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PaddleX
未验证 提交 bc53ec16 编写于 作者: J Jason 提交者： GitHub
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Merge pull request #138 from PaddlePaddle/develop 

upgrade
上级 86544569 9fe089f3
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Showing with 1182 addition and 442 deletion
deploy/cpp/src/paddlex.cpp
浏览文件 @ bc53ec16
......@@ -98,7 +98,7 @@ bool Model::load_config(const std::string& model_dir) {
bool Model::preprocess(const cv::Mat& input_im, ImageBlob* blob) {
cv::Mat im = input_im.clone();
if (!transforms_.Run(&im, &inputs_)) {
if (!transforms_.Run(&im, blob)) {
return false;
}
return true;
......
docs/FAQ.md
浏览文件 @ bc53ec16
......@@ -60,3 +60,9 @@
## 11. 每次训练新的模型，都需要重新下载预训练模型，怎样可以下载一次就搞定
> 1.可以按照9的方式来解决这个问题
> 2.每次训练前都设定`paddlex.pretrain_dir`路径，如设定`paddlex.pretrain_dir='/usrname/paddlex`，如此下载完的预训练模型会存放至`/usrname/paddlex`目录下，而已经下载在该目录的模型也不会再次重复下载
## 12. 程序启动时提示"Failed to execute script PaddleX"，如何解决？
> 1. 请检查目标机器上PaddleX程序所在路径是否包含中文。目前暂不支持中文路径，请尝试将程序移动到英文目录。
> 2. 如果您的系统是Windows 7或者Windows Server 2012时，原因是缺少MFPlat.DLL/MF.dll/MFReadWrite.dll等OpenCV依赖的DLL，请按如下方式安装桌面体验：通过“我的电脑”-->“属性”-->"管理"打开服务器管理器，点击右上角“管理”选择“添加角色和功能”。点击“服务器选择”-->“功能”，拖动滚动条到最下端，点开“用户界面和基础结构”，勾选“桌面体验”后点击“安装”，等安装完成尝试再次运行PaddleX。
> 3. 请检查目标机器上是否有其他的PaddleX程序或者进程在运行中，如有请退出或者重启机器看是否解决
> 4. 请确认运行程序的用户是否有管理员权限，如非管理员权限用户请尝试使用管理员运行看是否成功
\ No newline at end of file
docs/apis/models/classification.md
浏览文件 @ bc53ec16
......@@ -15,7 +15,7 @@ paddlex.cls.ResNet50(num_classes=1000)
### train 训练接口
```python
train(self, num_epochs, train_dataset, train_batch_size=64, eval_dataset=None, save_interval_epochs=1, log_interval_steps=2, save_dir='output', pretrain_weights='IMAGENET', optimizer=None, learning_rate=0.025, lr_decay_epochs=[30, 60, 90], lr_decay_gamma=0.1, use_vdl=False, sensitivities_file=None, eval_metric_loss=0.05, early_stop=False, early_stop_patience=5, resume_checkpoint=None)
train(self, num_epochs, train_dataset, train_batch_size=64, eval_dataset=None, save_interval_epochs=1, log_interval_steps=2, save_dir='output', pretrain_weights='IMAGENET', optimizer=None, learning_rate=0.025, warmup_steps=0, warmup_start_lr=0.0, lr_decay_epochs=[30, 60, 90], lr_decay_gamma=0.1, use_vdl=False, sensitivities_file=None, eval_metric_loss=0.05, early_stop=False, early_stop_patience=5, resume_checkpoint=None)
```
>
> **参数**
......@@ -30,6 +30,8 @@ train(self, num_epochs, train_dataset, train_batch_size=64, eval_dataset=None, s
> > - **pretrain_weights** (str): 若指定为路径时，则加载路径下预训练模型；若为字符串'IMAGENET'，则自动下载在ImageNet图片数据上预训练的模型权重；若为None，则不使用预训练模型。默认为'IMAGENET'。
> > - **optimizer** (paddle.fluid.optimizer): 优化器。当该参数为None时，使用默认优化器：fluid.layers.piecewise_decay衰减策略，fluid.optimizer.Momentum优化方法。
> > - **learning_rate** (float): 默认优化器的初始学习率。默认为0.025。
> > - **warmup_steps** (int): 默认优化器的warmup步数，学习率将在设定的步数内，从warmup_start_lr线性增长至设定的learning_rate，默认为0。
> > - **warmup_start_lr**(float): 默认优化器的warmup起始学习率，默认为0.0。
> > - **lr_decay_epochs** (list): 默认优化器的学习率衰减轮数。默认为[30, 60, 90]。
> > - **lr_decay_gamma** (float): 默认优化器的学习率衰减率。默认为0.1。
> > - **use_vdl** (bool): 是否使用VisualDL进行可视化。默认值为False。
......
docs/appendix/model_zoo.md
浏览文件 @ bc53ec16
......@@ -6,28 +6,28 @@
| 模型 | 模型大小 | 预测速度（毫秒） | Top1准确率（%） | Top5准确率（%） |
| :----| :------- | :----------- | :--------- | :--------- |
| ResNet18| 46.9MB | 1.499 | 71.0 | 89.9 |
| ResNet34| 87.5MB | 2.272 | 74.6 | 92.1 |
| ResNet50| 102.7MB | 2.939 | 76.5 | 93.0 |
| ResNet101 |179.1MB | 5.314 | 77.6 | 93.6 |
| ResNet50_vd |102.8MB | 3.165 | 79.1 | 94.4 |
| ResNet101_vd| 179.2MB | 5.252 | 80.2 | 95.0 |
| ResNet50_vd_ssld |102.8MB | 3.165 | 82.4 | 96.1 |
| ResNet101_vd_ssld| 179.2MB | 5.252 | 83.7 | 96.7 |
| DarkNet53|166.9MB | 3.139 | 78.0 | 94.1 |
| MobileNetV1 | 16.0MB | 32.523 | 71.0 | 89.7 |
| MobileNetV2 | 14.0MB | 23.318 | 72.2 | 90.7 |
| MobileNetV3_large| 21.0MB | 19.308 | 75.3 | 93.2 |
| MobileNetV3_small | 12.0MB | 6.546 | 68.2 | 88.1 |
| MobileNetV3_large_ssld| 21.0MB | 19.308 | 79.0 | 94.5 |
| MobileNetV3_small_ssld | 12.0MB | 6.546 | 71.3 | 90.1 |
| Xception41 |92.4MB | 4.408 | 79.6 | 94.4 |
| Xception65 | 144.6MB | 6.464 | 80.3 | 94.5 |
| DenseNet121 | 32.8MB | 4.371 | 75.7 | 92.6 |
| DenseNet161|116.3MB | 8.863 | 78.6 | 94.1 |
| DenseNet201| 84.6MB | 8.173 | 77.6 | 93.7 |
| ShuffleNetV2 | 9.0MB | 10.941 | 68.8 | 88.5 |
| HRNet_W18 | 21.29MB | 7.368 (V100 GPU) | 76.9 | 93.4 |
| ResNet18| 46.9MB | - | 71.0 | 89.9 |
| ResNet34| 87.5MB | - | 74.6 | 92.1 |
| ResNet50| 102.7MB | - | 76.5 | 93.0 |
| ResNet101 |179.1MB | - | 77.6 | 93.6 |
| ResNet50_vd |102.8MB |- | 79.1 | 94.4 |
| ResNet101_vd| 179.2MB | - | 80.2 | 95.0 |
| ResNet50_vd_ssld |102.8MB | - | 82.4 | 96.1 |
| ResNet101_vd_ssld| 179.2MB | - | 83.7 | 96.7 |
| DarkNet53|166.9MB | - | 78.0 | 94.1 |
| MobileNetV1 | 16.0MB | - | 71.0 | 89.7 |
| MobileNetV2 | 14.0MB | - | 72.2 | 90.7 |
| MobileNetV3_large| 21.0MB | - | 75.3 | 93.2 |
| MobileNetV3_small | 12.0MB | - | 68.2 | 88.1 |
| MobileNetV3_large_ssld| 21.0MB | - | 79.0 | 94.5 |
| MobileNetV3_small_ssld | 12.0MB | - | 71.3 | 90.1 |
| Xception41 |92.4MB | - | 79.6 | 94.4 |
| Xception65 | 144.6MB | - | 80.3 | 94.5 |
| DenseNet121 | 32.8MB | - | 75.7 | 92.6 |
| DenseNet161|116.3MB | - | 78.6 | 94.1 |
| DenseNet201| 84.6MB | - | 77.6 | 93.7 |
| ShuffleNetV2 | 9.0MB | - | 68.8 | 88.5 |
| HRNet_W18 | 21.29MB | - | 76.9 | 93.4 |
## 目标检测模型
......
docs/appendix/parameters.md
浏览文件 @ bc53ec16
......@@ -23,3 +23,35 @@ Batch Size指模型在训练过程中，一次性处理的样本数量, 如若
- [实例分割MaskRCNN-train](https://paddlex.readthedocs.io/zh_CN/latest/apis/models/instance_segmentation.html#train)
- [语义分割DeepLabv3p-train](https://paddlex.readthedocs.io/zh_CN/latest/apis/models/semantic_segmentation.html#train)
- [语义分割UNet](https://paddlex.readthedocs.io/zh_CN/latest/apis/models/semantic_segmentation.html#id2)
## 关于lr_decay_epoch, warmup_steps等参数的说明
在PaddleX或其它深度学习模型的训练过程中，经常见到lr_decay_epoch, warmup_steps, warmup_start_lr等参数设置，下面介绍一些这些参数的作用。
首先这些参数都是用于控制模型训练过程中学习率的变化方式，例如我们在训练时将learning_rate设为0.1, 通常情况，在模型的训练过程中，学习率一直以0.1不变训练下去, 但为了调出更好的模型效果，我们往往不希望学习率一直保持不变。
### warmup_steps和warmup_start_lr
我们在训练模型时，一般都会使用预训练模型，例如检测模型在训练时使用backbone在ImageNet数据集上的预训练权重。但由于在自行训练时，自己的数据与ImageNet数据集存在较大的差异，可能会一开始由于梯度过大使得训练出现问题，因此可以在刚开始训练时，让学习率以一个较小的值，慢慢增长到设定的学习率。因此`warmup_steps`和`warmup_start_lr`就是这个作用，模型开始训练时，学习率会从`warmup_start_lr`开始，在`warmup_steps`内线性增长到设定的学习率。
### lr_decay_epochs和lr_decay_gamma
`lr_decay_epochs`用于让学习率在模型训练后期逐步衰减，它一般是一个list，如[6, 8, 10]，表示学习率在第6个epoch时衰减一次，第8个epoch时再衰减一次，第10个epoch时再衰减一次。每次学习率衰减为之前的学习率*lr_decay_gamma
### Notice
在PaddleX中，限制warmup需要在第一个学习率decay衰减前结束，因此要满足下面的公式
```
warmup_steps <= lr_decay_epochs[0] * num_steps_each_epoch
```
其中公式中`num_steps_each_epoch = num_samples_in_train_dataset // train_batch_size`。
> 因此如若在训练时PaddleX提示`warmup_steps should be less than xxx`时，即可根据上述公式来调整你的`lr_decay_epochs`或者是`warmup_steps`使得两个参数满足上面的条件
> - 图像分类模型 [train接口文档](https://paddlex.readthedocs.io/zh_CN/latest/apis/models/classification.html#train)
> - FasterRCNN [train接口文档](https://paddlex.readthedocs.io/zh_CN/latest/apis/models/detection.html#fasterrcnn)
> - YOLOv3 [train接口文档](https://paddlex.readthedocs.io/zh_CN/latest/apis/models/detection.html#yolov3)
> - MaskRCNN [train接口文档](https://paddlex.readthedocs.io/zh_CN/latest/apis/models/instance_segmentation.html#maskrcnn)
> - DeepLab [train接口文档](https://paddlex.readthedocs.io/zh_CN/latest/apis/models/semantic_segmentation.html#deeplabv3p)
> - UNet [train接口文档](https://paddlex.readthedocs.io/zh_CN/latest/apis/models/semantic_segmentation.html#unet)
> - HRNet [train接口文档](https://paddlex.readthedocs.io/zh_CN/latest/apis/models/semantic_segmentation.html#hrnet)
paddlex/__init__.py
浏览文件 @ bc53ec16
......@@ -53,4 +53,4 @@ log_level = 2
from . import interpret
__version__ = '1.0.5'
__version__ = '1.0.6'
paddlex/cls.py
浏览文件 @ bc53ec16
......@@ -37,5 +37,6 @@ DenseNet161 = cv.models.DenseNet161
DenseNet201 = cv.models.DenseNet201
ShuffleNetV2 = cv.models.ShuffleNetV2
HRNet_W18 = cv.models.HRNet_W18
AlexNet = cv.models.AlexNet
transforms = cv.transforms.cls_transforms
paddlex/cv/models/__init__.py
浏览文件 @ bc53ec16
......@@ -35,6 +35,7 @@ from .classifier import DenseNet161
from .classifier import DenseNet201
from .classifier import ShuffleNetV2
from .classifier import HRNet_W18
from .classifier import AlexNet
from .base import BaseAPI
from .yolo_v3 import YOLOv3
from .faster_rcnn import FasterRCNN
......
paddlex/cv/models/base.py
浏览文件 @ bc53ec16
......@@ -221,8 +221,8 @@ class BaseAPI:
logging.info(
"Load pretrain weights from {}.".format(pretrain_weights),
use_color=True)
paddlex.utils.utils.load_pretrain_weights(
self.exe, self.train_prog, pretrain_weights, fuse_bn)
paddlex.utils.utils.load_pretrain_weights(self.exe, self.train_prog,
pretrain_weights, fuse_bn)
# 进行裁剪
if sensitivities_file is not None:
import paddleslim
......@@ -262,6 +262,7 @@ class BaseAPI:
info['_Attributes']['num_classes'] = self.num_classes
info['_Attributes']['labels'] = self.labels
info['_Attributes']['fixed_input_shape'] = self.fixed_input_shape
try:
primary_metric_key = list(self.eval_metrics.keys())[0]
primary_metric_value = float(self.eval_metrics[primary_metric_key])
......@@ -325,9 +326,7 @@ class BaseAPI:
logging.info("Model saved in {}.".format(save_dir))
def export_inference_model(self, save_dir):
test_input_names = [
var.name for var in list(self.test_inputs.values())
]
test_input_names = [var.name for var in list(self.test_inputs.values())]
test_outputs = list(self.test_outputs.values())
if self.__class__.__name__ == 'MaskRCNN':
from paddlex.utils.save import save_mask_inference_model
......@@ -364,8 +363,7 @@ class BaseAPI:
# 模型保存成功的标志
open(osp.join(save_dir, '.success'), 'w').close()
logging.info("Model for inference deploy saved in {}.".format(
save_dir))
logging.info("Model for inference deploy saved in {}.".format(save_dir))
def train_loop(self,
num_epochs,
......@@ -489,13 +487,11 @@ class BaseAPI:
eta = ((num_epochs - i) * total_num_steps - step - 1
) * avg_step_time
if time_eval_one_epoch is not None:
eval_eta = (
total_eval_times - i // save_interval_epochs
) * time_eval_one_epoch
eval_eta = (total_eval_times - i // save_interval_epochs
) * time_eval_one_epoch
else:
eval_eta = (
total_eval_times - i // save_interval_epochs
) * total_num_steps_eval * avg_step_time
eval_eta = (total_eval_times - i // save_interval_epochs
) * total_num_steps_eval * avg_step_time
eta_str = seconds_to_hms(eta + eval_eta)
logging.info(
......
paddlex/cv/models/classifier.py
浏览文件 @ bc53ec16
......@@ -48,12 +48,13 @@ class BaseClassifier(BaseAPI):
self.fixed_input_shape = None
def build_net(self, mode='train'):
if self.__class__.__name__ == "AlexNet":
assert self.fixed_input_shape is not None, "In AlexNet, input_shape should be defined, e.g. model = paddlex.cls.AlexNet(num_classes=1000, input_shape=[224, 224])"
if self.fixed_input_shape is not None:
input_shape = [
None, 3, self.fixed_input_shape[1], self.fixed_input_shape[0]
]
image = fluid.data(
dtype='float32', shape=input_shape, name='image')
image = fluid.data(dtype='float32', shape=input_shape, name='image')
else:
image = fluid.data(
dtype='float32', shape=[None, 3, None, None], name='image')
......@@ -81,7 +82,8 @@ class BaseClassifier(BaseAPI):
del outputs['loss']
return inputs, outputs
def default_optimizer(self, learning_rate, lr_decay_epochs, lr_decay_gamma,
def default_optimizer(self, learning_rate, warmup_steps, warmup_start_lr,
lr_decay_epochs, lr_decay_gamma,
num_steps_each_epoch):
boundaries = [b * num_steps_each_epoch for b in lr_decay_epochs]
values = [
......@@ -90,6 +92,24 @@ class BaseClassifier(BaseAPI):
]
lr_decay = fluid.layers.piecewise_decay(
boundaries=boundaries, values=values)
if warmup_steps > 0:
if warmup_steps > lr_decay_epochs[0] * num_steps_each_epoch:
logging.error(
"In function train(), parameters should satisfy: warmup_steps <= lr_decay_epochs[0]*num_samples_in_train_dataset",
exit=False)
logging.error(
"See this doc for more information: https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/appendix/parameters.md#notice",
exit=False)
logging.error(
"warmup_steps should less than {} or lr_decay_epochs[0] greater than {}, please modify 'lr_decay_epochs' or 'warmup_steps' in train function".
format(lr_decay_epochs[0] * num_steps_each_epoch,
warmup_steps // num_steps_each_epoch))
lr_decay = fluid.layers.linear_lr_warmup(
learning_rate=lr_decay,
warmup_steps=warmup_steps,
start_lr=warmup_start_lr,
end_lr=learning_rate)
optimizer = fluid.optimizer.Momentum(
lr_decay,
momentum=0.9,
......@@ -107,6 +127,8 @@ class BaseClassifier(BaseAPI):
pretrain_weights='IMAGENET',
optimizer=None,
learning_rate=0.025,
warmup_steps=0,
warmup_start_lr=0.0,
lr_decay_epochs=[30, 60, 90],
lr_decay_gamma=0.1,
use_vdl=False,
......@@ -129,6 +151,8 @@ class BaseClassifier(BaseAPI):
optimizer (paddle.fluid.optimizer): 优化器。当该参数为None时，使用默认优化器：
fluid.layers.piecewise_decay衰减策略，fluid.optimizer.Momentum优化方法。
learning_rate (float): 默认优化器的初始学习率。默认为0.025。
warmup_steps(int): 学习率从warmup_start_lr上升至设定的learning_rate，所需的步数，默认为0
warmup_start_lr(float): 学习率在warmup阶段时的起始值，默认为0.0
lr_decay_epochs (list): 默认优化器的学习率衰减轮数。默认为[30, 60, 90]。
lr_decay_gamma (float): 默认优化器的学习率衰减率。默认为0.1。
use_vdl (bool): 是否使用VisualDL进行可视化。默认值为False。
......@@ -149,6 +173,8 @@ class BaseClassifier(BaseAPI):
num_steps_each_epoch = train_dataset.num_samples // train_batch_size
optimizer = self.default_optimizer(
learning_rate=learning_rate,
warmup_steps=warmup_steps,
warmup_start_lr=warmup_start_lr,
lr_decay_epochs=lr_decay_epochs,
lr_decay_gamma=lr_decay_gamma,
num_steps_each_epoch=num_steps_each_epoch)
......@@ -193,8 +219,7 @@ class BaseClassifier(BaseAPI):
tuple (metrics, eval_details): 当return_details为True时，增加返回dict，
包含关键字：'true_labels'、'pred_scores'，分别代表真实类别id、每个类别的预测得分。
"""
self.arrange_transforms(
transforms=eval_dataset.transforms, mode='eval')
self.arrange_transforms(transforms=eval_dataset.transforms, mode='eval')
data_generator = eval_dataset.generator(
batch_size=batch_size, drop_last=False)
k = min(5, self.num_classes)
......@@ -206,9 +231,8 @@ class BaseClassifier(BaseAPI):
self.test_prog).with_data_parallel(
share_vars_from=self.parallel_train_prog)
batch_size_each_gpu = self._get_single_card_bs(batch_size)
logging.info(
"Start to evaluating(total_samples={}, total_steps={})...".format(
eval_dataset.num_samples, total_steps))
logging.info("Start to evaluating(total_samples={}, total_steps={})...".
format(eval_dataset.num_samples, total_steps))
for step, data in tqdm.tqdm(
enumerate(data_generator()), total=total_steps):
images = np.array([d[0] for d in data]).astype('float32')
......@@ -405,3 +429,10 @@ class HRNet_W18(BaseClassifier):
def __init__(self, num_classes=1000):
super(HRNet_W18, self).__init__(
model_name='HRNet_W18', num_classes=num_classes)
class AlexNet(BaseClassifier):
def __init__(self, num_classes=1000, input_shape=None):
super(AlexNet, self).__init__(
model_name='AlexNet', num_classes=num_classes)
self.fixed_input_shape = input_shape
paddlex/cv/models/faster_rcnn.py
浏览文件 @ bc53ec16
......@@ -138,8 +138,16 @@ class FasterRCNN(BaseAPI):
lr_decay_epochs, lr_decay_gamma,
num_steps_each_epoch):
if warmup_steps > lr_decay_epochs[0] * num_steps_each_epoch:
raise Exception("warmup_steps should less than {}".format(
lr_decay_epochs[0] * num_steps_each_epoch))
logging.error(
"In function train(), parameters should satisfy: warmup_steps <= lr_decay_epochs[0]*num_samples_in_train_dataset",
exit=False)
logging.error(
"See this doc for more information: https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/appendix/parameters.md#notice",
exit=False)
logging.error(
"warmup_steps should less than {} or lr_decay_epochs[0] greater than {}, please modify 'lr_decay_epochs' or 'warmup_steps' in train function".
format(lr_decay_epochs[0] * num_steps_each_epoch, warmup_steps
// num_steps_each_epoch))
boundaries = [b * num_steps_each_epoch for b in lr_decay_epochs]
values = [(lr_decay_gamma**i) * learning_rate
for i in range(len(lr_decay_epochs) + 1)]
......@@ -282,8 +290,7 @@ class FasterRCNN(BaseAPI):
eval_details为dict，包含关键字：'bbox'，对应元素预测结果列表，每个预测结果由图像id、
预测框类别id、预测框坐标、预测框得分；’gt‘：真实标注框相关信息。
"""
self.arrange_transforms(
transforms=eval_dataset.transforms, mode='eval')
self.arrange_transforms(transforms=eval_dataset.transforms, mode='eval')
if metric is None:
if hasattr(self, 'metric') and self.metric is not None:
metric = self.metric
......@@ -302,14 +309,12 @@ class FasterRCNN(BaseAPI):
logging.warning(
"Faster RCNN supports batch_size=1 only during evaluating, so batch_size is forced to be set to 1."
)
dataset = eval_dataset.generator(
batch_size=batch_size, drop_last=False)
dataset = eval_dataset.generator(batch_size=batch_size, drop_last=False)
total_steps = math.ceil(eval_dataset.num_samples * 1.0 / batch_size)
results = list()
logging.info(
"Start to evaluating(total_samples={}, total_steps={})...".format(
eval_dataset.num_samples, total_steps))
logging.info("Start to evaluating(total_samples={}, total_steps={})...".
format(eval_dataset.num_samples, total_steps))
for step, data in tqdm.tqdm(enumerate(dataset()), total=total_steps):
images = np.array([d[0] for d in data]).astype('float32')
im_infos = np.array([d[1] for d in data]).astype('float32')
......
paddlex/cv/models/load_model.py
浏览文件 @ bc53ec16
......@@ -41,7 +41,16 @@ def load_model(model_dir, fixed_input_shape=None):
if 'model_name' in info['_init_params']:
del info['_init_params']['model_name']
model = getattr(paddlex.cv.models, info['Model'])(**info['_init_params'])
model.fixed_input_shape = fixed_input_shape
if '_Attributes' in info:
if 'fixed_input_shape' in info['_Attributes']:
fixed_input_shape = info['_Attributes']['fixed_input_shape']
if fixed_input_shape is not None:
logging.info("Model already has fixed_input_shape with {}".
format(fixed_input_shape))
model.fixed_input_shape = fixed_input_shape
if status == "Normal" or \
status == "Prune" or status == "fluid.save":
startup_prog = fluid.Program()
......@@ -88,8 +97,8 @@ def load_model(model_dir, fixed_input_shape=None):
model.model_type, info['Transforms'], info['BatchTransforms'])
model.eval_transforms = copy.deepcopy(model.test_transforms)
else:
model.test_transforms = build_transforms(
model.model_type, info['Transforms'], to_rgb)
model.test_transforms = build_transforms(model.model_type,
info['Transforms'], to_rgb)
model.eval_transforms = copy.deepcopy(model.test_transforms)
if '_Attributes' in info:
......@@ -107,20 +116,7 @@ def fix_input_shape(info, fixed_input_shape=None):
resize = {'ResizeByShort': {}}
padding = {'Padding': {}}
if info['_Attributes']['model_type'] == 'classifier':
crop_size = 0
for transform in info['Transforms']:
if 'CenterCrop' in transform:
crop_size = transform['CenterCrop']['crop_size']
break
assert crop_size == fixed_input_shape[
0], "fixed_input_shape must == CenterCrop:crop_size:{}".format(
crop_size)
assert crop_size == fixed_input_shape[
1], "fixed_input_shape must == CenterCrop:crop_size:{}".format(
crop_size)
if crop_size == 0:
logging.warning(
"fixed_input_shape must == input shape when trainning")
pass
else:
resize['ResizeByShort']['short_size'] = min(fixed_input_shape)
resize['ResizeByShort']['max_size'] = max(fixed_input_shape)
......
paddlex/cv/models/mask_rcnn.py
浏览文件 @ bc53ec16
......@@ -97,8 +97,16 @@ class MaskRCNN(FasterRCNN):
lr_decay_epochs, lr_decay_gamma,
num_steps_each_epoch):
if warmup_steps > lr_decay_epochs[0] * num_steps_each_epoch:
raise Exception("warmup_step should less than {}".format(
lr_decay_epochs[0] * num_steps_each_epoch))
logging.error(
"In function train(), parameters should satisfy: warmup_steps <= lr_decay_epochs[0]*num_samples_in_train_dataset",
exit=False)
logging.error(
"See this doc for more information: https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/appendix/parameters.md#notice",
exit=False)
logging.error(
"warmup_steps should less than {} or lr_decay_epochs[0] greater than {}, please modify 'lr_decay_epochs' or 'warmup_steps' in train function".
format(lr_decay_epochs[0] * num_steps_each_epoch, warmup_steps
// num_steps_each_epoch))
boundaries = [b * num_steps_each_epoch for b in lr_decay_epochs]
values = [(lr_decay_gamma**i) * learning_rate
for i in range(len(lr_decay_epochs) + 1)]
......@@ -244,8 +252,7 @@ class MaskRCNN(FasterRCNN):
预测框坐标、预测框得分；'mask'，对应元素预测区域结果列表，每个预测结果由图像id、
预测区域类别id、预测区域坐标、预测区域得分；’gt‘：真实标注框和标注区域相关信息。
"""
self.arrange_transforms(
transforms=eval_dataset.transforms, mode='eval')
self.arrange_transforms(transforms=eval_dataset.transforms, mode='eval')
if metric is None:
if hasattr(self, 'metric') and self.metric is not None:
metric = self.metric
......@@ -266,9 +273,8 @@ class MaskRCNN(FasterRCNN):
total_steps = math.ceil(eval_dataset.num_samples * 1.0 / batch_size)
results = list()
logging.info(
"Start to evaluating(total_samples={}, total_steps={})...".format(
eval_dataset.num_samples, total_steps))
logging.info("Start to evaluating(total_samples={}, total_steps={})...".
format(eval_dataset.num_samples, total_steps))
for step, data in tqdm.tqdm(
enumerate(data_generator()), total=total_steps):
images = np.array([d[0] for d in data]).astype('float32')
......@@ -310,8 +316,7 @@ class MaskRCNN(FasterRCNN):
zip(['bbox_map', 'segm_map'],
[ap_stats[0][1], ap_stats[1][1]]))
else:
metrics = OrderedDict(
zip(['bbox_map', 'segm_map'], [0.0, 0.0]))
metrics = OrderedDict(zip(['bbox_map', 'segm_map'], [0.0, 0.0]))
elif metric == 'COCO':
if isinstance(ap_stats[0], np.ndarray) and isinstance(ap_stats[1],
np.ndarray):
......
paddlex/cv/models/slim/prune.py
浏览文件 @ bc53ec16
......@@ -115,6 +115,21 @@ def channel_prune(program, prune_names, prune_ratios, place, only_graph=False):
Returns:
paddle.fluid.Program: 裁剪后的Program。
"""
prog_var_shape_dict = {}
for var in program.list_vars():
try:
prog_var_shape_dict[var.name] = var.shape
except Exception:
pass
index = 0
for param, ratio in zip(prune_names, prune_ratios):
origin_num = prog_var_shape_dict[param][0]
pruned_num = int(round(origin_num * ratio))
while origin_num == pruned_num:
ratio -= 0.1
pruned_num = int(round(origin_num * (ratio)))
prune_ratios[index] = ratio
index += 1
scope = fluid.global_scope()
pruner = Pruner()
program, _, _ = pruner.prune(
......@@ -266,8 +281,8 @@ def get_params_ratios(sensitivities_file, eval_metric_loss=0.05):
if not osp.exists(sensitivities_file):
raise Exception('The sensitivities file is not exists!')
sensitivitives = paddleslim.prune.load_sensitivities(sensitivities_file)
params_ratios = paddleslim.prune.get_ratios_by_loss(sensitivitives,
eval_metric_loss)
params_ratios = paddleslim.prune.get_ratios_by_loss(
sensitivitives, eval_metric_loss)
return params_ratios
......@@ -286,6 +301,19 @@ def cal_model_size(program, place, sensitivities_file, eval_metric_loss=0.05):
"""
prune_params_ratios = get_params_ratios(sensitivities_file,
eval_metric_loss)
prog_var_shape_dict = {}
for var in program.list_vars():
try:
prog_var_shape_dict[var.name] = var.shape
except Exception:
pass
for param, ratio in prune_params_ratios.items():
origin_num = prog_var_shape_dict[param][0]
pruned_num = int(round(origin_num * ratio))
while origin_num == pruned_num:
ratio -= 0.1
pruned_num = int(round(origin_num * (ratio)))
prune_params_ratios[param] = ratio
prune_program = channel_prune(
program,
list(prune_params_ratios.keys()),
......
paddlex/cv/models/slim/prune_config.py
浏览文件 @ bc53ec16
......@@ -142,13 +142,16 @@ def get_prune_params(model):
program = model.test_prog
if model_type.startswith('ResNet') or \
model_type.startswith('DenseNet') or \
model_type.startswith('DarkNet'):
model_type.startswith('DarkNet') or \
model_type.startswith('AlexNet'):
for block in program.blocks:
for param in block.all_parameters():
pd_var = fluid.global_scope().find_var(param.name)
pd_param = pd_var.get_tensor()
if len(np.array(pd_param).shape) == 4:
prune_names.append(param.name)
if model_type == 'AlexNet':
prune_names.remove('conv5_weights')
elif model_type == "MobileNetV1":
prune_names.append("conv1_weights")
for param in program.global_block().all_parameters():
......@@ -162,7 +165,7 @@ def get_prune_params(model):
continue
prune_names.append(param.name)
elif model_type.startswith("MobileNetV3"):
if model_type == 'MobileNetV3_small':
if model_type.startswith('MobileNetV3_small'):
expand_prune_id = [3, 4]
else:
expand_prune_id = [2, 3, 4, 8, 9, 11]
......
paddlex/cv/models/utils/pretrain_weights.py
浏览文件 @ bc53ec16
......@@ -70,6 +70,8 @@ image_pretrain = {
'https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W60_C_pretrained.tar',
'HRNet_W64':
'https://paddle-imagenet-models-name.bj.bcebos.com/HRNet_W64_C_pretrained.tar',
'AlexNet':
'http://paddle-imagenet-models-name.bj.bcebos.com/AlexNet_pretrained.tar'
}
coco_pretrain = {
......@@ -99,10 +101,12 @@ def get_pretrain_weights(flag, model_type, backbone, save_dir):
backbone = 'DetResNet50'
assert backbone in image_pretrain, "There is not ImageNet pretrain weights for {}, you may try COCO.".format(
backbone)
# url = image_pretrain[backbone]
# fname = osp.split(url)[-1].split('.')[0]
# paddlex.utils.download_and_decompress(url, path=new_save_dir)
# return osp.join(new_save_dir, fname)
# if backbone == 'AlexNet':
# url = image_pretrain[backbone]
# fname = osp.split(url)[-1].split('.')[0]
# paddlex.utils.download_and_decompress(url, path=new_save_dir)
# return osp.join(new_save_dir, fname)
try:
hub.download(backbone, save_path=new_save_dir)
except Exception as e:
......
paddlex/cv/models/yolo_v3.py
浏览文件 @ bc53ec16
......@@ -128,8 +128,16 @@ class YOLOv3(BaseAPI):
lr_decay_epochs, lr_decay_gamma,
num_steps_each_epoch):
if warmup_steps > lr_decay_epochs[0] * num_steps_each_epoch:
raise Exception("warmup_steps should less than {}".format(
lr_decay_epochs[0] * num_steps_each_epoch))
logging.error(
"In function train(), parameters should satisfy: warmup_steps <= lr_decay_epochs[0]*num_samples_in_train_dataset",
exit=False)
logging.error(
"See this doc for more information: https://github.com/PaddlePaddle/PaddleX/blob/develop/docs/appendix/parameters.md#notice",
exit=False)
logging.error(
"warmup_steps should less than {} or lr_decay_epochs[0] greater than {}, please modify 'lr_decay_epochs' or 'warmup_steps' in train function".
format(lr_decay_epochs[0] * num_steps_each_epoch, warmup_steps
// num_steps_each_epoch))
boundaries = [b * num_steps_each_epoch for b in lr_decay_epochs]
values = [(lr_decay_gamma**i) * learning_rate
for i in range(len(lr_decay_epochs) + 1)]
......@@ -277,8 +285,7 @@ class YOLOv3(BaseAPI):
eval_details为dict，包含关键字：'bbox'，对应元素预测结果列表，每个预测结果由图像id、
预测框类别id、预测框坐标、预测框得分；’gt‘：真实标注框相关信息。
"""
self.arrange_transforms(
transforms=eval_dataset.transforms, mode='eval')
self.arrange_transforms(transforms=eval_dataset.transforms, mode='eval')
if metric is None:
if hasattr(self, 'metric') and self.metric is not None:
metric = self.metric
......@@ -298,9 +305,8 @@ class YOLOv3(BaseAPI):
data_generator = eval_dataset.generator(
batch_size=batch_size, drop_last=False)
logging.info(
"Start to evaluating(total_samples={}, total_steps={})...".format(
eval_dataset.num_samples, total_steps))
logging.info("Start to evaluating(total_samples={}, total_steps={})...".
format(eval_dataset.num_samples, total_steps))
for step, data in tqdm.tqdm(
enumerate(data_generator()), total=total_steps):
images = np.array([d[0] for d in data])
......
paddlex/cv/nets/__init__.py
浏览文件 @ bc53ec16
......@@ -24,6 +24,7 @@ from .xception import Xception
from .densenet import DenseNet
from .shufflenet_v2 import ShuffleNetV2
from .hrnet import HRNet
from .alexnet import AlexNet
def resnet18(input, num_classes=1000):
......@@ -153,3 +154,8 @@ def shufflenetv2(input, num_classes=1000):
def hrnet_w18(input, num_classes=1000):
model = HRNet(width=18, num_classes=num_classes)
return model(input)
def alexnet(input, num_classes=1000):
model = AlexNet(num_classes=num_classes)
return model(input)
paddlex/cv/nets/alexnet.py 0 → 100644
浏览文件 @ bc53ec16
#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
import paddle.fluid as fluid
class AlexNet():
def __init__(self, num_classes=1000):
assert num_classes is not None, "In AlextNet, num_classes cannot be None"
self.num_classes = num_classes
def __call__(self, input):
stdv = 1.0 / math.sqrt(input.shape[1] * 11 * 11)
layer_name = [
"conv1", "conv2", "conv3", "conv4", "conv5", "fc6", "fc7", "fc8"
]
conv1 = fluid.layers.conv2d(
input=input,
num_filters=64,
filter_size=11,
stride=4,
padding=2,
groups=1,
act='relu',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[0] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[0] + "_weights"))
pool1 = fluid.layers.pool2d(
input=conv1,
pool_size=3,
pool_stride=2,
pool_padding=0,
pool_type='max')
stdv = 1.0 / math.sqrt(pool1.shape[1] * 5 * 5)
conv2 = fluid.layers.conv2d(
input=pool1,
num_filters=192,
filter_size=5,
stride=1,
padding=2,
groups=1,
act='relu',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[1] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[1] + "_weights"))
pool2 = fluid.layers.pool2d(
input=conv2,
pool_size=3,
pool_stride=2,
pool_padding=0,
pool_type='max')
stdv = 1.0 / math.sqrt(pool2.shape[1] * 3 * 3)
conv3 = fluid.layers.conv2d(
input=pool2,
num_filters=384,
filter_size=3,
stride=1,
padding=1,
groups=1,
act='relu',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[2] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[2] + "_weights"))
stdv = 1.0 / math.sqrt(conv3.shape[1] * 3 * 3)
conv4 = fluid.layers.conv2d(
input=conv3,
num_filters=256,
filter_size=3,
stride=1,
padding=1,
groups=1,
act='relu',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[3] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[3] + "_weights"))
stdv = 1.0 / math.sqrt(conv4.shape[1] * 3 * 3)
conv5 = fluid.layers.conv2d(
input=conv4,
num_filters=256,
filter_size=3,
stride=1,
padding=1,
groups=1,
act='relu',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[4] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[4] + "_weights"))
pool5 = fluid.layers.pool2d(
input=conv5,
pool_size=3,
pool_stride=2,
pool_padding=0,
pool_type='max')
drop6 = fluid.layers.dropout(x=pool5, dropout_prob=0.5)
stdv = 1.0 / math.sqrt(drop6.shape[1] * drop6.shape[2] *
drop6.shape[3] * 1.0)
fc6 = fluid.layers.fc(
input=drop6,
size=4096,
act='relu',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[5] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[5] + "_weights"))
drop7 = fluid.layers.dropout(x=fc6, dropout_prob=0.5)
stdv = 1.0 / math.sqrt(drop7.shape[1] * 1.0)
fc7 = fluid.layers.fc(
input=drop7,
size=4096,
act='relu',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[6] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[6] + "_weights"))
stdv = 1.0 / math.sqrt(fc7.shape[1] * 1.0)
out = fluid.layers.fc(
input=fc7,
size=self.num_classes,
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[7] + "_offset"),
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[7] + "_weights"))
return out
paddlex/cv/nets/hrnet.py
浏览文件 @ bc53ec16
......@@ -71,7 +71,7 @@ class HRNet(object):
self.end_points = []
return
def net(self, input, class_dim=1000):
def net(self, input):
width = self.width
channels_2, channels_3, channels_4 = self.channels[width]
num_modules_2, num_modules_3, num_modules_4 = 1, 4, 3
......@@ -125,7 +125,7 @@ class HRNet(object):
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
out = fluid.layers.fc(
input=pool,
size=class_dim,
size=self.num_classes,
param_attr=ParamAttr(
name='fc_weights',
initializer=fluid.initializer.Uniform(-stdv, stdv)),
......
paddlex/interpret/core/_session_preparation.py
浏览文件 @ bc53ec16
......@@ -20,6 +20,7 @@ import numpy as np
from paddle.fluid.param_attr import ParamAttr
from paddlex.interpret.as_data_reader.readers import preprocess_image
def gen_user_home():
if "HOME" in os.environ:
home_path = os.environ["HOME"]
......@@ -34,10 +35,20 @@ def paddle_get_fc_weights(var_name="fc_0.w_0"):
def paddle_resize(extracted_features, outsize):
resized_features = fluid.layers.resize_bilinear(extracted_features, outsize)
resized_features = fluid.layers.resize_bilinear(extracted_features,
outsize)
return resized_features
def get_precomputed_normlime_weights():
root_path = gen_user_home()
root_path = osp.join(root_path, '.paddlex')
h_pre_models = osp.join(root_path, "pre_models")
normlime_weights_file = osp.join(
h_pre_models, "normlime_weights_imagenet_resnet50vc.npy")
return np.load(normlime_weights_file, allow_pickle=True).item()
def compute_features_for_kmeans(data_content):
root_path = gen_user_home()
root_path = osp.join(root_path, '.paddlex')
......@@ -47,6 +58,7 @@ def compute_features_for_kmeans(data_content):
os.makedirs(root_path)
url = "https://bj.bcebos.com/paddlex/interpret/pre_models.tar.gz"
pdx.utils.download_and_decompress(url, path=root_path)
def conv_bn_layer(input,
num_filters,
filter_size,
......@@ -55,7 +67,7 @@ def compute_features_for_kmeans(data_content):
act=None,
name=None,
is_test=True,
global_name=''):
global_name='for_kmeans_'):
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
......@@ -79,14 +91,14 @@ def compute_features_for_kmeans(data_content):
bias_attr=ParamAttr(global_name + bn_name + '_offset'),
moving_mean_name=global_name + bn_name + '_mean',
moving_variance_name=global_name + bn_name + '_variance',
use_global_stats=is_test
)
use_global_stats=is_test)
startup_prog = fluid.default_startup_program().clone(for_test=True)
prog = fluid.Program()
with fluid.program_guard(prog, startup_prog):
with fluid.unique_name.guard():
image_op = fluid.data(name='image', shape=[None, 3, 224, 224], dtype='float32')
image_op = fluid.data(
name='image', shape=[None, 3, 224, 224], dtype='float32')
conv = conv_bn_layer(
input=image_op,
......@@ -110,7 +122,8 @@ def compute_features_for_kmeans(data_content):
act='relu',
name='conv1_3')
extracted_features = conv
resized_features = fluid.layers.resize_bilinear(extracted_features, image_op.shape[2:])
resized_features = fluid.layers.resize_bilinear(extracted_features,
image_op.shape[2:])
gpu_id = int(os.environ.get('FLAGS_selected_gpus', 0))
place = fluid.CUDAPlace(gpu_id)
......@@ -119,7 +132,10 @@ def compute_features_for_kmeans(data_content):
exe.run(startup_prog)
fluid.io.load_persistables(exe, h_pre_models, prog)
images = preprocess_image(data_content) # transpose to [N, 3, H, W], scaled to [0.0, 1.0]
result = exe.run(prog, fetch_list=[resized_features], feed={'image': images})
images = preprocess_image(
data_content) # transpose to [N, 3, H, W], scaled to [0.0, 1.0]
result = exe.run(prog,
fetch_list=[resized_features],
feed={'image': images})
return result[0][0]
paddlex/interpret/core/interpretation.py
浏览文件 @ bc53ec16
......@@ -20,12 +20,10 @@ class Interpretation(object):
"""
Base class for all interpretation algorithms.
"""
def __init__(self, interpretation_algorithm_name, predict_fn, label_names, **kwargs):
supported_algorithms = {
'cam': CAM,
'lime': LIME,
'normlime': NormLIME
}
def __init__(self, interpretation_algorithm_name, predict_fn, label_names,
**kwargs):
supported_algorithms = {'cam': CAM, 'lime': LIME, 'normlime': NormLIME}
self.algorithm_name = interpretation_algorithm_name.lower()
assert self.algorithm_name in supported_algorithms.keys()
......@@ -33,19 +31,17 @@ class Interpretation(object):
# initialization for the interpretation algorithm.
self.algorithm = supported_algorithms[self.algorithm_name](
self.predict_fn, label_names, **kwargs
)
self.predict_fn, label_names, **kwargs)
def interpret(self, data_, visualization=True, save_to_disk=True, save_dir='./tmp'):
def interpret(self, data_, visualization=True, save_dir='./'):
"""
Args:
data_: data_ can be a path or numpy.ndarray.
visualization: whether to show using matplotlib.
save_to_disk: whether to save the figure in local disk.
save_dir: dir to save figure if save_to_disk is True.
Returns:
"""
return self.algorithm.interpret(data_, visualization, save_to_disk, save_dir)
return self.algorithm.interpret(data_, visualization, save_dir)
paddlex/interpret/core/lime_base.py
浏览文件 @ bc53ec16
......@@ -27,7 +27,6 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
The code in this file (lime_base.py) is modified from https://github.com/marcotcr/lime.
"""
import numpy as np
import scipy as sp
......@@ -39,10 +38,8 @@ import paddlex.utils.logging as logging
class LimeBase(object):
"""Class for learning a locally linear sparse model from perturbed data"""
def __init__(self,
kernel_fn,
verbose=False,
random_state=None):
def __init__(self, kernel_fn, verbose=False, random_state=None):
"""Init function
Args:
......@@ -72,15 +69,14 @@ class LimeBase(object):
"""
from sklearn.linear_model import lars_path
x_vector = weighted_data
alphas, _, coefs = lars_path(x_vector,
weighted_labels,
method='lasso',
verbose=False)
alphas, _, coefs = lars_path(
x_vector, weighted_labels, method='lasso', verbose=False)
return alphas, coefs
def forward_selection(self, data, labels, weights, num_features):
"""Iteratively adds features to the model"""
clf = Ridge(alpha=0, fit_intercept=True, random_state=self.random_state)
clf = Ridge(
alpha=0, fit_intercept=True, random_state=self.random_state)
used_features = []
for _ in range(min(num_features, data.shape[1])):
max_ = -100000000
......@@ -88,11 +84,13 @@ class LimeBase(object):
for feature in range(data.shape[1]):
if feature in used_features:
continue
clf.fit(data[:, used_features + [feature]], labels,
clf.fit(data[:, used_features + [feature]],
labels,
sample_weight=weights)
score = clf.score(data[:, used_features + [feature]],
labels,
sample_weight=weights)
score = clf.score(
data[:, used_features + [feature]],
labels,
sample_weight=weights)
if score > max_:
best = feature
max_ = score
......@@ -108,8 +106,8 @@ class LimeBase(object):
elif method == 'forward_selection':
return self.forward_selection(data, labels, weights, num_features)
elif method == 'highest_weights':
clf = Ridge(alpha=0.01, fit_intercept=True,
random_state=self.random_state)
clf = Ridge(
alpha=0.01, fit_intercept=True, random_state=self.random_state)
clf.fit(data, labels, sample_weight=weights)
coef = clf.coef_
......@@ -125,7 +123,8 @@ class LimeBase(object):
nnz_indexes = argsort_data[::-1]
indices = weighted_data.indices[nnz_indexes]
num_to_pad = num_features - sdata
indices = np.concatenate((indices, np.zeros(num_to_pad, dtype=indices.dtype)))
indices = np.concatenate((indices, np.zeros(
num_to_pad, dtype=indices.dtype)))
indices_set = set(indices)
pad_counter = 0
for i in range(data.shape[1]):
......@@ -135,7 +134,8 @@ class LimeBase(object):
if pad_counter >= num_to_pad:
break
else:
nnz_indexes = argsort_data[sdata - num_features:sdata][::-1]
nnz_indexes = argsort_data[sdata - num_features:sdata][::
-1]
indices = weighted_data.indices[nnz_indexes]
return indices
else:
......@@ -146,13 +146,13 @@ class LimeBase(object):
reverse=True)
return np.array([x[0] for x in feature_weights[:num_features]])
elif method == 'lasso_path':
weighted_data = ((data - np.average(data, axis=0, weights=weights))
* np.sqrt(weights[:, np.newaxis]))
weighted_labels = ((labels - np.average(labels, weights=weights))
* np.sqrt(weights))
weighted_data = ((data - np.average(
data, axis=0, weights=weights)) *
np.sqrt(weights[:, np.newaxis]))
weighted_labels = ((labels - np.average(
labels, weights=weights)) * np.sqrt(weights))
nonzero = range(weighted_data.shape[1])
_, coefs = self.generate_lars_path(weighted_data,
weighted_labels)
_, coefs = self.generate_lars_path(weighted_data, weighted_labels)
for i in range(len(coefs.T) - 1, 0, -1):
nonzero = coefs.T[i].nonzero()[0]
if len(nonzero) <= num_features:
......@@ -164,8 +164,8 @@ class LimeBase(object):
n_method = 'forward_selection'
else:
n_method = 'highest_weights'
return self.feature_selection(data, labels, weights,
num_features, n_method)
return self.feature_selection(data, labels, weights, num_features,
n_method)
def interpret_instance_with_data(self,
neighborhood_data,
......@@ -214,30 +214,31 @@ class LimeBase(object):
weights = self.kernel_fn(distances)
labels_column = neighborhood_labels[:, label]
used_features = self.feature_selection(neighborhood_data,
labels_column,
weights,
num_features,
feature_selection)
labels_column, weights,
num_features, feature_selection)
if model_regressor is None:
model_regressor = Ridge(alpha=1, fit_intercept=True,
random_state=self.random_state)
model_regressor = Ridge(
alpha=1, fit_intercept=True, random_state=self.random_state)
easy_model = model_regressor
easy_model.fit(neighborhood_data[:, used_features],
labels_column, sample_weight=weights)
labels_column,
sample_weight=weights)
prediction_score = easy_model.score(
neighborhood_data[:, used_features],
labels_column, sample_weight=weights)
labels_column,
sample_weight=weights)
local_pred = easy_model.predict(neighborhood_data[0, used_features].reshape(1, -1))
local_pred = easy_model.predict(neighborhood_data[0, used_features]
.reshape(1, -1))
if self.verbose:
logging.info('Intercept' + str(easy_model.intercept_))
logging.info('Prediction_local' + str(local_pred))
logging.info('Right:' + str(neighborhood_labels[0, label]))
return (easy_model.intercept_,
sorted(zip(used_features, easy_model.coef_),
key=lambda x: np.abs(x[1]), reverse=True),
prediction_score, local_pred)
return (easy_model.intercept_, sorted(
zip(used_features, easy_model.coef_),
key=lambda x: np.abs(x[1]),
reverse=True), prediction_score, local_pred)
class ImageInterpretation(object):
......@@ -254,8 +255,13 @@ class ImageInterpretation(object):
self.local_weights = {}
self.local_pred = None
def get_image_and_mask(self, label, positive_only=True, negative_only=False, hide_rest=False,
num_features=5, min_weight=0.):
def get_image_and_mask(self,
label,
positive_only=True,
negative_only=False,
hide_rest=False,
num_features=5,
min_weight=0.):
"""Init function.
Args:
......@@ -279,7 +285,9 @@ class ImageInterpretation(object):
if label not in self.local_weights:
raise KeyError('Label not in interpretation')
if positive_only & negative_only:
raise ValueError("Positive_only and negative_only cannot be true at the same time.")
raise ValueError(
"Positive_only and negative_only cannot be true at the same time."
)
segments = self.segments
image = self.image
local_weights_label = self.local_weights[label]
......@@ -289,14 +297,20 @@ class ImageInterpretation(object):
else:
temp = self.image.copy()
if positive_only:
fs = [x[0] for x in local_weights_label
if x[1] > 0 and x[1] > min_weight][:num_features]
fs = [
x[0] for x in local_weights_label
if x[1] > 0 and x[1] > min_weight
][:num_features]
if negative_only:
fs = [x[0] for x in local_weights_label
if x[1] < 0 and abs(x[1]) > min_weight][:num_features]
fs = [
x[0] for x in local_weights_label
if x[1] < 0 and abs(x[1]) > min_weight
][:num_features]
if positive_only or negative_only:
c = 1 if positive_only else 0
for f in fs:
temp[segments == f] = image[segments == f].copy()
temp[segments == f] = [0, 255, 0]
# temp[segments == f, c] = np.max(image)
mask[segments == f] = 1
return temp, mask
else:
......@@ -330,8 +344,11 @@ class ImageInterpretation(object):
temp = np.zeros_like(image)
weight_max = abs(local_weights_label[0][1])
local_weights_label = [(f, w/weight_max) for f, w in local_weights_label]
local_weights_label = sorted(local_weights_label, key=lambda x: x[1], reverse=True) # negatives are at last.
local_weights_label = [(f, w / weight_max)
for f, w in local_weights_label]
local_weights_label = sorted(
local_weights_label, key=lambda x: x[1],
reverse=True) # negatives are at last.
cmaps = cm.get_cmap('Spectral')
colors = cmaps(np.linspace(0, 1, len(local_weights_label)))
......@@ -354,8 +371,12 @@ class LimeImageInterpreter(object):
feature that is 1 when the value is the same as the instance being
interpreted."""
def __init__(self, kernel_width=.25, kernel=None, verbose=False,
feature_selection='auto', random_state=None):
def __init__(self,
kernel_width=.25,
kernel=None,
verbose=False,
feature_selection='auto',
random_state=None):
"""Init function.
Args:
......@@ -377,22 +398,27 @@ class LimeImageInterpreter(object):
kernel_width = float(kernel_width)
if kernel is None:
def kernel(d, kernel_width):
return np.sqrt(np.exp(-(d ** 2) / kernel_width ** 2))
return np.sqrt(np.exp(-(d**2) / kernel_width**2))
kernel_fn = partial(kernel, kernel_width=kernel_width)
self.random_state = check_random_state(random_state)
self.feature_selection = feature_selection
self.base = LimeBase(kernel_fn, verbose, random_state=self.random_state)
self.base = LimeBase(
kernel_fn, verbose, random_state=self.random_state)
def interpret_instance(self, image, classifier_fn, labels=(1,),
def interpret_instance(self,
image,
classifier_fn,
labels=(1, ),
hide_color=None,
num_features=100000, num_samples=1000,
num_features=100000,
num_samples=1000,
batch_size=10,
distance_metric='cosine',
model_regressor=None
):
model_regressor=None):
"""Generates interpretations for a prediction.
First, we generate neighborhood data by randomly perturbing features
......@@ -435,6 +461,7 @@ class LimeImageInterpreter(object):
self.segments = segments
fudged_image = image.copy()
# global_mean = np.mean(image, (0, 1))
if hide_color is None:
# if no hide_color, use the mean
for x in np.unique(segments):
......@@ -461,24 +488,30 @@ class LimeImageInterpreter(object):
top = labels
data, labels = self.data_labels(image, fudged_image, segments,
classifier_fn, num_samples,
batch_size=batch_size)
data, labels = self.data_labels(
image,
fudged_image,
segments,
classifier_fn,
num_samples,
batch_size=batch_size)
distances = sklearn.metrics.pairwise_distances(
data,
data[0].reshape(1, -1),
metric=distance_metric
).ravel()
data, data[0].reshape(1, -1), metric=distance_metric).ravel()
interpretation_image = ImageInterpretation(image, segments)
for label in top:
(interpretation_image.intercept[label],
interpretation_image.local_weights[label],
interpretation_image.score, interpretation_image.local_pred) = self.base.interpret_instance_with_data(
data, labels, distances, label, num_features,
model_regressor=model_regressor,
feature_selection=self.feature_selection)
interpretation_image.score, interpretation_image.local_pred
) = self.base.interpret_instance_with_data(
data,
labels,
distances,
label,
num_features,
model_regressor=model_regressor,
feature_selection=self.feature_selection)
return interpretation_image
def data_labels(self,
......@@ -511,6 +544,9 @@ class LimeImageInterpreter(object):
labels = []
data[0, :] = 1
imgs = []
logging.info("Computing LIME.", use_color=True)
for row in tqdm.tqdm(data):
temp = copy.deepcopy(image)
zeros = np.where(row == 0)[0]
......
paddlex/interpret/core/normlime_base.py
浏览文件 @ bc53ec16
......@@ -16,6 +16,7 @@ import os
import os.path as osp
import numpy as np
import glob
import tqdm
from paddlex.interpret.as_data_reader.readers import read_image
import paddlex.utils.logging as logging
......@@ -38,18 +39,24 @@ def combine_normlime_and_lime(lime_weights, g_weights):
for y in pred_labels:
normlized_lime_weights_y = lime_weights[y]
lime_weights_dict = {tuple_w[0]: tuple_w[1] for tuple_w in normlized_lime_weights_y}
lime_weights_dict = {
tuple_w[0]: tuple_w[1]
for tuple_w in normlized_lime_weights_y
}
normlized_g_weight_y = g_weights[y]
normlime_weights_dict = {tuple_w[0]: tuple_w[1] for tuple_w in normlized_g_weight_y}
normlime_weights_dict = {
tuple_w[0]: tuple_w[1]
for tuple_w in normlized_g_weight_y
}
combined_weights[y] = [
(seg_k, lime_weights_dict[seg_k] * normlime_weights_dict[seg_k])
for seg_k in lime_weights_dict.keys()
]
combined_weights[y] = sorted(combined_weights[y],
key=lambda x: np.abs(x[1]), reverse=True)
combined_weights[y] = sorted(
combined_weights[y], key=lambda x: np.abs(x[1]), reverse=True)
return combined_weights
......@@ -67,7 +74,8 @@ def centroid_using_superpixels(features, segments):
regions = regionprops(segments + 1)
one_list = np.zeros((len(np.unique(segments)), features.shape[2]))
for i, r in enumerate(regions):
one_list[i] = features[int(r.centroid[0] + 0.5), int(r.centroid[1] + 0.5), :]
one_list[i] = features[int(r.centroid[0] + 0.5), int(r.centroid[1] +
0.5), :]
return one_list
......@@ -80,30 +88,39 @@ def get_feature_for_kmeans(feature_map, segments):
return x
def precompute_normlime_weights(list_data_, predict_fn, num_samples=3000, batch_size=50, save_dir='./tmp'):
def precompute_normlime_weights(list_data_,
predict_fn,
num_samples=3000,
batch_size=50,
save_dir='./tmp'):
# save lime weights and kmeans cluster labels
precompute_lime_weights(list_data_, predict_fn, num_samples, batch_size, save_dir)
precompute_lime_weights(list_data_, predict_fn, num_samples, batch_size,
save_dir)
# load precomputed results, compute normlime weights and save.
fname_list = glob.glob(os.path.join(save_dir, 'lime_weights_s{}*.npy'.format(num_samples)))
fname_list = glob.glob(
os.path.join(save_dir, 'lime_weights_s{}*.npy'.format(num_samples)))
return compute_normlime_weights(fname_list, save_dir, num_samples)
def save_one_lime_predict_and_kmean_labels(lime_all_weights, image_pred_labels, cluster_labels, save_path):
def save_one_lime_predict_and_kmean_labels(lime_all_weights, image_pred_labels,
cluster_labels, save_path):
lime_weights = {}
for label in image_pred_labels:
lime_weights[label] = lime_all_weights[label]
for_normlime_weights = {
'lime_weights': lime_weights, # a dict: class_label: (seg_label, weight)
'lime_weights':
lime_weights, # a dict: class_label: (seg_label, weight)
'cluster': cluster_labels # a list with segments as indices.
}
np.save(save_path, for_normlime_weights)
def precompute_lime_weights(list_data_, predict_fn, num_samples, batch_size, save_dir):
def precompute_lime_weights(list_data_, predict_fn, num_samples, batch_size,
save_dir):
root_path = gen_user_home()
root_path = osp.join(root_path, '.paddlex')
h_pre_models = osp.join(root_path, "pre_models")
......@@ -117,17 +134,24 @@ def precompute_lime_weights(list_data_, predict_fn, num_samples, batch_size, sav
for data_index, each_data_ in enumerate(list_data_):
if isinstance(each_data_, str):
save_path = "lime_weights_s{}_{}.npy".format(num_samples, each_data_.split('/')[-1].split('.')[0])
save_path = "lime_weights_s{}_{}.npy".format(
num_samples, each_data_.split('/')[-1].split('.')[0])
save_path = os.path.join(save_dir, save_path)
else:
save_path = "lime_weights_s{}_{}.npy".format(num_samples, data_index)
save_path = "lime_weights_s{}_{}.npy".format(num_samples,
data_index)
save_path = os.path.join(save_dir, save_path)
if os.path.exists(save_path):
logging.info(save_path + ' exists, not computing this one.', use_color=True)
logging.info(
save_path + ' exists, not computing this one.', use_color=True)
continue
img_file_name = each_data_ if isinstance(each_data_, str) else data_index
logging.info('processing '+ img_file_name + ' [{}/{}]'.format(data_index, len(list_data_)), use_color=True)
img_file_name = each_data_ if isinstance(each_data_,
str) else data_index
logging.info(
'processing ' + img_file_name + ' [{}/{}]'.format(data_index,
len(list_data_)),
use_color=True)
image_show = read_image(each_data_)
result = predict_fn(image_show)
......@@ -156,32 +180,38 @@ def precompute_lime_weights(list_data_, predict_fn, num_samples, batch_size, sav
pred_label = pred_label[:top_k]
algo = lime_base.LimeImageInterpreter()
interpreter = algo.interpret_instance(image_show[0], predict_fn, pred_label, 0,
num_samples=num_samples, batch_size=batch_size)
X = get_feature_for_kmeans(compute_features_for_kmeans(image_show).transpose((1, 2, 0)), interpreter.segments)
interpreter = algo.interpret_instance(
image_show[0],
predict_fn,
pred_label,
0,
num_samples=num_samples,
batch_size=batch_size)
X = get_feature_for_kmeans(
compute_features_for_kmeans(image_show).transpose((1, 2, 0)),
interpreter.segments)
try:
cluster_labels = kmeans_model.predict(X)
except AttributeError:
from sklearn.metrics import pairwise_distances_argmin_min
cluster_labels, _ = pairwise_distances_argmin_min(X, kmeans_model.cluster_centers_)
cluster_labels, _ = pairwise_distances_argmin_min(
X, kmeans_model.cluster_centers_)
save_one_lime_predict_and_kmean_labels(
interpreter.local_weights, pred_label,
cluster_labels,
save_path
)
interpreter.local_weights, pred_label, cluster_labels, save_path)
def compute_normlime_weights(a_list_lime_fnames, save_dir, lime_num_samples):
normlime_weights_all_labels = {}
for f in a_list_lime_fnames:
try:
lime_weights_and_cluster = np.load(f, allow_pickle=True).item()
lime_weights = lime_weights_and_cluster['lime_weights']
cluster = lime_weights_and_cluster['cluster']
except:
logging.info('When loading precomputed LIME result, skipping' + str(f))
logging.info('When loading precomputed LIME result, skipping' +
str(f))
continue
logging.info('Loading precomputed LIME result,' + str(f))
pred_labels = lime_weights.keys()
......@@ -203,10 +233,12 @@ def compute_normlime_weights(a_list_lime_fnames, save_dir, lime_num_samples):
for y in normlime_weights_all_labels:
normlime_weights = normlime_weights_all_labels.get(y, {})
for k in normlime_weights:
normlime_weights[k] = sum(normlime_weights[k]) / len(normlime_weights[k])
normlime_weights[k] = sum(normlime_weights[k]) / len(
normlime_weights[k])
# check normlime
if len(normlime_weights_all_labels.keys()) < max(normlime_weights_all_labels.keys()) + 1:
if len(normlime_weights_all_labels.keys()) < max(
normlime_weights_all_labels.keys()) + 1:
logging.info(
"\n" + \
"Warning: !!! \n" + \
......@@ -218,17 +250,166 @@ def compute_normlime_weights(a_list_lime_fnames, save_dir, lime_num_samples):
)
n = 0
f_out = 'normlime_weights_s{}_samples_{}-{}.npy'.format(lime_num_samples, len(a_list_lime_fnames), n)
while os.path.exists(
os.path.join(save_dir, f_out)
):
f_out = 'normlime_weights_s{}_samples_{}-{}.npy'.format(
lime_num_samples, len(a_list_lime_fnames), n)
while os.path.exists(os.path.join(save_dir, f_out)):
n += 1
f_out = 'normlime_weights_s{}_samples_{}-{}.npy'.format(lime_num_samples, len(a_list_lime_fnames), n)
f_out = 'normlime_weights_s{}_samples_{}-{}.npy'.format(
lime_num_samples, len(a_list_lime_fnames), n)
continue
np.save(
os.path.join(save_dir, f_out),
normlime_weights_all_labels
)
np.save(os.path.join(save_dir, f_out), normlime_weights_all_labels)
return os.path.join(save_dir, f_out)
def precompute_global_classifier(dataset,
predict_fn,
save_path,
batch_size=50,
max_num_samples=1000):
from sklearn.linear_model import LogisticRegression
root_path = gen_user_home()
root_path = osp.join(root_path, '.paddlex')
h_pre_models = osp.join(root_path, "pre_models")
if not osp.exists(h_pre_models):
if not osp.exists(root_path):
os.makedirs(root_path)
url = "https://bj.bcebos.com/paddlex/interpret/pre_models.tar.gz"
pdx.utils.download_and_decompress(url, path=root_path)
h_pre_models_kmeans = osp.join(h_pre_models, "kmeans_model.pkl")
kmeans_model = load_kmeans_model(h_pre_models_kmeans)
image_list = []
for item in dataset.file_list:
image_list.append(item[0])
x_data = []
y_labels = []
num_features = len(kmeans_model.cluster_centers_)
logging.info(
"Initialization for NormLIME: Computing each sample in the test list.",
use_color=True)
for each_data_ in tqdm.tqdm(image_list):
x_data_i = np.zeros((num_features))
image_show = read_image(each_data_)
result = predict_fn(image_show)
result = result[0] # only one image here.
c = compute_features_for_kmeans(image_show).transpose((1, 2, 0))
segments = np.zeros((image_show.shape[1], image_show.shape[2]),
np.int32)
num_blocks = 10
height_per_i = segments.shape[0] // num_blocks + 1
width_per_i = segments.shape[1] // num_blocks + 1
for i in range(segments.shape[0]):
for j in range(segments.shape[1]):
segments[i,
j] = i // height_per_i * num_blocks + j // width_per_i
# segments = quickshift(image_show[0], sigma=1)
X = get_feature_for_kmeans(c, segments)
try:
cluster_labels = kmeans_model.predict(X)
except AttributeError:
from sklearn.metrics import pairwise_distances_argmin_min
cluster_labels, _ = pairwise_distances_argmin_min(
X, kmeans_model.cluster_centers_)
for c in cluster_labels:
x_data_i[c] = 1
# x_data_i /= len(cluster_labels)
pred_y_i = np.argmax(result)
y_labels.append(pred_y_i)
x_data.append(x_data_i)
if len(np.unique(y_labels)) < 2:
logging.info("Warning: The test samples in the dataset is limited.\n \
NormLIME may have no effect on the results.\n \
Try to add more test samples, or see the results of LIME.")
num_classes = np.max(np.unique(y_labels)) + 1
normlime_weights_all_labels = {}
for class_index in range(num_classes):
w = np.ones((num_features)) / num_features
normlime_weights_all_labels[class_index] = {
i: wi
for i, wi in enumerate(w)
}
logging.info("Saving the computed normlime_weights in {}".format(
save_path))
np.save(save_path, normlime_weights_all_labels)
return save_path
clf = LogisticRegression(multi_class='multinomial', max_iter=1000)
clf.fit(x_data, y_labels)
num_classes = np.max(np.unique(y_labels)) + 1
normlime_weights_all_labels = {}
if len(y_labels) / len(np.unique(y_labels)) < 3:
logging.info("Warning: The test samples in the dataset is limited.\n \
NormLIME may have no effect on the results.\n \
Try to add more test samples, or see the results of LIME.")
if len(np.unique(y_labels)) == 2:
# binary: clf.coef_ has shape of [1, num_features]
for class_index in range(num_classes):
if class_index not in clf.classes_:
w = np.ones((num_features)) / num_features
normlime_weights_all_labels[class_index] = {
i: wi
for i, wi in enumerate(w)
}
continue
if clf.classes_[0] == class_index:
w = -clf.coef_[0]
else:
w = clf.coef_[0]
# softmax
w = w - np.max(w)
exp_w = np.exp(w * 10)
w = exp_w / np.sum(exp_w)
normlime_weights_all_labels[class_index] = {
i: wi
for i, wi in enumerate(w)
}
else:
# clf.coef_ has shape of [len(np.unique(y_labels)), num_features]
for class_index in range(num_classes):
if class_index not in clf.classes_:
w = np.ones((num_features)) / num_features
normlime_weights_all_labels[class_index] = {
i: wi
for i, wi in enumerate(w)
}
continue
coef_class_index = np.where(clf.classes_ == class_index)[0][0]
w = clf.coef_[coef_class_index]
# softmax
w = w - np.max(w)
exp_w = np.exp(w * 10)
w = exp_w / np.sum(exp_w)
normlime_weights_all_labels[class_index] = {
i: wi
for i, wi in enumerate(w)
}
logging.info("Saving the computed normlime_weights in {}".format(
save_path))
np.save(save_path, normlime_weights_all_labels)
return save_path
paddlex/interpret/interpretation_predict.py
浏览文件 @ bc53ec16
......@@ -13,17 +13,26 @@
# limitations under the License.
import numpy as np
import cv2
import copy
def interpretation_predict(model, images):
model.arrange_transforms(
transforms=model.test_transforms, mode='test')
images = images.astype('float32')
model.arrange_transforms(transforms=model.test_transforms, mode='test')
tmp_transforms = copy.deepcopy(model.test_transforms.transforms)
model.test_transforms.transforms = model.test_transforms.transforms[-2:]
new_imgs = []
for i in range(images.shape[0]):
img = images[i]
new_imgs.append(model.test_transforms(img)[0])
images[i] = cv2.cvtColor(images[i], cv2.COLOR_RGB2BGR)
new_imgs.append(model.test_transforms(images[i])[0])
new_imgs = np.array(new_imgs)
result = model.exe.run(
model.test_prog,
feed={'image': new_imgs},
fetch_list=list(model.interpretation_feats.values()))
return result
\ No newline at end of file
out = model.exe.run(model.test_prog,
feed={'image': new_imgs},
fetch_list=list(model.interpretation_feats.values()))
model.test_transforms.transforms = tmp_transforms
return out
paddlex/interpret/visualize.py
浏览文件 @ bc53ec16
......@@ -20,79 +20,79 @@ import numpy as np
import paddlex as pdx
from .interpretation_predict import interpretation_predict
from .core.interpretation import Interpretation
from .core.normlime_base import precompute_normlime_weights
from .core.normlime_base import precompute_global_classifier
from .core._session_preparation import gen_user_home
def lime(img_file,
model,
num_samples=3000,
batch_size=50,
save_dir='./'):
"""使用LIME算法将模型预测结果的可解释性可视化。
def lime(img_file, model, num_samples=3000, batch_size=50, save_dir='./'):
"""使用LIME算法将模型预测结果的可解释性可视化。
LIME表示与模型无关的局部可解释性，可以解释任何模型。LIME的思想是以输入样本为中心，
在其附近的空间中进行随机采样，每个采样通过原模型得到新的输出，这样得到一系列的输入
和对应的输出，LIME用一个简单的、可解释的模型（比如线性回归模型）来拟合这个映射关系，
得到每个输入维度的权重，以此来解释模型。
得到每个输入维度的权重，以此来解释模型。
注意：LIME可解释性结果可视化目前只支持分类模型。
Args:
img_file (str): 预测图像路径。
model (paddlex.cv.models): paddlex中的模型。
num_samples (int): LIME用于学习线性模型的采样数，默认为3000。
batch_size (int): 预测数据batch大小，默认为50。
save_dir (str): 可解释性可视化结果（保存为png格式文件）和中间文件存储路径。
save_dir (str): 可解释性可视化结果（保存为png格式文件）和中间文件存储路径。
"""
assert model.model_type == 'classifier', \
'Now the interpretation visualize only be supported in classifier!'
if model.status != 'Normal':
raise Exception('The interpretation only can deal with the Normal model')
raise Exception(
'The interpretation only can deal with the Normal model')
if not osp.exists(save_dir):
os.makedirs(save_dir)
model.arrange_transforms(
transforms=model.test_transforms, mode='test')
model.arrange_transforms(transforms=model.test_transforms, mode='test')
tmp_transforms = copy.deepcopy(model.test_transforms)
tmp_transforms.transforms = tmp_transforms.transforms[:-2]
img = tmp_transforms(img_file)[0]
img = np.around(img).astype('uint8')
img = np.expand_dims(img, axis=0)
interpreter = None
interpreter = get_lime_interpreter(img, model, num_samples=num_samples, batch_size=batch_size)
interpreter = get_lime_interpreter(
img, model, num_samples=num_samples, batch_size=batch_size)
img_name = osp.splitext(osp.split(img_file)[-1])[0]
interpreter.interpret(img, save_dir=save_dir)
def normlime(img_file,
model,
dataset=None,
num_samples=3000,
batch_size=50,
save_dir='./'):
interpreter.interpret(img, save_dir=osp.join(save_dir, img_name))
def normlime(img_file,
model,
dataset=None,
num_samples=3000,
batch_size=50,
save_dir='./',
normlime_weights_file=None):
"""使用NormLIME算法将模型预测结果的可解释性可视化。
NormLIME是利用一定数量的样本来出一个全局的解释。NormLIME会提前计算一定数量的测
试样本的LIME结果，然后对相同的特征进行权重的归一化，这样来得到一个全局的输入和输出的关系。
注意1：dataset读取的是一个数据集，该数据集不宜过大，否则计算时间会较长，但应包含所有类别的数据。
注意2：NormLIME可解释性结果可视化目前只支持分类模型。
Args:
img_file (str): 预测图像路径。
model (paddlex.cv.models): paddlex中的模型。
dataset (paddlex.datasets): 数据集读取器，默认为None。
num_samples (int): LIME用于学习线性模型的采样数，默认为3000。
batch_size (int): 预测数据batch大小，默认为50。
save_dir (str): 可解释性可视化结果（保存为png格式文件）和中间文件存储路径。
save_dir (str): 可解释性可视化结果（保存为png格式文件）和中间文件存储路径。
normlime_weights_file (str): NormLIME初始化文件名，若不存在，则计算一次，保存于该路径；若存在，则直接载入。
"""
assert model.model_type == 'classifier', \
'Now the interpretation visualize only be supported in classifier!'
if model.status != 'Normal':
raise Exception('The interpretation only can deal with the Normal model')
raise Exception(
'The interpretation only can deal with the Normal model')
if not osp.exists(save_dir):
os.makedirs(save_dir)
model.arrange_transforms(
transforms=model.test_transforms, mode='test')
model.arrange_transforms(transforms=model.test_transforms, mode='test')
tmp_transforms = copy.deepcopy(model.test_transforms)
tmp_transforms.transforms = tmp_transforms.transforms[:-2]
img = tmp_transforms(img_file)[0]
......@@ -100,52 +100,48 @@ def normlime(img_file,
img = np.expand_dims(img, axis=0)
interpreter = None
if dataset is None:
raise Exception('The dataset is None. Cannot implement this kind of interpretation')
interpreter = get_normlime_interpreter(img, model, dataset,
num_samples=num_samples, batch_size=batch_size,
save_dir=save_dir)
raise Exception(
'The dataset is None. Cannot implement this kind of interpretation')
interpreter = get_normlime_interpreter(
img,
model,
dataset,
num_samples=num_samples,
batch_size=batch_size,
save_dir=save_dir,
normlime_weights_file=normlime_weights_file)
img_name = osp.splitext(osp.split(img_file)[-1])[0]
interpreter.interpret(img, save_dir=save_dir)
interpreter.interpret(img, save_dir=osp.join(save_dir, img_name))
def get_lime_interpreter(img, model, num_samples=3000, batch_size=50):
def predict_func(image):
image = image.astype('float32')
for i in range(image.shape[0]):
image[i] = cv2.cvtColor(image[i], cv2.COLOR_RGB2BGR)
tmp_transforms = copy.deepcopy(model.test_transforms.transforms)
model.test_transforms.transforms = model.test_transforms.transforms[-2:]
out = interpretation_predict(model, image)
model.test_transforms.transforms = tmp_transforms
return out[0]
labels_name = None
if hasattr(model, 'labels'):
labels_name = model.labels
interpreter = Interpretation('lime',
predict_func,
labels_name,
num_samples=num_samples,
batch_size=batch_size)
interpreter = Interpretation(
'lime',
predict_func,
labels_name,
num_samples=num_samples,
batch_size=batch_size)
return interpreter
def get_normlime_interpreter(img, model, dataset, num_samples=3000, batch_size=50, save_dir='./'):
def precompute_predict_func(image):
image = image.astype('float32')
tmp_transforms = copy.deepcopy(model.test_transforms.transforms)
model.test_transforms.transforms = model.test_transforms.transforms[-2:]
out = interpretation_predict(model, image)
model.test_transforms.transforms = tmp_transforms
return out[0]
def get_normlime_interpreter(img,
model,
dataset,
num_samples=3000,
batch_size=50,
save_dir='./',
normlime_weights_file=None):
def predict_func(image):
image = image.astype('float32')
for i in range(image.shape[0]):
image[i] = cv2.cvtColor(image[i], cv2.COLOR_RGB2BGR)
tmp_transforms = copy.deepcopy(model.test_transforms.transforms)
model.test_transforms.transforms = model.test_transforms.transforms[-2:]
out = interpretation_predict(model, image)
model.test_transforms.transforms = tmp_transforms
return out[0]
labels_name = None
if dataset is not None:
labels_name = dataset.labels
......@@ -157,28 +153,29 @@ def get_normlime_interpreter(img, model, dataset, num_samples=3000, batch_size=5
os.makedirs(root_path)
url = "https://bj.bcebos.com/paddlex/interpret/pre_models.tar.gz"
pdx.utils.download_and_decompress(url, path=root_path)
npy_dir = precompute_for_normlime(precompute_predict_func,
dataset,
num_samples=num_samples,
batch_size=batch_size,
save_dir=save_dir)
interpreter = Interpretation('normlime',
predict_func,
labels_name,
num_samples=num_samples,
batch_size=batch_size,
normlime_weights=npy_dir)
return interpreter
def precompute_for_normlime(predict_func, dataset, num_samples=3000, batch_size=50, save_dir='./'):
image_list = []
for item in dataset.file_list:
image_list.append(item[0])
return precompute_normlime_weights(
image_list,
if osp.exists(osp.join(save_dir, normlime_weights_file)):
normlime_weights_file = osp.join(save_dir, normlime_weights_file)
try:
np.load(normlime_weights_file, allow_pickle=True).item()
except:
normlime_weights_file = precompute_global_classifier(
dataset,
predict_func,
save_path=normlime_weights_file,
batch_size=batch_size)
else:
normlime_weights_file = precompute_global_classifier(
dataset,
predict_func,
num_samples=num_samples,
batch_size=batch_size,
save_dir=save_dir)
save_path=osp.join(save_dir, normlime_weights_file),
batch_size=batch_size)
interpreter = Interpretation(
'normlime',
predict_func,
labels_name,
num_samples=num_samples,
batch_size=batch_size,
normlime_weights=normlime_weights_file)
return interpreter
paddlex/utils/logging.py
浏览文件 @ bc53ec16
......@@ -29,13 +29,11 @@ def log(level=2, message="", use_color=False):
current_time = time.strftime("%Y-%m-%d %H:%M:%S", time_array)
if paddlex.log_level >= level:
if use_color:
print("\033[1;31;40m{} [{}]\t{}\033[0m".format(
current_time, levels[level],
message).encode("utf-8").decode("latin1"))
print("\033[1;31;40m{} [{}]\t{}\033[0m".format(current_time, levels[
level], message).encode("utf-8").decode("latin1"))
else:
print(
"{} [{}]\t{}".format(current_time, levels[level],
message).encode("utf-8").decode("latin1"))
print("{} [{}]\t{}".format(current_time, levels[level], message)
.encode("utf-8").decode("latin1"))
sys.stdout.flush()
......@@ -51,6 +49,7 @@ def warning(message="", use_color=True):
log(level=1, message=message, use_color=use_color)
def error(message="", use_color=True):
def error(message="", use_color=True, exit=True):
log(level=0, message=message, use_color=use_color)
sys.exit(-1)
if exit:
sys.exit(-1)
setup.py
浏览文件 @ bc53ec16
......@@ -19,7 +19,7 @@ long_description = "PaddleX. A end-to-end deeplearning model development toolkit
setuptools.setup(
name="paddlex",
version='1.0.5',
version='1.0.6',
author="paddlex",
author_email="paddlex@baidu.com",
description=long_description,
......
tutorials/interpret/normlime.py
浏览文件 @ bc53ec16
......@@ -14,18 +14,22 @@ model_file = 'https://bj.bcebos.com/paddlex/interpret/mini_imagenet_veg_mobilene
pdx.utils.download_and_decompress(model_file, path='./')
# 加载模型
model = pdx.load_model('mini_imagenet_veg_mobilenetv2')
model_file = 'mini_imagenet_veg_mobilenetv2'
model = pdx.load_model(model_file)
# 定义测试所用的数据集
dataset = 'mini_imagenet_veg'
test_dataset = pdx.datasets.ImageNet(
data_dir='mini_imagenet_veg',
file_list=osp.join('mini_imagenet_veg', 'test_list.txt'),
label_list=osp.join('mini_imagenet_veg', 'labels.txt'),
data_dir=dataset,
file_list=osp.join(dataset, 'test_list.txt'),
label_list=osp.join(dataset, 'labels.txt'),
transforms=model.test_transforms)
# 可解释性可视化
pdx.interpret.normlime(
'mini_imagenet_veg/mushroom/n07734744_1106.JPEG',
model,
test_dataset,
save_dir='./')
test_dataset.file_list[0][0],
model,
test_dataset,
save_dir='./',
normlime_weights_file='{}_{}.npy'.format(
dataset.split('/')[-1], model.model_name))
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