fluid.layers import utils
import numpy as np
def Fconv2d(input,
filter,
stride=1,
padding=0,
dilation=1,
groups=None,
use_cudnn=True,
name=None):
"""
Similar with conv2d, this is a convolution2D layers. Difference
is filter can be token as input directly instead of setting filter size
and number of fliters. Filter is a 4-D tensor with shape
[num_filter, num_channel, filter_size_h, filter_size_w].
Args:
input (Variable): The input image with [N, C, H, W] format.
filter(Variable): The input filter with [out_channels, in_channels, H, W] format.
stride (int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride. Default: stride = 1.
padding (int|tuple): The padding size. If padding is a tuple, it must
contain two integers, (padding_H, padding_W). Otherwise, the
padding_H = padding_W = padding. Default: padding = 0.
dilation (int|tuple): The dilation size. If dilation is a tuple, it must
contain two integers, (dilation_H, dilation_W). Otherwise, the
dilation_H = dilation_W = dilation. Default: dilation = 1.
bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv2d.
If it is set to False, no bias will be added to the output units.
If it is set to None or one attribute of ParamAttr, conv2d
will create ParamAttr as bias_attr. If the Initializer of the bias_attr
is not set, the bias is initialized zero. Default: None.
use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
act (str): Activation type, if it is set to None, activation is not appended.
Default: None
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically. Default: None
Returns:
Variable: The tensor variable storing the convolution and \
non-linearity activation result.
Raises:
ValueError: If the shapes of input, filter_size, stride, padding and
groups mismatch.
Examples:
.. code-block:: python
data = fluid.layers.data(name='data', shape=[3, 32, 32], \
dtype='float32')
filter = fluid.layers.data(name='filter',shape=[10,3,3,3], \
dtype='float32',append_batch_size=False)
conv2d = fluid.layers.conv2d(input=data,
filter=filter,
act="relu")
"""
helper = LayerHelper("conv2d_with_filter", **locals())
num_channels = input.shape[1]
num_filters = filter.shape[0]
num_filter_channels = filter.shape[1]
l_type = 'conv2d'
# if (num_channels == groups and
if (num_channels == groups and num_filters % num_channels == 0 and
not use_cudnn):
l_type = 'depthwise_conv2d'
if groups is None:
assert num_filter_channels == num_channels
groups = 1
else:
if num_channels % groups != 0:
raise ValueError("num_channels must be divisible by groups.")
if num_channels // groups != num_filter_channels:
raise ValueError("num_filter_channels must equal to num_channels\
divided by groups.")
stride = utils.convert_to_list(stride, 2, 'stride')
padding = utils.convert_to_list(padding, 2, 'padding')
dilation = utils.convert_to_list(dilation, 2, 'dilation')
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
pre_bias = helper.create_variable_for_type_inference(dtype=input.dtype)
helper.append_op(
type=l_type,
inputs={
'Input': input,
'Filter': filter,
},
outputs={"Output": pre_bias},
attrs={
'strides': stride,
'paddings': padding,
'dilations': dilation,
'groups': groups,
'use_cudnn': use_cudnn,
'use_mkldnn': False
})
return pre_bias
def test_conv2d_with_filter():
exemplar = np.random.random((8, 4, 6, 6)).astype(np.float32)
instance = np.random.random((8, 4, 22, 22)).astype(np.float32)
# fluid.layers.data(append_batch_size=)
use_gpu = False
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
train_program = fluid.Program()
start_program = fluid.Program()
with fluid.program_guard(train_program, start_program):
x = fluid.layers.data(
name="inst", shape=[8, 4, 22, 22], append_batch_size=False)
y = fluid.layers.data(
name="exem", shape=[8, 4, 6, 6], append_batch_size=False)
bias_att = fluid.ParamAttr(
name="bias_", initializer=fluid.initializer.ConstantInitializer(1.))
out = conv2d_with_filter(x, y, groups=1)
weight_att = fluid.ParamAttr(
name='weight',
initializer=fluid.initializer.NumpyArrayInitializer(exemplar))
bias_att = fluid.ParamAttr(
name="bias", initializer=fluid.initializer.ConstantInitializer(0.))
res = fluid.layers.conv2d(
x,
8,
6,
param_attr=weight_att,
bias_attr=bias_att,
stride=1,
padding=0,
dilation=1)
exe = fluid.Executor(place)
exe.run(program=fluid.default_startup_program())
print(out.shape)
compiled_prog = fluid.compiler.CompiledProgram(train_program)
out, res = exe.run(compiled_prog,
feed={"inst": instance,
"exem": exemplar},
fetch_list=[out.name, res.name])
print(np.sum(out - res))
np.testing.assert_allclose(out, res, rtol=1e-5, atol=0)
with fluid.dygraph.guard():
exem = fluid.dygraph.to_variable(exemplar)
inst = fluid.dygraph.to_variable(instance)
out = conv2d_with_filter(inst, exem, groups=1)
print(np.sum(out.numpy() - res))
np.testing.assert_allclose(out.numpy(), res, rtol=1e-5, atol=0)
if __name__ == '__main__':
test_conv2d_with_filter()
PaddleCV/tracking/pytracking/libs/__init__.py 0 → 100644
浏览文件 @ 7ec3ec11
from .tensorlist import TensorList
from .tensordict import TensorDict
PaddleCV/tracking/pytracking/libs/complex.py 0 → 100644
浏览文件 @ 7ec3ec11
import numpy as np
from pytracking.libs.tensorlist import tensor_operation
def is_complex(a: np.array) -> bool:
return a.ndim >= 4 and a.shape[-1] == 2
def is_real(a: np.array) -> bool:
return not is_complex(a)
@tensor_operation
def mult(a: np.array, b: np.array):
"""Pointwise complex multiplication of complex tensors."""
if is_real(a):
if a.ndim >= b.ndim:
raise ValueError('Incorrect dimensions.')
# a is real
return mult_real_cplx(a, b)
if is_real(b):
if b.ndim >= a.ndim:
raise ValueError('Incorrect dimensions.')
# b is real
return mult_real_cplx(b, a)
# Both complex
c = mult_real_cplx(a[..., 0], b)
c[..., 0] -= a[..., 1] * b[..., 1]
c[..., 1] += a[..., 1] * b[..., 0]
return c
@tensor_operation
def mult_conj(a: np.array, b: np.array):
"""Pointwise complex multiplication of complex tensors, with conjugate on b: a*conj(b)."""
if is_real(a):
if a.ndim >= b.ndim:
raise ValueError('Incorrect dimensions.')
# a is real
return mult_real_cplx(a, conj(b))
if is_real(b):
if b.ndim >= a.ndim:
raise ValueError('Incorrect dimensions.')
# b is real
return mult_real_cplx(b, a)
# Both complex
c = mult_real_cplx(b[..., 0], a)
c[..., 0] += a[..., 1] * b[..., 1]
c[..., 1] -= a[..., 0] * b[..., 1]
return c
@tensor_operation
def mult_real_cplx(a: np.array, b: np.array):
"""Pointwise complex multiplication of real tensor a with complex tensor b."""
if is_real(b):
raise ValueError('Last dimension must have length 2.')
return np.expand_dims(a, -1) * b
@tensor_operation
def div(a: np.array, b: np.array):
"""Pointwise complex division of complex tensors."""
if is_real(b):
if b.ndim >= a.ndim:
raise ValueError('Incorrect dimensions.')
# b is real
return div_cplx_real(a, b)
return div_cplx_real(mult_conj(a, b), abs_sqr(b))
@tensor_operation
def div_cplx_real(a: np.array, b: np.array):
"""Pointwise complex division of complex tensor a with real tensor b."""
if is_real(a):
raise ValueError('Last dimension must have length 2.')
return a / np.expand_dims(b, -1)
@tensor_operation
def abs_sqr(a: np.array):
"""Squared absolute value."""
if is_real(a):
raise ValueError('Last dimension must have length 2.')
return np.sum(a * a, -1)
@tensor_operation
def abs(a: np.array):
"""Absolute value."""
if is_real(a):
raise ValueError('Last dimension must have length 2.')
return np.sqrt(abs_sqr(a))
@tensor_operation
def conj(a: np.array):
"""Complex conjugate."""
if is_real(a):
raise ValueError('Last dimension must have length 2.')
# return a * np.array([1, -1], device=a.device)
return complex(a[..., 0], -a[..., 1])
@tensor_operation
def real(a: np.array):
"""Real part."""
if is_real(a):
raise ValueError('Last dimension must have length 2.')
return a[..., 0]
@tensor_operation
def imag(a: np.array):
"""Imaginary part."""
if is_real(a):
raise ValueError('Last dimension must have length 2.')
return a[..., 1]
@tensor_operation
def complex(a: np.array, b: np.array=None):
"""Create complex tensor from real and imaginary part."""
if b is None:
b = np.zeros(a.shape, a.dtype)
elif a is None:
a = np.zeros(b.shape, b.dtype)
return np.concatenate((np.expand_dims(a, -1), np.expand_dims(b, -1)), -1)
@tensor_operation
def mtimes(a: np.array, b: np.array, conj_a=False, conj_b=False):
"""Complex matrix multiplication of complex tensors.
The dimensions (-3, -2) are matrix multiplied. -1 is the complex dimension."""
if is_real(a):
if a.ndim >= b.ndim:
raise ValueError('Incorrect dimensions.')
return mtimes_real_complex(a, b, conj_b=conj_b)
if is_real(b):
if b.ndim >= a.ndim:
raise ValueError('Incorrect dimensions.')
return mtimes_complex_real(a, b, conj_a=conj_a)
if not conj_a and not conj_b:
return complex(
np.matmul(a[..., 0], b[..., 0]) - np.matmul(a[..., 1], b[..., 1]),
np.matmul(a[..., 0], b[..., 1]) + np.matmul(a[..., 1], b[..., 0]))
if conj_a and not conj_b:
return complex(
np.matmul(a[..., 0], b[..., 0]) + np.matmul(a[..., 1], b[..., 1]),
np.matmul(a[..., 0], b[..., 1]) - np.matmul(a[..., 1], b[..., 0]))
if not conj_a and conj_b:
return complex(
np.matmul(a[..., 0], b[..., 0]) + np.matmul(a[..., 1], b[..., 1]),
np.matmul(a[..., 1], b[..., 0]) - np.matmul(a[..., 0], b[..., 1]))
if conj_a and conj_b:
return complex(
np.matmul(a[..., 0], b[..., 0]) - np.matmul(a[..., 1], b[..., 1]),
-np.matmul(a[..., 0], b[..., 1]) - np.matmul(a[..., 1], b[..., 0]))
@tensor_operation
def mtimes_real_complex(a: np.array, b: np.array, conj_b=False):
if is_real(b):
raise ValueError('Incorrect dimensions.')
if not conj_b:
return complex(np.matmul(a, b[..., 0]), np.matmul(a, b[..., 1]))
if conj_b:
return complex(np.matmul(a, b[..., 0]), -np.matmul(a, b[..., 1]))
@tensor_operation
def mtimes_complex_real(a: np.array, b: np.array, conj_a=False):
if is_real(a):
raise ValueError('Incorrect dimensions.')
if not conj_a:
return complex(np.matmul(a[..., 0], b), np.matmul(a[..., 1], b))
if conj_a:
return complex(np.matmul(a[..., 0], b), -np.matmul(a[..., 1], b))
@tensor_operation
def exp_imag(a: np.array):
"""Complex exponential with imaginary input: e^(i*a)"""
a = np.expand_dims(a, -1)
return np.concatenate((np.cos(a), np.sin(a)), -1)
PaddleCV/tracking/pytracking/libs/dcf.py 0 → 100644
浏览文件 @ 7ec3ec11
import math
import numpy as np
from pytracking.libs import fourier
from pytracking.libs import complex
from pytracking.libs.paddle_utils import _padding
def hann1d(sz: int, centered=True) -> np.ndarray:
"""1D cosine window."""
if centered:
return 0.5 * (1 - np.cos(
(2 * math.pi / (sz + 2)) * np.arange(1, sz + 1, 1, 'float32')))
w = 0.5 * (1 + np.cos(
(2 * math.pi / (sz + 2)) * np.arange(0, sz // 2 + 1, 1, 'float32')))
return np.concatenate([w, np.flip(w[1:sz - sz // 2], 0)])
def hann2d(sz: np.ndarray, centered=True) -> np.ndarray:
"""2D cosine window."""
return np.reshape(hann1d(sz[0], centered), (1, 1, -1, 1)) * \
np.reshape(hann1d(sz[1], centered), (1, 1, 1, -1))
def hann2d_clipped(sz: np.ndarray, effective_sz: np.ndarray,
centered=True) -> np.ndarray:
"""1D clipped cosine window."""
# Ensure that the difference is even
effective_sz += (effective_sz - sz) % 2
effective_window = np.reshape(hann1d(effective_sz[0], True), (1, 1, -1, 1)) * \
np.reshape(hann1d(effective_sz[1], True), (1, 1, 1, -1))
pad = np.int32((sz - effective_sz) / 2)
window = _padding(
effective_window, (pad[1], pad[1], pad[0], pad[0]), mode='replicate')
if centered:
return window
else:
mid = np.int32((sz / 2))
window_shift_lr = np.concatenate(
(window[..., mid[1]:], window[..., :mid[1]]), 3)
return np.concatenate((window_shift_lr[..., mid[0]:, :],
window_shift_lr[..., :mid[0], :]), 2)
def gauss_fourier(sz: int, sigma: float, half: bool=False) -> np.ndarray:
if half:
k = np.arange(0, int(sz / 2 + 1), 1, 'float32')
else:
k = np.arange(-int((sz - 1) / 2), int(sz / 2 + 1), 1, 'float32')
return (math.sqrt(2 * math.pi) * sigma / sz) * np.exp(-2 * np.square(
math.pi * sigma * k / sz))
def gauss_spatial(sz, sigma, center=0, end_pad=0):
k = np.arange(-(sz - 1) / 2, (sz + 1) / 2 + end_pad, 1, 'float32')
return np.exp(-1.0 / (2 * sigma**2) * np.square(k - center))
def label_function(sz: np.ndarray, sigma: np.ndarray):
return np.reshape(gauss_fourier(sz[0], sigma[0]), (1, 1, -1, 1)) * \
np.reshape(gauss_fourier(sz[1], sigma[1], True), (1, 1, 1, -1))
def label_function_spatial(sz: np.ndarray,
sigma: np.ndarray,
center: np.ndarray=None,
end_pad: np.ndarray=None):
"""The origin is in the middle of the image."""
if center is None: center = np.zeros((2, ), 'float32')
if end_pad is None: end_pad = np.zeros((2, ), 'float32')
return np.reshape(gauss_spatial(sz[0], sigma[0], center[0], end_pad[0]), (1, 1, -1, 1)) * \
np.reshape(gauss_spatial(sz[1], sigma[1], center[1], end_pad[1]), (1, 1, 1, -1))
def cubic_spline_fourier(f, a):
"""The continuous Fourier transform of a cubic spline kernel."""
bf = (6 * (1 - np.cos(2 * math.pi * f)) + 3 * a * (1 - np.cos(4 * math.pi * f))
- (6 + 8 * a) * math.pi * f * np.sin(2 * math.pi * f) - 2 * a * math.pi * f * np.sin(4 * math.pi * f)) \
/ (4 * math.pi ** 4 * f ** 4)
bf[f == 0] = 1
return bf
def get_interp_fourier(sz: np.ndarray,
method='ideal',
bicubic_param=0.5,
centering=True,
windowing=False,
device='cpu'):
ky, kx = fourier.get_frequency_coord(sz)
if method == 'ideal':
interp_y = np.ones(ky.shape) / sz[0]
interp_x = np.ones(kx.shape) / sz[1]
elif method == 'bicubic':
interp_y = cubic_spline_fourier(ky / sz[0], bicubic_param) / sz[0]
interp_x = cubic_spline_fourier(kx / sz[1], bicubic_param) / sz[1]
else:
raise ValueError('Unknown method.')
if centering:
interp_y = complex.mult(interp_y,
complex.exp_imag((-math.pi / sz[0]) * ky))
interp_x = complex.mult(interp_x,
complex.exp_imag((-math.pi / sz[1]) * kx))
if windowing:
raise NotImplementedError
return interp_y, interp_x
def interpolate_dft(a: np.ndarray, interp_fs) -> np.ndarray:
if isinstance(interp_fs, np.ndarray):
return complex.mult(a, interp_fs)
if isinstance(interp_fs, (tuple, list)):
return complex.mult(complex.mult(a, interp_fs[0]), interp_fs[1])
raise ValueError('"interp_fs" must be tensor or tuple of tensors.')
def max2d(a: np.ndarray) -> (np.ndarray, np.ndarray):
"""Computes maximum and argmax in the last two dimensions."""
argmax_row = np.argmax(a, axis=-2)
max_val_row = np.max(a, axis=-2)
argmax_col = np.argmax(max_val_row, axis=-1)
max_val = np.max(max_val_row, axis=-1)
argmax_row = np.reshape(argmax_row, (
argmax_col.size, -1))[np.arange(argmax_col.size), argmax_col.flatten()]
argmax_row = argmax_row.reshape(argmax_col.shape)
argmax = np.concatenate(
(np.expand_dims(argmax_row, -1), np.expand_dims(argmax_col, -1)), -1)
return max_val, argmax
PaddleCV/tracking/pytracking/libs/fourier.py 0 → 100644
浏览文件 @ 7ec3ec11
import numpy as np
from pytracking.libs import complex, TensorList
from pytracking.libs.tensorlist import tensor_operation
from pytracking.libs.paddle_utils import _padding
@tensor_operation
def rfftshift2(a: np.array):
h = a.shape[2] + 2
return np.concatenate([a[:, :, (h - 1) // 2:], a[:, :, :h // 2]], 2)
@tensor_operation
def irfftshift2(a: np.array):
mid = int((a.shape[2] - 1) / 2)
return np.concatenate([a[:, :, mid:], a[:, :, :mid]], 2)
@tensor_operation
def cfft2(a):
"""Do FFT and center the low frequency component.
Always produces odd (full) output sizes."""
out = rfftshift2(np.fft.rfft2(a))
return np.stack([out.real, out.imag], axis=-1)
@tensor_operation
def cifft2(a, signal_sizes=None):
"""Do inverse FFT corresponding to cfft2."""
out = irfftshift2(a)
return np.fft.irfft2(out[..., 0] + 1j * out[..., 1], s=signal_sizes)
@tensor_operation
def sample_fs(a: np.array, grid_sz: np.array=None, rescale=True):
"""Samples the Fourier series."""
# Size of the fourier series
sz = np.array([a.shape[2], 2 * a.shape[3] - 1], 'float32')
# Default grid
if grid_sz is None or sz[0] == grid_sz[0] and sz[1] == grid_sz[1]:
if rescale:
return np.prod(sz) * cifft2(a)
return cifft2(a)
if sz[0] > grid_sz[0] or sz[1] > grid_sz[1]:
raise ValueError(
"Only grid sizes that are smaller than the Fourier series size are supported."
)
tot_pad = (grid_sz - sz).tolist()
is_even = [s % 2 == 0 for s in sz]
# Compute paddings
pad_top = int((tot_pad[0] + 1) / 2) if is_even[0] else int(tot_pad[0] / 2)
pad_bottom = int(tot_pad[0] - pad_top)
pad_right = int((tot_pad[1] + 1) / 2)
if rescale:
return np.prod(grid_sz) * cifft2(
_padding(a, (0, 0, 0, pad_right, pad_top, pad_bottom)),
signal_sizes=grid_sz.astype('long').tolist())
else:
return cifft2(
_padding(a, (0, 0, 0, pad_right, pad_top, pad_bottom)),
signal_sizes=grid_sz.astype('long').tolist())
def get_frequency_coord(sz, add_complex_dim=False, device='cpu'):
"""Frequency coordinates."""
ky = np.reshape(
np.arange(
-int((sz[0] - 1) / 2), int(sz[0] / 2 + 1), dtype='float32'),
(1, 1, -1, 1))
kx = np.reshape(
np.arange(
0, int(sz[1] / 2 + 1), dtype='float32'), (1, 1, 1, -1))
if add_complex_dim:
ky = np.expand_dims(ky, -1)
kx = np.expand_dims(kx, -1)
return ky, kx
@tensor_operation
def shift_fs(a: np.array, shift: np.array):
"""Shift a sample a in the Fourier domain.
Params:
a : The fourier coefficiens of the sample.
shift : The shift to be performed normalized to the range [-pi, pi]."""
if a.ndim != 5:
raise ValueError(
'a must be the Fourier coefficients, a 5-dimensional tensor.')
if shift[0] == 0 and shift[1] == 0:
return a
ky, kx = get_frequency_coord((a.shape[2], 2 * a.shape[3] - 1))
return complex.mult(
complex.mult(a, complex.exp_imag(shift[0] * ky)),
complex.exp_imag(shift[1] * kx))
def sum_fs(a: TensorList) -> np.array:
"""Sum a list of Fourier series expansions."""
s = None
mid = None
for e in sorted(a, key=lambda elem: elem.shape[-3], reverse=True):
if s is None:
s = e.copy()
mid = int((s.shape[-3] - 1) / 2)
else:
# Compute coordinates
top = mid - int((e.shape[-3] - 1) / 2)
bottom = mid + int(e.shape[-3] / 2) + 1
right = e.shape[-2]
# Add the data
s[..., top:bottom, :right, :] += e
return s
def sum_fs12(a: TensorList) -> np.array:
"""Sum a list of Fourier series expansions."""
s = None
mid = None
for e in sorted(a, key=lambda elem: elem.shape[0], reverse=True):
if s is None:
s = e.copy()
mid = int((s.shape[0] - 1) / 2)
else:
# Compute coordinates
top = mid - int((e.shape[0] - 1) / 2)
bottom = mid + int(e.shape[0] / 2) + 1
right = e.shape[1]
# Add the data
s[top:bottom, :right, ...] += e
return s
@tensor_operation
def inner_prod_fs(a: np.array, b: np.array):
if complex.is_complex(a) and complex.is_complex(b):
return 2 * (a.flatten() @b.flatten()
) - a[:, :, :, 0, :].flatten() @b[:, :, :, 0, :].flatten()
elif complex.is_real(a) and complex.is_real(b):
return 2 * (a.flatten() @b.flatten()
) - a[:, :, :, 0].flatten() @b[:, :, :, 0].flatten()
else:
raise NotImplementedError('Not implemented for mixed real and complex.')
PaddleCV/tracking/pytracking/libs/operation.py 0 → 100644
浏览文件 @ 7ec3ec11
from paddle import fluid
from paddle.fluid import layers
from pytracking.libs.Fconv2d import Fconv2d
from pytracking.libs.tensorlist import tensor_operation, TensorList
from paddle.fluid.framework import Variable as PTensor
@tensor_operation
def conv2d(input: PTensor,
weight: PTensor,
bias: PTensor=None,
stride=1,
padding=0,
dilation=1,
groups=1,
mode=None):
"""Standard conv2d. Returns the input if weight=None."""
if weight is None:
return input
ind = None
if mode is not None:
if padding != 0:
raise ValueError('Cannot input both padding and mode.')
if mode == 'same':
padding = (weight.shape[2] // 2, weight.shape[3] // 2)
if weight.shape[2] % 2 == 0 or weight.shape[3] % 2 == 0:
ind = (slice(-1)
if weight.shape[2] % 2 == 0 else slice(None), slice(-1)
if weight.shape[3] % 2 == 0 else slice(None))
elif mode == 'valid':
padding = (0, 0)
elif mode == 'full':
padding = (weight.shape[2] - 1, weight.shape[3] - 1)
else:
raise ValueError('Unknown mode for padding.')
assert bias is None
out = Fconv2d(
input,
weight,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups)
if ind is None:
return out
return out[:, :, ind[0], ind[1]]
@tensor_operation
def conv1x1(input: PTensor, weight: PTensor):
"""Do a convolution with a 1x1 kernel weights. Implemented with matmul, which can be faster than using conv."""
if weight is None:
return input
return Fconv2d(input, weight)
PaddleCV/tracking/pytracking/libs/optimization.py 0 → 100644
浏览文件 @ 7ec3ec11
import numpy as np
from paddle.fluid import layers
from paddle import fluid
from pytracking.libs.tensorlist import TensorList
from pytracking.utils.plotting import plot_graph
from pytracking.libs.paddle_utils import n2p, clone, static_clone
class L2Problem:
"""Base class for representing an L2 optimization problem."""
def __call__(self, x: TensorList) -> TensorList:
"""Shall compute the residuals of the problem."""
raise NotImplementedError
def ip_input(self, a, b):
"""Inner product of the input space."""
return sum(a.view(-1) @b.view(-1))
def ip_output(self, a, b):
"""Inner product of the output space."""
return sum(a.view(-1) @b.view(-1))
def M1(self, x):
"""M1 preconditioner."""
return x
def M2(self, x):
"""M2 preconditioner."""
return x
def get_feed_dict(self):
raise NotImplementedError
class MinimizationProblem:
"""General minimization problem."""
def __call__(self, x: TensorList) -> TensorList:
"""Shall compute the loss."""
raise NotImplementedError
def ip_input(self, a, b):
"""Inner product of the input space."""
return sum(a.view(-1) @b.view(-1))
def M1(self, x):
return x
def M2(self, x):
return x
def get_feed_dict(self):
raise NotImplementedError
class ConjugateGradientBase:
"""Conjugate Gradient optimizer base class. Implements the CG loop."""
def __init__(self,
fletcher_reeves=True,
standard_alpha=True,
direction_forget_factor=0,
debug=False):
self.fletcher_reeves = fletcher_reeves
self.standard_alpha = standard_alpha
self.direction_forget_factor = direction_forget_factor
self.debug = debug
# State
self.p = None
self.rho = np.ones((1, ), 'float32')
self.r_prev = None
# Right hand side
self.b = None
def reset_state(self):
self.p = None
self.rho = np.ones((1, ), 'float32')
self.r_prev = None
def run_CG(self, num_iter, x=None, eps=0.0):
"""Main conjugate gradient method.
args:
num_iter: Number of iterations.
x: Initial guess. Assumed zero if None.
eps: Stop if the residual norm gets smaller than this.
"""
# Apply forgetting factor
if self.direction_forget_factor == 0:
self.reset_state()
elif self.p is not None:
self.rho /= self.direction_forget_factor
if x is None:
r = self.b.clone()
else:
r = self.b - self.A(x)
# Norms of residuals etc for debugging
resvec = None
# Loop over iterations
for ii in range(num_iter):
# Preconditioners
y = self.M1(r)
z = self.M2(y)
rho1 = self.rho
self.rho = self.ip(r, z)
if self.check_zero(self.rho):
if self.debug:
print('Stopped CG since rho = 0')
if resvec is not None:
resvec = resvec[:ii + 1]
return x, resvec
if self.p is None:
self.p = z.clone()
else:
if self.fletcher_reeves:
beta = self.rho / rho1
else:
rho2 = self.ip(self.r_prev, z)
beta = (self.rho - rho2) / rho1
beta = beta.apply(lambda a: np.clip(a, 0, 1e10))
self.p = z + self.p * beta
q = self.A(self.p)
pq = self.ip(self.p, q)
if self.standard_alpha:
alpha = self.rho / pq
else:
alpha = self.ip(self.p, r) / pq
# Save old r for PR formula
if not self.fletcher_reeves:
self.r_prev = r.clone()
# Form new iterate
if x is None:
x = self.p * alpha
else:
x += self.p * alpha
if ii < num_iter - 1 or self.debug:
r -= q * alpha
if eps > 0.0 or self.debug:
normr = self.residual_norm(r)
# if self.debug:
if True:
self.evaluate_CG_iteration(x)
# resvec[ii + 1] = normr
if eps > 0 and normr <= eps:
if self.debug:
print('Stopped CG since norm smaller than eps')
break
if resvec is not None:
resvec = resvec[:ii + 2]
return x, resvec
def A(self, x):
# Implements the left hand operation
raise NotImplementedError
def ip(self, a, b):
# Implements the inner product
return a.view(-1) @b.view(-1)
def residual_norm(self, r):
res = self.ip(r, r).sum()
if isinstance(res, (TensorList, list, tuple)):
res = sum(res)
return np.sqrt(res)
def check_zero(self, s, eps=0.0):
ss = s.abs() <= eps
if isinstance(ss, (TensorList, list, tuple)):
ss = sum(ss)
return ss > 0
def M1(self, x):
# M1 preconditioner
return x
def M2(self, x):
# M2 preconditioner
return x
def evaluate_CG_iteration(self, x):
pass
class ConjugateGradient(ConjugateGradientBase):
"""Conjugate Gradient optimizer, performing single linearization of the residuals in the start."""
def __init__(self,
problem: L2Problem,
variable: TensorList,
cg_eps=0.0,
fletcher_reeves=True,
standard_alpha=True,
direction_forget_factor=0,
debug=False,
analyze=False,
plotting=False,
fig_num=(10, 11)):
super().__init__(fletcher_reeves, standard_alpha,
direction_forget_factor, debug or plotting)
self.problem = problem
self.x = variable
self.plotting = plotting
self.fig_num = fig_num
self.cg_eps = cg_eps
self.f0 = None
self.g = None
self.dfdxt_g = None
self.residuals = np.zeros(0)
self.losses = np.zeros(0)
self._construct_graph()
self.analyze_convergence = analyze
def clear_temp(self):
pass
def _construct_graph(self):
train_program = fluid.Program()
start_program = fluid.Program()
with fluid.program_guard(train_program, start_program):
scope = 'first/'
self.x_ph = TensorList([
fluid.layers.data(
'{}x_{}'.format(scope, idx),
v.shape,
append_batch_size=False,
stop_gradient=False) for idx, v in enumerate(self.x)
])
self.p_ph = TensorList([
fluid.layers.data(
'{}p_{}'.format(scope, idx),
v.shape,
append_batch_size=False,
stop_gradient=False) for idx, v in enumerate(self.x)
])
# problem forward
self.f0 = self.problem(self.x_ph, scope)
self.g = self.f0.apply(static_clone)
# self.g = self.f0
# Get df/dx^t @ f0
self.dfdxt_g = TensorList(
fluid.gradients(self.f0, self.x_ph, self.g))
# For computing A
tmp = [a * b for a, b in zip(self.dfdxt_g, self.p_ph)]
self.dfdx_x = TensorList(fluid.gradients(tmp, self.g))
# self.dfdx_x = TensorList(fluid.gradients(self.dfdxt_g, self.g, self.p_ph))
train_program2 = fluid.Program()
start_program2 = fluid.Program()
with fluid.program_guard(train_program2, start_program2):
scope = 'second/'
self.x_ph_2 = TensorList([
fluid.layers.data(
'{}x_{}'.format(scope, idx),
v.shape,
append_batch_size=False,
stop_gradient=False) for idx, v in enumerate(self.x)
])
self.dfdx_x_ph = TensorList([
fluid.layers.data(
'{}dfdx_x_{}'.format(scope, idx),
v.shape,
append_batch_size=False,
stop_gradient=False) for idx, v in enumerate(self.g)
])
self.f0_2 = self.problem(self.x_ph_2, scope)
self.dfdx_dfdx = TensorList(
fluid.gradients(self.f0_2 * self.dfdx_x_ph, self.x_ph_2))
place = fluid.CUDAPlace(0)
self.exe = fluid.Executor(place)
self.exe.run(program=fluid.default_startup_program())
self.compiled_prog = fluid.compiler.CompiledProgram(train_program)
place2 = fluid.CUDAPlace(0)
self.exe2 = fluid.Executor(place2)
self.exe2.run(program=fluid.default_startup_program())
self.compiled_prog2 = fluid.compiler.CompiledProgram(train_program2)
def get_dfdxt_g(self):
scope = 'first/'
feed_dict = self.problem.get_feed_dict(scope)
# add variable feed
for idx, v in enumerate(self.x):
feed_dict['{}x_{}'.format(scope, idx)] = v
for idx, v in enumerate(self.x):
feed_dict['{}p_{}'.format(scope, idx)] = v
res = self.exe.run(self.compiled_prog,
feed=feed_dict,
fetch_list=[v.name for v in self.dfdxt_g])
return TensorList(res)
def run(self, num_cg_iter):
"""Run the oprimizer with the provided number of iterations."""
if num_cg_iter == 0:
return
# Get the right hand side
self.b = -self.get_dfdxt_g()
self.evaluate_CG_iteration(0)
# Run CG
delta_x, res = self.run_CG(num_cg_iter, eps=self.cg_eps)
self.x += delta_x
# reset problem training samples
self.problem.training_samples_stack = None
def A(self, x):
# First pass
scope = 'first/'
feed_dict = self.problem.get_feed_dict(scope)
# add variable feed
for idx, v in enumerate(self.x):
feed_dict['{}x_{}'.format(scope, idx)] = v
# add p feed
for idx, v in enumerate(x):
feed_dict['{}p_{}'.format(scope, idx)] = v
dfdx_x = TensorList(
self.exe.run(self.compiled_prog,
feed=feed_dict,
fetch_list=[v.name for v in self.dfdx_x]))
# Second pass
scope = 'second/'
feed_dict = self.problem.get_feed_dict(scope)
# add variable feed
for idx, v in enumerate(self.x):
feed_dict['{}x_{}'.format(scope, idx)] = v
# add p feed
for idx, v in enumerate(dfdx_x):
feed_dict['{}dfdx_x_{}'.format(scope, idx)] = v
res = TensorList(
self.exe2.run(self.compiled_prog2,
feed=feed_dict,
fetch_list=[v.name for v in self.dfdx_dfdx]))
return res
def ip(self, a, b):
return self.problem.ip_input(a, b)
def M1(self, x):
return self.problem.M1(x)
def M2(self, x):
return self.problem.M2(x)
def evaluate_CG_iteration(self, delta_x):
if self.analyze_convergence:
scope = 'first/'
x = self.x + delta_x
feed_dict = self.problem.get_feed_dict(scope)
for idx, v in enumerate(x):
feed_dict['{}x_{}'.format(scope, idx)] = v
for idx, v in enumerate(x):
feed_dict['{}p_{}'.format(scope, idx)] = v
res = self.exe.run(self.compiled_prog,
feed=feed_dict,
fetch_list=[v.name for v in self.f0])
res = TensorList(res)
loss = self.problem.ip_output(res, res)
print('Paddle Loss: {}'.format(loss))
class GaussNewtonCG(ConjugateGradientBase):
"""Gauss-Newton with Conjugate Gradient optimizer."""
def __init__(self,
problem: L2Problem,
variable: TensorList,
cg_eps=0.0,
fletcher_reeves=True,
standard_alpha=True,
direction_forget_factor=0,
debug=False,
analyze=False,
plotting=False,
fig_num=(10, 11, 12)):
super().__init__(fletcher_reeves, standard_alpha,
direction_forget_factor, debug or analyze or plotting)
self.problem = problem
self.x = variable
self.analyze_convergence = analyze
self.plotting = plotting
self.fig_num = fig_num
self.cg_eps = cg_eps
self.f0 = None
self.g = None
self.dfdxt_g = None
self.residuals = np.zeros(0)
self.losses = np.zeros(0)
self.gradient_mags = np.zeros(0)
self._construct_graph()
def clear_temp(self):
self.f0 = None
self.g = None
self.dfdxt_g = None
def run_GN(self, *args, **kwargs):
return self.run(*args, **kwargs)
def _construct_graph(self):
train_program = fluid.Program()
start_program = fluid.Program()
with fluid.program_guard(train_program, start_program):
scope = 'first/'
self.x_ph = TensorList([
fluid.layers.data(
'{}x_{}'.format(scope, idx),
v.shape,
append_batch_size=False,
stop_gradient=False) for idx, v in enumerate(self.x)
])
self.p_ph = TensorList([
fluid.layers.data(
'{}p_{}'.format(scope, idx),
v.shape,
append_batch_size=False,
stop_gradient=False) for idx, v in enumerate(self.x)
])
# problem forward
self.f0 = self.problem(self.x_ph, scope)
self.g = self.f0.apply(static_clone)
# Get df/dx^t @ f0
self.dfdxt_g = TensorList(
fluid.gradients(self.f0, self.x_ph, self.g))
# For computing A
tmp = [a * b for a, b in zip(self.dfdxt_g, self.p_ph)]
self.dfdx_x = TensorList(fluid.gradients(tmp, self.g))
# self.dfdx_x = TensorList(fluid.gradients(self.dfdxt_g, self.g, self.p_ph))
train_program2 = fluid.Program()
start_program2 = fluid.Program()
with fluid.program_guard(train_program2, start_program2):
scope = 'second/'
self.x_ph_2 = TensorList([
fluid.layers.data(
'{}x_{}'.format(scope, idx),
v.shape,
append_batch_size=False,
stop_gradient=False) for idx, v in enumerate(self.x)
])
self.dfdx_x_ph = TensorList([
fluid.layers.data(
'{}dfdx_x_{}'.format(scope, idx),
v.shape,
append_batch_size=False,
stop_gradient=False) for idx, v in enumerate(self.g)
])
self.f0_2 = self.problem(self.x_ph_2, scope)
self.dfdx_dfdx = TensorList(
fluid.gradients(self.f0_2 * self.dfdx_x_ph, self.x_ph_2))
place = fluid.CUDAPlace(0)
self.exe = fluid.Executor(place)
self.exe.run(program=fluid.default_startup_program())
self.compiled_prog = fluid.compiler.CompiledProgram(train_program)
place2 = fluid.CUDAPlace(0)
self.exe2 = fluid.Executor(place2)
self.exe2.run(program=fluid.default_startup_program())
self.compiled_prog2 = fluid.compiler.CompiledProgram(train_program2)
def get_dfdxt_g(self):
scope = 'first/'
feed_dict = self.problem.get_feed_dict(scope)
# add variable feed
for idx, v in enumerate(self.x):
feed_dict['{}x_{}'.format(scope, idx)] = v
for idx, v in enumerate(self.x):
feed_dict['{}p_{}'.format(scope, idx)] = v
res = self.exe.run(self.compiled_prog,
feed=feed_dict,
fetch_list=[v.name for v in self.dfdxt_g])
return TensorList(res)
def run(self, num_cg_iter, num_gn_iter=None):
"""Run the optimizer.
args:
num_cg_iter: Number of CG iterations per GN iter. If list, then each entry specifies number of CG iterations
and number of GN iterations is given by the length of the list.
num_gn_iter: Number of GN iterations. Shall only be given if num_cg_iter is an integer.
"""
if isinstance(num_cg_iter, int):
if num_gn_iter is None:
raise ValueError(
'Must specify number of GN iter if CG iter is constant')
num_cg_iter = [num_cg_iter] * num_gn_iter
num_gn_iter = len(num_cg_iter)
if num_gn_iter == 0:
return
if self.analyze_convergence:
self.evaluate_CG_iteration(0)
# Outer loop for running the GN iterations.
for cg_iter in num_cg_iter:
self.run_GN_iter(cg_iter)
# reset problem training samples
self.problem.training_samples_stack = None
return self.losses, self.residuals
def run_GN_iter(self, num_cg_iter):
"""Runs a single GN iteration."""
self.b = -self.get_dfdxt_g()
# Run CG
if num_cg_iter > 0:
delta_x, res = self.run_CG(num_cg_iter, eps=self.cg_eps)
self.x += delta_x
def A(self, x):
# First pass
scope = 'first/'
feed_dict = self.problem.get_feed_dict(scope)
# add variable feed
for idx, v in enumerate(self.x):
feed_dict['{}x_{}'.format(scope, idx)] = v
# add p feed
for idx, v in enumerate(x):
feed_dict['{}p_{}'.format(scope, idx)] = v
dfdx_x = TensorList(
self.exe.run(self.compiled_prog,
feed=feed_dict,
fetch_list=[v.name for v in self.dfdx_x]))
# Second pass
scope = 'second/'
feed_dict = self.problem.get_feed_dict(scope)
# add variable feed
for idx, v in enumerate(self.x):
feed_dict['{}x_{}'.format(scope, idx)] = v
# add p feed
for idx, v in enumerate(dfdx_x):
feed_dict['{}dfdx_x_{}'.format(scope, idx)] = v
res = TensorList(
self.exe2.run(self.compiled_prog2,
feed=feed_dict,
fetch_list=[v.name for v in self.dfdx_dfdx]))
return res
def ip(self, a, b):
return self.problem.ip_input(a, b)
def M1(self, x):
return self.problem.M1(x)
def M2(self, x):
return self.problem.M2(x)
def evaluate_CG_iteration(self, delta_x):
if self.analyze_convergence:
scope = 'first/'
x = self.x + delta_x
feed_dict = self.problem.get_feed_dict(scope)
for idx, v in enumerate(x):
feed_dict['{}x_{}'.format(scope, idx)] = v
for idx, v in enumerate(x):
feed_dict['{}p_{}'.format(scope, idx)] = v
res = self.exe.run(self.compiled_prog,
feed=feed_dict,
fetch_list=[v.name for v in self.f0])
res = TensorList(res)
loss = self.problem.ip_output(res, res)
print('Paddle Loss: {}'.format(loss))
class GradientDescentL2:
"""Gradient descent with momentum for L2 problems."""
def __init__(self,
problem: L2Problem,
variable: TensorList,
step_length: float,
momentum: float=0.0,
debug=False,
plotting=False,
fig_num=(10, 11)):
self.problem = problem
self.x = variable # Numpy arrays
self.step_legnth = step_length
self.momentum = momentum
self.debug = debug or plotting
self.plotting = plotting
self.fig_num = fig_num
self.losses = np.zeros(0)
self.gradient_mags = np.zeros(0)
self.residuals = None
self.clear_temp()
self._construct_graph()
def clear_temp(self):
self.f0 = None
self.dir = None
def _construct_graph(self):
train_program = fluid.Program()
start_program = fluid.Program()
with fluid.program_guard(train_program, start_program):
self.x_ph = TensorList([
fluid.layers.data(
'x_{}'.format(idx),
v.shape,
append_batch_size=False,
stop_gradient=False) for idx, v in enumerate(self.x)
])
# problem forward
self.f0 = self.problem(self.x_ph)
self.loss = self.problem.ip_output(self.f0, self.f0)
# problem backward
self.grad = TensorList(fluid.gradients(self.loss, self.x_ph))
place = fluid.CUDAPlace(0)
self.exe = fluid.Executor(place)
self.exe.run(program=fluid.default_startup_program())
self.compiled_prog = fluid.compiler.CompiledProgram(train_program)
def get_feed_dict(self, x_list):
feed_dict = self.problem.get_feed_dict()
# add variable feed
for idx, v in enumerate(x_list):
feed_dict['x_{}'.format(idx)] = v
return feed_dict
def run(self, num_iter, dummy=None):
if num_iter == 0:
return
grad_names = [v.name for v in self.grad]
for i in range(num_iter):
res = self.exe.run(self.compiled_prog,
feed=self.get_feed_dict(self.x),
fetch_list=[self.loss.name] + grad_names)
if self.debug:
loss = res[0]
print('Paddle Loss: {}'.format(loss))
grad = TensorList(res[1:])
# update parameters
if self.dir is None:
self.dir = grad
else:
self.dir = grad + self.momentum * self.dir
self.x = self.x - self.step_legnth * self.dir
# reset problem training samples
self.problem.training_samples_stack = None
class GradientDescent:
"""Gradient descent for general minimization problems."""
def __init__(self,
problem: MinimizationProblem,
variable: TensorList,
step_length: float,
momentum: float=0.0,
debug=False,
plotting=False,
fig_num=(10, 11)):
self.problem = problem
self.x = variable
self.step_legnth = step_length
self.momentum = momentum
self.debug = debug or plotting
self.plotting = plotting
self.fig_num = fig_num
self.losses = layers.zeros((0, ), 'float32')
self.gradient_mags = layers.zeros((0, ), 'float32')
self.residuals = None
self.clear_temp()
def clear_temp(self):
self.dir = None
def run(self, num_iter, dummy=None):
if num_iter == 0:
return
lossvec = None
if self.debug:
lossvec = np.zeros((num_iter + 1, ))
grad_mags = np.zeros((num_iter + 1, ))
for i in range(num_iter):
self.x.stop_gradient = False
# Evaluate function at current estimate
loss = self.problem(self.x)
# Compute grad
loss.backward()
grad = TensorList(self.x.gradient()).apply(n2p)
self.x.clear_gradient()
# Update direction
if self.dir is None:
self.dir = grad
else:
self.dir = grad + self.momentum * self.dir
self.x = self.x.detach()
self.x -= self.step_legnth * self.dir
if self.debug:
lossvec[i] = loss.numpy()
grad_mags[i] = self.problem.ip_input(
grad, grad).apply(layers.sqrt).numpy()
self.problem.training_samples_stack = None
self.x = self.x.detach()
self.x.stop_gradient = True
self.clear_temp()
PaddleCV/tracking/pytracking/libs/paddle_utils.py 0 → 100644
浏览文件 @ 7ec3ec11
import numpy as np
import paddle
from paddle.fluid import dygraph
from paddle.fluid import layers
from paddle.fluid.framework import Variable
import cv2 as cv
PTensor = Variable
def broadcast_op(a, b, op='mul'):
a_expand_factors = []
b_expand_factors = []
assert len(a.shape) == len(
b.shape), 'a.shape = {} while b.shape = {}'.format(a.shape, b.shape)
for a_s, b_s in zip(a.shape, b.shape):
if a_s != b_s:
if a_s == 1:
a_expand_factors.append(b_s)
b_expand_factors.append(1)
elif b_s == 1:
a_expand_factors.append(1)
b_expand_factors.append(a_s)
else:
raise NotImplementedError
else:
a_expand_factors.append(1)
b_expand_factors.append(1)
if op == 'mul':
op = layers.elementwise_mul
elif op == 'add':
op = layers.elementwise_add
elif op == 'sub':
op = layers.elementwise_sub
elif op == 'div':
op = layers.elementwise_div
else:
raise NotImplementedError
return op(
layers.expand(a, a_expand_factors), layers.expand(b, b_expand_factors))
def paddle_prod(x):
prod = 1
num_elems = x.shape[0]
for idx in range(num_elems):
prod *= x[idx]
return prod
def n2p(x, dtype=None):
if dtype is None:
x = np.array(x)
if x.dtype == np.float64:
x = x.astype('float32')
else:
x = np.array(x, dtype=dtype)
return dygraph.to_variable(x)
def p2n(x):
return x.numpy()
def clone(x):
v = dygraph.to_variable(x.numpy())
v.stop_gradient = x.stop_gradient
return v
def static_identity(x):
x = layers.reshape(x, x.shape)
return x
def static_clone(x):
x1 = static_identity(x)
x1.stop_gradient = True
x2 = static_identity(x1)
x2.stop_gradient = x.stop_gradient
return x2
def detach(x):
v = dygraph.to_variable(x.numpy())
v.stop_gradient = True
return v
def squeeze(input, axes):
new_shape = []
for i, s in enumerate(input.shape):
if i in axes:
assert s == 1
else:
new_shape.append(s)
return layers.reshape(input, new_shape)
def unsqueeze(input, axes):
new_shape = []
for i, s in enumerate(input.shape):
for a in axes:
if i == a:
new_shape.append(1)
new_shape.append(s)
return layers.reshape(input, new_shape)
def crop(x, crops):
slices = []
for c in crops:
c1 = None if c[1] == 0 else -c[1]
slices.append(slice(c[0], c1))
return x[tuple(slices)]
def _padding(x, pads, mode='constant'):
return_tensor = False
if isinstance(x, PTensor):
x = x.numpy()
return_tensor = True
assert len(pads) % 2 == 0
pads = list(pads) + [0] * (len(x.shape) * 2 - len(pads))
# convert to numpy pad format
pads_np, pad_per_dim = [], []
for i, p in enumerate(pads):
if i % 2 == 0:
pad_per_dim = [p]
else:
pad_per_dim.append(p)
pads_np.insert(0, pad_per_dim)
# handle negative pads (cropping)
pads_np_pos, pads_np_neg = [], []
for pad_per_dim in pads_np:
pad_per_dim_pos, pad_per_dim_neg = [], []
for p in pad_per_dim:
if p < 0:
pad_per_dim_pos.append(0)
pad_per_dim_neg.append(-p)
else:
pad_per_dim_pos.append(p)
pad_per_dim_neg.append(0)
pads_np_pos.append(pad_per_dim_pos)
pads_np_neg.append(pad_per_dim_neg)
# cropping
x = crop(x, pads_np_neg)
# padding
# if x is an image
if len(x.shape) == 3 and pads_np_pos[-1][0] == 0 and pads_np_pos[-1][
1] == 0:
if mode == 'replicate':
pad_mode = cv.BORDER_REPLICATE
else:
pad_mode = cv.BORDER_CONSTANT
y1_pad, y2_pad = pads_np_pos[0]
x1_pad, x2_pad = pads_np_pos[1]
x = cv.copyMakeBorder(x, y1_pad, y2_pad, x1_pad, x2_pad, pad_mode)
else:
np_mode = 'edge' if mode == 'replicate' else 'constant'
x = np.pad(x, pads_np_pos, mode=np_mode)
out = dygraph.to_variable(x) if return_tensor else x
return out
def mod(a, b):
arg_list, new_arg_list = [a, b], []
return_PTensor = False
for x in arg_list:
if isinstance(x, PTensor):
x = p2n(x)
return_PTensor = True
new_arg_list.append(x)
out = new_arg_list[0] % new_arg_list[1]
return n2p(out) if return_PTensor else out
def floordiv(a, b):
arg_list, new_arg_list = [a, b], []
return_PTensor = False
for x in arg_list:
if isinstance(x, PTensor):
x = p2n(x)
return_PTensor = True
new_arg_list.append(x)
out = new_arg_list[0] // new_arg_list[1]
return n2p(out) if return_PTensor else out
def stack_sum(x):
return layers.reduce_sum(layers.stack(x))
def leaky_relu(x, alpha):
return layers.relu(x) + alpha * (-1 * layers.relu(-1 * x))
def elu(x, alpha):
return layers.relu(x) + alpha * (layers.exp(-1 * layers.relu(-1 * x)) - 1)
def dropout2d(input, prob, is_train=False):
if not is_train:
return input
channels = input.shape[1]
keep_prob = 1.0 - prob
random_tensor = keep_prob + layers.uniform_random_batch_size_like(
input, [-1, channels, 1, 1], min=0., max=1.)
binary_tensor = layers.floor(random_tensor)
output = input / keep_prob * binary_tensor
return output
PaddleCV/tracking/pytracking/libs/tensordict.py 0 → 100644
浏览文件 @ 7ec3ec11
from collections import OrderedDict
class TensorDict(OrderedDict):
"""Container mainly used for dicts of Variable."""
def concat(self, other):
"""Concatenates two dicts without copying internal data."""
return TensorDict(self, **other)
def copy(self):
return TensorDict(super(TensorDict, self).copy())
def __getattr__(self, name):
for n, e in self.items():
if not hasattr(e, name):
raise AttributeError('\'{}\' object has not attribute \'{}\''.
format(type(e), name))
def apply_attr(*args, **kwargs):
return TensorDict({
n: getattr(e, name)(*args, **kwargs) if hasattr(e, name) else e
for n, e in self.items()
})
return apply_attr
def attribute(self, attr: str, *args):
return TensorDict({n: getattr(e, attr, *args) for n, e in self.items()})
def apply(self, fn, *args, **kwargs):
return TensorDict({n: fn(e, *args, **kwargs) for n, e in self.items()})
@staticmethod
def _iterable(a):
return isinstance(a, (TensorDict, list))
PaddleCV/tracking/pytracking/libs/tensorlist.py 0 → 100644
浏览文件 @ 7ec3ec11
import functools
import numpy as np
from paddle.fluid import layers
from pytracking.libs.paddle_utils import clone as clone_fn
from pytracking.libs.paddle_utils import detach as detach_fn
from pytracking.libs.paddle_utils import PTensor
def matmul(a, b):
if isinstance(a, PTensor) or isinstance(b, PTensor):
return layers.matmul(a, b)
else:
return np.matmul(a, b)
class TensorList(list):
"""Container mainly used for lists of paddle tensors. Extends lists with paddle functionality."""
def __init__(self, list_of_tensors=list()):
super(TensorList, self).__init__(list_of_tensors)
def __getitem__(self, item):
if isinstance(item, int):
return super(TensorList, self).__getitem__(item)
elif isinstance(item, (tuple, list)):
return TensorList(
[super(TensorList, self).__getitem__(i) for i in item])
else:
return TensorList(super(TensorList, self).__getitem__(item))
def __add__(self, other):
if TensorList._iterable(other):
return TensorList([e1 + e2 for e1, e2 in zip(self, other)])
return TensorList([e + other for e in self])
def __radd__(self, other):
if TensorList._iterable(other):
return TensorList([e2 + e1 for e1, e2 in zip(self, other)])
return TensorList([other + e for e in self])
def __iadd__(self, other):
if TensorList._iterable(other):
for i, e2 in enumerate(other):
self[i] += e2
else:
for i in range(len(self)):
self[i] += other
return self
def __sub__(self, other):
if TensorList._iterable(other):
return TensorList([e1 - e2 for e1, e2 in zip(self, other)])
return TensorList([e - other for e in self])
def __rsub__(self, other):
if TensorList._iterable(other):
return TensorList([e2 - e1 for e1, e2 in zip(self, other)])
return TensorList([other - e for e in self])
def __isub__(self, other):
if TensorList._iterable(other):
for i, e2 in enumerate(other):
self[i] -= e2
else:
for i in range(len(self)):
self[i] -= other
return self
def __mul__(self, other):
if TensorList._iterable(other):
return TensorList([e1 * e2 for e1, e2 in zip(self, other)])
return TensorList([e * other for e in self])
def __rmul__(self, other):
if TensorList._iterable(other):
return TensorList([e2 * e1 for e1, e2 in zip(self, other)])
return TensorList([other * e for e in self])
def __imul__(self, other):
if TensorList._iterable(other):
for i, e2 in enumerate(other):
self[i] *= e2
else:
for i in range(len(self)):
self[i] *= other
return self
def __truediv__(self, other):
if TensorList._iterable(other):
return TensorList([e1 / e2 for e1, e2 in zip(self, other)])
return TensorList([e / other for e in self])
def __rtruediv__(self, other):
if TensorList._iterable(other):
return TensorList([e2 / e1 for e1, e2 in zip(self, other)])
return TensorList([other / e for e in self])
def __itruediv__(self, other):
if TensorList._iterable(other):
for i, e2 in enumerate(other):
self[i] /= e2
else:
for i in range(len(self)):
self[i] /= other
return self
def __matmul__(self, other):
if TensorList._iterable(other):
return TensorList([matmul(e1, e2) for e1, e2 in zip(self, other)])
return TensorList([matmul(e, other) for e in self])
def __rmatmul__(self, other):
if TensorList._iterable(other):
return TensorList([matmul(e2, e1) for e1, e2 in zip(self, other)])
return TensorList([matmul(other, e) for e in self])
def __imatmul__(self, other):
if TensorList._iterable(other):
for i, e2 in enumerate(other):
self[i] = matmul(self[i], e2)
else:
for i in range(len(self)):
self[i] = matmul(self[i], other)
return self
def __mod__(self, other):
if TensorList._iterable(other):
return TensorList([e1 % e2 for e1, e2 in zip(self, other)])
return TensorList([e % other for e in self])
def __rmod__(self, other):
if TensorList._iterable(other):
return TensorList([e2 % e1 for e1, e2 in zip(self, other)])
return TensorList([other % e for e in self])
def __pos__(self):
return TensorList([+e for e in self])
def __neg__(self):
return TensorList([-e for e in self])
def __le__(self, other):
if TensorList._iterable(other):
return TensorList([e1 <= e2 for e1, e2 in zip(self, other)])
return TensorList([e <= other for e in self])
def __ge__(self, other):
if TensorList._iterable(other):
return TensorList([e1 >= e2 for e1, e2 in zip(self, other)])
return TensorList([e >= other for e in self])
def view(self, *args):
def reshape(x):
if isinstance(x, PTensor):
return layers.reshape(x, args)
else:
return np.reshape(x, args)
return self.apply(reshape)
def clone(self):
def _clone(x):
if isinstance(x, PTensor):
return clone_fn(x)
else:
return x.copy()
return self.apply(_clone)
def detach(self):
return self.apply(detach_fn)
def sqrt(self):
def _sqrt(x):
if isinstance(x, PTensor):
return layers.sqrt(x)
else:
return np.sqrt(x)
return self.apply(_sqrt)
def abs(self):
def _abs(x):
if isinstance(x, PTensor):
return layers.abs(x)
else:
return np.abs(x)
return self.apply(_abs)
def size(self, axis=None):
def get_size(x):
if axis is None:
return x.shape
else:
return x.shape[axis]
return self.apply(get_size)
def concat(self, other):
return TensorList(super(TensorList, self).__add__(other))
def copy(self):
return TensorList(super(TensorList, self).copy())
def unroll(self):
if not any(isinstance(t, TensorList) for t in self):
return self
new_list = TensorList()
for t in self:
if isinstance(t, TensorList):
new_list.extend(t.unroll())
else:
new_list.append(t)
return new_list
def attribute(self, attr: str, *args):
return TensorList([getattr(e, attr, *args) for e in self])
def apply(self, fn):
return TensorList([fn(e) for e in self])
def __getattr__(self, name):
for e in self:
if not hasattr(e, name):
raise AttributeError('\'{}\' object has not attribute \'{}\''.
format(type(e), name))
def apply_attr(*args, **kwargs):
return TensorList([getattr(e, name)(*args, **kwargs) for e in self])
return apply_attr
@staticmethod
def _iterable(a):
return isinstance(a, (TensorList, list))
def tensor_operation(op):
def islist(a):
return isinstance(a, TensorList)
@functools.wraps(op)
def oplist(*args, **kwargs):
if len(args) == 0:
raise ValueError(
'Must be at least one argument without keyword (i.e. operand).')
if len(args) == 1:
if islist(args[0]):
return TensorList([op(a, **kwargs) for a in args[0]])
else:
# Multiple operands, assume max two
if islist(args[0]) and islist(args[1]):
return TensorList(
[op(a, b, *args[2:], **kwargs) for a, b in zip(*args[:2])])
if islist(args[0]):
return TensorList([op(a, *args[1:], **kwargs) for a in args[0]])
if islist(args[1]):
return TensorList(
[op(args[0], b, *args[2:], **kwargs) for b in args[1]])
# None of the operands are lists
return op(*args, **kwargs)
return oplist
PaddleCV/tracking/pytracking/parameter/atom/default_vot.py 0 → 100644
浏览文件 @ 7ec3ec11
import numpy as np
from pytracking.features.deep import ResNet50
from pytracking.features.extractor import MultiResolutionExtractor
from pytracking.utils import TrackerParams, FeatureParams
def parameters():
params = TrackerParams()
# These are usually set from outside
params.debug = 0 # Debug level
params.visualization = False # Do visualization
# Use GPU or not (IoUNet requires this to be True)
params.use_gpu = True
# Feature specific parameters
deep_params = TrackerParams()
# Patch sampling parameters
params.max_image_sample_size = (14 * 16)**2 # Maximum image sample size
params.min_image_sample_size = (14 * 16)**2 # Minimum image sample size
params.search_area_scale = 4 # Scale relative to target size
params.feature_size_odd = False # Good to use False for even-sized kernels and vice versa
# Optimization parameters
params.CG_iter = 5 # The number of Conjugate Gradient iterations in each update after the first frame
params.init_CG_iter = 60 # The total number of Conjugate Gradient iterations used in the first frame
params.init_GN_iter = 6 # The number of Gauss-Newton iterations used in the first frame (only if the projection matrix is updated)
params.post_init_CG_iter = 0 # CG iterations to run after GN
params.fletcher_reeves = False # Use the Fletcher-Reeves (true) or Polak-Ribiere (false) formula in the Conjugate Gradient
params.standard_alpha = True # Use the standard formula for computing the step length in Conjugate Gradient
params.CG_forgetting_rate = None # Forgetting rate of the last conjugate direction
# Learning parameters for each feature type
deep_params.learning_rate = 0.0075 # Learning rate
deep_params.output_sigma_factor = 1 / 4 # Standard deviation of Gaussian label relative to target size
# Training parameters
params.sample_memory_size = 250 # Memory size
params.train_skipping = 10 # How often to run training (every n-th frame)
# Online model parameters
deep_params.kernel_size = (4, 4) # Kernel size of filter
deep_params.compressed_dim = 64 # Dimension output of projection matrix
deep_params.filter_reg = 1e-1 # Filter regularization factor
deep_params.projection_reg = 1e-4 # Projection regularization factor
# Windowing
params.feature_window = False # Perform windowing of features
params.window_output = True # Perform windowing of output scores
# Detection parameters
params.scale_factors = np.array(
[1], dtype='float32'
) # What scales to use for localization (only one scale if IoUNet is used)
params.score_upsample_factor = 1 # How much Fourier upsampling to use
# Init data augmentation parameters
params.augmentation = {
'fliplr': True,
'rotate': [5, -5, 10, -10, 20, -20, 30, -30, 45, -45, -60, 60],
'blur': [(2, 0.2), (0.2, 2), (3, 1), (1, 3), (2, 2)],
'relativeshift': [(0.6, 0.6), (-0.6, 0.6), (0.6, -0.6), (-0.6, -0.6)],
'dropout': (7, 0.2)
}
params.augmentation_expansion_factor = 2 # How much to expand sample when doing augmentation
params.random_shift_factor = 1 / 3 # How much random shift to do on each augmented sample
deep_params.use_augmentation = True # Whether to use augmentation for this feature
# Factorized convolution parameters
# params.use_projection_matrix = True # Use projection matrix, i.e. use the factorized convolution formulation
params.update_projection_matrix = True # Whether the projection matrix should be optimized or not
params.proj_init_method = 'randn' # Method for initializing the projection matrix
params.filter_init_method = 'randn' # Method for initializing the spatial filter
params.projection_activation = 'none' # Activation function after projection ('none', 'relu', 'elu' or 'mlu')
params.response_activation = (
'mlu', 0.05
) # Activation function on the output scores ('none', 'relu', 'elu' or 'mlu')
# Advanced localization parameters
params.advanced_localization = True # Use this or not
params.target_not_found_threshold = -1 # Absolute score threshold to detect target missing
params.distractor_threshold = 100 # Relative threshold to find distractors
params.hard_negative_threshold = 0.3 # Relative threshold to find hard negative samples
params.target_neighborhood_scale = 2.2 # Target neighborhood to remove
params.dispalcement_scale = 0.7 # Dispacement to consider for distractors
params.hard_negative_learning_rate = 0.02 # Learning rate if hard negative detected
params.hard_negative_CG_iter = 5 # Number of optimization iterations to use if hard negative detected
params.update_scale_when_uncertain = True # Update scale or not if distractor is close
# IoUNet parameters
params.iounet_augmentation = False # Use the augmented samples to compute the modulation vector
params.iounet_k = 3 # Top-k average to estimate final box
params.num_init_random_boxes = 9 # Num extra random boxes in addition to the classifier prediction
params.box_jitter_pos = 0.1 # How much to jitter the translation for random boxes
params.box_jitter_sz = 0.5 # How much to jitter the scale for random boxes
params.maximal_aspect_ratio = 6 # Limit on the aspect ratio
params.box_refinement_iter = 5 # Number of iterations for refining the boxes
params.box_refinement_step_length = 1 # Gradient step length in the bounding box refinement
params.box_refinement_step_decay = 1 # Multiplicative step length decay (1 means no decay)
# Setup the feature extractor (which includes the IoUNet)
deep_fparams = FeatureParams(feature_params=[deep_params])
deep_feat = ResNet50(
net_path='/home/vis/bily/code/baidu/personal-code/libi-13/paddle_ATOMnet-ep0040',
output_layers=['block2'],
fparams=deep_fparams,
normalize_power=2)
params.features = MultiResolutionExtractor([deep_feat])
params.vot_anno_conversion_type = 'preserve_area'
return params
PaddleCV/tracking/pytracking/parameter/siamfc/default.py 0 → 100644
浏览文件 @ 7ec3ec11
import numpy as np
from pytracking.features import deep
from pytracking.features.extractor import MultiResolutionExtractor
from pytracking.utils import TrackerParams, FeatureParams
def parameters():
params = TrackerParams()
# These are usually set from outside
params.debug = 0 # Debug level
params.visualization = False # Do visualization
# Use GPU or not (IoUNet requires this to be True)
params.use_gpu = True
# Feature specific parameters
deep_params = TrackerParams()
# Patch sampling parameters
params.exemplar_size = 127
params.max_image_sample_size = 255 * 255 # Maximum image sample size
params.min_image_sample_size = 255 * 255 # Minimum image sample size
# Detection parameters
params.scale_factors = 1.0375**np.array(
[-1, 0, 1]
) # What scales to use for localization (only one scale if IoUNet is used)
params.score_upsample_factor = 16 # How much Fourier upsampling to use
params.scale_penalty = 0.9745
params.scale_lr = 0.59
params.window_influence = 0.176
params.total_stride = 8
# Setup the feature extractor (which includes the IoUNet)
deep_fparams = FeatureParams(feature_params=[deep_params])
deep_feat = deep.SFCAlexnet(
net_path='/ssd2/bily/code/baidu/personal-code/pytracking/ltr/checkpoints/ltr/fs/siamrpn50/SiamRPN_ep0001.pth.tar',
output_layers=['conv5'],
fparams=deep_fparams)
params.features = MultiResolutionExtractor([deep_feat])
params.net_path = None
params.response_up = 16
params.response_sz = 17
params.context = 0.5
params.instance_sz = 255
params.exemplar_sz = 127
params.scale_num = 3
params.scale_step = 1.0375
params.scale_lr = 0.59
params.scale_penalty = 0.9745
params.window_influence = 0.176
params.total_stride = 8
return params
PaddleCV/tracking/pytracking/tracker/atom/__init__.py 0 → 100644
浏览文件 @ 7ec3ec11
from .atom import ATOM
def get_tracker_class():
return ATOM
PaddleCV/tracking/pytracking/tracker/atom/atom.py 0 → 100644
浏览文件 @ 7ec3ec11
import math
import os
import time
import numpy as np
from paddle import fluid
from paddle.fluid import layers
from pytracking.features import augmentation
from pytracking.libs import dcf, operation, fourier
from pytracking.libs.optimization import ConjugateGradient, GaussNewtonCG, GradientDescentL2
from pytracking.libs.paddle_utils import mod, n2p, \
leaky_relu, dropout2d
from pytracking.libs.tensorlist import TensorList
from pytracking.tracker.atom.optim import FactorizedConvProblem, ConvProblem
from pytracking.tracker.base.basetracker import BaseTracker
class ATOM(BaseTracker):
def initialize_features(self):
if not getattr(self, 'features_initialized', False):
self.params.features.initialize()
self.features_initialized = True
def initialize(self, image, state, *args, **kwargs):
# Initialize some stuff
self.frame_num = 1
# TODO: for now, we don't support explictly setting up device
# if not hasattr(self.params, 'device'):
# self.params.device = 'cuda' if self.params.use_gpu else 'cpu'
# Initialize features
self.initialize_features()
# Check if image is color
self.params.features.set_is_color(image.shape[2] == 3)
# Get feature specific params
self.fparams = self.params.features.get_fparams('feature_params')
self.time = 0
tic = time.time()
# Get position and size
self.pos = np.array(
[state[1] + (state[3] - 1) / 2, state[0] + (state[2] - 1) / 2],
'float32')
self.target_sz = np.array([state[3], state[2]], 'float32')
# Set search area
self.target_scale = 1.0
search_area = np.prod(self.target_sz * self.params.search_area_scale)
if search_area > self.params.max_image_sample_size:
self.target_scale = math.sqrt(search_area /
self.params.max_image_sample_size)
elif search_area < self.params.min_image_sample_size:
self.target_scale = math.sqrt(search_area /
self.params.min_image_sample_size)
# Check if IoUNet is used
self.use_iou_net = getattr(self.params, 'use_iou_net', True)
# Target size in base scale
self.base_target_sz = self.target_sz / self.target_scale
# Use odd square search area and set sizes
feat_max_stride = max(self.params.features.stride())
if getattr(self.params, 'search_area_shape', 'square') == 'square':
self.img_sample_sz = np.ones((2, ), 'float32') * np.round(
np.sqrt(
np.prod(self.base_target_sz *
self.params.search_area_scale)))
elif self.params.search_area_shape == 'initrect':
self.img_sample_sz = np.round(self.base_target_sz *
self.params.search_area_scale)
else:
raise ValueError('Unknown search area shape')
if self.params.feature_size_odd:
self.img_sample_sz += feat_max_stride - mod(self.img_sample_sz,
(2 * feat_max_stride))
else:
self.img_sample_sz += feat_max_stride - mod(
(self.img_sample_sz + feat_max_stride), (2 * feat_max_stride))
# Set sizes
self.img_support_sz = self.img_sample_sz
self.feature_sz = self.params.features.size(self.img_sample_sz)
self.output_sz = self.params.score_upsample_factor * self.img_support_sz # Interpolated size of the output
self.kernel_size = self.fparams.attribute('kernel_size')
self.iou_img_sample_sz = self.img_sample_sz
# Optimization options
self.params.precond_learning_rate = self.fparams.attribute(
'learning_rate')
if self.params.CG_forgetting_rate is None or max(
self.params.precond_learning_rate) >= 1:
self.params.direction_forget_factor = 0
else:
self.params.direction_forget_factor = (
1 - max(self.params.precond_learning_rate)
)**self.params.CG_forgetting_rate
self.output_window = None
if getattr(self.params, 'window_output', False):
if getattr(self.params, 'use_clipped_window', False):
self.output_window = dcf.hann2d_clipped(
self.output_sz.astype('long'),
self.output_sz.astype('long') *
self.params.effective_search_area /
self.params.search_area_scale,
centered=False)
else:
self.output_window = dcf.hann2d(
self.output_sz.astype('long'), centered=False)
# Initialize some learning things
self.init_learning()
# Convert image
im = image.astype('float32')
self.im = im # For debugging only
# Setup scale bounds
self.image_sz = np.array([im.shape[0], im.shape[1]], 'float32')
self.min_scale_factor = np.max(10 / self.base_target_sz)
self.max_scale_factor = np.min(self.image_sz / self.base_target_sz)
# Extract and transform sample
x = self.generate_init_samples(im)
# Initialize iounet
if self.use_iou_net:
self.init_iou_net()
# Initialize projection matrix
self.init_projection_matrix(x)
# Transform to get the training sample
train_x = self.preprocess_sample(x)
# Generate label function
init_y = self.init_label_function(train_x)
# Init memory
self.init_memory(train_x)
# Init optimizer and do initial optimization
self.init_optimization(train_x, init_y)
self.pos_iounet = self.pos.copy()
self.time += time.time() - tic
def track(self, image):
self.frame_num += 1
# Convert image
# im = numpy_to_paddle(image)
im = image.astype('float32')
self.im = im # For debugging only
# ------- LOCALIZATION ------- #
# Get sample
sample_pos = self.pos.round()
sample_scales = self.target_scale * self.params.scale_factors
test_x = self.extract_processed_sample(im, self.pos, sample_scales,
self.img_sample_sz)
# Compute scores
scores_raw = self.apply_filter(test_x)
translation_vec, scale_ind, s, flag = self.localize_target(scores_raw)
# Update position and scale
if flag != 'not_found':
if self.use_iou_net:
update_scale_flag = getattr(self.params,
'update_scale_when_uncertain',
True) or flag != 'uncertain'
if getattr(self.params, 'use_classifier', True):
self.update_state(sample_pos + translation_vec)
self.refine_target_box(sample_pos, sample_scales[scale_ind],
scale_ind, update_scale_flag)
elif getattr(self.params, 'use_classifier', True):
self.update_state(sample_pos + translation_vec,
sample_scales[scale_ind])
# ------- UPDATE ------- #
# Check flags and set learning rate if hard negative
update_flag = flag not in ['not_found', 'uncertain']
hard_negative = (flag == 'hard_negative')
learning_rate = self.params.hard_negative_learning_rate if hard_negative else None
if update_flag:
# Get train sample
train_x = TensorList([x[scale_ind:scale_ind + 1] for x in test_x])
# Create label for sample
train_y = self.get_label_function(sample_pos,
sample_scales[scale_ind])
# Update memory
self.update_memory(train_x, train_y, learning_rate)
# Train filter
if hard_negative:
self.filter_optimizer.run(self.params.hard_negative_CG_iter)
elif (self.frame_num - 1) % self.params.train_skipping == 0:
self.filter_optimizer.run(self.params.CG_iter)
self.filter = self.filter_optimizer.x
# Set the pos of the tracker to iounet pos
if self.use_iou_net and flag != 'not_found':
self.pos = self.pos_iounet.copy()
# Return new state
yx = self.pos - (self.target_sz - 1) / 2
new_state = np.array(
[yx[1], yx[0], self.target_sz[1], self.target_sz[0]], 'float32')
return new_state.tolist()
def update_memory(self,
sample_x: TensorList,
sample_y: TensorList,
learning_rate=None):
replace_ind = self.update_sample_weights(
self.sample_weights, self.previous_replace_ind,
self.num_stored_samples, self.num_init_samples, self.fparams,
learning_rate)
self.previous_replace_ind = replace_ind
for train_samp, x, ind in zip(self.training_samples, sample_x,
replace_ind):
train_samp[ind] = x[0]
for y_memory, y, ind in zip(self.y, sample_y, replace_ind):
y_memory[ind] = y[0]
if self.hinge_mask is not None:
for m, y, ind in zip(self.hinge_mask, sample_y, replace_ind):
m[ind] = layers.cast(y >= self.params.hinge_threshold,
'float32')[0]
self.num_stored_samples += 1
def update_sample_weights(self,
sample_weights,
previous_replace_ind,
num_stored_samples,
num_init_samples,
fparams,
learning_rate=None):
# Update weights and get index to replace in memory
replace_ind = []
for sw, prev_ind, num_samp, num_init, fpar in zip(
sample_weights, previous_replace_ind, num_stored_samples,
num_init_samples, fparams):
lr = learning_rate
if lr is None:
lr = fpar.learning_rate
init_samp_weight = getattr(fpar, 'init_samples_minimum_weight',
None)
if init_samp_weight == 0:
init_samp_weight = None
s_ind = 0 if init_samp_weight is None else num_init
if num_samp == 0 or lr == 1:
sw[:] = 0
sw[0] = 1
r_ind = 0
else:
# Get index to replace
r_ind = np.argmin(sw[s_ind:], 0)
r_ind = int(r_ind + s_ind)
# Update weights
if prev_ind is None:
sw /= 1 - lr
sw[r_ind] = lr
else:
sw[r_ind] = sw[prev_ind] / (1 - lr)
sw /= sw.sum()
if init_samp_weight is not None and sw[:num_init].sum(
) < init_samp_weight:
sw /= init_samp_weight + sw[num_init:].sum()
sw[:num_init] = init_samp_weight / num_init
replace_ind.append(r_ind)
return replace_ind
def localize_target(self, scores_raw):
# Weighted sum (if multiple features) with interpolation in fourier domain
weight = self.fparams.attribute('translation_weight', 1.0)
scores_raw = weight * scores_raw
sf_weighted = fourier.cfft2(scores_raw) / (scores_raw.size(2) *
scores_raw.size(3))
for i, (sz, ksz) in enumerate(zip(self.feature_sz, self.kernel_size)):
sf_weighted[i] = fourier.shift_fs(sf_weighted[i], math.pi * (
1 - np.array([ksz[0] % 2, ksz[1] % 2]) / sz))
scores_fs = fourier.sum_fs(sf_weighted)
scores = fourier.sample_fs(scores_fs, self.output_sz)
if self.output_window is not None and not getattr(
self.params, 'perform_hn_without_windowing', False):
scores *= self.output_window
if getattr(self.params, 'advanced_localization', False):
return self.localize_advanced(scores)
# Get maximum
max_score, max_disp = dcf.max2d(scores)
scale_ind = np.argmax(max_score, axis=0)[0]
max_disp = max_disp.astype('float32')
# Convert to displacements in the base scale
output_sz = self.output_sz.copy()
disp = mod((max_disp + output_sz / 2), output_sz) - output_sz / 2
# Compute translation vector and scale change factor
translation_vec = np.reshape(
disp[scale_ind].astype('float32'), [-1]) * (
self.img_support_sz / self.output_sz) * self.target_scale
translation_vec *= self.params.scale_factors[scale_ind]
# Shift the score output for visualization purposes
if self.params.debug >= 2:
sz = scores.shape[-2:]
scores = np.concatenate(
[scores[..., sz[0] // 2:, :], scores[..., :sz[0] // 2, :]], -2)
scores = np.concatenate(
[scores[..., sz[1] // 2:], scores[..., :sz[1] // 2]], -1)
return translation_vec, scale_ind, scores, None
def update_state(self, new_pos, new_scale=None):
# Update scale
if new_scale is not None:
self.target_scale = np.clip(new_scale, self.min_scale_factor,
self.max_scale_factor)
self.target_sz = self.base_target_sz * self.target_scale
# Update pos
inside_ratio = 0.2
inside_offset = (inside_ratio - 0.5) * self.target_sz
self.pos = np.maximum(
np.minimum(new_pos,
self.image_sz.astype('float32') - inside_offset),
inside_offset)
def get_label_function(self, sample_pos, sample_scale):
# Generate label function
train_y = TensorList()
target_center_norm = (self.pos - sample_pos) / (self.img_support_sz *
sample_scale)
for sig, sz, ksz in zip(self.sigma, self.feature_sz, self.kernel_size):
center = sz * target_center_norm + 0.5 * np.array(
[(ksz[0] + 1) % 2, (ksz[1] + 1) % 2], 'float32')
train_y.append(dcf.label_function_spatial(sz, sig, center))
return train_y
def extract_sample(self,
im: np.ndarray,
pos: np.ndarray,
scales,
sz: np.ndarray,
debug_save_name):
return self.params.features.extract(im, pos, scales, sz,
debug_save_name)
def extract_processed_sample(self,
im: np.ndarray,
pos: np.ndarray,
scales,
sz: np.ndarray,
debug_save_name=None) -> (TensorList,
TensorList):
x = self.extract_sample(im, pos, scales, sz, debug_save_name)
return self.preprocess_sample(self.project_sample(x))
def apply_filter(self, sample_x: TensorList):
with fluid.dygraph.guard():
sample_x = sample_x.apply(n2p)
filter = self.filter.apply(n2p)
return operation.conv2d(sample_x, filter, mode='same').numpy()
def init_projection_matrix(self, x):
# Set if using projection matrix
self.params.use_projection_matrix = getattr(
self.params, 'use_projection_matrix', True)
if self.params.use_projection_matrix:
self.compressed_dim = self.fparams.attribute('compressed_dim', None)
proj_init_method = getattr(self.params, 'proj_init_method', 'pca')
if proj_init_method == 'pca':
raise NotImplementedError
elif proj_init_method == 'randn':
with fluid.dygraph.guard():
self.projection_matrix = TensorList([
None if cdim is None else layers.gaussian_random(
(cdim, ex.shape[1], 1, 1), 0.0,
1 / math.sqrt(ex.shape[1])).numpy()
for ex, cdim in zip(x, self.compressed_dim)
])
elif proj_init_method == 'np_randn':
rng = np.random.RandomState(0)
self.projection_matrix = TensorList([
None if cdim is None else rng.normal(
size=(cdim, ex.shape[1], 1, 1),
loc=0.0,
scale=1 / math.sqrt(ex.shape[1])).astype('float32')
for ex, cdim in zip(x, self.compressed_dim)
])
elif proj_init_method == 'ones':
self.projection_matrix = TensorList([
None if cdim is None else
np.ones((cdim, ex.shape[1], 1, 1),
'float32') / math.sqrt(ex.shape[1])
for ex, cdim in zip(x, self.compressed_dim)
])
else:
self.compressed_dim = x.size(1)
self.projection_matrix = TensorList([None] * len(x))
def preprocess_sample(self, x: TensorList) -> (TensorList, TensorList):
if getattr(self.params, '_feature_window', False):
x = x * self.feature_window
return x
def init_label_function(self, train_x):
# Allocate label function
self.y = TensorList([
np.zeros(
[self.params.sample_memory_size, 1, x.shape[2], x.shape[3]],
'float32') for x in train_x
])
# Output sigma factor
output_sigma_factor = self.fparams.attribute('output_sigma_factor')
self.sigma = output_sigma_factor * np.ones((2, ), 'float32') * (
self.feature_sz / self.img_support_sz *
self.base_target_sz).apply(np.prod).apply(np.sqrt)
# Center pos in normalized coords
target_center_norm = (self.pos - np.round(self.pos)) / (
self.target_scale * self.img_support_sz)
# Generate label functions
for y, sig, sz, ksz, x in zip(self.y, self.sigma, self.feature_sz,
self.kernel_size, train_x):
center_pos = sz * target_center_norm + 0.5 * np.array(
[(ksz[0] + 1) % 2, (ksz[1] + 1) % 2], 'float32')
for i, T in enumerate(self.transforms[:x.shape[0]]):
sample_center = center_pos + np.array(
T.shift, 'float32') / self.img_support_sz * sz
y[i] = dcf.label_function_spatial(sz, sig, sample_center)
# Return only the ones to use for initial training
return TensorList([y[:x.shape[0]] for y, x in zip(self.y, train_x)])
def init_memory(self, train_x):
# Initialize first-frame training samples
self.num_init_samples = train_x.size(0)
self.init_sample_weights = TensorList(
[np.ones(x.shape[0], 'float32') / x.shape[0] for x in train_x])
self.init_training_samples = train_x
# Sample counters and weights
self.num_stored_samples = self.num_init_samples.copy()
self.previous_replace_ind = [None] * len(self.num_stored_samples)
self.sample_weights = TensorList([
np.zeros(self.params.sample_memory_size, 'float32') for x in train_x
])
for sw, init_sw, num in zip(self.sample_weights,
self.init_sample_weights,
self.num_init_samples):
sw[:num] = init_sw
# Initialize memory
self.training_samples = TensorList(
[[np.zeros([cdim, x.shape[2], x.shape[3]], 'float32')] *
self.params.sample_memory_size
for x, cdim in zip(train_x, self.compressed_dim)])
def init_learning(self):
# Get window function
self.feature_window = TensorList(
[dcf.hann2d(sz) for sz in self.feature_sz])
# Filter regularization
self.filter_reg = self.fparams.attribute('filter_reg')
# Activation function after the projection matrix (phi_1 in the paper)
projection_activation = getattr(self.params, 'projection_activation',
'none')
if isinstance(projection_activation, tuple):
projection_activation, act_param = projection_activation
if projection_activation == 'none':
self.projection_activation = lambda x: x
elif projection_activation == 'relu':
self.projection_activation = layers.relu
elif projection_activation == 'elu':
self.projection_activation = layers.elu
elif projection_activation == 'mlu':
self.projection_activation = lambda x: layers.elu(leaky_relu(x, 1 / act_param), act_param)
else:
raise ValueError('Unknown activation')
# Activation function after the output scores (phi_2 in the paper)
response_activation = getattr(self.params, 'response_activation',
'none')
if isinstance(response_activation, tuple):
response_activation, act_param = response_activation
if response_activation == 'none':
self.response_activation = lambda x: x
elif response_activation == 'relu':
self.response_activation = layers.relu
elif response_activation == 'elu':
self.response_activation = layers.elu
elif response_activation == 'mlu':
self.response_activation = lambda x: layers.elu(leaky_relu(x, 1 / act_param), act_param)
else:
raise ValueError('Unknown activation')
def generate_init_samples(self, im: np.ndarray) -> TensorList:
"""Generate augmented initial samples."""
# Compute augmentation size
aug_expansion_factor = getattr(self.params,
'augmentation_expansion_factor', None)
aug_expansion_sz = self.img_sample_sz.copy()
aug_output_sz = None
if aug_expansion_factor is not None and aug_expansion_factor != 1:
aug_expansion_sz = (self.img_sample_sz *
aug_expansion_factor).astype('long')
aug_expansion_sz += (
aug_expansion_sz - self.img_sample_sz.astype('long')) % 2
aug_expansion_sz = aug_expansion_sz.astype('float32')
aug_output_sz = self.img_sample_sz.astype('long').tolist()
# Random shift operator
get_rand_shift = lambda: None
random_shift_factor = getattr(self.params, 'random_shift_factor', 0)
if random_shift_factor > 0:
get_rand_shift = lambda: ((np.random.uniform(size=[2]) - 0.5) * self.img_sample_sz * random_shift_factor).astype('long').tolist()
# Create transofmations
self.transforms = [augmentation.Identity(aug_output_sz)]
if 'shift' in self.params.augmentation:
self.transforms.extend([
augmentation.Translation(shift, aug_output_sz)
for shift in self.params.augmentation['shift']
])
if 'relativeshift' in self.params.augmentation:
get_absolute = lambda shift: (np.array(shift, 'float32') * self.img_sample_sz / 2).astype('long').tolist()
self.transforms.extend([
augmentation.Translation(get_absolute(shift), aug_output_sz)
for shift in self.params.augmentation['relativeshift']
])
if 'fliplr' in self.params.augmentation and self.params.augmentation[
'fliplr']:
self.transforms.append(
augmentation.FlipHorizontal(aug_output_sz, get_rand_shift()))
if 'blur' in self.params.augmentation:
self.transforms.extend([
augmentation.Blur(sigma, aug_output_sz, get_rand_shift())
for sigma in self.params.augmentation['blur']
])
if 'scale' in self.params.augmentation:
self.transforms.extend([
augmentation.Scale(scale_factor, aug_output_sz,
get_rand_shift())
for scale_factor in self.params.augmentation['scale']
])
if 'rotate' in self.params.augmentation:
self.transforms.extend([
augmentation.Rotate(angle, aug_output_sz, get_rand_shift())
for angle in self.params.augmentation['rotate']
])
# Generate initial samples
init_samples = self.params.features.extract_transformed(
im, self.pos, self.target_scale, aug_expansion_sz, self.transforms)
# Remove augmented samples for those that shall not have
for i, use_aug in enumerate(self.fparams.attribute('use_augmentation')):
if not use_aug:
init_samples[i] = init_samples[i][0:1]
# Add dropout samples
if 'dropout' in self.params.augmentation:
num, prob = self.params.augmentation['dropout']
self.transforms.extend(self.transforms[:1] * num)
with fluid.dygraph.guard():
for i, use_aug in enumerate(
self.fparams.attribute('use_augmentation')):
if use_aug:
init_samples[i] = np.concatenate([
init_samples[i], dropout2d(
layers.expand(
n2p(init_samples[i][0:1]), (num, 1, 1, 1)),
prob,
is_train=True).numpy()
])
return init_samples
def init_optimization(self, train_x, init_y):
# Initialize filter
filter_init_method = getattr(self.params, 'filter_init_method', 'zeros')
self.filter = TensorList([
np.zeros([1, cdim, sz[0], sz[1]], 'float32')
for x, cdim, sz in zip(train_x, self.compressed_dim,
self.kernel_size)
])
if filter_init_method == 'zeros':
pass
elif filter_init_method == 'ones':
for idx, f in enumerate(self.filter):
self.filter[idx] = np.ones(f.shape,
'float32') / np.prod(f.shape)
elif filter_init_method == 'np_randn':
rng = np.random.RandomState(0)
for idx, f in enumerate(self.filter):
self.filter[idx] = rng.normal(
size=f.shape, loc=0,
scale=1 / np.prod(f.shape)).astype('float32')
elif filter_init_method == 'randn':
for idx, f in enumerate(self.filter):
with fluid.dygraph.guard():
self.filter[idx] = layers.gaussian_random(
f.shape, std=1 / np.prod(f.shape)).numpy()
else:
raise ValueError('Unknown "filter_init_method"')
# Get parameters
self.params.update_projection_matrix = getattr(
self.params, 'update_projection_matrix',
True) and self.params.use_projection_matrix
optimizer = getattr(self.params, 'optimizer', 'GaussNewtonCG')
# Setup factorized joint optimization
if self.params.update_projection_matrix:
self.joint_problem = FactorizedConvProblem(
self.init_training_samples, init_y, self.filter_reg,
self.fparams.attribute('projection_reg'), self.params,
self.init_sample_weights, self.projection_activation,
self.response_activation)
# Variable containing both filter and projection matrix
joint_var = self.filter.concat(self.projection_matrix)
# Initialize optimizer
analyze_convergence = getattr(self.params, 'analyze_convergence',
False)
if optimizer == 'GaussNewtonCG':
self.joint_optimizer = GaussNewtonCG(
self.joint_problem,
joint_var,
plotting=(self.params.debug >= 3),
analyze=True,
fig_num=(12, 13, 14))
elif optimizer == 'GradientDescentL2':
self.joint_optimizer = GradientDescentL2(
self.joint_problem,
joint_var,
self.params.optimizer_step_length,
self.params.optimizer_momentum,
plotting=(self.params.debug >= 3),
debug=analyze_convergence,
fig_num=(12, 13))
# Do joint optimization
if isinstance(self.params.init_CG_iter, (list, tuple)):
self.joint_optimizer.run(self.params.init_CG_iter)
else:
self.joint_optimizer.run(self.params.init_CG_iter //
self.params.init_GN_iter,
self.params.init_GN_iter)
# Get back filter and optimizer
len_x = len(self.joint_optimizer.x)
self.filter = self.joint_optimizer.x[:len_x // 2] # w2 in paper
self.projection_matrix = self.joint_optimizer.x[len_x //
2:] # w1 in paper
if analyze_convergence:
opt_name = 'CG' if getattr(self.params, 'CG_optimizer',
True) else 'GD'
for val_name, values in zip(['loss', 'gradient'], [
self.joint_optimizer.losses,
self.joint_optimizer.gradient_mags
]):
val_str = ' '.join(
['{:.8e}'.format(v.item()) for v in values])
file_name = '{}_{}.txt'.format(opt_name, val_name)
with open(file_name, 'a') as f:
f.write(val_str + '\n')
raise RuntimeError('Exiting')
# Re-project samples with the new projection matrix
compressed_samples = self.project_sample(self.init_training_samples,
self.projection_matrix)
for train_samp, init_samp in zip(self.training_samples,
compressed_samples):
for idx in range(init_samp.shape[0]):
train_samp[idx] = init_samp[idx]
self.hinge_mask = None
# Initialize optimizer
self.conv_problem = ConvProblem(self.training_samples, self.y,
self.filter_reg, self.sample_weights,
self.response_activation)
if optimizer == 'GaussNewtonCG':
self.filter_optimizer = ConjugateGradient(
self.conv_problem,
self.filter,
fletcher_reeves=self.params.fletcher_reeves,
direction_forget_factor=self.params.direction_forget_factor,
debug=(self.params.debug >= 3),
fig_num=(12, 13))
elif optimizer == 'GradientDescentL2':
self.filter_optimizer = GradientDescentL2(
self.conv_problem,
self.filter,
self.params.optimizer_step_length,
self.params.optimizer_momentum,
debug=(self.params.debug >= 3),
fig_num=12)
# Transfer losses from previous optimization
if self.params.update_projection_matrix:
self.filter_optimizer.residuals = self.joint_optimizer.residuals
self.filter_optimizer.losses = self.joint_optimizer.losses
if not self.params.update_projection_matrix:
self.filter_optimizer.run(self.params.init_CG_iter)
# Post optimization
self.filter_optimizer.run(self.params.post_init_CG_iter)
self.filter = self.filter_optimizer.x
# Free memory
del self.init_training_samples
if self.params.use_projection_matrix:
del self.joint_problem, self.joint_optimizer
def project_sample(self, x: TensorList, proj_matrix=None):
# Apply projection matrix
if proj_matrix is None:
proj_matrix = self.projection_matrix
with fluid.dygraph.guard():
return operation.conv2d(x.apply(n2p), proj_matrix.apply(n2p)).apply(
self.projection_activation).numpy()
def get_iounet_box(self, pos, sz, sample_pos, sample_scale):
"""All inputs in original image coordinates"""
box_center = (pos - sample_pos) / sample_scale + (self.iou_img_sample_sz
- 1) / 2
box_sz = sz / sample_scale
target_ul = box_center - (box_sz - 1) / 2
return np.concatenate([np.flip(target_ul, 0), np.flip(box_sz, 0)])
def get_iou_features(self):
return self.params.features.get_unique_attribute('iounet_features')
def get_iou_backbone_features(self):
return self.params.features.get_unique_attribute(
'iounet_backbone_features')
def init_iou_net(self):
# Setup IoU net
self.iou_predictor = self.params.features.get_unique_attribute(
'iou_predictor')
# Get target boxes for the different augmentations
self.iou_target_box = self.get_iounet_box(self.pos, self.target_sz,
self.pos.round(),
self.target_scale)
target_boxes = TensorList()
if self.params.iounet_augmentation:
for T in self.transforms:
if not isinstance(
T, (augmentation.Identity, augmentation.Translation,
augmentation.FlipHorizontal,
augmentation.FlipVertical, augmentation.Blur)):
break
target_boxes.append(self.iou_target_box + np.array(
[T.shift[1], T.shift[0], 0, 0]))
else:
target_boxes.append(self.iou_target_box.copy())
target_boxes = np.concatenate(target_boxes.view(1, 4), 0)
# Get iou features
iou_backbone_features = self.get_iou_backbone_features()
# Remove other augmentations such as rotation
iou_backbone_features = TensorList(
[x[:target_boxes.shape[0], ...] for x in iou_backbone_features])
# Extract target feat
with fluid.dygraph.guard():
iou_backbone_features = iou_backbone_features.apply(n2p)
target_boxes = n2p(target_boxes)
target_feat = self.iou_predictor.get_filter(iou_backbone_features,
target_boxes)
self.target_feat = TensorList(
[layers.reduce_mean(x, 0).numpy() for x in target_feat])
if getattr(self.params, 'iounet_not_use_reference', False):
self.target_feat = TensorList([
np.full_like(tf, tf.norm() / tf.numel())
for tf in self.target_feat
])
def optimize_boxes(self, iou_features, init_boxes):
with fluid.dygraph.guard():
# Optimize iounet boxes
init_boxes = np.reshape(init_boxes, (1, -1, 4))
step_length = self.params.box_refinement_step_length
target_feat = self.target_feat.apply(n2p)
iou_features = iou_features.apply(n2p)
output_boxes = n2p(init_boxes)
for f in iou_features:
f.stop_gradient = False
for i_ in range(self.params.box_refinement_iter):
# forward pass
bb_init = output_boxes
bb_init.stop_gradient = False
outputs = self.iou_predictor.predict_iou(target_feat,
iou_features, bb_init)
if isinstance(outputs, (list, tuple)):
outputs = outputs[0]
outputs.backward()
# Update proposal
bb_init_np = bb_init.numpy()
bb_init_gd = bb_init.gradient()
output_boxes = bb_init_np + step_length * bb_init_gd * np.tile(
bb_init_np[:, :, 2:], (1, 1, 2))
output_boxes = n2p(output_boxes)
step_length *= self.params.box_refinement_step_decay
return layers.reshape(output_boxes, (
-1, 4)).numpy(), layers.reshape(outputs, (-1, )).numpy()
def refine_target_box(self,
sample_pos,
sample_scale,
scale_ind,
update_scale=True):
# Initial box for refinement
init_box = self.get_iounet_box(self.pos, self.target_sz, sample_pos,
sample_scale)
# Extract features from the relevant scale
iou_features = self.get_iou_features()
iou_features = TensorList(
[x[scale_ind:scale_ind + 1, ...] for x in iou_features])
init_boxes = np.reshape(init_box, (1, 4)).copy()
rand_fn = lambda a, b: np.random.rand(a, b).astype('float32')
if self.params.num_init_random_boxes > 0:
# Get random initial boxes
square_box_sz = np.sqrt(init_box[2:].prod())
rand_factor = square_box_sz * np.concatenate([
self.params.box_jitter_pos * np.ones(2),
self.params.box_jitter_sz * np.ones(2)
])
minimal_edge_size = init_box[2:].min() / 3
rand_bb = (rand_fn(self.params.num_init_random_boxes, 4) - 0.5
) * rand_factor
new_sz = np.clip(init_box[2:] + rand_bb[:, 2:], minimal_edge_size,
1e10)
new_center = (init_box[:2] + init_box[2:] / 2) + rand_bb[:, :2]
init_boxes = np.concatenate([new_center - new_sz / 2, new_sz], 1)
init_boxes = np.concatenate(
[np.reshape(init_box, (1, 4)), init_boxes])
# Refine boxes by maximizing iou
output_boxes, output_iou = self.optimize_boxes(iou_features, init_boxes)
# Remove weird boxes with extreme aspect ratios
output_boxes[:, 2:] = np.clip(output_boxes[:, 2:], 1, 1e10)
aspect_ratio = output_boxes[:, 2] / output_boxes[:, 3]
keep_ind = (aspect_ratio < self.params.maximal_aspect_ratio) * \
(aspect_ratio > 1 / self.params.maximal_aspect_ratio)
output_boxes = output_boxes[keep_ind, :]
output_iou = output_iou[keep_ind]
# If no box found
if output_boxes.shape[0] == 0:
return
# Take average of top k boxes
k = getattr(self.params, 'iounet_k', 5)
topk = min(k, output_boxes.shape[0])
inds = np.argsort(-output_iou)[:topk]
predicted_box = np.mean(output_boxes[inds, :], axis=0)
predicted_iou = np.mean(
np.reshape(output_iou, (-1, 1))[inds, :], axis=0)
# Update position
new_pos = predicted_box[:2] + predicted_box[2:] / 2 - (
self.iou_img_sample_sz - 1) / 2
new_pos = np.flip(new_pos, 0) * sample_scale + sample_pos
new_target_sz = np.flip(predicted_box[2:], 0) * sample_scale
new_scale = np.sqrt(
np.prod(new_target_sz) / np.prod(self.base_target_sz))
self.pos_iounet = new_pos.copy()
if getattr(self.params, 'use_iounet_pos_for_learning', True):
self.pos = new_pos.copy()
self.target_sz = new_target_sz
if update_scale:
self.target_scale = new_scale
def localize_advanced(self, scores):
"""Does the advanced localization with hard negative detection and target not found."""
sz = scores.shape[-2:]
if self.output_window is not None and getattr(
self.params, 'perform_hn_without_windowing', False):
scores_orig = scores.copy()
scores_orig = np.concatenate([
scores_orig[..., (sz[0] + 1) // 2:, :],
scores_orig[..., :(sz[0] + 1) // 2, :]
], -2)
scores_orig = np.concatenate([
scores_orig[..., :, (sz[1] + 1) // 2:],
scores_orig[..., :, :(sz[1] + 1) // 2]
], -1)
scores *= self.output_window
# Shift scores back
scores = np.concatenate([
scores[..., (sz[0] + 1) // 2:, :], scores[..., :(sz[0] + 1) // 2, :]
], -2)
scores = np.concatenate([
scores[..., :, (sz[1] + 1) // 2:], scores[..., :, :(sz[1] + 1) // 2]
], -1)
# Find maximum
max_score1, max_disp1 = dcf.max2d(scores)
scale_ind = np.argmax(max_score1, axis=0)[0]
max_score1 = max_score1[scale_ind]
max_disp1 = np.reshape(max_disp1[scale_ind].astype('float32'), (-1))
target_disp1 = max_disp1 - self.output_sz // 2
translation_vec1 = target_disp1 * (self.img_support_sz /
self.output_sz) * self.target_scale
if max_score1 < self.params.target_not_found_threshold:
return translation_vec1, scale_ind, scores, 'not_found'
if self.output_window is not None and getattr(
self.params, 'perform_hn_without_windowing', False):
scores = scores_orig
# Mask out target neighborhood
target_neigh_sz = self.params.target_neighborhood_scale * self.target_sz / self.target_scale
tneigh_top = int(max(round(max_disp1[0] - target_neigh_sz[0] / 2), 0))
tneigh_bottom = int(
min(round(max_disp1[0] + target_neigh_sz[0] / 2 + 1), sz[0]))
tneigh_left = int(max(round(max_disp1[1] - target_neigh_sz[1] / 2), 0))
tneigh_right = int(
min(round(max_disp1[1] + target_neigh_sz[1] / 2 + 1), sz[1]))
scores_masked = scores[scale_ind:scale_ind + 1, ...].copy()
scores_masked[..., tneigh_top:tneigh_bottom, tneigh_left:
tneigh_right] = 0
# Find new maximum
max_score2, max_disp2 = dcf.max2d(scores_masked)
max_disp2 = np.reshape(max_disp2.astype('float32'), (-1))
target_disp2 = max_disp2 - self.output_sz // 2
translation_vec2 = target_disp2 * (self.img_support_sz /
self.output_sz) * self.target_scale
# Handle the different cases
if max_score2 > self.params.distractor_threshold * max_score1:
disp_norm1 = np.sqrt(np.sum(target_disp1**2))
disp_norm2 = np.sqrt(np.sum(target_disp2**2))
disp_threshold = self.params.dispalcement_scale * math.sqrt(
sz[0] * sz[1]) / 2
if disp_norm2 > disp_threshold and disp_norm1 < disp_threshold:
return translation_vec1, scale_ind, scores, 'hard_negative'
if disp_norm2 < disp_threshold and disp_norm1 > disp_threshold:
return translation_vec2, scale_ind, scores, 'hard_negative'
if disp_norm2 > disp_threshold and disp_norm1 > disp_threshold:
return translation_vec1, scale_ind, scores, 'uncertain'
# If also the distractor is close, return with highest score
return translation_vec1, scale_ind, scores, 'uncertain'
if max_score2 > self.params.hard_negative_threshold * max_score1 and max_score2 > self.params.target_not_found_threshold:
return translation_vec1, scale_ind, scores, 'hard_negative'
return translation_vec1, scale_ind, scores, None
PaddleCV/tracking/pytracking/tracker/atom/optim.py 0 → 100644
浏览文件 @ 7ec3ec11
import numpy as np
from paddle.fluid import layers
from paddle import fluid
from pytracking.libs import optimization, TensorList, operation
from pytracking.libs.paddle_utils import PTensor, broadcast_op, n2p, static_identity
import math
def stack_input(e):
if isinstance(e, list):
e_exist = []
for x in e:
if x is not None:
e_exist.append(x)
e = np.stack(e_exist)
else:
assert isinstance(e, np.ndarray)
if len(e.shape) == 1:
e = np.expand_dims(e, 1)
return e
class FactorizedConvProblem(optimization.L2Problem):
def __init__(self,
training_samples: TensorList,
y: TensorList,
filter_reg: TensorList,
projection_reg,
params,
sample_weights: TensorList,
projection_activation,
response_activation):
self.training_samples = training_samples
self.y = y
self.filter_reg = filter_reg
self.sample_weights = sample_weights
self.params = params
self.projection_reg = projection_reg
self.projection_activation = projection_activation
self.response_activation = response_activation
self.diag_M = self.filter_reg.concat(projection_reg)
self.inputs_dict = {}
# stack tensors
self.training_samples_stack = None
self.y_stack = None
self.sample_weights_stack = None
def get_inputs(self, scope=''):
if scope not in self.inputs_dict:
training_samples_p = TensorList([
fluid.layers.data(
'{}training_samples_{}'.format(scope, idx),
shape=[None] + list(v[0].shape),
stop_gradient=False,
append_batch_size=False)
for idx, v in enumerate(self.training_samples)
])
y_p = TensorList([
fluid.layers.data(
'{}y_{}'.format(scope, idx),
shape=[None] + list(v[0].shape),
stop_gradient=False,
append_batch_size=False) for idx, v in enumerate(self.y)
])
sample_weights_p = TensorList([
fluid.layers.data(
'{}sample_weights_{}'.format(scope, idx),
shape=[None, 1],
stop_gradient=False,
append_batch_size=False)
for idx, v in enumerate(self.sample_weights)
])
self.inputs_dict[scope] = (training_samples_p, y_p,
sample_weights_p)
return self.inputs_dict[scope]
def get_feed_dict(self, scope=''):
if self.training_samples_stack is None or self.y_stack is None or self.sample_weights_stack is None:
self.training_samples_stack = self.training_samples.apply(
stack_input)
self.y_stack = self.y.apply(stack_input)
self.sample_weights_stack = self.sample_weights.apply(stack_input)
feed_dict = {}
for idx, v in enumerate(self.training_samples_stack):
feed_dict['{}training_samples_{}'.format(scope, idx)] = v
for idx, v in enumerate(self.y_stack):
feed_dict['{}y_{}'.format(scope, idx)] = v
for idx, v in enumerate(self.sample_weights_stack):
feed_dict['{}sample_weights_{}'.format(scope, idx)] = v
return feed_dict
def __call__(self, x: TensorList, scope=''):
"""
Compute residuals
:param x: [filters, projection_matrices]
:return: [data_terms, filter_regularizations, proj_mat_regularizations]
"""
training_samples, y, samples_weights = self.get_inputs(scope)
filter = x[:len(x) // 2] # w2 in paper
P = x[len(x) // 2:] # w1 in paper
# Do first convolution
compressed_samples = operation.conv1x1(
training_samples, P).apply(self.projection_activation)
# Do second convolution
residuals = operation.conv2d(
compressed_samples, filter,
mode='same').apply(self.response_activation)
# Compute data residuals
residuals = residuals - y
residuals = residuals * samples_weights.sqrt()
# Add regularization for projection matrix
# TODO: remove static_identity
# for now, this is needed. Otherwise the gradient is None
residuals.extend(
filter.apply(static_identity) * self.filter_reg.apply(math.sqrt))
# Add regularization for projection matrix
residuals.extend(
P.apply(static_identity) * self.projection_reg.apply(math.sqrt))
return residuals
def ip_input(self, a: TensorList, b: TensorList):
# return a.reshape(-1) @ b.reshape(-1)
num = len(a) // 2 # Number of filters
a_filter = a[:num]
b_filter = b[:num]
a_P = a[num:]
b_P = b[num:]
# Filter inner product
ip_out = a_filter.reshape(-1) @b_filter.reshape(-1)
# ip_out = operation.conv2d(a_filter, b_filter).view(-1)
# Add projection matrix part
ip_out += a_P.reshape(-1) @b_P.reshape(-1)
# ip_out += operation.conv2d(a_P.view(1, -1, 1, 1), b_P.view(1, -1, 1, 1)).view(-1)
# Have independent inner products for each filter
return ip_out.concat(ip_out.clone())
def M1(self, x: TensorList):
return x / self.diag_M
class ConvProblem(optimization.L2Problem):
def __init__(self,
training_samples: TensorList,
y: TensorList,
filter_reg: TensorList,
sample_weights: TensorList,
response_activation):
self.training_samples = training_samples
self.y = y
self.filter_reg = filter_reg
self.sample_weights = sample_weights
self.response_activation = response_activation
self.inputs_dict = {}
# stack tensors
self.training_samples_stack = None
self.y_stack = None
self.sample_weights_stack = None
def get_feed_dict(self, scope=''):
if self.training_samples_stack is None or self.y_stack is None or self.sample_weights_stack is None:
self.training_samples_stack = self.training_samples.apply(
stack_input)
self.y_stack = self.y.apply(stack_input)
self.sample_weights_stack = self.sample_weights.apply(stack_input)
feed_dict = {}
for idx, v in enumerate(self.training_samples_stack):
feed_dict['{}training_samples_{}'.format(scope, idx)] = v
for idx, v in enumerate(self.y_stack):
feed_dict['{}y_{}'.format(scope, idx)] = v
for idx, v in enumerate(self.sample_weights_stack):
feed_dict['{}sample_weights_{}'.format(scope, idx)] = v
return feed_dict
def get_inputs(self, scope=''):
if scope not in self.inputs_dict:
training_samples_p = TensorList([
fluid.layers.data(
'{}training_samples_{}'.format(scope, idx),
shape=[None] + list(v[0].shape),
stop_gradient=False,
append_batch_size=False)
for idx, v in enumerate(self.training_samples)
])
y_p = TensorList([
fluid.layers.data(
'{}y_{}'.format(scope, idx),
shape=[None] + list(v[0].shape),
stop_gradient=False,
append_batch_size=False) for idx, v in enumerate(self.y)
])
sample_weights_p = TensorList([
fluid.layers.data(
'{}sample_weights_{}'.format(scope, idx),
shape=[None] + list(v[0].shape),
stop_gradient=False,
append_batch_size=False)
for idx, v in enumerate(self.sample_weights)
])
self.inputs_dict[scope] = (training_samples_p, y_p,
sample_weights_p)
return self.inputs_dict[scope]
def __call__(self, x: TensorList, scope=''):
"""
Compute residuals
:param x: [filters]
:return: [data_terms, filter_regularizations]
"""
training_samples, y, samples_weights = self.get_inputs(scope)
# Do convolution and compute residuals
residuals = operation.conv2d(
training_samples, x, mode='same').apply(self.response_activation)
residuals = residuals - y
residuals = residuals * samples_weights.sqrt()
# Add regularization for projection matrix
residuals.extend(
x.apply(static_identity) * self.filter_reg.apply(math.sqrt))
return residuals
def ip_input(self, a: TensorList, b: TensorList):
return a.reshape(-1) @b.reshape(-1)
# return (a * b).sum()
# return operation.conv2d(a, b).view(-1)
PaddleCV/tracking/pytracking/tracker/base/basetracker.py 0 → 100644
浏览文件 @ 7ec3ec11
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import cv2 as cv
import time
import os
class BaseTracker:
"""Base class for all trackers."""
def __init__(self, params):
self.params = params
def initialize(self, image, state, class_info=None):
"""Overload this function in your tracker. This should initialize the model."""
raise NotImplementedError
def track(self, image):
"""Overload this function in your tracker. This should track in the frame and update the model."""
raise NotImplementedError
def track_sequence(self, sequence):
"""Run tracker on a sequence."""
# Initialize
image = self._read_image(sequence.frames[0])
times = []
start_time = time.time()
self.initialize(image, sequence.init_state)
init_time = getattr(self, 'time', time.time() - start_time)
times.append(init_time)
if self.params.visualization:
self.init_visualization()
self.visualize(image, sequence.init_state)
# Track
tracked_bb = [sequence.init_state]
for frame in sequence.frames[1:]:
image = self._read_image(frame)
start_time = time.time()
state = self.track(image)
times.append(time.time() - start_time)
tracked_bb.append(state)
if self.params.visualization:
self.visualize(image, state)
return tracked_bb, times
def track_videofile(self, videofilepath, optional_box=None):
"""Run track with a video file input."""
assert os.path.isfile(videofilepath), "Invalid param {}".format(
videofilepath)
", videofilepath must be a valid videofile"
if hasattr(self, 'initialize_features'):
self.initialize_features()
cap = cv.VideoCapture(videofilepath)
display_name = 'Display: ' + self.params.tracker_name
cv.namedWindow(display_name, cv.WINDOW_NORMAL | cv.WINDOW_KEEPRATIO)
cv.resizeWindow(display_name, 960, 720)
success, frame = cap.read()
cv.imshow(display_name, frame)
if success is not True:
print("Read frame from {} failed.".format(videofilepath))
exit(-1)
if optional_box is not None:
assert isinstance(optional_box, list, tuple)
assert len(optional_box) == 4, "valid box's foramt is [x,y,w,h]"
self.initialize(frame, optional_box)
else:
while True:
# cv.waitKey()
frame_disp = frame.copy()
cv.putText(frame_disp, 'Select target ROI and press ENTER',
(20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1.5,
(0, 0, 0), 1)
x, y, w, h = cv.selectROI(
display_name, frame_disp, fromCenter=False)
init_state = [x, y, w, h]
self.initialize(frame, init_state)
break
while True:
ret, frame = cap.read()
if frame is None:
return
frame_disp = frame.copy()
# Draw box
state = self.track(frame)
state = [int(s) for s in state]
cv.rectangle(frame_disp, (state[0], state[1]),
(state[2] + state[0], state[3] + state[1]),
(0, 255, 0), 5)
font_color = (0, 0, 0)
cv.putText(frame_disp, 'Tracking!', (20, 30),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
cv.putText(frame_disp, 'Press r to reset', (20, 55),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
cv.putText(frame_disp, 'Press q to quit', (20, 80),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
# Display the resulting frame
cv.imshow(display_name, frame_disp)
key = cv.waitKey(1)
if key == ord('q'):
break
elif key == ord('r'):
ret, frame = cap.read()
frame_disp = frame.copy()
cv.putText(frame_disp, 'Select target ROI and press ENTER',
(20, 30), cv.FONT_HERSHEY_COMPLEX_SMALL, 1.5,
(0, 0, 0), 1)
cv.imshow(display_name, frame_disp)
x, y, w, h = cv.selectROI(
display_name, frame_disp, fromCenter=False)
init_state = [x, y, w, h]
self.initialize(frame, init_state)
# When everything done, release the capture
cap.release()
cv.destroyAllWindows()
def track_webcam(self):
"""Run tracker with webcam."""
class UIControl:
def __init__(self):
self.mode = 'init' # init, select, track
self.target_tl = (-1, -1)
self.target_br = (-1, -1)
self.mode_switch = False
def mouse_callback(self, event, x, y, flags, param):
if event == cv.EVENT_LBUTTONDOWN and self.mode == 'init':
self.target_tl = (x, y)
self.target_br = (x, y)
self.mode = 'select'
self.mode_switch = True
elif event == cv.EVENT_MOUSEMOVE and self.mode == 'select':
self.target_br = (x, y)
elif event == cv.EVENT_LBUTTONDOWN and self.mode == 'select':
self.target_br = (x, y)
self.mode = 'track'
self.mode_switch = True
def get_tl(self):
return self.target_tl if self.target_tl[0] < self.target_br[
0] else self.target_br
def get_br(self):
return self.target_br if self.target_tl[0] < self.target_br[
0] else self.target_tl
def get_bb(self):
tl = self.get_tl()
br = self.get_br()
bb = [tl[0], tl[1], br[0] - tl[0], br[1] - tl[1]]
return bb
ui_control = UIControl()
cap = cv.VideoCapture(0)
display_name = 'Display: ' + self.params.tracker_name
cv.namedWindow(display_name, cv.WINDOW_NORMAL | cv.WINDOW_KEEPRATIO)
cv.resizeWindow(display_name, 960, 720)
cv.setMouseCallback(display_name, ui_control.mouse_callback)
if hasattr(self, 'initialize_features'):
self.initialize_features()
while True:
# Capture frame-by-frame
ret, frame = cap.read()
frame_disp = frame.copy()
if ui_control.mode == 'track' and ui_control.mode_switch:
ui_control.mode_switch = False
init_state = ui_control.get_bb()
self.initialize(frame, init_state)
# Draw box
if ui_control.mode == 'select':
cv.rectangle(frame_disp,
ui_control.get_tl(),
ui_control.get_br(), (255, 0, 0), 2)
elif ui_control.mode == 'track':
state = self.track(frame)
state = [int(s) for s in state]
cv.rectangle(frame_disp, (state[0], state[1]),
(state[2] + state[0], state[3] + state[1]),
(0, 255, 0), 5)
# Put text
font_color = (0, 0, 0)
if ui_control.mode == 'init' or ui_control.mode == 'select':
cv.putText(frame_disp, 'Select target', (20, 30),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
cv.putText(frame_disp, 'Press q to quit', (20, 55),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
elif ui_control.mode == 'track':
cv.putText(frame_disp, 'Tracking!', (20, 30),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
cv.putText(frame_disp, 'Press r to reset', (20, 55),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
cv.putText(frame_disp, 'Press q to quit', (20, 80),
cv.FONT_HERSHEY_COMPLEX_SMALL, 1, font_color, 1)
# Display the resulting frame
cv.imshow(display_name, frame_disp)
key = cv.waitKey(1)
if key == ord('q'):
break
elif key == ord('r'):
ui_control.mode = 'init'
# When everything done, release the capture
cap.release()
cv.destroyAllWindows()
def reset_tracker(self):
pass
def press(self, event):
if event.key == 'p':
self.pause_mode = not self.pause_mode
print("Switching pause mode!")
elif event.key == 'r':
self.reset_tracker()
print("Resetting target pos to gt!")
def init_visualization(self):
# plt.ion()
self.pause_mode = False
self.fig, self.ax = plt.subplots(1)
self.fig.canvas.mpl_connect('key_press_event', self.press)
plt.tight_layout()
def visualize(self, image, state):
self.ax.cla()
self.ax.imshow(image)
rect = patches.Rectangle(
(state[0], state[1]),
state[2],
state[3],
linewidth=1,
edgecolor='r',
facecolor='none')
self.ax.add_patch(rect)
if hasattr(self, 'gt_state') and False:
gt_state = self.gt_state
rect = patches.Rectangle(
(gt_state[0], gt_state[1]),
gt_state[2],
gt_state[3],
linewidth=1,
edgecolor='g',
facecolor='none')
self.ax.add_patch(rect)
self.ax.set_axis_off()
self.ax.axis('equal')
plt.draw()
plt.pause(0.001)
if self.pause_mode:
plt.waitforbuttonpress()
def _read_image(self, image_file: str):
return cv.cvtColor(cv.imread(image_file), cv.COLOR_BGR2RGB)
PaddleCV/tracking/pytracking/tracker/siamfc/__init__.py 0 → 100644
浏览文件 @ 7ec3ec11
from .siamfc import SiamFC
def get_tracker_class():
return SiamFC
PaddleCV/tracking/pytracking/tracker/siamfc/eval_siamfc_otb.py 0 → 100644
浏览文件 @ 7ec3ec11
import os
import numpy as np
from PIL import Image
import os.path as osp
import sys
CURRENT_DIR = osp.dirname(__file__)
sys.path.append(osp.join(CURRENT_DIR, '..', '..', '..'))
from pytracking.pysot_toolkit.utils import success_overlap, success_error
import json
from pytracking.tracker.siamfc.siamfc import SiamFC
from tqdm import tqdm
from pytracking.parameter.siamfc.default import parameters
class ValidOTB(SiamFC):
def __init__(self, dataset_root, dataset_name, params):
super(ValidOTB, self).__init__(params)
"""
dataset_root: the root directory of dataset
dataset_name: the name of OTB dataste, [CVPR2013, OTB50, OTB100]
"""
self.params = self.params
self.root_path = dataset_root
if not os.path.exists(self.root_path):
raise Exception("'{}' does not exists".format(self.root_path))
dataset_list = ['CVPR13', 'OTB2013', 'OTB100', 'OTB50']
if dataset_name not in dataset_list:
raise Exception("ValidOTB's dataset_name can only be one of {}".
format(dataset_list))
if dataset_name == 'OTB2013':
dataset_name = 'CVPR13'
self.dataset_name = dataset_name
self.otb2013_json = os.path.join(self.root_path, dataset_name + '.json')
self.meta_data = json.load(open(self.otb2013_json, 'rb'))
self.video_name = list(self.meta_data.keys())
def inference(self, epoch):
gtbb = []
prebb = []
""" add save dir """
save_dir = "./eval_otb13/epoch_" + str(epoch)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# load videos
pbar = tqdm(
self.meta_data.keys(),
desc='loading ' + self.dataset_name,
ncols=100)
for idx, vid in enumerate(pbar):
pbar.set_postfix_str(vid)
gt_boxs = self.meta_data[vid]['gt_rect']
start_frame, end_frame = 0, len(gt_boxs)
img_list = self.meta_data[vid]['img_names']
assert len(img_list) == len(gt_boxs)
gt_box_list = []
pre_box_list = []
for i in range(start_frame, end_frame):
img = Image.open(os.path.join(self.root_path, img_list[i]))
if len(img.size) < 3 or img.size[-1] == 1:
img = img.convert('RGB')
gt_box = gt_boxs[i - start_frame]
if i == start_frame:
self.initialize(image=img, state=gt_box)
pre_box_list.append(gt_box)
gt_box_list.append(gt_box)
continue
else:
pre_box = self.track(img)
pre_box_list.append(list(pre_box))
gt_box_list.append(gt_box)
gtbb += gt_box_list
prebb += pre_box_list
""" add save_dir"""
vid_save_dir = os.path.join(save_dir, vid + '.txt')
with open(vid_save_dir, 'w') as f:
outputs = []
for res in pre_box_list:
outputs.append('{},{},{},{}'.format(res[0], res[1], res[2],
res[3]))
f.write('\n'.join(outputs))
auc = success_overlap(np.array(gtbb), np.array(prebb), len(gtbb))
thresholds = np.arange(0, 51, 1)
gt_center = self.convert_bb_to_center(np.array(gtbb))
tracker_center = self.convert_bb_to_center(np.array(prebb))
precision = success_error(
np.array(gt_center),
np.array(tracker_center), thresholds, len(gtbb))
print("####AUC:{}, Precision:{}".format(
np.mean(auc), np.mean(precision)))
return np.mean(auc), np.mean(precision)
def convert_bb_to_center(self, bboxes):
return np.array([(bboxes[:, 0] + (bboxes[:, 2] - 1) / 2),
(bboxes[:, 1] + (bboxes[:, 3] - 1) / 2)]).T
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint',
type=str,
default="./checkpoint/",
help="the path of saved siamfc params file")
parser.add_argument(
'--dataset_dir',
type=str,
default="/paddle/Datasets/OTB100",
help="the path of OTB dataset")
parser.add_argument(
'--dataset_name',
type=str,
default="CVPR13",
help="can only be one of [CVPR13, OTB2013, OTB50, OTB100]")
parser.add_argument(
'--start_epoch',
type=int,
default=1,
help="evaluate from start_epoch epoch, greater than 1")
parser.add_argument(
'--end_epoch',
type=int,
default=50,
help="evaluate ends at end_epoch epoch, smaller than 50 ")
args = parser.parse_args()
if __name__ == '__main__':
params = parameters()
params.net_path = args.checkpoint
start_epoch = args.start_epoch
end_epoch = args.end_epoch
assert start_epoch >= 1 and end_epoch <= 50 and start_epoch < end_epoch
best_auc, best_epoch = 0, start_epoch
for i in range(start_epoch, end_epoch, 1):
params.net_path = os.path.join(args.checkpoint, "SiamNet_ep%004d" % i)
valid = ValidOTB(
dataset_root=args.dataset_dir,
dataset_name=args.dataset_name,
params=params)
auc, precision = valid.inference(epoch=i)
if auc > best_auc:
best_auc = auc
best_epoch = i
print("####Best AUC is {}, corresponding epoch is {}".format(
best_auc, best_epoch))
PaddleCV/tracking/pytracking/tracker/siamfc/eval_siamfc_vot.py 0 → 100644
浏览文件 @ 7ec3ec11
import os
import numpy as np
from PIL import Image
import os.path as osp
import sys
CURRENT_DIR = osp.dirname(__file__)
sys.path.append(osp.join(CURRENT_DIR, '..', '..', '..'))
import json
from pytracking.tracker.siamfc.siamfc import SiamFC
from tqdm import tqdm
from pytracking.parameter.siamfc.default import parameters
class ValidVOT(SiamFC):
def __init__(self, dataset_root, dataset_name, params):
super(ValidVOT, self).__init__(params)
"""
dataset_root: the root directory of dataset
dataset_name: the name of VOT dataste, [VOt2015, VOT2018, ...]
"""
self.params = self.params
self.root_path = dataset_root
if not os.path.exists(self.root_path):
raise Exception("'{}' does not exists".format(self.root_path))
dataset_list = ['VOT2015', 'VOT2018']
if dataset_name not in dataset_list:
raise Exception("ValidVOT's dataset_name can only be one of {}".
format(dataset_list))
self.dataset_name = dataset_name
self.vot2013_json = os.path.join(self.root_path, dataset_name + '.json')
# self.otb2013_json = "/paddle/Datasets/OTB100/CVPR13.json"
self.meta_data = json.load(open(self.vot2013_json, 'rb'))
self.video_name = list(self.meta_data.keys())
def inference_reinit(self, epoch, start_frame=0):
# video-wised
vid_num = len(self.video_name)
vid_ious = np.zeros(vid_num)
vid_length = np.zeros(vid_num)
fail_num = np.zeros(vid_num)
burn_in_period = 5
pbar = tqdm(
self.meta_data.keys(),
desc='loading ' + self.dataset_name,
ncols=100)
for idx, vid in enumerate(pbar):
pbar.set_postfix_str(vid)
gt_boxs = self.meta_data[vid]['gt_rect']
img_list = self.meta_data[vid]['img_names']
imgs_num = len(img_list)
gt_box_list = []
pre_box_list = []
valid_frames_num = imgs_num - start_frame
step = start_frame
reinit = True
re_init_frame = step
while step < imgs_num:
img = Image.open(os.path.join(self.root_path, img_list[step]))
gt_box = list(map(float, self.region_to_bbox(gt_boxs[step])))
if reinit:
# the tracker was initialized
# five frames after the failure
self.initialize(img, gt_box)
reinit = False
# print("reinit, vid: {}, step: {}, failnum: {}".format(vid, step, fail_num[idx]))
continue
pre_box = self.track(img)
if step - re_init_frame < 10:
# burn in period
step += 1
valid_frames_num -= 1
continue
pre_box_list.append(list(pre_box))
gt_box_list.append(gt_box)
iou = self._compute_iou(pre_box, gt_box)
vid_ious[idx] += iou
if iou == 0.:
reinit = True
fail_num[idx] += 1
# the tracker was initialized
# five frames after the failure
step += burn_in_period
re_init_frame = step
valid_frames_num -= burn_in_period
step += 1
vid_length[idx] = valid_frames_num
#print("idx: {}, vid: {}, failure: {}, miou: {}\n".format(idx, vid, fail_num[idx],
# vid_ious[idx]/valid_frames_num))
acc = np.sum(vid_ious) / np.sum(vid_length)
print("##########Evaluation##########")
print("##acc = {}".format(acc))
print("##failure = {}".format(np.sum(fail_num)))
return acc, np.sum(fail_num)
def _compute_iou(self, box1, box2):
"""
computing IoU
print("acc shape", acc.shape, "vid_length shape: ", vid_length.shape)
print("acc shape", acc.shape, "vid_length shape: ", vid_length.shape)
:param rec1: (x0, y0, w, h), which reflects
(top, left, bottom, right)
:param rec2: (x0, y0, w, h)
:return: scala value of IoU
"""
rec1 = box1
rec2 = box2
# computing area of each rectangles
S_rec1 = (rec1[2] + 1) * (rec1[3] + 1)
S_rec2 = (rec2[2] + 1) * (rec2[3] + 1)
# computing the sum_area
sum_area = S_rec1 + S_rec2
# find the each edge of intersect rectangle
left_line = max(rec1[1], rec2[1])
right_line = min(rec1[3] + rec1[1], rec2[3] + rec2[1])
top_line = max(rec1[0], rec2[0])
bottom_line = min(rec1[2] + rec2[0], rec2[2] + rec2[0])
# judge if there is an intersect
if left_line >= right_line or top_line >= bottom_line:
iou = 0.
else:
intersect = (right_line - left_line + 1) * (
bottom_line - top_line + 1)
iou = (intersect / (sum_area - intersect)) * 1.0
assert iou >= 0
assert iou <= 1.01
return iou
def region_to_bbox(self, region, center=False):
n = len(region)
region = np.array(region)
assert n == 4 or n == 8, (
'GT region format is invalid, should have 4 or 8 entries.')
# we assume the grountruth bounding boxes are saved with 0-indexing
def _rect(region, center):
if center:
x = region[0]
y = region[1]
w = region[2]
h = region[3]
cx = x + w / 2
cy = y + h / 2
return cx, cy, w, h
else:
region[0] -= 1
region[1] -= 1
return region
def _poly(region, center):
cx = np.mean(region[::2])
cy = np.mean(region[1::2])
x1 = np.min(region[::2])
x2 = np.max(region[::2])
y1 = np.min(region[1::2])
y2 = np.max(region[1::2])
A1 = np.linalg.norm(region[0:2] - region[2:4]) * np.linalg.norm(
region[2:4] - region[4:6])
A2 = (x2 - x1) * (y2 - y1)
s = np.sqrt(A1 / A2)
w = s * (x2 - x1) + 1
h = s * (y2 - y1) + 1
if center:
return cx, cy, w, h
else:
return cx - w / 2, cy - h / 2, w, h
if n == 4:
return _rect(region, center)
else:
return _poly(region, center)
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint',
type=str,
default="./checkpoint/",
help="the path of saved siamfc params file")
parser.add_argument(
'--dataset_dir',
type=str,
default="/paddle/Datasets/VOT2015",
help="the path of VOT dataset")
parser.add_argument(
'--dataset_name',
type=str,
default="VOT2015",
help="can only be one of [VOT2015, VOT2018]")
parser.add_argument(
'--start_epoch',
type=int,
default=1,
help="evaluate from start_epoch epoch, greater than 1")
parser.add_argument(
'--end_epoch',
type=int,
default=50,
help="evaluate ends at end_epoch epoch, smaller than 50 ")
args = parser.parse_args()
if __name__ == '__main__':
params = parameters()
params.net_path = args.checkpoint
start_epoch = args.start_epoch
end_epoch = args.end_epoch
assert start_epoch >= 1 and end_epoch <= 50 and start_epoch < end_epoch
best_acc, best_failure, best_epoch = 0, 100, start_epoch
for i in range(start_epoch, end_epoch, 2):
params.net_path = os.path.join(args.checkpoint, "SiamNet_ep%004d" % i)
valid = ValidVOT(
dataset_root=args.dataset_dir,
dataset_name=args.dataset_name,
params=params)
acc, failure = valid.inference_reinit(epoch=i)
print("####Epoch: {}, ACC: {}, Failure: {}".format(i, acc, failure))
if acc > best_acc and failure <= 84:
best_acc = acc
best_epoch = i
print("####Best ACC: {}, Failure: {}, corresponding epoch: {}".
format(best_acc, failure, best_epoch))
PaddleCV/tracking/pytracking/tracker/siamfc/siamfc.py 0 → 100644
浏览文件 @ 7ec3ec11
import time
import numpy as np
import paddle.fluid as fluid
from paddle.fluid import dygraph
from pytracking.tracker.base.basetracker import BaseTracker
from ltr.models.siamese.siam import siamfc_alexnet
import cv2
# for debug
from pytracking.parameter.siamfc.default import parameters
class SiamFC(BaseTracker):
def __init__(self, params=parameters()):
self.params = params
self.model_initializer()
def initialize_features(self):
if not getattr(self, 'features_initialized', False):
self.params.features.initialize()
self.features_initialized = True
def model_initializer(self):
import os
net_path = self.params.net_path
if net_path is None:
net_path = self.params.features.features[0].net_path
if not os.path.exists(net_path):
raise Exception("not found {}".format(net_path))
with dygraph.guard():
self.model = siamfc_alexnet(backbone_is_test=True)
#state_dict, _ = fluid.load_dygraph(net_path)
weight_params, opt_params = fluid.load_dygraph(net_path)
state_dict = self.model.state_dict()
for k1, k2 in zip(state_dict.keys(), weight_params.keys()):
if list(state_dict[k1].shape) == list(weight_params[k2].shape):
state_dict[k1].set_value(weight_params[k2])
else:
raise Exception("ERROR, shape not match")
self.model.load_dict(state_dict)
self.model.eval()
def _cosine_window(self, size):
"""
get the cosine window
"""
cos_window = np.hanning(int(size[0]))[:, np.newaxis].dot(
np.hanning(int(size[1]))[np.newaxis, :])
cos_window = cos_window.astype(np.float32)
cos_window /= np.sum(cos_window)
return cos_window
def initialize(self, image, state, *args, **kwargs):
# state (x, y, w, h)
# Initialize some stuff
self.frame_num = 1
self.time = 0
# Get position and size
box = state
image = np.asarray(image)
# convert box to 0-indexed and center based [y, x, h, w]
box = np.array(
[
box[1] - 1 + (box[3] - 1) / 2, box[0] - 1 + (box[2] - 1) / 2,
box[3], box[2]
],
dtype=np.float32)
self.center, self.target_sz = box[:2], box[2:]
# create hanning window
self.upscale_sz = self.params.response_up * self.params.response_sz
self.hann_window = np.outer(
np.hanning(self.upscale_sz), np.hanning(self.upscale_sz))
self.hann_window /= self.hann_window.sum()
# search scale factors
self.scale_factors = self.params.scale_step**np.linspace(
-(self.params.scale_num // 2), self.params.scale_num // 2,
self.params.scale_num)
# exemplar and search sizes
context = self.params.context * np.sum(self.target_sz)
self.z_sz = np.sqrt(np.prod(self.target_sz + context))
self.x_sz = self.z_sz * \
self.params.instance_sz / self.params.exemplar_sz
# exemplar image
self.avg_color = np.mean(image, axis=(0, 1))
exemplar_image = self._crop_and_resize(
image,
self.center,
self.z_sz,
out_size=self.params.exemplar_sz,
pad_color=self.avg_color)
self.exemplar_img_1s = exemplar_image[np.newaxis, :, :, :]
self.exemplar_img = np.transpose(self.exemplar_img_1s,
[0, 3, 1, 2]).astype(np.float32)
self.exemplar_img = np.repeat(
self.exemplar_img, self.params.scale_num, axis=0)
def _crop_and_resize(self, image, center, size, out_size, pad_color):
# convert box to corners (0-indexed)
size = round(size)
corners = np.concatenate((np.round(center - (size - 1) / 2),
np.round(center - (size - 1) / 2) + size))
corners = np.round(corners).astype(int)
# pad image if necessary
pads = np.concatenate((-corners[:2], corners[2:] - image.shape[:2]))
npad = max(0, int(pads.max()))
if npad > 0:
image = cv2.copyMakeBorder(
image,
npad,
npad,
npad,
npad,
cv2.BORDER_CONSTANT,
value=pad_color)
# crop image patch
corners = (corners + npad).astype(int)
patch = image[corners[0]:corners[2], corners[1]:corners[3]]
# resize to out_size
patch = cv2.resize(patch, (out_size, out_size))
return patch
def track(self, image):
#print("## track, input image shape:", image.shape)
self.frame_num += 1
image = np.asarray(image)
# search images
instance_images = [
self._crop_and_resize(
image,
self.center,
self.x_sz * f,
out_size=self.params.instance_sz,
pad_color=self.avg_color) for f in self.scale_factors
]
instance_images = np.stack(instance_images, axis=0)
instance_images = np.transpose(instance_images,
[0, 3, 1, 2]).astype(np.float32)
# calculate response
# exemplar features
with fluid.dygraph.guard():
instance_images = fluid.dygraph.to_variable(instance_images)
self.exemplar_img = fluid.dygraph.to_variable(self.exemplar_img)
responses = self.model(self.exemplar_img, instance_images)
responses = responses.numpy()
responses = np.squeeze(responses, axis=1)
# upsample responses and penalize scale changes
responses = np.stack(
[
cv2.resize(
t, (self.upscale_sz, self.upscale_sz),
interpolation=cv2.INTER_CUBIC) for t in responses
],
axis=0)
responses[:self.params.scale_num // 2] *= self.params.scale_penalty
responses[self.params.scale_num // 2 + 1:] *= self.params.scale_penalty
# peak scale
scale_list = np.amax(responses, axis=(1, 2))
scale_id = np.argmax(scale_list)
#scale_id = np.argmax(np.amax(responses, axis=(1, 2)))
# peak location
response = responses[scale_id]
response -= response.min()
response /= response.sum() + 1e-16
response = (1 - self.params.window_influence) * response + \
self.params.window_influence * self.hann_window
loc = np.unravel_index(response.argmax(), response.shape)
# locate target center
disp_in_response = np.array(loc) - (self.upscale_sz - 1.) / 2
disp_in_instance = disp_in_response * \
self.params.total_stride / self.params.response_up
disp_in_image = disp_in_instance * self.x_sz * \
self.scale_factors[scale_id] / self.params.instance_sz
self.center += disp_in_image
# update target size
scale = (1 - self.params.scale_lr) * 1.0 + \
self.params.scale_lr * self.scale_factors[scale_id]
self.target_sz *= scale
self.z_sz *= scale
self.x_sz *= scale
# return 1-indexed and left-top based bounding box
box = np.array([
self.center[1] + 1 - (self.target_sz[1] - 1) / 2,
self.center[0] + 1 - (self.target_sz[0] - 1) / 2, self.target_sz[1],
self.target_sz[0]
])
return box
PaddleCV/tracking/pytracking/utils/__init__.py 0 → 100644
浏览文件 @ 7ec3ec11
# from .evaluation import *
from .params import *
PaddleCV/tracking/pytracking/utils/params.py 0 → 100644
浏览文件 @ 7ec3ec11
from pytracking.libs import TensorList
import random
class TrackerParams:
"""Class for tracker parameters."""
def free_memory(self):
for a in dir(self):
if not a.startswith('__') and hasattr(
getattr(self, a), 'free_memory'):
getattr(self, a).free_memory()
class FeatureParams:
"""Class for feature specific parameters"""
def __init__(self, *args, **kwargs):
if len(args) > 0:
raise ValueError
for name, val in kwargs.items():
if isinstance(val, list):
setattr(self, name, TensorList(val))
else:
setattr(self, name, val)
def Choice(*args):
"""Can be used to sample random parameter values."""
return random.choice(args)
PaddleCV/tracking/pytracking/utils/plotting.py 0 → 100644
浏览文件 @ 7ec3ec11
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import numpy as np
from pytracking.libs.paddle_utils import p2n, PTensor
def save_tensor(a: PTensor, save_name):
a_np = p2n(a)
np.save(save_name, a_np)
def show_tensor(a: PTensor, fig_num=None, title=None):
"""Display a 2D tensor.
args:
fig_num: Figure number.
title: Title of figure.
"""
a_np = a.squeeze().cpu().clone().detach().numpy()
if a_np.ndim == 3:
a_np = np.transpose(a_np, (1, 2, 0))
plt.figure(fig_num)
plt.tight_layout()
plt.cla()
plt.imshow(a_np)
plt.axis('off')
plt.axis('equal')
if title is not None:
plt.title(title)
plt.draw()
plt.pause(0.001)
def plot_graph(a: PTensor, fig_num=None, title=None):
"""Plot graph. Data is a 1D tensor.
args:
fig_num: Figure number.
title: Title of figure.
"""
a_np = a.squeeze().cpu().clone().detach().numpy()
if a_np.ndim > 1:
raise ValueError
plt.figure(fig_num)
# plt.tight_layout()
plt.cla()
plt.plot(a_np)
if title is not None:
plt.title(title)
plt.draw()
plt.pause(0.001)
PaddleCV/tracking/pytracking/visualize_results_on_benchmark.ipynb 0 → 100644
浏览文件 @ 7ec3ec11
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"import cv2 as cv\n",
"import numpy as np\n",
"from matplotlib.pyplot import Rectangle\n",
"from videofig import videofig\n",
"\n",
"sys.path.append('..')\n",
"from pytracking.pysot_toolkit.datasets import DatasetFactory\n",
"from pytracking.pysot_toolkit.environment import env_settings\n",
"\n",
"# set the dataset name here\n",
"dataset_name = 'CVPR13'\n",
"\n",
"if dataset_name in ['CVPR13', 'OTB50', 'OTB100']:\n",
" # for OTB datasets, we save results into the same directory\n",
" save_dataset_name = 'OTB100'\n",
"else:\n",
" save_dataset_name = dataset_name\n",
"\n",
"dataset_root = os.path.join(env_settings().dataset_path, save_dataset_name)\n",
"\n",
"# load dataset\n",
"dataset = DatasetFactory.create_dataset(name=dataset_name, dataset_root=dataset_root, load_img=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dataset.videos.keys()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Select results to show"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tracker_test_params = 'siamfc.default'\n",
"exp_id = 'siamfc.siamfc_alexnet_vid.epoch49'\n",
"videoname = 'Bolt'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Show"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib notebook\n",
"\n",
"if 'OTB100' == save_dataset_name:\n",
" filename = '{}.txt'.format(videoname)\n",
"elif 'VOT' in save_dataset_name:\n",
" filename = 'baseline/{vname}/{vname}_001.txt'.format(vname=videoname)\n",
"else:\n",
" raise NotImplemented\n",
" \n",
"video = dataset[videoname]\n",
"\n",
"# load tracking results\n",
"boxs = []\n",
"with open(os.path.join(env_settings().results_path, save_dataset_name, tracker_test_params, exp_id, filename), 'r') as file_handle:\n",
" for line in file_handle:\n",
" boxs.append([float(v) for v in line.strip().split(',')])\n",
"\n",
"def redraw_fn(f, ax):\n",
" img_path, _ = video[f]\n",
" img = cv.cvtColor(cv.imread(img_path), cv.COLOR_BGR2RGB)\n",
" \n",
" box = boxs[f]\n",
" if len(box) == 4:\n",
" x, y, w, h = box\n",
" else:\n",
" x, y, w, h = 0, 0, 0, 0\n",
" \n",
" if not redraw_fn.initialized:\n",
" redraw_fn.img_handle = ax.imshow(img)\n",
" box_artist = Rectangle((x, y), w, h,\n",
" fill=False, # remove background\n",
" lw=2,\n",
" edgecolor=\"red\")\n",
" ax.add_patch(box_artist)\n",
" redraw_fn.box_handle = box_artist\n",
" redraw_fn.text_handle = ax.text(0., 1 - 0.05,\n",
" 'Frame: {}'.format(f + 1),\n",
" transform=ax.transAxes,\n",
" color='yellow', size=12)\n",
" redraw_fn.initialized = True\n",
" else:\n",
" redraw_fn.img_handle.set_array(img)\n",
" redraw_fn.box_handle.set_xy((x, y))\n",
" redraw_fn.box_handle.set_width(w)\n",
" redraw_fn.box_handle.set_height(h)\n",
" redraw_fn.text_handle.set_text('Frame: {}'.format(f + 1))\n",
"\n",
"redraw_fn.initialized = False\n",
"\n",
"videofig(len(video), redraw_fn)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
PaddleCV/tracking/requirements.txt 0 → 100644
浏览文件 @ 7ec3ec11
git+https://github.com/tensorpack/dataflow.git
cython
pycocotools
lmdb
pandas
jpeg4py
opencv-python
tensorboardX
videofig
jupyter
tqdm
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