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README.md
浏览文件 @ e6ed31ef
......@@ -8,8 +8,66 @@ PaddlePaddle provides a rich set of computational units to enable users to adopt
- [fluid models](fluid): use PaddlePaddle's Fluid APIs. We especially recommend users to use Fluid models.
- [legacy models](legacy): use PaddlePaddle's v2 APIs.
PaddlePaddle 提供了丰富的计算单元，使得用户可以采用模块化的方法解决各种学习问题。在此repo中，我们展示了如何用 PaddlePaddle 来解决常见的机器学习任务，提供若干种不同的易学易用的神经网络模型。
- [fluid模型](fluid): 使用 PaddlePaddle Fluid版本的 APIs，我们特别推荐您使用Fluid模型。
## PaddleCV
模型|简介|模型优势|参考论文
--|:--:|:--:|:--:
[AlexNet](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/image_classification/models)|图像分类经典模型|首次在CNN中成功的应用了ReLU、Dropout和LRN，并使用GPU进行运算加速|[ImageNet Classification with Deep Convolutional Neural Networks](https://www.researchgate.net/publication/267960550_ImageNet_Classification_with_Deep_Convolutional_Neural_Networks)
[VGG](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/image_classification/models)|图像分类经典模型|在AlexNet的基础上使用3*3小卷积核，增加网络深度，具有很好的泛化能力|[Very Deep ConvNets for Large-Scale Inage Recognition](https://arxiv.org/pdf/1409.1556.pdf)
[GoogleNet](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/image_classification/models)|图像分类经典模型|在不增加计算负载的前提下增加了网络的深度和宽度，性能更加优越|[Going deeper with convolutions](https://ieeexplore.ieee.org/document/7298594)
[ResNet](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/image_classification/models)|残差网络|引入了新的残差结构，解决了随着网络加深，准确率下降的问题|[Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)
[Inception-v4](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/image_classification/models)|图像分类经典模型|更加deeper和wider的inception结构|[Inception-ResNet and the Impact of Residual Connections on Learning](http://arxiv.org/abs/1602.07261)
[MobileNet](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/image_classification/models)|轻量级网络模型|为移动和嵌入式设备提出的高效模型|[MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861)
[DPN](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/image_classification/models)|图像分类模型|结合了DenseNet和ResNeXt的网络结构，对图像分类效果有所提升|[Dual Path Networks](https://arxiv.org/abs/1707.01629)
[SE-ResNeXt](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/image_classification/models)|图像分类模型|ResNeXt中加入了SE block，提高了模型准确率|[Squeeze-and-excitation networks](https://arxiv.org/abs/1709.01507)
[SSD](https://github.com/PaddlePaddle/models/blob/develop/fluid/PaddleCV/object_detection/README_cn.md)|单阶段目标检测器|在不同尺度的特征图上检测对应尺度的目标,可以方便地插入到任何一种标准卷积网络中|[SSD: Single Shot MultiBox Detector](https://arxiv.org/abs/1512.02325)
[Face Detector: PyramidBox](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/face_detection/README_cn.md)|基于SSD的单阶段人脸检测器|利用上下文信息解决困难人脸的检测问题，网络表达能力高，鲁棒性强|[PyramidBox: A Context-assisted Single Shot Face Detector](https://arxiv.org/pdf/1803.07737.pdf)
[Faster RCNN](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/faster_rcnn/README_cn.md)|典型的两阶段目标检测器|创造性地采用卷积网络自行产生建议框，并且和目标检测网络共享卷积网络，建议框数目减少，质量提高|[Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/abs/1506.01497)
[ICNet](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/icnet)|图像实时语义分割模型|即考虑了速度，也考虑了准确性，在高分辨率图像的准确性和低复杂度网络的效率之间获得平衡|[ICNet for Real-Time Semantic Segmentation on High-Resolution Images](https://arxiv.org/abs/1704.08545)
[DCGAN](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/gan/c_gan)|图像生成模型|深度卷积生成对抗网络，将GAN和卷积网络结合起来，以解决GAN训练不稳定的问题|[Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/pdf/1511.06434.pdf)
[ConditionalGAN](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/gan/c_gan)|图像生成模型|条件生成对抗网络，一种带条件约束的GAN，使用额外信息对模型增加条件，可以指导数据生成过程|[Conditional Generative Adversarial Nets](https://arxiv.org/abs/1411.1784)
[CycleGAN](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/gan/cycle_gan)|图片转化模型|自动将某一类图片转换成另外一类图片，可用于风格迁移|[Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://arxiv.org/abs/1703.10593)
[CRNN-CTC模型](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/ocr_recognition)|场景文字识别模型|使用CTC model识别图片中单行英文字符|[Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks](https://www.researchgate.net/publication/221346365_Connectionist_temporal_classification_Labelling_unsegmented_sequence_data_with_recurrent_neural_'networks)
[Attention模型](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/ocr_recognition)|场景文字识别模型|使用attention 识别图片中单行英文字符|[Recurrent Models of Visual Attention](https://arxiv.org/abs/1406.6247)
[Metric Learning](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/metric_learning)|度量学习模型|能够用于分析对象时间的关联、比较关系，可应用于辅助分类、聚类问题，也广泛用于图像检索、人脸识别等领域|-
[TSN](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/video_classification)|视频分类模型|基于长范围时间结构建模，结合了稀疏时间采样策略和视频级监督来保证使用整段视频时学习得有效和高效|[Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://arxiv.org/abs/1608.00859)
[caffe2fluid](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/caffe2fluid)|将Caffe模型转换为Paddle Fluid配置和模型文件工具|-|-
## PaddleNLP
模型|简介|模型优势|参考论文
--|:--:|:--:|:--:
[Transformer](https://github.com/PaddlePaddle/models/blob/develop/fluid/PaddleNLP/neural_machine_translation/transformer/README_cn.md)|机器翻译模型|基于self-attention，计算复杂度小，并行度高，容易学习长程依赖，翻译效果更好|[Attention Is All You Need](https://arxiv.org/abs/1706.03762)
[LAC](https://github.com/baidu/lac/blob/master/README.md)|联合的词法分析模型|能够整体性地完成中文分词、词性标注、专名识别任务|[Chinese Lexical Analysis with Deep Bi-GRU-CRF Network](https://arxiv.org/abs/1807.01882)
[Senta](https://github.com/baidu/Senta/blob/master/README.md)|情感倾向分析模型集|百度AI开放平台中情感倾向分析模型|-
[DAM](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleNLP/deep_attention_matching_net)|语义匹配模型|百度自然语言处理部发表于ACL-2018的工作,用于检索式聊天机器人多轮对话中应答的选择|[Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network](http://aclweb.org/anthology/P18-1103)
[SimNet](https://github.com/baidu/AnyQ/blob/master/tools/simnet/train/paddle/README.md)|语义匹配框架|使用SimNet构建出的模型可以便捷的加入AnyQ系统中，增强AnyQ系统的语义匹配能力|-
[DuReader](https://github.com/PaddlePaddle/models/blob/develop/fluid/PaddleNLP/machine_reading_comprehension/README.md)|阅读理解模型|百度MRC数据集上的机器阅读理解模型|-
[Bi-GRU-CRF](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleNLP/sequence_tagging_for_ner/README.md)|命名实体识别|结合了CRF和双向GRU的命名实体识别模型|-
## PaddleRec
模型|简介|模型优势|参考论文
--|:--:|:--:|:--:
[TagSpace](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleRec/tagspace)|文本及标签的embedding表示学习模型|应用于工业级的标签推荐，具体应用场景有feed新闻标签推荐等|[#TagSpace: Semantic embeddings from hashtags](https://www.bibsonomy.org/bibtex/0ed4314916f8e7c90d066db45c293462)
[GRU4Rec](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleRec/gru4rec)|个性化推荐模型|首次将RNN（GRU）运用于session-based推荐，相比传统的KNN和矩阵分解，效果有明显的提升|[Session-based Recommendations with Recurrent Neural Networks](https://arxiv.org/abs/1511.06939)
[SSR](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleRec/ssr)|序列语义检索推荐模型|使用参考论文中的思想，使用多种时间粒度进行用户行为预测|[Multi-Rate Deep Learning for Temporal Recommendation](https://dl.acm.org/citation.cfm?id=2914726)
[DeepCTR](https://github.com/PaddlePaddle/models/blob/develop/fluid/PaddleRec/ctr/README.cn.md)|点击率预估模型|只实现了DeepFM论文中介绍的模型的DNN部分，DeepFM会在其他例子中给出|[DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://arxiv.org/abs/1703.04247)
[Multiview-Simnet](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleRec/multiview_simnet)|个性化推荐模型|基于多元视图，将用户和项目的多个功能视图合并为一个统一模型|[A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems](http://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/frp1159-songA.pdf)
## Other Models
模型|简介|模型优势|参考论文
--|:--:|:--:|:--:
[DeepASR](https://github.com/PaddlePaddle/models/blob/develop/fluid/DeepASR/README_cn.md)|语音识别系统|利用Fluid框架完成语音识别中声学模型的配置和训练，并集成 Kaldi 的解码器|-
[DQN](https://github.com/PaddlePaddle/models/blob/develop/fluid/DeepQNetwork/README_cn.md)|深度Q网络|value based强化学习算法，第一个成功地将深度学习和强化学习结合起来的模型|[Human-level control through deep reinforcement learning](https://www.nature.com/articles/nature14236)
[DoubleDQN](https://github.com/PaddlePaddle/models/blob/develop/fluid/DeepQNetwork/README_cn.md)|DQN的变体|将Double Q的想法应用在DQN上，解决过优化问题|[Font Size: Deep Reinforcement Learning with Double Q-Learning](https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/viewPaper/12389)
[DuelingDQN](https://github.com/PaddlePaddle/models/blob/develop/fluid/DeepQNetwork/README_cn.md)|DQN的变体|改进了DQN模型，提高了模型的性能|[Dueling Network Architectures for Deep Reinforcement Learning](http://proceedings.mlr.press/v48/wangf16.html)
## License
This tutorial is contributed by [PaddlePaddle](https://github.com/PaddlePaddle/Paddle) and licensed under the [Apache-2.0 license](LICENSE).
## 许可证书
此向导由[PaddlePaddle](https://github.com/PaddlePaddle/Paddle)贡献，受[Apache-2.0 license](LICENSE)许可认证.
fluid/PaddleCV/HiNAS_models/nn_paddle.py
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......@@ -21,6 +21,7 @@ import math
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.contrib.trainer import *
from paddle.fluid.layers.learning_rate_scheduler import _decay_step_counter
import reader
......@@ -104,7 +105,7 @@ class Model(object):
accs = []
def event_handler(event):
if isinstance(event, fluid.EndStepEvent):
if isinstance(event, EndStepEvent):
costs.append(event.metrics[0])
accs.append(event.metrics[1])
if event.step % 20 == 0:
......@@ -113,7 +114,7 @@ class Model(object):
del costs[:]
del accs[:]
if isinstance(event, fluid.EndEpochEvent):
if isinstance(event, EndEpochEvent):
if event.epoch % 3 == 0 or event.epoch == FLAGS.num_epochs - 1:
avg_cost, accuracy = trainer.test(
reader=test_reader, feed_order=['pixel', 'label'])
......@@ -126,7 +127,7 @@ class Model(object):
event_handler.best_acc = 0.0
place = fluid.CUDAPlace(0)
trainer = fluid.Trainer(
trainer = Trainer(
train_func=self.train_network,
optimizer_func=self.optimizer_program,
place=place)
......
fluid/PaddleCV/caffe2fluid/kaffe/paddle/network.py
浏览文件 @ e6ed31ef
......@@ -440,7 +440,8 @@ class Network(object):
if need_transpose:
order = range(dims)
order.remove(axis).append(axis)
order.remove(axis)
order.append(axis)
input = fluid.layers.transpose(
input,
perm=order,
......@@ -525,11 +526,21 @@ class Network(object):
scale_shape = input.shape[axis:axis + num_axes]
param_attr = fluid.ParamAttr(name=prefix + 'scale')
scale_param = fluid.layers.create_parameter(
shape=scale_shape, dtype=input.dtype, name=name, attr=param_attr)
shape=scale_shape,
dtype=input.dtype,
name=name,
attr=param_attr,
is_bias=True,
default_initializer=fluid.initializer.Constant(value=1.0))
offset_attr = fluid.ParamAttr(name=prefix + 'offset')
offset_param = fluid.layers.create_parameter(
shape=scale_shape, dtype=input.dtype, name=name, attr=offset_attr)
shape=scale_shape,
dtype=input.dtype,
name=name,
attr=offset_attr,
is_bias=True,
default_initializer=fluid.initializer.Constant(value=0.0))
output = fluid.layers.elementwise_mul(
input,
......
fluid/PaddleCV/deeplabv3+/.run_ce.sh 0 → 100755
浏览文件 @ e6ed31ef
#!/bin/bash
export MKL_NUM_THREADS=1
export OMP_NUM_THREADS=1
DATASET_PATH=${HOME}/.cache/paddle/dataset/cityscape/
cudaid=${deeplabv3plus:=0} # use 0-th card as default
export CUDA_VISIBLE_DEVICES=$cudaid
FLAGS_benchmark=true python train.py \
--batch_size=2 \
--train_crop_size=769 \
--total_step=50 \
--save_weights_path=output1 \
--dataset_path=$DATASET_PATH \
--enable_ce | python _ce.py
cudaid=${deeplabv3plus_m:=0,1,2,3} # use 0,1,2,3 card as default
export CUDA_VISIBLE_DEVICES=$cudaid
FLAGS_benchmark=true python train.py \
--batch_size=2 \
--train_crop_size=769 \
--total_step=50 \
--save_weights_path=output4 \
--dataset_path=$DATASET_PATH \
--enable_ce | python _ce.py
fluid/PaddleCV/deeplabv3+/README.md
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......@@ -76,7 +76,7 @@ python ./train.py \
--train_crop_size=769 \
--total_step=90000 \
--init_weights_path=deeplabv3plus_xception65_initialize.params \
--save_weights_path=output \
--save_weights_path=output/ \
--dataset_path=$DATASET_PATH
```
......
fluid/PaddleCV/deeplabv3+/_ce.py 0 → 100644
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# this file is only used for continuous evaluation test!
import os
import sys
sys.path.append(os.environ['ceroot'])
from kpi import CostKpi
from kpi import DurationKpi
each_pass_duration_card1_kpi = DurationKpi('each_pass_duration_card1', 0.1, 0, actived=True)
train_loss_card1_kpi = CostKpi('train_loss_card1', 0.05, 0)
each_pass_duration_card4_kpi = DurationKpi('each_pass_duration_card4', 0.1, 0, actived=True)
train_loss_card4_kpi = CostKpi('train_loss_card4', 0.05, 0)
tracking_kpis = [
each_pass_duration_card1_kpi,
train_loss_card1_kpi,
each_pass_duration_card4_kpi,
train_loss_card4_kpi,
]
def parse_log(log):
'''
This method should be implemented by model developers.
The suggestion:
each line in the log should be key, value, for example:
"
train_cost\t1.0
test_cost\t1.0
train_cost\t1.0
train_cost\t1.0
train_acc\t1.2
"
'''
for line in log.split('\n'):
fs = line.strip().split('\t')
print(fs)
if len(fs) == 3 and fs[0] == 'kpis':
kpi_name = fs[1]
kpi_value = float(fs[2])
yield kpi_name, kpi_value
def log_to_ce(log):
kpi_tracker = {}
for kpi in tracking_kpis:
kpi_tracker[kpi.name] = kpi
for (kpi_name, kpi_value) in parse_log(log):
print(kpi_name, kpi_value)
kpi_tracker[kpi_name].add_record(kpi_value)
kpi_tracker[kpi_name].persist()
if __name__ == '__main__':
log = sys.stdin.read()
log_to_ce(log)
fluid/PaddleCV/deeplabv3+/eval.py
浏览文件 @ e6ed31ef
......@@ -26,6 +26,7 @@ def add_arguments():
add_argument('dataset_path', str, None, "Cityscape dataset path.")
add_argument('verbose', bool, False, "Print mIoU for each step if verbose.")
add_argument('use_gpu', bool, True, "Whether use GPU or CPU.")
add_argument('num_classes', int, 19, "Number of classes.")
def mean_iou(pred, label):
......@@ -69,7 +70,7 @@ tp = fluid.Program()
batch_size = 1
reader.default_config['crop_size'] = -1
reader.default_config['shuffle'] = False
num_classes = 19
num_classes = args.num_classes
with fluid.program_guard(tp, sp):
img = fluid.layers.data(name='img', shape=[3, 0, 0], dtype='float32')
......@@ -84,7 +85,7 @@ tp = tp.clone(True)
fluid.memory_optimize(
tp,
print_log=False,
skip_opt_set=[pred.name, miou, out_wrong, out_correct],
skip_opt_set=set([pred.name, miou, out_wrong, out_correct]),
level=1)
place = fluid.CPUPlace()
......
fluid/PaddleCV/deeplabv3+/models.py
浏览文件 @ e6ed31ef
......@@ -20,6 +20,11 @@ op_results = {}
default_epsilon = 1e-3
default_norm_type = 'bn'
default_group_number = 32
depthwise_use_cudnn = False
bn_regularizer = fluid.regularizer.L2DecayRegularizer(regularization_coeff=0.0)
depthwise_regularizer = fluid.regularizer.L2DecayRegularizer(
regularization_coeff=0.0)
@contextlib.contextmanager
......@@ -52,20 +57,39 @@ def append_op_result(result, name):
def conv(*args, **kargs):
kargs['param_attr'] = name_scope + 'weights'
if "xception" in name_scope:
init_std = 0.09
elif "logit" in name_scope:
init_std = 0.01
elif name_scope.endswith('depthwise/'):
init_std = 0.33
else:
init_std = 0.06
if name_scope.endswith('depthwise/'):
regularizer = depthwise_regularizer
else:
regularizer = None
kargs['param_attr'] = fluid.ParamAttr(
name=name_scope + 'weights',
regularizer=regularizer,
initializer=fluid.initializer.TruncatedNormal(
loc=0.0, scale=init_std))
if 'bias_attr' in kargs and kargs['bias_attr']:
kargs['bias_attr'] = name_scope + 'biases'
kargs['bias_attr'] = fluid.ParamAttr(
name=name_scope + 'biases',
regularizer=regularizer,
initializer=fluid.initializer.ConstantInitializer(value=0.0))
else:
kargs['bias_attr'] = False
kargs['name'] = name_scope + 'conv'
return append_op_result(fluid.layers.conv2d(*args, **kargs), 'conv')
def group_norm(input, G, eps=1e-5, param_attr=None, bias_attr=None):
helper = fluid.layer_helper.LayerHelper('group_norm', **locals())
N, C, H, W = input.shape
if C % G != 0:
print("group can not divide channle:", C, G)
# print "group can not divide channle:", C, G
for d in range(10):
for t in [d, -d]:
if G + t <= 0: continue
......@@ -73,29 +97,16 @@ def group_norm(input, G, eps=1e-5, param_attr=None, bias_attr=None):
G = G + t
break
if C % G == 0:
print("use group size:", G)
# print "use group size:", G
break
assert C % G == 0
param_shape = (G, )
x = input
x = fluid.layers.reshape(x, [N, G, C // G * H * W])
mean = fluid.layers.reduce_mean(x, dim=2, keep_dim=True)
x = x - mean
var = fluid.layers.reduce_mean(fluid.layers.square(x), dim=2, keep_dim=True)
x = x / fluid.layers.sqrt(var + eps)
scale = helper.create_parameter(
attr=helper.param_attr,
shape=param_shape,
dtype='float32',
default_initializer=fluid.initializer.Constant(1.0))
bias = helper.create_parameter(
attr=helper.bias_attr, shape=param_shape, dtype='float32', is_bias=True)
x = fluid.layers.elementwise_add(
fluid.layers.elementwise_mul(
x, scale, axis=1), bias, axis=1)
return fluid.layers.reshape(x, input.shape)
x = fluid.layers.group_norm(
input,
groups=G,
param_attr=param_attr,
bias_attr=bias_attr,
name=name_scope + 'group_norm')
return x
def bn(*args, **kargs):
......@@ -106,8 +117,10 @@ def bn(*args, **kargs):
*args,
epsilon=default_epsilon,
momentum=bn_momentum,
param_attr=name_scope + 'gamma',
bias_attr=name_scope + 'beta',
param_attr=fluid.ParamAttr(
name=name_scope + 'gamma', regularizer=bn_regularizer),
bias_attr=fluid.ParamAttr(
name=name_scope + 'beta', regularizer=bn_regularizer),
moving_mean_name=name_scope + 'moving_mean',
moving_variance_name=name_scope + 'moving_variance',
**kargs),
......@@ -119,8 +132,10 @@ def bn(*args, **kargs):
args[0],
default_group_number,
eps=default_epsilon,
param_attr=name_scope + 'gamma',
bias_attr=name_scope + 'beta'),
param_attr=fluid.ParamAttr(
name=name_scope + 'gamma', regularizer=bn_regularizer),
bias_attr=fluid.ParamAttr(
name=name_scope + 'beta', regularizer=bn_regularizer)),
'gn')
else:
raise "Unsupport norm type:" + default_norm_type
......@@ -143,7 +158,8 @@ def seq_conv(input, channel, stride, filter, dilation=1, act=None):
stride,
groups=input.shape[1],
padding=(filter // 2) * dilation,
dilation=dilation)
dilation=dilation,
use_cudnn=depthwise_use_cudnn)
input = bn(input)
if act: input = act(input)
with scope('pointwise'):
......
fluid/PaddleCV/deeplabv3+/train.py 100644 → 100755
浏览文件 @ e6ed31ef
......@@ -13,6 +13,7 @@ import reader
import models
import time
def add_argument(name, type, default, help):
parser.add_argument('--' + name, default=default, type=type, help=help)
......@@ -32,15 +33,35 @@ def add_arguments():
add_argument('dataset_path', str, None, "Cityscape dataset path.")
add_argument('parallel', bool, False, "using ParallelExecutor.")
add_argument('use_gpu', bool, True, "Whether use GPU or CPU.")
add_argument('num_classes', int, 19, "Number of classes.")
parser.add_argument(
'--enable_ce',
action='store_true',
help='If set, run the task with continuous evaluation logs.')
def load_model():
myvars = [
x for x in tp.list_vars()
if isinstance(x, fluid.framework.Parameter) and x.name.find('logit') ==
-1
]
if args.init_weights_path.endswith('/'):
if args.num_classes == 19:
fluid.io.load_params(
exe, dirname=args.init_weights_path, main_program=tp)
else:
fluid.io.load_vars(exe, dirname=args.init_weights_path, vars=myvars)
else:
if args.num_classes == 19:
fluid.io.load_params(
exe, dirname="", filename=args.init_weights_path, main_program=tp)
exe,
dirname="",
filename=args.init_weights_path,
main_program=tp)
else:
fluid.io.load_vars(
exe, dirname="", filename=args.init_weights_path, vars=myvars)
def save_model():
......@@ -70,6 +91,15 @@ def loss(logit, label):
return loss, label_nignore
def get_cards(args):
if args.enable_ce:
cards = os.environ.get('CUDA_VISIBLE_DEVICES')
num = len(cards.split(","))
return num
else:
return args.num_devices
CityscapeDataset = reader.CityscapeDataset
parser = argparse.ArgumentParser()
......@@ -80,16 +110,24 @@ args = parser.parse_args()
models.clean()
models.bn_momentum = 0.9997
models.dropout_keep_prop = 0.9
models.label_number = args.num_classes
deeplabv3p = models.deeplabv3p
sp = fluid.Program()
tp = fluid.Program()
# only for ce
if args.enable_ce:
SEED = 102
sp.random_seed = SEED
tp.random_seed = SEED
crop_size = args.train_crop_size
batch_size = args.batch_size
image_shape = [crop_size, crop_size]
reader.default_config['crop_size'] = crop_size
reader.default_config['shuffle'] = True
num_classes = 19
num_classes = args.num_classes
weight_decay = 0.00004
base_lr = args.base_lr
......@@ -120,7 +158,7 @@ with fluid.program_guard(tp, sp):
retv = opt.minimize(loss_mean, startup_program=sp, no_grad_set=no_grad_set)
fluid.memory_optimize(
tp, print_log=False, skip_opt_set=[pred.name, loss_mean.name], level=1)
tp, print_log=False, skip_opt_set=set([pred.name, loss_mean.name]), level=1)
place = fluid.CPUPlace()
if args.use_gpu:
......@@ -140,7 +178,13 @@ if args.parallel:
batches = dataset.get_batch_generator(batch_size, total_step)
total_time = 0.0
epoch_idx = 0
train_loss = 0
for i, imgs, labels, names in batches:
epoch_idx += 1
begin_time = time.time()
prev_start_time = time.time()
if args.parallel:
retv = exe_p.run(fetch_list=[pred.name, loss_mean.name],
......@@ -152,11 +196,21 @@ for i, imgs, labels, names in batches:
'label': labels},
fetch_list=[pred, loss_mean])
end_time = time.time()
total_time += end_time - begin_time
if i % 100 == 0:
print("Model is saved to", args.save_weights_path)
save_model()
print("step {:d}, loss: {:.6f}, step_time_cost: {:.3f}" .format(i,
np.mean(retv[1]), end_time - prev_start_time))
print("step {:d}, loss: {:.6f}, step_time_cost: {:.3f}".format(
i, np.mean(retv[1]), end_time - prev_start_time))
# only for ce
train_loss = np.mean(retv[1])
if args.enable_ce:
gpu_num = get_cards(args)
print("kpis\teach_pass_duration_card%s\t%s" %
(gpu_num, total_time / epoch_idx))
print("kpis\ttrain_loss_card%s\t%s" % (gpu_num, train_loss))
print("Training done. Model is saved to", args.save_weights_path)
save_model()
fluid/PaddleCV/face_detection/.run_ce.sh 0 → 100755
浏览文件 @ e6ed31ef
#!/bin/bash
export MKL_NUM_THREADS=1
export OMP_NUM_THREADS=1
cudaid=${face_detection:=0} # use 0-th card as default
export CUDA_VISIBLE_DEVICES=$cudaid
FLAGS_benchmark=true python train.py --batch_size=2 --epoc_num=1 --batch_num=200 --parallel=False --enable_ce | python _ce.py
cudaid=${face_detection_m:=0,1,2,3} # use 0,1,2,3 card as default
export CUDA_VISIBLE_DEVICES=$cudaid
FLAGS_benchmark=true python train.py --batch_size=8 --epoc_num=1 --batch_num=200 --parallel=False --enable_ce | python _ce.py
fluid/PaddleCV/face_detection/README_cn.md
浏览文件 @ e6ed31ef
......@@ -99,7 +99,7 @@ python -u train.py --batch_size=16 --pretrained_model=vgg_ilsvrc_16_fc_reduced
模型训练所采用的数据增强：
**数据增强**：数据的读取行为定义在 `reader.py` 中，所有的图片都会被缩放到640x640。在训练时还会对图片进行数据增强，包括随机扰动、翻转、裁剪等，和[物体检测SSD算法](https://github.com/PaddlePaddle/models/blob/develop/fluid/object_detection/README_cn.md#%E8%AE%AD%E7%BB%83-pascal-voc-%E6%95%B0%E6%8D%AE%E9%9B%86)中数据增强类似，除此之外，增加了上面提到的Data-anchor-sampling:
**数据增强**：数据的读取行为定义在 `reader.py` 中，所有的图片都会被缩放到640x640。在训练时还会对图片进行数据增强，包括随机扰动、翻转、裁剪等，和[物体检测SSD算法](https://github.com/PaddlePaddle/models/blob/develop/fluid/PaddleCV/object_detection/README.md)中数据增强类似，除此之外，增加了上面提到的Data-anchor-sampling:
**尺度变换(Data-anchor-sampling)**：随机将图片尺度变换到一定范围的尺度，大大增强人脸的尺度变化。具体操作为根据随机选择的人脸高(height)和宽(width)，得到$v=\\sqrt{width * height}$，判断$v$的值位于缩放区间$[16，32，64，128，256，512]$中的的哪一个。假设$v=45$，则选定$32<v<64$，以均匀分布的概率选取$[16，32，64]$中的任意一个值。若选中$64$，则该人脸的缩放区间在 $[64 / 2，min(v * 2, 64 * 2)]$中选定。
......
fluid/PaddleCV/face_detection/_ce.py 0 → 100644
浏览文件 @ e6ed31ef
# this file is only used for continuous evaluation test!
import os
import sys
sys.path.append(os.environ['ceroot'])
from kpi import CostKpi
from kpi import DurationKpi
each_pass_duration_card1_kpi = DurationKpi('each_pass_duration_card1', 0.08, 0, actived=True)
train_face_loss_card1_kpi = CostKpi('train_face_loss_card1', 0.08, 0)
train_head_loss_card1_kpi = CostKpi('train_head_loss_card1', 0.08, 0)
each_pass_duration_card4_kpi = DurationKpi('each_pass_duration_card4', 0.08, 0, actived=True)
train_face_loss_card4_kpi = CostKpi('train_face_loss_card4', 0.08, 0)
train_head_loss_card4_kpi = CostKpi('train_head_loss_card4', 0.08, 0)
tracking_kpis = [
each_pass_duration_card1_kpi,
train_face_loss_card1_kpi,
train_head_loss_card1_kpi,
each_pass_duration_card4_kpi,
train_face_loss_card4_kpi,
train_head_loss_card4_kpi,
]
def parse_log(log):
'''
This method should be implemented by model developers.
The suggestion:
each line in the log should be key, value, for example:
"
train_cost\t1.0
test_cost\t1.0
train_cost\t1.0
train_cost\t1.0
train_acc\t1.2
"
'''
for line in log.split('\n'):
fs = line.strip().split('\t')
print(fs)
if len(fs) == 3 and fs[0] == 'kpis':
kpi_name = fs[1]
kpi_value = float(fs[2])
yield kpi_name, kpi_value
def log_to_ce(log):
kpi_tracker = {}
for kpi in tracking_kpis:
kpi_tracker[kpi.name] = kpi
for (kpi_name, kpi_value) in parse_log(log):
print(kpi_name, kpi_value)
kpi_tracker[kpi_name].add_record(kpi_value)
kpi_tracker[kpi_name].persist()
if __name__ == '__main__':
log = sys.stdin.read()
log_to_ce(log)
fluid/PaddleCV/face_detection/data_util.py 已删除 100644 → 0
浏览文件 @ 366d6612
"""
This code is based on https://github.com/fchollet/keras/blob/master/keras/utils/data_utils.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
import numpy as np
import threading
import multiprocessing
import traceback
try:
import queue
except ImportError:
import Queue as queue
class GeneratorEnqueuer(object):
"""
Builds a queue out of a data generator.
Args:
generator: a generator function which endlessly yields data
use_multiprocessing (bool): use multiprocessing if True,
otherwise use threading.
wait_time (float): time to sleep in-between calls to `put()`.
random_seed (int): Initial seed for workers,
will be incremented by one for each workers.
"""
def __init__(self,
generator,
use_multiprocessing=False,
wait_time=0.05,
random_seed=None):
self.wait_time = wait_time
self._generator = generator
self._use_multiprocessing = use_multiprocessing
self._threads = []
self._stop_event = None
self.queue = None
self._manager = None
self.seed = random_seed
def start(self, workers=1, max_queue_size=10):
"""
Start worker threads which add data from the generator into the queue.
Args:
workers (int): number of worker threads
max_queue_size (int): queue size
(when full, threads could block on `put()`)
"""
def data_generator_task():
"""
Data generator task.
"""
def task():
if (self.queue is not None and
self.queue.qsize() < max_queue_size):
generator_output = next(self._generator)
self.queue.put((generator_output))
else:
time.sleep(self.wait_time)
if not self._use_multiprocessing:
while not self._stop_event.is_set():
with self.genlock:
try:
task()
except Exception:
traceback.print_exc()
self._stop_event.set()
break
else:
while not self._stop_event.is_set():
try:
task()
except Exception:
traceback.print_exc()
self._stop_event.set()
break
try:
if self._use_multiprocessing:
self._manager = multiprocessing.Manager()
self.queue = self._manager.Queue(maxsize=max_queue_size)
self._stop_event = multiprocessing.Event()
else:
self.genlock = threading.Lock()
self.queue = queue.Queue()
self._stop_event = threading.Event()
for _ in range(workers):
if self._use_multiprocessing:
# Reset random seed else all children processes
# share the same seed
np.random.seed(self.seed)
thread = multiprocessing.Process(target=data_generator_task)
thread.daemon = True
if self.seed is not None:
self.seed += 1
else:
thread = threading.Thread(target=data_generator_task)
self._threads.append(thread)
thread.start()
except:
self.stop()
raise
def is_running(self):
"""
Returns:
bool: Whether the worker theads are running.
"""
return self._stop_event is not None and not self._stop_event.is_set()
def stop(self, timeout=None):
"""
Stops running threads and wait for them to exit, if necessary.
Should be called by the same thread which called `start()`.
Args:
timeout(int|None): maximum time to wait on `thread.join()`.
"""
if self.is_running():
self._stop_event.set()
for thread in self._threads:
if self._use_multiprocessing:
if thread.is_alive():
thread.terminate()
else:
thread.join(timeout)
if self._manager:
self._manager.shutdown()
self._threads = []
self._stop_event = None
self.queue = None
def get(self):
"""
Creates a generator to extract data from the queue.
Skip the data if it is `None`.
# Yields
tuple of data in the queue.
"""
while self.is_running():
if not self.queue.empty():
inputs = self.queue.get()
if inputs is not None:
yield inputs
else:
time.sleep(self.wait_time)
fluid/PaddleCV/face_detection/reader.py
浏览文件 @ e6ed31ef
......@@ -16,8 +16,6 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import image_util
from paddle.utils.image_util import *
from PIL import Image
from PIL import ImageDraw
import numpy as np
......@@ -28,7 +26,10 @@ import copy
import random
import cv2
import six
from data_util import GeneratorEnqueuer
import math
from itertools import islice
import paddle
import image_util
class Settings(object):
......@@ -199,7 +200,7 @@ def load_file_list(input_txt):
else:
file_dict[num_class].append(line_txt)
return file_dict
return list(file_dict.values())
def expand_bboxes(bboxes,
......@@ -227,13 +228,12 @@ def expand_bboxes(bboxes,
def train_generator(settings, file_list, batch_size, shuffle=True):
file_dict = load_file_list(file_list)
while True:
def reader():
if shuffle:
np.random.shuffle(file_dict)
np.random.shuffle(file_list)
batch_out = []
for index_image in file_dict.keys():
image_name = file_dict[index_image][0]
for item in file_list:
image_name = item[0]
image_path = os.path.join(settings.data_dir, image_name)
im = Image.open(image_path)
if im.mode == 'L':
......@@ -242,10 +242,10 @@ def train_generator(settings, file_list, batch_size, shuffle=True):
# layout: label | xmin | ymin | xmax | ymax
bbox_labels = []
for index_box in range(len(file_dict[index_image])):
for index_box in range(len(item)):
if index_box >= 2:
bbox_sample = []
temp_info_box = file_dict[index_image][index_box].split(' ')
temp_info_box = item[index_box].split(' ')
xmin = float(temp_info_box[0])
ymin = float(temp_info_box[1])
w = float(temp_info_box[2])
......@@ -277,43 +277,25 @@ def train_generator(settings, file_list, batch_size, shuffle=True):
yield batch_out
batch_out = []
return reader
def train(settings,
file_list,
batch_size,
shuffle=True,
use_multiprocessing=True,
num_workers=8,
max_queue=24):
def reader():
try:
enqueuer = GeneratorEnqueuer(
train_generator(settings, file_list, batch_size, shuffle),
use_multiprocessing=use_multiprocessing)
enqueuer.start(max_queue_size=max_queue, workers=num_workers)
generator_output = None
while True:
while enqueuer.is_running():
if not enqueuer.queue.empty():
generator_output = enqueuer.queue.get()
break
else:
time.sleep(0.01)
yield generator_output
generator_output = None
finally:
if enqueuer is not None:
enqueuer.stop()
return reader
def train(settings, file_list, batch_size, shuffle=True, num_workers=8):
file_lists = load_file_list(file_list)
n = int(math.ceil(len(file_lists) // num_workers))
split_lists = [file_lists[i:i + n] for i in range(0, len(file_lists), n)]
readers = []
for iterm in split_lists:
readers.append(train_generator(settings, iterm, batch_size, shuffle))
return paddle.reader.multiprocess_reader(readers, False)
def test(settings, file_list):
file_dict = load_file_list(file_list)
file_lists = load_file_list(file_list)
def reader():
for index_image in file_dict.keys():
image_name = file_dict[index_image][0]
for image in file_lists:
image_name = image[0]
image_path = os.path.join(settings.data_dir, image_name)
im = Image.open(image_path)
if im.mode == 'L':
......
fluid/PaddleCV/face_detection/train.py
浏览文件 @ e6ed31ef
......@@ -32,6 +32,9 @@ add_arg('mean_BGR', str, '104., 117., 123.', "Mean value for B,G,R cha
add_arg('with_mem_opt', bool, True, "Whether to use memory optimization or not.")
add_arg('pretrained_model', str, './vgg_ilsvrc_16_fc_reduced/', "The init model path.")
add_arg('data_dir', str, 'data', "The base dir of dataset")
parser.add_argument('--enable_ce', action='store_true', help='If set, run the task with continuous evaluation logs.')
parser.add_argument('--batch_num', type=int, help="batch num for ce")
parser.add_argument('--num_devices', type=int, default=1, help='Number of GPU devices')
#yapf: enable
train_parameters = {
......@@ -119,6 +122,16 @@ def train(args, config, train_params, train_file_list):
startup_prog = fluid.Program()
train_prog = fluid.Program()
#only for ce
if args.enable_ce:
SEED = 102
startup_prog.random_seed = SEED
train_prog.random_seed = SEED
num_workers = 1
pretrained_model = ""
if args.batch_num != None:
iters_per_epoc = args.batch_num
train_py_reader, fetches, loss = build_program(
train_params = train_params,
main_prog = train_prog,
......@@ -150,9 +163,7 @@ def train(args, config, train_params, train_file_list):
train_file_list,
batch_size_per_device,
shuffle = is_shuffle,
use_multiprocessing=True,
num_workers = num_workers,
max_queue=24)
num_workers = num_workers)
train_py_reader.decorate_paddle_reader(train_reader)
if args.parallel:
......@@ -169,42 +180,69 @@ def train(args, config, train_params, train_file_list):
print('save models to %s' % (model_path))
fluid.io.save_persistables(exe, model_path, main_program=program)
train_py_reader.start()
try:
total_time = 0.0
epoch_idx = 0
face_loss = 0
head_loss = 0
for pass_id in range(start_epoc, epoc_num):
epoch_idx += 1
start_time = time.time()
prev_start_time = start_time
end_time = 0
batch_id = 0
for batch_id in range(iters_per_epoc):
train_py_reader.start()
while True:
try:
prev_start_time = start_time
start_time = time.time()
if args.parallel:
fetch_vars = train_exe.run(fetch_list=
[v.name for v in fetches])
else:
fetch_vars = exe.run(train_prog,
fetch_list=fetches)
fetch_vars = exe.run(train_prog, fetch_list=fetches)
end_time = time.time()
fetch_vars = [np.mean(np.array(v)) for v in fetch_vars]
face_loss = fetch_vars[0]
head_loss = fetch_vars[1]
if batch_id % 10 == 0:
if not args.use_pyramidbox:
print("Pass {:d}, batch {:d}, loss {:.6f}, time {:.5f}".format(
pass_id, batch_id, fetch_vars[0],
pass_id, batch_id, face_loss,
start_time - prev_start_time))
else:
print("Pass {:d}, batch {:d}, face loss {:.6f}, " \
"head loss {:.6f}, " \
"time {:.5f}".format(pass_id,
batch_id, fetch_vars[0], fetch_vars[1],
batch_id, face_loss, head_loss,
start_time - prev_start_time))
if pass_id % 1 == 0 or pass_id == epoc_num - 1:
save_model(str(pass_id), train_prog)
except fluid.core.EOFException:
train_py_reader.reset()
except StopIteration:
train_py_reader.reset()
batch_id += 1
except (fluid.core.EOFException, StopIteration):
train_py_reader.reset()
break
epoch_end_time = time.time()
total_time += epoch_end_time - start_time
save_model(str(pass_id), train_prog)
# only for ce
if args.enable_ce:
gpu_num = get_cards(args)
print("kpis\teach_pass_duration_card%s\t%s" %
(gpu_num, total_time / epoch_idx))
print("kpis\ttrain_face_loss_card%s\t%s" %
(gpu_num, face_loss))
print("kpis\ttrain_head_loss_card%s\t%s" %
(gpu_num, head_loss))
def get_cards(args):
if args.enable_ce:
cards = os.environ.get('CUDA_VISIBLE_DEVICES')
num = len(cards.split(","))
return num
else:
return args.num_devices
if __name__ == '__main__':
args = parser.parse_args()
......
fluid/PaddleCV/faster_rcnn/.run_ce.sh 0 → 100755
浏览文件 @ e6ed31ef
#!/bin/bash
export MKL_NUM_THREADS=1
export OMP_NUM_THREADS=1
cudaid=${face_detection:=0} # use 0-th card as default
export CUDA_VISIBLE_DEVICES=$cudaid
FLAGS_benchmark=true python train.py --model_save_dir=output/ --data_dir=dataset/coco/ --max_iter=10 --enable_ce --pretrained_model=./imagenet_resnet50_fusebn | python _ce.py
cudaid=${face_detection_m:=0,1,2,3} # use 0,1,2,3 card as default
export CUDA_VISIBLE_DEVICES=$cudaid
FLAGS_benchmark=true python train.py --model_save_dir=output/ --data_dir=dataset/coco/ --max_iter=10 --enable_ce --pretrained_model=./imagenet_resnet50_fusebn | python _ce.py
fluid/PaddleCV/faster_rcnn/README.md
浏览文件 @ e6ed31ef
......@@ -38,18 +38,6 @@ Train the model on [MS-COCO dataset](http://cocodataset.org/#download), download
## Training
After data preparation, one can start the training step by:
python train.py \
--model_save_dir=output/ \
--pretrained_model=${path_to_pretrain_model}
--data_dir=${path_to_data}
- Set ```export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7``` to specifiy 8 GPU to train.
- For more help on arguments:
python train.py --help
**download the pre-trained model:** This sample provides Resnet-50 pre-trained model which is converted from Caffe. The model fuses the parameters in batch normalization layer. One can download pre-trained model as:
sh ./pretrained/download.sh
......@@ -72,6 +60,18 @@ To train the model, [cocoapi](https://github.com/cocodataset/cocoapi) is needed.
# not to install the COCO API into global site-packages
python2 setup.py install --user
After data preparation, one can start the training step by:
python train.py \
--model_save_dir=output/ \
--pretrained_model=${path_to_pretrain_model}
--data_dir=${path_to_data}
- Set ```export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7``` to specifiy 8 GPU to train.
- For more help on arguments:
python train.py --help
**data reader introduction:**
* Data reader is defined in `reader.py`.
......@@ -128,7 +128,7 @@ Inference is used to get prediction score or image features based on trained mod
python infer.py \
--dataset=coco2017 \
--pretrained_model=${path_to_pretrain_model} \
--image_path=data/COCO17/val2017/ \
--image_path=dataset/coco/val2017/ \
--image_name=000000000139.jpg \
--draw_threshold=0.6
......
fluid/PaddleCV/faster_rcnn/README_cn.md
浏览文件 @ e6ed31ef
......@@ -37,18 +37,6 @@ Faster RCNN 目标检测模型
## 模型训练
数据准备完毕后，可以通过如下的方式启动训练：
python train.py \
--model_save_dir=output/ \
--pretrained_model=${path_to_pretrain_model}
--data_dir=${path_to_data}
- 通过设置export CUDA\_VISIBLE\_DEVICES=0,1,2,3,4,5,6,7指定8卡GPU训练。
- 可选参数见：
python train.py --help
**下载预训练模型：** 本示例提供Resnet-50预训练模型，该模性转换自Caffe，并对批标准化层(Batch Normalization Layer)进行参数融合。采用如下命令下载预训练模型：
sh ./pretrained/download.sh
......@@ -71,6 +59,18 @@ Faster RCNN 目标检测模型
# not to install the COCO API into global site-packages
python2 setup.py install --user
数据准备完毕后，可以通过如下的方式启动训练：
python train.py \
--model_save_dir=output/ \
--pretrained_model=${path_to_pretrain_model}
--data_dir=${path_to_data}
- 通过设置export CUDA\_VISIBLE\_DEVICES=0,1,2,3,4,5,6,7指定8卡GPU训练。
- 可选参数见：
python train.py --help
**数据读取器说明：** 数据读取器定义在reader.py中。所有图像将短边等比例缩放至`scales`，若长边大于`max_size`, 则再次将长边等比例缩放至`max_size`。在训练阶段，对图像采用水平翻转。支持将同一个batch内的图像padding为相同尺寸。
**模型设置：**
......@@ -124,7 +124,7 @@ Faster RCNN 目标检测模型
python infer.py \
--dataset=coco2017 \
--pretrained_model=${path_to_pretrain_model} \
--image_path=data/COCO17/val2017/ \
--image_path=dataset/coco/val2017/ \
--image_name=000000000139.jpg \
--draw_threshold=0.6
......
fluid/PaddleCV/faster_rcnn/_ce.py 0 → 100644
浏览文件 @ e6ed31ef
# this file is only used for continuous evaluation test!
import os
import sys
sys.path.append(os.environ['ceroot'])
from kpi import CostKpi
from kpi import DurationKpi
each_pass_duration_card1_kpi = DurationKpi('each_pass_duration_card1', 0.08, 0, actived=True)
train_loss_card1_kpi = CostKpi('train_loss_card1', 0.08, 0)
each_pass_duration_card4_kpi = DurationKpi('each_pass_duration_card4', 0.08, 0, actived=True)
train_loss_card4_kpi = CostKpi('train_loss_card4', 0.08, 0)
tracking_kpis = [
each_pass_duration_card1_kpi,
train_loss_card1_kpi,
each_pass_duration_card4_kpi,
train_loss_card4_kpi,
]
def parse_log(log):
'''
This method should be implemented by model developers.
The suggestion:
each line in the log should be key, value, for example:
"
train_cost\t1.0
test_cost\t1.0
train_cost\t1.0
train_cost\t1.0
train_acc\t1.2
"
'''
for line in log.split('\n'):
fs = line.strip().split('\t')
print(fs)
if len(fs) == 3 and fs[0] == 'kpis':
kpi_name = fs[1]
kpi_value = float(fs[2])
yield kpi_name, kpi_value
def log_to_ce(log):
kpi_tracker = {}
for kpi in tracking_kpis:
kpi_tracker[kpi.name] = kpi
for (kpi_name, kpi_value) in parse_log(log):
print(kpi_name, kpi_value)
kpi_tracker[kpi_name].add_record(kpi_value)
kpi_tracker[kpi_name].persist()
if __name__ == '__main__':
log = sys.stdin.read()
log_to_ce(log)
fluid/PaddleCV/faster_rcnn/data_utils.py
浏览文件 @ e6ed31ef
......@@ -28,6 +28,7 @@ from __future__ import unicode_literals
import cv2
import numpy as np
from config import cfg
import os
def get_image_blob(roidb, mode):
......@@ -43,8 +44,11 @@ def get_image_blob(roidb, mode):
target_size = cfg.TEST.scales[0]
max_size = cfg.TEST.max_size
im = cv2.imread(roidb['image'])
assert im is not None, \
'Failed to read image \'{}\''.format(roidb['image'])
try:
assert im is not None
except AssertionError as e:
print('Failed to read image \'{}\''.format(roidb['image']))
os._exit(0)
if roidb['flipped']:
im = im[:, ::-1, :]
im, im_scale = prep_im_for_blob(im, cfg.pixel_means, target_size, max_size)
......
fluid/PaddleCV/faster_rcnn/train.py
浏览文件 @ e6ed31ef
......@@ -35,7 +35,7 @@ def train():
learning_rate = cfg.learning_rate
image_shape = [3, cfg.TRAIN.max_size, cfg.TRAIN.max_size]
if cfg.debug:
if cfg.debug or cfg.enable_ce:
fluid.default_startup_program().random_seed = 1000
fluid.default_main_program().random_seed = 1000
import random
......@@ -46,11 +46,14 @@ def train():
devices_num = len(devices.split(","))
total_batch_size = devices_num * cfg.TRAIN.im_per_batch
use_random = True
if cfg.enable_ce:
use_random = False
model = model_builder.FasterRCNN(
add_conv_body_func=resnet.add_ResNet50_conv4_body,
add_roi_box_head_func=resnet.add_ResNet_roi_conv5_head,
use_pyreader=cfg.use_pyreader,
use_random=True)
use_random=use_random)
model.build_model(image_shape)
loss_cls, loss_bbox, rpn_cls_loss, rpn_reg_loss = model.loss()
loss_cls.persistable = True
......@@ -92,16 +95,19 @@ def train():
train_exe = fluid.ParallelExecutor(
use_cuda=bool(cfg.use_gpu), loss_name=loss.name)
shuffle = True
if cfg.enable_ce:
shuffle = False
if cfg.use_pyreader:
train_reader = reader.train(
batch_size=cfg.TRAIN.im_per_batch,
total_batch_size=total_batch_size,
padding_total=cfg.TRAIN.padding_minibatch,
shuffle=True)
shuffle=shuffle)
py_reader = model.py_reader
py_reader.decorate_paddle_reader(train_reader)
else:
train_reader = reader.train(batch_size=total_batch_size, shuffle=True)
train_reader = reader.train(batch_size=total_batch_size, shuffle=shuffle)
feeder = fluid.DataFeeder(place=place, feed_list=model.feeds())
def save_model(postfix):
......@@ -118,6 +124,8 @@ def train():
try:
start_time = time.time()
prev_start_time = start_time
total_time = 0
last_loss = 0
every_pass_loss = []
for iter_id in range(cfg.max_iter):
prev_start_time = start_time
......@@ -131,9 +139,23 @@ def train():
iter_id, lr[0],
smoothed_loss.get_median_value(
), start_time - prev_start_time))
end_time = time.time()
total_time += end_time - start_time
last_loss = np.mean(np.array(losses[0]))
sys.stdout.flush()
if (iter_id + 1) % cfg.TRAIN.snapshot_iter == 0:
save_model("model_iter{}".format(iter_id))
# only for ce
if cfg.enable_ce:
gpu_num = devices_num
epoch_idx = iter_id + 1
loss = last_loss
print("kpis\teach_pass_duration_card%s\t%s" %
(gpu_num, total_time / epoch_idx))
print("kpis\ttrain_loss_card%s\t%s" %
(gpu_num, loss))
except fluid.core.EOFException:
py_reader.reset()
return np.mean(every_pass_loss)
......@@ -142,6 +164,8 @@ def train():
start_time = time.time()
prev_start_time = start_time
start = start_time
total_time = 0
last_loss = 0
every_pass_loss = []
smoothed_loss = SmoothedValue(cfg.log_window)
for iter_id, data in enumerate(train_reader()):
......@@ -154,6 +178,9 @@ def train():
smoothed_loss.add_value(loss_v)
lr = np.array(fluid.global_scope().find_var('learning_rate')
.get_tensor())
end_time = time.time()
total_time += end_time - start_time
last_loss = loss_v
print("Iter {:d}, lr {:.6f}, loss {:.6f}, time {:.5f}".format(
iter_id, lr[0],
smoothed_loss.get_median_value(), start_time - prev_start_time))
......@@ -162,6 +189,16 @@ def train():
save_model("model_iter{}".format(iter_id))
if (iter_id + 1) == cfg.max_iter:
break
# only for ce
if cfg.enable_ce:
gpu_num = devices_num
epoch_idx = iter_id + 1
loss = last_loss
print("kpis\teach_pass_duration_card%s\t%s" %
(gpu_num, total_time / epoch_idx))
print("kpis\ttrain_loss_card%s\t%s" %
(gpu_num, loss))
return np.mean(every_pass_loss)
if cfg.use_pyreader:
......
fluid/PaddleCV/faster_rcnn/utility.py
浏览文件 @ e6ed31ef
......@@ -98,7 +98,7 @@ def parse_args():
add_arg('pretrained_model', str, 'imagenet_resnet50_fusebn', "The init model path.")
add_arg('dataset', str, 'coco2017', "coco2014, coco2017.")
add_arg('class_num', int, 81, "Class number.")
add_arg('data_dir', str, 'data/COCO17', "The data root path.")
add_arg('data_dir', str, 'dataset/coco', "The data root path.")
add_arg('use_pyreader', bool, True, "Use pyreader.")
add_arg('use_profile', bool, False, "Whether use profiler.")
add_arg('padding_minibatch',bool, False,
......@@ -127,8 +127,11 @@ def parse_args():
add_arg('debug', bool, False, "Debug mode")
# SINGLE EVAL AND DRAW
add_arg('draw_threshold', float, 0.8, "Confidence threshold to draw bbox.")
add_arg('image_path', str, 'data/COCO17/val2017', "The image path used to inference and visualize.")
add_arg('image_path', str, 'dataset/coco/val2017', "The image path used to inference and visualize.")
add_arg('image_name', str, '', "The single image used to inference and visualize.")
# ce
parser.add_argument(
'--enable_ce', action='store_true', help='If set, run the task with continuous evaluation logs.')
# yapf: enable
args = parser.parse_args()
file_name = sys.argv[0]
......
fluid/PaddleCV/gan/c_gan/.run_ce.sh
浏览文件 @ e6ed31ef
......@@ -3,7 +3,7 @@
# This file is only used for continuous evaluation.
export FLAGS_cudnn_deterministic=True
export ce_mode=1
(CUDA_VISIBLE_DEVICES=6 python c_gan.py --batch_size=121 --epoch=1 --run_ce=True --use_gpu=True & \
CUDA_VISIBLE_DEVICES=7 python dc_gan.py --batch_size=121 --epoch=1 --run_ce=True --use_gpu=True) | python _ce.py
(CUDA_VISIBLE_DEVICES=2 python c_gan.py --batch_size=121 --epoch=1 --run_ce=True --use_gpu=True & \
CUDA_VISIBLE_DEVICES=3 python dc_gan.py --batch_size=121 --epoch=1 --run_ce=True --use_gpu=True) | python _ce.py
fluid/PaddleCV/gan/c_gan/c_gan.py
浏览文件 @ e6ed31ef
......@@ -165,7 +165,8 @@ def train(args):
'conditions': conditions_data},
fetch_list={dg_loss})[0][0]
losses[1].append(dg_loss_n)
t_time += (time.time() - s_time)
batch_time = time.time() - s_time
t_time += batch_time
......@@ -180,8 +181,9 @@ def train(args):
fetch_list={g_img})[0]
total_images = np.concatenate([real_image, generated_images])
fig = plot(total_images)
msg = "Epoch ID={0}\n Batch ID={1}\n D-Loss={2}\n DG-Loss={3}\n gen={4}".format(
pass_id, batch_id, d_loss_n, dg_loss_n, check(generated_images))
msg = "Epoch ID={0}\n Batch ID={1}\n D-Loss={2}\n DG-Loss={3}\n gen={4}\n " \
"Batch_time_cost={5:.2f}".format(
pass_id, batch_id, d_loss_n, dg_loss_n, check(generated_images), batch_time)
print(msg)
plt.title(msg)
plt.savefig(
......
fluid/PaddleCV/gan/cycle_gan/train.py
浏览文件 @ e6ed31ef
......@@ -187,10 +187,12 @@ def train(args):
fetch_list=[d_A_trainer.d_loss_A],
feed={"input_A": tensor_A,
"fake_pool_A": fake_pool_A})[0]
t_time += (time.time() - s_time)
print("epoch{}; batch{}; g_A_loss: {}; d_B_loss: {}; g_B_loss: {}; d_A_loss: {};".format(
batch_time = time.time() - s_time
t_time += batch_time
print("epoch{}; batch{}; g_A_loss: {}; d_B_loss: {}; g_B_loss: {}; d_A_loss: {}; "
"Batch_time_cost: {:.2f}".format(
epoch, batch_id, g_A_loss[0], d_B_loss[0], g_B_loss[0],
d_A_loss[0]))
d_A_loss[0], batch_time))
losses[0].append(g_A_loss[0])
losses[1].append(d_A_loss[0])
sys.stdout.flush()
......
fluid/PaddleCV/image_classification/.run_ce.sh
浏览文件 @ e6ed31ef
......@@ -7,6 +7,7 @@ cudaid=${object_detection_cudaid:=0}
export CUDA_VISIBLE_DEVICES=$cudaid
python train.py --batch_size=${BATCH_SIZE} --num_epochs=5 --enable_ce=True --lr_strategy=cosine_decay | python _ce.py
BATCH_SIZE=224
cudaid=${object_detection_cudaid_m:=0, 1, 2, 3}
export CUDA_VISIBLE_DEVICES=$cudaid
python train.py --batch_size=${BATCH_SIZE} --num_epochs=5 --enable_ce=True --lr_strategy=cosine_decay | python _ce.py
fluid/PaddleCV/image_classification/README.md
浏览文件 @ e6ed31ef
......@@ -6,6 +6,7 @@ Image classification, which is an important field of computer vision, is to clas
- [Installation](#installation)
- [Data preparation](#data-preparation)
- [Training a model with flexible parameters](#training-a-model)
- [Using Mixed-Precision Training](#using-mixed-precision-training)
- [Finetuning](#finetuning)
- [Evaluation](#evaluation)
- [Inference](#inference)
......@@ -112,6 +113,13 @@ The error rate curves of AlexNet, ResNet50 and SE-ResNeXt-50 are shown in the fi
Training and validation Curves
</p>
## Using Mixed-Precision Training
You may add `--fp16 1` to start train using mixed precisioin training, which the training process will use float16 and the output model ("master" parameters) is saved as float32. You also may need to pass `--scale_loss` to overcome accuracy issues, usually `--scale_loss 8.0` will do.
Note that currently `--fp16` can not use together with `--with_mem_opt`, so pass `--with_mem_opt 0` to disable memory optimization pass.
## Finetuning
Finetuning is to finetune model weights in a specific task by loading pretrained weights. After initializing ```path_to_pretrain_model```, one can finetune a model as:
......@@ -196,10 +204,19 @@ Models are trained by starting with learning rate ```0.1``` and decaying it by `
|model | top-1/top-5 accuracy(PIL)| top-1/top-5 accuracy(CV2) |
|- |:-: |:-:|
|[AlexNet](http://paddle-imagenet-models-name.bj.bcebos.com/AlexNet_pretrained.zip) | 56.71%/79.18% | 55.88%/78.65% |
|[VGG11](http://paddle-imagenet-models-name.bj.bcebos.com/VGG11_pretained.zip) | 68.92%/88.66% | 68.61%/88.60% |
|[VGG11](https://paddle-imagenet-models-name.bj.bcebos.com/VGG11_pretrained.zip) | 69.22%/89.09% | 69.01%/88.90% |
|[VGG13](https://paddle-imagenet-models-name.bj.bcebos.com/VGG13_pretrained.zip) | 70.14%/89.48% | 69.83%/89.13% |
|[VGG16](https://paddle-imagenet-models-name.bj.bcebos.com/VGG16_pretrained.zip) | 72.08%/90.63% | 71.65%/90.57% |
|[VGG19](https://paddle-imagenet-models-name.bj.bcebos.com/VGG19_pretrained.zip) | 72.56%/90.83% | 72.32%/90.98% |
|[MobileNetV1](http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.zip) | 70.91%/89.54% | 70.51%/89.35% |
|[ResNet50](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.zip) | 76.35%/92.80% | 76.22%/92.92% |
|[ResNet101](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.zip) | 77.49%/93.57% | 77.56%/93.64% |
|[ResNet152](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet152_pretrained.zip) | 78.12%/93.93% | 77.92%/93.87% |
|[SE_ResNeXt50_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNext50_32x4d_pretrained.zip) | 78.50%/94.01% | 78.44%/93.96% |
|[SE_ResNeXt101_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt101_32x4d_pretrained.zip) | 79.26%/94.22% | 79.12%/94.20% |
- Released models: not specify parameter names
......
fluid/PaddleCV/image_classification/README_cn.md
浏览文件 @ e6ed31ef
......@@ -109,6 +109,11 @@ End pass 9, train_loss 3.3745200634, train_acc1 0.303871691227, train_acc5 0.545
训练集合与验证集合上的错误率曲线
</p>
## 混合精度训练
可以通过开启`--fp16 1`启动混合精度训练，这样训练过程会使用float16数据，并输出float32的模型参数（"master"参数）。您可能需要同时传入`--scale_loss`来解决fp16训练的精度问题，通常传入`--scale_loss 8.0`即可。
注意，目前混合精度训练不能和内存优化功能同时使用，所以需要传`--with_mem_opt 0`这个参数来禁用内存优化功能。
## 参数微调
......@@ -194,10 +199,16 @@ Models包括两种模型：带有参数名字的模型，和不带有参数名
|model | top-1/top-5 accuracy(PIL)| top-1/top-5 accuracy(CV2) |
|- |:-: |:-:|
|[AlexNet](http://paddle-imagenet-models-name.bj.bcebos.com/AlexNet_pretrained.zip) | 56.71%/79.18% | 55.88%/78.65% |
|[VGG11](http://paddle-imagenet-models-name.bj.bcebos.com/VGG11_pretained.zip) | 68.92%/88.66% | 68.61%/88.60% |
|[VGG11](https://paddle-imagenet-models-name.bj.bcebos.com/VGG11_pretrained.zip) | 69.22%/89.09% | 69.01%/88.90% |
|[VGG13](https://paddle-imagenet-models-name.bj.bcebos.com/VGG13_pretrained.zip) | 70.14%/89.48% | 69.83%/89.13% |
|[VGG16](https://paddle-imagenet-models-name.bj.bcebos.com/VGG16_pretrained.zip) | 72.08%/90.63% | 71.65%/90.57% |
|[VGG19](https://paddle-imagenet-models-name.bj.bcebos.com/VGG19_pretrained.zip) | 72.56%/90.83% | 72.32%/90.98% |
|[MobileNetV1](http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.zip) | 70.91%/89.54% | 70.51%/89.35% |
|[ResNet50](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.zip) | 76.35%/92.80% | 76.22%/92.92% |
|[ResNet101](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.zip) | 77.49%/93.57% | 77.56%/93.64% |
|[ResNet152](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet152_pretrained.zip) | 78.12%/93.93% | 77.92%/93.87% |
|[SE_ResNeXt50_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNext50_32x4d_pretrained.zip) | 78.50%/94.01% | 78.44%/93.96% |
|[SE_ResNeXt101_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt101_32x4d_pretrained.zip) | 79.26%/94.22% | 79.12%/94.20% |
- Released models: not specify parameter names
......
fluid/PaddleCV/image_classification/dist_train/README.md
浏览文件 @ e6ed31ef
......@@ -7,13 +7,15 @@ large-scaled distributed training with two distributed mode: parameter server mo
Before getting started, please make sure you have go throught the imagenet [Data Preparation](../README.md#data-preparation).
1. The entrypoint file is `dist_train.py`, some important flags are as follows:
1. The entrypoint file is `dist_train.py`, the commandline arguments are almost the same as the original `train.py`, with the following arguments specific to distributed training.
- `model`, the model to run with, default is the fine tune model `DistResnet`.
- `batch_size`, the batch_size per device.
- `update_method`, specify the update method, can choose from local, pserver or nccl2.
- `device`, use CPU or GPU device.
- `gpus`, the GPU device count that the process used.
- `multi_batch_repeat`, set this greater than 1 to merge batches before pushing gradients to pservers.
- `start_test_pass`, when to start running tests.
- `num_threads`, how many threads will be used for ParallelExecutor.
- `split_var`, in pserver mode, whether to split one parameter to several pservers, default True.
- `async_mode`, do async training, defalt False.
- `reduce_strategy`, choose from "reduce", "allreduce".
you can check out more details of the flags by `python dist_train.py --help`.
......@@ -21,66 +23,27 @@ Before getting started, please make sure you have go throught the imagenet [Data
We use the environment variable to distinguish the different training role of a distributed training job.
- `PADDLE_TRAINING_ROLE`, the current training role, should be in [PSERVER, TRAINER].
- `PADDLE_TRAINERS`, the trainer count of a job.
- `PADDLE_CURRENT_IP`, the current instance IP.
- `PADDLE_PSERVER_IPS`, the parameter server IP list, separated by "," only be used with update_method is pserver.
- General envs:
- `PADDLE_TRAINER_ID`, the unique trainer ID of a job, the ranging is [0, PADDLE_TRAINERS).
- `PADDLE_PSERVER_PORT`, the port of the parameter pserver listened on.
- `PADDLE_TRAINER_IPS`, the trainer IP list, separated by ",", only be used with upadte_method is nccl2.
### Parameter Server Mode
In this example, we launched 4 parameter server instances and 4 trainer instances in the cluster:
1. launch parameter server process
``` bash
PADDLE_TRAINING_ROLE=PSERVER \
PADDLE_TRAINERS=4 \
PADDLE_PSERVER_IPS=192.168.0.100,192.168.0.101,192.168.0.102,192.168.0.103 \
PADDLE_CURRENT_IP=192.168.0.100 \
PADDLE_PSERVER_PORT=7164 \
python dist_train.py \
--model=DistResnet \
--batch_size=32 \
--update_method=pserver \
--device=CPU \
--data_dir=../data/ILSVRC2012
```
1. launch trainer process
``` bash
PADDLE_TRAINING_ROLE=TRAINER \
PADDLE_TRAINERS=4 \
PADDLE_PSERVER_IPS=192.168.0.100,192.168.0.101,192.168.0.102,192.168.0.103 \
PADDLE_TRAINER_ID=0 \
PADDLE_PSERVER_PORT=7164 \
python dist_train.py \
--model=DistResnet \
--batch_size=32 \
--update_method=pserver \
--device=GPU \
--data_dir=../data/ILSVRC2012
```
### NCCL2 Collective Mode
1. launch trainer process
``` bash
PADDLE_TRAINING_ROLE=TRAINER \
PADDLE_TRAINERS=4 \
PADDLE_TRAINER_IPS=192.168.0.100,192.168.0.101,192.168.0.102,192.168.0.103 \
PADDLE_TRAINER_ID=0 \
python dist_train.py \
--model=DistResnet \
--batch_size=32 \
--update_method=nccl2 \
--device=GPU \
--data_dir=../data/ILSVRC2012
```
- `PADDLE_TRAINERS_NUM`, the trainer count of a distributed job.
- `PADDLE_CURRENT_ENDPOINT`, current process endpoint.
- Pserver mode:
- `PADDLE_TRAINING_ROLE`, the current training role, should be in [PSERVER, TRAINER].
- `PADDLE_PSERVER_ENDPOINTS`, the parameter server endpoint list, separated by ",".
- NCCL2 mode:
- `PADDLE_TRAINER_ENDPOINTS`, endpoint list for each worker, separated by ",".
### Try Out Different Distributed Training Modes
You can test if distributed training works on a single node before deploying to the "real" cluster.
***NOTE: for best performance, we recommend using multi-process mode, see No.3. And together with fp16.***
1. simply run `python dist_train.py` to start local training with default configuratioins.
2. for pserver mode, run `bash run_ps_mode.sh` to start 2 pservers and 2 trainers, these 2 trainers
will use GPU 0 and 1 to simulate 2 workers.
3. for nccl2 mode, run `bash run_nccl2_mode.sh` to start 2 workers.
4. for local/distributed multi-process mode, run `run_mp_mode.sh` (this test use 4 GPUs).
### Visualize the Training Process
......@@ -88,16 +51,10 @@ It's easy to draw the learning curve accroding to the training logs, for example
the logs of ResNet50 is as follows:
``` text
Pass 0, batch 0, loss 7.0336914, accucacys: [0.0, 0.00390625]
Pass 0, batch 1, loss 7.094781, accucacys: [0.0, 0.0]
Pass 0, batch 2, loss 7.007068, accucacys: [0.0, 0.0078125]
Pass 0, batch 3, loss 7.1056547, accucacys: [0.00390625, 0.00390625]
Pass 0, batch 4, loss 7.133543, accucacys: [0.0, 0.0078125]
Pass 0, batch 5, loss 7.3055463, accucacys: [0.0078125, 0.01171875]
Pass 0, batch 6, loss 7.341838, accucacys: [0.0078125, 0.01171875]
Pass 0, batch 7, loss 7.290557, accucacys: [0.0, 0.0]
Pass 0, batch 8, loss 7.264951, accucacys: [0.0, 0.00390625]
Pass 0, batch 9, loss 7.43522, accucacys: [0.00390625, 0.00390625]
Pass 0, batch 30, loss 7.569439, acc1: 0.0125, acc5: 0.0125, avg batch time 0.1720
Pass 0, batch 60, loss 7.027379, acc1: 0.0, acc5: 0.0, avg batch time 0.1551
Pass 0, batch 90, loss 6.819984, acc1: 0.0, acc5: 0.0125, avg batch time 0.1492
Pass 0, batch 120, loss 6.9076853, acc1: 0.0, acc5: 0.0125, avg batch time 0.1464
```
The below figure shows top 1 train accuracy for local training with 8 GPUs and distributed training
......
fluid/PaddleCV/image_classification/dist_train/batch_merge.py 0 → 100644
浏览文件 @ e6ed31ef
import paddle.fluid as fluid
def copyback_repeat_bn_params(main_prog):
repeat_vars = set()
for op in main_prog.global_block().ops:
if op.type == "batch_norm":
repeat_vars.add(op.input("Mean")[0])
repeat_vars.add(op.input("Variance")[0])
for vname in repeat_vars:
real_var = fluid.global_scope().find_var("%s.repeat.0" % vname).get_tensor()
orig_var = fluid.global_scope().find_var(vname).get_tensor()
orig_var.set(np.array(real_var), fluid.CUDAPlace(0)) # test on GPU0
def append_bn_repeat_init_op(main_prog, startup_prog, num_repeats):
repeat_vars = set()
for op in main_prog.global_block().ops:
if op.type == "batch_norm":
repeat_vars.add(op.input("Mean")[0])
repeat_vars.add(op.input("Variance")[0])
for i in range(num_repeats):
for op in startup_prog.global_block().ops:
if op.type == "fill_constant":
for oname in op.output_arg_names:
if oname in repeat_vars:
var = startup_prog.global_block().var(oname)
repeat_var_name = "%s.repeat.%d" % (oname, i)
repeat_var = startup_prog.global_block().create_var(
name=repeat_var_name,
type=var.type,
dtype=var.dtype,
shape=var.shape,
persistable=var.persistable
)
main_prog.global_block()._clone_variable(repeat_var)
startup_prog.global_block().append_op(
type="fill_constant",
inputs={},
outputs={"Out": repeat_var},
attrs=op.all_attrs()
)
fluid/PaddleCV/image_classification/dist_train/dist_train.py
浏览文件 @ e6ed31ef
......@@ -16,6 +16,8 @@ import argparse
import time
import os
import traceback
import functools
import subprocess
import numpy as np
......@@ -28,127 +30,121 @@ sys.path.append("..")
import models
import utils
from reader import train, val
from utility import add_arguments, print_arguments
from batch_merge import copyback_repeat_bn_params, append_bn_repeat_init_op
from dist_utils import pserver_prepare, nccl2_prepare
from env import dist_env
def parse_args():
parser = argparse.ArgumentParser('Distributed Image Classification Training.')
parser.add_argument(
'--model',
type=str,
default='DistResNet',
help='The model to run.')
parser.add_argument(
'--batch_size', type=int, default=32, help='The minibatch size per device.')
parser.add_argument(
'--multi_batch_repeat', type=int, default=1, help='Batch merge repeats.')
parser.add_argument(
'--learning_rate', type=float, default=0.1, help='The learning rate.')
parser.add_argument(
'--pass_num', type=int, default=90, help='The number of passes.')
parser.add_argument(
'--data_format',
type=str,
default='NCHW',
choices=['NCHW', 'NHWC'],
help='The data data_format, now only support NCHW.')
parser.add_argument(
'--device',
type=str,
default='GPU',
choices=['CPU', 'GPU'],
help='The device type.')
parser.add_argument(
'--gpus',
type=int,
default=1,
help='If gpus > 1, will use ParallelExecutor to run, else use Executor.')
parser.add_argument(
'--cpus',
type=int,
default=1,
help='If cpus > 1, will set ParallelExecutor to use multiple threads.')
parser.add_argument(
'--no_test',
action='store_true',
help='If set, do not test the testset during training.')
parser.add_argument(
'--memory_optimize',
action='store_true',
help='If set, optimize runtime memory before start.')
parser.add_argument(
'--update_method',
type=str,
default='local',
choices=['local', 'pserver', 'nccl2'],
help='Choose parameter update method, can be local, pserver, nccl2.')
parser.add_argument(
'--no_split_var',
action='store_true',
default=False,
help='Whether split variables into blocks when update_method is pserver')
parser.add_argument(
'--async_mode',
action='store_true',
default=False,
help='Whether start pserver in async mode to support ASGD')
parser.add_argument(
'--reduce_strategy',
type=str,
choices=['reduce', 'all_reduce'],
default='all_reduce',
help='Specify the reduce strategy, can be reduce, all_reduce')
parser.add_argument(
'--data_dir',
type=str,
default="../data/ILSVRC2012",
help="The ImageNet dataset root dir."
)
parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable
add_arg('batch_size', int, 256, "Minibatch size.")
add_arg('use_gpu', bool, True, "Whether to use GPU or not.")
add_arg('total_images', int, 1281167, "Training image number.")
add_arg('num_epochs', int, 120, "number of epochs.")
add_arg('class_dim', int, 1000, "Class number.")
add_arg('image_shape', str, "3,224,224", "input image size")
add_arg('model_save_dir', str, "output", "model save directory")
add_arg('with_mem_opt', bool, False, "Whether to use memory optimization or not.")
add_arg('pretrained_model', str, None, "Whether to use pretrained model.")
add_arg('checkpoint', str, None, "Whether to resume checkpoint.")
add_arg('lr', float, 0.1, "set learning rate.")
add_arg('lr_strategy', str, "piecewise_decay", "Set the learning rate decay strategy.")
add_arg('model', str, "DistResNet", "Set the network to use.")
add_arg('enable_ce', bool, False, "If set True, enable continuous evaluation job.")
add_arg('data_dir', str, "./data/ILSVRC2012", "The ImageNet dataset root dir.")
add_arg('model_category', str, "models", "Whether to use models_name or not, valid value:'models','models_name'" )
add_arg('fp16', bool, False, "Enable half precision training with fp16." )
add_arg('scale_loss', float, 1.0, "Scale loss for fp16." )
# for distributed
add_arg('update_method', str, "local", "Can be local, pserver, nccl2.")
add_arg('multi_batch_repeat', int, 1, "Batch merge repeats.")
add_arg('start_test_pass', int, 0, "Start test after x passes.")
add_arg('num_threads', int, 8, "Use num_threads to run the fluid program.")
add_arg('split_var', bool, True, "Split params on pserver.")
add_arg('async_mode', bool, False, "Async distributed training, only for pserver mode.")
add_arg('reduce_strategy', str, "allreduce", "Choose from reduce or allreduce.")
add_arg('skip_unbalanced_data', bool, False, "Skip data not if data not balanced on nodes.")
# yapf: enable
args = parser.parse_args()
return args
def get_model(args, is_train, main_prog, startup_prog):
pyreader = None
class_dim = 1000
if args.data_format == 'NCHW':
dshape = [3, 224, 224]
def get_device_num():
if os.getenv("CPU_NUM"):
return int(os.getenv("CPU_NUM"))
visible_device = os.getenv('CUDA_VISIBLE_DEVICES')
if visible_device:
device_num = len(visible_device.split(','))
else:
dshape = [224, 224, 3]
device_num = subprocess.check_output(['nvidia-smi', '-L']).decode().count('\n')
return device_num
def prepare_reader(is_train, pyreader, args, pass_id=0):
if is_train:
reader = train(data_dir=args.data_dir)
reader = train(data_dir=args.data_dir, pass_id_as_seed=pass_id)
else:
reader = val(data_dir=args.data_dir)
if is_train:
bs = args.batch_size / get_device_num()
else:
bs = 16
pyreader.decorate_paddle_reader(
paddle.batch(
reader,
batch_size=bs))
def build_program(is_train, main_prog, startup_prog, args):
pyreader = None
class_dim = args.class_dim
image_shape = [int(m) for m in args.image_shape.split(",")]
trainer_count = int(os.getenv("PADDLE_TRAINERS", "1"))
trainer_count = args.dist_env["num_trainers"]
device_num_per_worker = get_device_num()
with fluid.program_guard(main_prog, startup_prog):
with fluid.unique_name.guard():
pyreader = fluid.layers.py_reader(
capacity=args.batch_size * args.gpus,
shapes=([-1] + dshape, (-1, 1)),
capacity=16,
shapes=([-1] + image_shape, (-1, 1)),
dtypes=('float32', 'int64'),
name="train_reader" if is_train else "test_reader",
use_double_buffer=True)
input, label = fluid.layers.read_file(pyreader)
with fluid.unique_name.guard():
image, label = fluid.layers.read_file(pyreader)
if args.fp16:
image = fluid.layers.cast(image, "float16")
model_def = models.__dict__[args.model](layers=50, is_train=is_train)
predict = model_def.net(input, class_dim=class_dim)
cost = fluid.layers.cross_entropy(input=predict, label=label)
predict = model_def.net(image, class_dim=class_dim)
cost, pred = fluid.layers.softmax_with_cross_entropy(predict, label, return_softmax=True)
if args.scale_loss > 1:
avg_cost = fluid.layers.mean(x=cost) * float(args.scale_loss)
else:
avg_cost = fluid.layers.mean(x=cost)
batch_acc1 = fluid.layers.accuracy(input=predict, label=label, k=1)
batch_acc5 = fluid.layers.accuracy(input=predict, label=label, k=5)
batch_acc1 = fluid.layers.accuracy(input=pred, label=label, k=1)
batch_acc5 = fluid.layers.accuracy(input=pred, label=label, k=5)
optimizer = None
if is_train:
start_lr = args.learning_rate
# n * worker * repeat
end_lr = args.learning_rate * trainer_count * args.multi_batch_repeat
total_images = 1281167 / trainer_count
step = int(total_images / (args.batch_size * args.gpus * args.multi_batch_repeat) + 1)
start_lr = args.lr
end_lr = args.lr * trainer_count * args.multi_batch_repeat
if os.getenv("FLAGS_selected_gpus"):
# in multi process mode, "trainer_count" will be total devices
# in the whole cluster, and we need to scale num_of nodes.
end_lr *= device_num_per_worker
total_images = args.total_images / trainer_count
step = int(total_images / (args.batch_size * args.multi_batch_repeat) + 1)
warmup_steps = step * 5 # warmup 5 passes
epochs = [30, 60, 80]
bd = [step * e for e in epochs]
base_lr = end_lr
lr = []
lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
print("start lr: %s, end lr: %s, decay boundaries: %s" % (
start_lr,
end_lr,
bd
))
# NOTE: we put weight decay in layers config, and remove
# weight decay on bn layers, so don't add weight decay in
......@@ -159,151 +155,77 @@ def get_model(args, is_train, main_prog, startup_prog):
boundaries=bd, values=lr),
warmup_steps, start_lr, end_lr),
momentum=0.9)
if args.fp16:
params_grads = optimizer.backward(avg_cost)
master_params_grads = utils.create_master_params_grads(
params_grads, main_prog, startup_prog, args.scale_loss)
optimizer.apply_gradients(master_params_grads)
utils.master_param_to_train_param(master_params_grads, params_grads, main_prog)
else:
optimizer.minimize(avg_cost)
batched_reader = None
pyreader.decorate_paddle_reader(
paddle.batch(
reader,
batch_size=args.batch_size))
return avg_cost, optimizer, [batch_acc1,
batch_acc5], batched_reader, pyreader
def append_nccl2_prepare(trainer_id, startup_prog):
trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
port = os.getenv("PADDLE_PSERVER_PORT")
worker_ips = os.getenv("PADDLE_TRAINER_IPS")
worker_endpoints = []
for ip in worker_ips.split(","):
worker_endpoints.append(':'.join([ip, port]))
current_endpoint = os.getenv("PADDLE_CURRENT_IP") + ":" + port
num_trainers = len(worker_endpoints)
config = fluid.DistributeTranspilerConfig()
config.mode = "nccl2"
t = fluid.DistributeTranspiler(config=config)
t.transpile(trainer_id, trainers=','.join(worker_endpoints),
current_endpoint=current_endpoint,
startup_program=startup_prog)
return num_trainers, trainer_id
def dist_transpile(trainer_id, args, train_prog, startup_prog):
port = os.getenv("PADDLE_PSERVER_PORT", "6174")
pserver_ips = os.getenv("PADDLE_PSERVER_IPS", "")
eplist = []
for ip in pserver_ips.split(","):
eplist.append(':'.join([ip, port]))
pserver_endpoints = ",".join(eplist)
trainers = int(os.getenv("PADDLE_TRAINERS"))
current_endpoint = os.getenv("PADDLE_CURRENT_IP", "") + ":" + port
training_role = os.getenv("PADDLE_TRAINING_ROLE")
config = fluid.DistributeTranspilerConfig()
config.slice_var_up = not args.no_split_var
t = fluid.DistributeTranspiler(config=config)
t.transpile(
trainer_id,
program=train_prog,
pservers=pserver_endpoints,
trainers=trainers,
sync_mode=not args.async_mode,
startup_program=startup_prog)
if training_role == "PSERVER":
pserver_program = t.get_pserver_program(current_endpoint)
pserver_startup_program = t.get_startup_program(
current_endpoint, pserver_program, startup_program=startup_prog)
return pserver_program, pserver_startup_program
elif training_role == "TRAINER":
train_program = t.get_trainer_program()
return train_program, startup_prog
else:
raise ValueError(
'PADDLE_TRAINING_ROLE environment variable must be either TRAINER or PSERVER'
)
def append_bn_repeat_init_op(main_prog, startup_prog, num_repeats):
repeat_vars = set()
for op in main_prog.global_block().ops:
if op.type == "batch_norm":
repeat_vars.add(op.input("Mean")[0])
repeat_vars.add(op.input("Variance")[0])
for i in range(num_repeats):
for op in startup_prog.global_block().ops:
if op.type == "fill_constant":
for oname in op.output_arg_names:
if oname in repeat_vars:
var = startup_prog.global_block().var(oname)
repeat_var_name = "%s.repeat.%d" % (oname, i)
repeat_var = startup_prog.global_block().create_var(
name=repeat_var_name,
type=var.type,
dtype=var.dtype,
shape=var.shape,
persistable=var.persistable
)
main_prog.global_block()._clone_variable(repeat_var)
startup_prog.global_block().append_op(
type="fill_constant",
inputs={},
outputs={"Out": repeat_var},
attrs=op.all_attrs()
)
def copyback_repeat_bn_params(main_prog):
repeat_vars = set()
for op in main_prog.global_block().ops:
if op.type == "batch_norm":
repeat_vars.add(op.input("Mean")[0])
repeat_vars.add(op.input("Variance")[0])
for vname in repeat_vars:
real_var = fluid.global_scope().find_var("%s.repeat.0" % vname).get_tensor()
orig_var = fluid.global_scope().find_var(vname).get_tensor()
orig_var.set(np.array(real_var), fluid.CUDAPlace(0)) # test on GPU0
def test_single(exe, test_args, args, test_prog):
acc_evaluators = []
for i in xrange(len(test_args[2])):
acc_evaluators.append(fluid.metrics.Accuracy())
to_fetch = [v.name for v in test_args[2]]
test_args[4].start()
# prepare reader for current program
prepare_reader(is_train, pyreader, args)
return pyreader, avg_cost, batch_acc1, batch_acc5
def test_single(exe, test_prog, args, pyreader, fetch_list):
acc1 = fluid.metrics.Accuracy()
acc5 = fluid.metrics.Accuracy()
test_losses = []
pyreader.start()
while True:
try:
acc_rets = exe.run(program=test_prog, fetch_list=to_fetch)
for i, e in enumerate(acc_evaluators):
e.update(
value=np.array(acc_rets[i]), weight=args.batch_size)
except fluid.core.EOFException as eof:
test_args[4].reset()
acc_rets = exe.run(program=test_prog, fetch_list=fetch_list)
test_losses.append(acc_rets[0])
acc1.update(value=np.array(acc_rets[1]), weight=args.batch_size)
acc5.update(value=np.array(acc_rets[2]), weight=args.batch_size)
except fluid.core.EOFException:
pyreader.reset()
break
test_avg_loss = np.mean(np.array(test_losses))
return test_avg_loss, np.mean(acc1.eval()), np.mean(acc5.eval())
def run_pserver(train_prog, startup_prog):
server_exe = fluid.Executor(fluid.CPUPlace())
server_exe.run(startup_prog)
server_exe.run(train_prog)
return [e.eval() for e in acc_evaluators]
def train_parallel(args):
train_prog = fluid.Program()
test_prog = fluid.Program()
startup_prog = fluid.Program()
train_pyreader, train_cost, train_acc1, train_acc5 = build_program(True, train_prog, startup_prog, args)
test_pyreader, test_cost, test_acc1, test_acc5 = build_program(False, test_prog, startup_prog, args)
def train_parallel(train_args, test_args, args, train_prog, test_prog,
startup_prog, num_trainers, trainer_id):
over_all_start = time.time()
place = core.CPUPlace() if args.device == 'CPU' else core.CUDAPlace(0)
if args.update_method == "pserver":
train_prog, startup_prog = pserver_prepare(args, train_prog, startup_prog)
elif args.update_method == "nccl2":
nccl2_prepare(args, startup_prog)
if args.update_method == "nccl2" and trainer_id == 0:
#FIXME(typhoonzero): wait other trainer to start listening
time.sleep(30)
if args.dist_env["training_role"] == "PSERVER":
run_pserver(train_prog, startup_prog)
exit(0)
if args.use_gpu:
# NOTE: for multi process mode: one process per GPU device.
gpu_id = 0
if os.getenv("FLAGS_selected_gpus"):
gpu_id = int(os.getenv("FLAGS_selected_gpus"))
place = core.CUDAPlace(gpu_id) if args.use_gpu else core.CPUPlace()
startup_exe = fluid.Executor(place)
if args.multi_batch_repeat > 1:
append_bn_repeat_init_op(train_prog, startup_prog, args.multi_batch_repeat)
startup_exe.run(startup_prog)
strategy = fluid.ExecutionStrategy()
strategy.num_threads = args.cpus
strategy.allow_op_delay = False
strategy.num_threads = args.num_threads
build_strategy = fluid.BuildStrategy()
if args.multi_batch_repeat > 1:
pass_builder = build_strategy._create_passes_from_strategy()
pass_builder = build_strategy._finalize_strategy_and_create_passes()
mypass = pass_builder.insert_pass(
len(pass_builder.all_passes()) - 2, "multi_batch_merge_pass")
mypass.set_int("num_repeats", args.multi_batch_repeat)
......@@ -314,73 +236,70 @@ def train_parallel(train_args, test_args, args, train_prog, test_prog,
build_strategy.reduce_strategy = fluid.BuildStrategy(
).ReduceStrategy.AllReduce
avg_loss = train_args[0]
if args.update_method == "pserver":
if args.update_method == "pserver" or args.update_method == "local":
# parameter server mode distributed training, merge
# gradients on local server, do not initialize
# ParallelExecutor with multi server all-reduce mode.
num_trainers = 1
trainer_id = 0
else:
num_trainers = args.dist_env["num_trainers"]
trainer_id = args.dist_env["trainer_id"]
exe = fluid.ParallelExecutor(
True,
avg_loss.name,
train_cost.name,
main_program=train_prog,
exec_strategy=strategy,
build_strategy=build_strategy,
num_trainers=num_trainers,
trainer_id=trainer_id)
pyreader = train_args[4]
for pass_id in range(args.pass_num):
over_all_start = time.time()
fetch_list = [train_cost.name, train_acc1.name, train_acc5.name]
steps_per_pass = args.total_images / args.batch_size / args.dist_env["num_trainers"]
for pass_id in range(args.num_epochs):
num_samples = 0
start_time = time.time()
batch_id = 0
pyreader.start()
batch_id = 1
# use pass_id+1 as per pass global shuffle for distributed training
prepare_reader(True, train_pyreader, args, pass_id + 1)
train_pyreader.start()
while True:
fetch_list = [avg_loss.name]
acc_name_list = [v.name for v in train_args[2]]
fetch_list.extend(acc_name_list)
try:
if batch_id % 30 == 0:
fetch_ret = exe.run(fetch_list)
fetched_data = [np.mean(np.array(d)) for d in fetch_ret]
print("Pass %d, batch %d, loss %s, acc1: %s, acc5: %s, avg batch time %.4f" %
(pass_id, batch_id, fetched_data[0], fetched_data[1],
fetched_data[2], (time.time()-start_time) / batch_id))
else:
fetch_ret = exe.run([])
except fluid.core.EOFException as eof:
except fluid.core.EOFException:
break
except fluid.core.EnforceNotMet as ex:
except fluid.core.EnforceNotMet:
traceback.print_exc()
break
num_samples += args.batch_size * args.gpus
if batch_id % 30 == 0:
fetched_data = [np.mean(np.array(d)) for d in fetch_ret]
print("Pass %d, batch %d, loss %s, accucacys: %s" %
(pass_id, batch_id, fetched_data[0], fetched_data[1:]))
num_samples += args.batch_size
batch_id += 1
if args.skip_unbalanced_data and batch_id >= steps_per_pass:
break
print_train_time(start_time, time.time(), num_samples)
pyreader.reset()
train_pyreader.reset()
if not args.no_test and test_args[2]:
if pass_id > args.start_test_pass:
if args.multi_batch_repeat > 1:
copyback_repeat_bn_params(train_prog)
test_ret = test_single(startup_exe, test_args, args, test_prog)
print("Pass: %d, Test Accuracy: %s\n" %
(pass_id, [np.mean(np.array(v)) for v in test_ret]))
test_fetch_list = [test_cost.name, test_acc1.name, test_acc5.name]
test_ret = test_single(startup_exe, test_prog, args, test_pyreader,test_fetch_list)
print("Pass: %d, Test Loss %s, test acc1: %s, test acc5: %s\n" %
(pass_id, test_ret[0], test_ret[1], test_ret[2]))
startup_exe.close()
print("total train time: ", time.time() - over_all_start)
def print_arguments(args):
print('----------- Configuration Arguments -----------')
for arg, value in sorted(six.iteritems(vars(args))):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
def print_train_time(start_time, end_time, num_samples):
train_elapsed = end_time - start_time
examples_per_sec = num_samples / train_elapsed
......@@ -400,47 +319,8 @@ def main():
args = parse_args()
print_arguments(args)
print_paddle_envs()
# the unique trainer id, starting from 0, needed by trainer
# only
num_trainers, trainer_id = (
1, int(os.getenv("PADDLE_TRAINER_ID", "0")))
train_prog = fluid.Program()
test_prog = fluid.Program()
startup_prog = fluid.Program()
train_args = list(get_model(args, True, train_prog, startup_prog))
test_args = list(get_model(args, False, test_prog, startup_prog))
all_args = [train_args, test_args, args]
if args.update_method == "pserver":
train_prog, startup_prog = dist_transpile(trainer_id, args, train_prog,
startup_prog)
if not train_prog:
raise Exception(
"Must configure correct environments to run dist train.")
all_args.extend([train_prog, test_prog, startup_prog])
if os.getenv("PADDLE_TRAINING_ROLE") == "TRAINER":
all_args.extend([num_trainers, trainer_id])
train_parallel(*all_args)
elif os.getenv("PADDLE_TRAINING_ROLE") == "PSERVER":
# start pserver with Executor
server_exe = fluid.Executor(fluid.CPUPlace())
server_exe.run(startup_prog)
server_exe.run(train_prog)
exit(0)
# for other update methods, use default programs
all_args.extend([train_prog, test_prog, startup_prog])
if args.update_method == "nccl2":
num_trainers, trainer_id = append_nccl2_prepare(
trainer_id, startup_prog)
all_args.extend([num_trainers, trainer_id])
train_parallel(*all_args)
args.dist_env = dist_env()
train_parallel(args)
if __name__ == "__main__":
main()
fluid/PaddleCV/image_classification/dist_train/dist_utils.py 0 → 100644
浏览文件 @ e6ed31ef
import os
import paddle.fluid as fluid
def nccl2_prepare(args, startup_prog):
config = fluid.DistributeTranspilerConfig()
config.mode = "nccl2"
t = fluid.DistributeTranspiler(config=config)
envs = args.dist_env
t.transpile(envs["trainer_id"],
trainers=','.join(envs["trainer_endpoints"]),
current_endpoint=envs["current_endpoint"],
startup_program=startup_prog)
def pserver_prepare(args, train_prog, startup_prog):
config = fluid.DistributeTranspilerConfig()
config.slice_var_up = args.split_var
t = fluid.DistributeTranspiler(config=config)
envs = args.dist_env
training_role = envs["training_role"]
t.transpile(
envs["trainer_id"],
program=train_prog,
pservers=envs["pserver_endpoints"],
trainers=envs["num_trainers"],
sync_mode=not args.async_mode,
startup_program=startup_prog)
if training_role == "PSERVER":
pserver_program = t.get_pserver_program(envs["current_endpoint"])
pserver_startup_program = t.get_startup_program(
envs["current_endpoint"], pserver_program, startup_program=startup_prog)
return pserver_program, pserver_startup_program
elif training_role == "TRAINER":
train_program = t.get_trainer_program()
return train_program, startup_prog
else:
raise ValueError(
'PADDLE_TRAINING_ROLE environment variable must be either TRAINER or PSERVER'
)
fluid/PaddleCV/image_classification/dist_train/env.py 0 → 100644
浏览文件 @ e6ed31ef
import os
def dist_env():
"""
Return a dict of all variable that distributed training may use.
NOTE: you may rewrite this function to suit your cluster environments.
"""
trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
num_trainers = 1
training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
assert(training_role == "PSERVER" or training_role == "TRAINER")
# - PADDLE_TRAINER_ENDPOINTS means nccl2 mode.
# - PADDLE_PSERVER_ENDPOINTS means pserver mode.
# - PADDLE_CURRENT_ENDPOINT means current process endpoint.
trainer_endpoints = os.getenv("PADDLE_TRAINER_ENDPOINTS")
pserver_endpoints = os.getenv("PADDLE_PSERVER_ENDPOINTS")
current_endpoint = os.getenv("PADDLE_CURRENT_ENDPOINT")
if trainer_endpoints:
trainer_endpoints = trainer_endpoints.split(",")
num_trainers = len(trainer_endpoints)
elif pserver_endpoints:
num_trainers = int(os.getenv("PADDLE_TRAINERS_NUM"))
return {
"trainer_id": trainer_id,
"num_trainers": num_trainers,
"current_endpoint": current_endpoint,
"training_role": training_role,
"pserver_endpoints": pserver_endpoints,
"trainer_endpoints": trainer_endpoints
}
fluid/PaddleCV/image_classification/dist_train/run_mp_mode.sh 0 → 100755
浏览文件 @ e6ed31ef
#!/bin/bash
# Test using 4 GPUs
export CUDA_VISIBLE_DEVICES="0,1,2,3"
export MODEL="DistResNet"
export PADDLE_TRAINER_ENDPOINTS="127.0.0.1:7160,127.0.0.1:7161,127.0.0.1:7162,127.0.0.1:7163"
# PADDLE_TRAINERS_NUM is used only for reader when nccl2 mode
export PADDLE_TRAINERS_NUM="4"
mkdir -p logs
for i in {0..3}
do
PADDLE_TRAINING_ROLE="TRAINER" \
PADDLE_CURRENT_ENDPOINT="127.0.0.1:716${i}" \
PADDLE_TRAINER_ID="${i}" \
FLAGS_selected_gpus="${i}" \
python dist_train.py --model $MODEL --update_method nccl2 --batch_size 32 --fp16 1 --scale_loss 8 &> logs/tr$i.log &
done
fluid/PaddleCV/image_classification/dist_train/run_nccl2_mode.sh 0 → 100755
浏览文件 @ e6ed31ef
#!/bin/bash
export MODEL="DistResNet"
export PADDLE_TRAINER_ENDPOINTS="127.0.0.1:7160,127.0.0.1:7161"
# PADDLE_TRAINERS_NUM is used only for reader when nccl2 mode
export PADDLE_TRAINERS_NUM="2"
mkdir -p logs
# NOTE: set NCCL_P2P_DISABLE so that can run nccl2 distribute train on one node.
PADDLE_TRAINING_ROLE="TRAINER" \
PADDLE_CURRENT_ENDPOINT="127.0.0.1:7160" \
PADDLE_TRAINER_ID="0" \
CUDA_VISIBLE_DEVICES="0" \
NCCL_P2P_DISABLE="1" \
python dist_train.py --model $MODEL --update_method nccl2 --batch_size 32 &> logs/tr0.log &
PADDLE_TRAINING_ROLE="TRAINER" \
PADDLE_CURRENT_ENDPOINT="127.0.0.1:7161" \
PADDLE_TRAINER_ID="1" \
CUDA_VISIBLE_DEVICES="1" \
NCCL_P2P_DISABLE="1" \
python dist_train.py --model $MODEL --update_method nccl2 --batch_size 32 &> logs/tr1.log &
fluid/PaddleCV/image_classification/dist_train/run_ps_mode.sh 0 → 100755
浏览文件 @ e6ed31ef
#!/bin/bash
export MODEL="DistResNet"
export PADDLE_PSERVER_ENDPOINTS="127.0.0.1:7160,127.0.0.1:7161"
export PADDLE_TRAINERS_NUM="2"
mkdir -p logs
PADDLE_TRAINING_ROLE="PSERVER" \
PADDLE_CURRENT_ENDPOINT="127.0.0.1:7160" \
python dist_train.py --model $MODEL --update_method pserver --batch_size 32 &> logs/ps0.log &
PADDLE_TRAINING_ROLE="PSERVER" \
PADDLE_CURRENT_ENDPOINT="127.0.0.1:7161" \
python dist_train.py --model $MODEL --update_method pserver --batch_size 32 &> logs/ps1.log &
PADDLE_TRAINING_ROLE="TRAINER" \
PADDLE_CURRENT_ENDPOINT="127.0.0.1:7160" \
PADDLE_TRAINER_ID="0" \
CUDA_VISIBLE_DEVICES="0" \
python dist_train.py --model $MODEL --update_method pserver --batch_size 32 &> logs/tr0.log &
PADDLE_TRAINING_ROLE="TRAINER" \
PADDLE_CURRENT_ENDPOINT="127.0.0.1:7161" \
PADDLE_TRAINER_ID="1" \
CUDA_VISIBLE_DEVICES="1" \
python dist_train.py --model $MODEL --update_method pserver --batch_size 32 &> logs/tr1.log &
fluid/PaddleCV/image_classification/eval.py
浏览文件 @ e6ed31ef
......@@ -7,12 +7,13 @@ import time
import sys
import paddle
import paddle.fluid as fluid
import models
#import models
import models_name as models
#import reader_cv2 as reader
import reader as reader
import argparse
import functools
from models.learning_rate import cosine_decay
from utils.learning_rate import cosine_decay
from utility import add_arguments, print_arguments
import math
......@@ -48,7 +49,7 @@ def eval(args):
# model definition
model = models.__dict__[model_name]()
if model_name is "GoogleNet":
if model_name == "GoogleNet":
out0, out1, out2 = model.net(input=image, class_dim=class_dim)
cost0 = fluid.layers.cross_entropy(input=out0, label=label)
cost1 = fluid.layers.cross_entropy(input=out1, label=label)
......@@ -70,8 +71,10 @@ def eval(args):
test_program = fluid.default_main_program().clone(for_test=True)
fetch_list = [avg_cost.name, acc_top1.name, acc_top5.name]
if with_memory_optimization:
fluid.memory_optimize(fluid.default_main_program())
fluid.memory_optimize(
fluid.default_main_program(), skip_opt_set=set(fetch_list))
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
......@@ -84,11 +87,9 @@ def eval(args):
fluid.io.load_vars(exe, pretrained_model, predicate=if_exist)
val_reader = paddle.batch(reader.val(""), batch_size=args.batch_size)
val_reader = paddle.batch(reader.val(), batch_size=args.batch_size)
feeder = fluid.DataFeeder(place=place, feed_list=[image, label])
fetch_list = [avg_cost.name, acc_top1.name, acc_top5.name]
test_info = [[], [], []]
cnt = 0
for batch_id, data in enumerate(val_reader()):
......
fluid/PaddleCV/image_classification/infer.py
浏览文件 @ e6ed31ef
......@@ -11,7 +11,6 @@ import models
import reader
import argparse
import functools
from models.learning_rate import cosine_decay
from utility import add_arguments, print_arguments
import math
......@@ -44,7 +43,6 @@ def infer(args):
# model definition
model = models.__dict__[model_name]()
if model_name is "GoogleNet":
out, _, _ = model.net(input=image, class_dim=class_dim)
else:
......@@ -52,8 +50,10 @@ def infer(args):
test_program = fluid.default_main_program().clone(for_test=True)
fetch_list = [out.name]
if with_memory_optimization:
fluid.memory_optimize(fluid.default_main_program())
fluid.memory_optimize(
fluid.default_main_program(), skip_opt_set=set(fetch_list))
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
......@@ -70,8 +70,6 @@ def infer(args):
test_reader = paddle.batch(reader.test(), batch_size=test_batch_size)
feeder = fluid.DataFeeder(place=place, feed_list=[image])
fetch_list = [out.name]
TOPK = 1
for batch_id, data in enumerate(test_reader()):
result = exe.run(test_program,
......
fluid/PaddleCV/image_classification/models/alexnet.py
浏览文件 @ e6ed31ef
......@@ -142,7 +142,6 @@ class AlexNet():
out = fluid.layers.fc(
input=fc7,
size=class_dim,
act='softmax',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv)),
param_attr=fluid.param_attr.ParamAttr(
......
fluid/PaddleCV/image_classification/models/dpn.py
浏览文件 @ e6ed31ef
......@@ -94,7 +94,6 @@ class DPN(object):
initializer=fluid.initializer.Uniform(-stdv, stdv))
fc6 = fluid.layers.fc(input=pool5,
size=class_dim,
act='softmax',
param_attr=param_attr)
return fc6
......
fluid/PaddleCV/image_classification/models/inception_v4.py
浏览文件 @ e6ed31ef
......@@ -47,7 +47,6 @@ class InceptionV4():
out = fluid.layers.fc(
input=drop,
size=class_dim,
act='softmax',
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv)))
return out
......
fluid/PaddleCV/image_classification/models/mobilenet.py
浏览文件 @ e6ed31ef
......@@ -120,7 +120,6 @@ class MobileNet():
output = fluid.layers.fc(input=input,
size=class_dim,
act='softmax',
param_attr=ParamAttr(initializer=MSRA()))
return output
......
fluid/PaddleCV/image_classification/models/mobilenet_v2.py
浏览文件 @ e6ed31ef
......@@ -73,7 +73,6 @@ class MobileNetV2():
output = fluid.layers.fc(input=input,
size=class_dim,
act='softmax',
param_attr=ParamAttr(initializer=MSRA()))
return output
......
fluid/PaddleCV/image_classification/models/resnet.py
浏览文件 @ e6ed31ef
......@@ -60,7 +60,6 @@ class ResNet():
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
out = fluid.layers.fc(input=pool,
size=class_dim,
act='softmax',
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv,
stdv)))
......
fluid/PaddleCV/image_classification/models/resnet_dist.py
浏览文件 @ e6ed31ef
......@@ -14,8 +14,9 @@ train_parameters = {
"learning_strategy": {
"name": "piecewise_decay",
"batch_size": 256,
"epochs": [30, 60, 90],
"steps": [0.1, 0.01, 0.001, 0.0001]
"epochs": [30, 60, 80],
"steps": [0.1, 0.01, 0.001, 0.0001],
"warmup_passes": 5
}
}
......@@ -62,7 +63,6 @@ class DistResNet():
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
out = fluid.layers.fc(input=pool,
size=class_dim,
act='softmax',
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv,
stdv),
......@@ -119,3 +119,4 @@ class DistResNet():
short = self.shortcut(input, num_filters * 4, stride)
return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
fluid/PaddleCV/image_classification/models/se_resnext.py
浏览文件 @ e6ed31ef
......@@ -110,7 +110,6 @@ class SE_ResNeXt():
stdv = 1.0 / math.sqrt(drop.shape[1] * 1.0)
out = fluid.layers.fc(input=drop,
size=class_dim,
act='softmax',
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv,
stdv)))
......
fluid/PaddleCV/image_classification/models/shufflenet_v2.py
浏览文件 @ e6ed31ef
......@@ -93,7 +93,6 @@ class ShuffleNetV2():
output = fluid.layers.fc(input=pool_last,
size=class_dim,
act='softmax',
param_attr=ParamAttr(initializer=MSRA()))
return output
......
fluid/PaddleCV/image_classification/models/vgg.py
浏览文件 @ e6ed31ef
......@@ -64,7 +64,6 @@ class VGGNet():
out = fluid.layers.fc(
input=fc2,
size=class_dim,
act='softmax',
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Normal(scale=0.005)),
bias_attr=fluid.param_attr.ParamAttr(
......
fluid/PaddleCV/image_classification/models_name/alexnet.py
浏览文件 @ e6ed31ef
......@@ -159,7 +159,6 @@ class AlexNet():
out = fluid.layers.fc(
input=fc7,
size=class_dim,
act='softmax',
bias_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name=layer_name[7] + "_offset"),
......
fluid/PaddleCV/image_classification/models_name/dpn.py
浏览文件 @ e6ed31ef
......@@ -122,7 +122,6 @@ class DPN(object):
initializer=fluid.initializer.Uniform(-stdv, stdv))
fc6 = fluid.layers.fc(input=pool5,
size=class_dim,
act='softmax',
param_attr=param_attr,
name="fc6")
......
fluid/PaddleCV/image_classification/models_name/inception_v4.py
浏览文件 @ e6ed31ef
......@@ -48,7 +48,6 @@ class InceptionV4():
out = fluid.layers.fc(
input=drop,
size=class_dim,
act='softmax',
param_attr=ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name="final_fc_weights"),
......
fluid/PaddleCV/image_classification/models_name/mobilenet.py
浏览文件 @ e6ed31ef
......@@ -130,7 +130,6 @@ class MobileNet():
output = fluid.layers.fc(input=input,
size=class_dim,
act='softmax',
param_attr=ParamAttr(
initializer=MSRA(), name="fc7_weights"),
bias_attr=ParamAttr(name="fc7_offset"))
......
fluid/PaddleCV/image_classification/models_name/mobilenet_v2.py
浏览文件 @ e6ed31ef
......@@ -80,7 +80,6 @@ class MobileNetV2():
output = fluid.layers.fc(input=input,
size=class_dim,
act='softmax',
param_attr=ParamAttr(name='fc10_weights'),
bias_attr=ParamAttr(name='fc10_offset'))
return output
......
fluid/PaddleCV/image_classification/models_name/resnet.py
浏览文件 @ e6ed31ef
......@@ -74,7 +74,6 @@ class ResNet():
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
out = fluid.layers.fc(input=pool,
size=class_dim,
act='softmax',
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv,
stdv)))
......
fluid/PaddleCV/image_classification/models_name/se_resnext.py
浏览文件 @ e6ed31ef
......@@ -123,7 +123,6 @@ class SE_ResNeXt():
out = fluid.layers.fc(
input=drop,
size=class_dim,
act='softmax',
param_attr=ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),
name='fc6_weights'),
......
fluid/PaddleCV/image_classification/models_name/shufflenet_v2.py
浏览文件 @ e6ed31ef
......@@ -97,7 +97,6 @@ class ShuffleNetV2():
output = fluid.layers.fc(input=pool_last,
size=class_dim,
act='softmax',
param_attr=ParamAttr(
initializer=MSRA(), name='fc6_weights'),
bias_attr=ParamAttr(name='fc6_offset'))
......
fluid/PaddleCV/image_classification/models_name/vgg.py
浏览文件 @ e6ed31ef
......@@ -61,7 +61,6 @@ class VGGNet():
out = fluid.layers.fc(
input=fc2,
size=class_dim,
act='softmax',
param_attr=fluid.param_attr.ParamAttr(name=fc_name[2] + "_weights"),
bias_attr=fluid.param_attr.ParamAttr(name=fc_name[2] + "_offset"))
......
fluid/PaddleCV/image_classification/reader.py
浏览文件 @ e6ed31ef
......@@ -130,16 +130,19 @@ def _reader_creator(file_list,
shuffle=False,
color_jitter=False,
rotate=False,
data_dir=DATA_DIR):
data_dir=DATA_DIR,
pass_id_as_seed=0):
def reader():
with open(file_list) as flist:
full_lines = [line.strip() for line in flist]
if shuffle:
if pass_id_as_seed:
np.random.seed(pass_id_as_seed)
np.random.shuffle(full_lines)
if mode == 'train' and os.getenv('PADDLE_TRAINING_ROLE'):
# distributed mode if the env var `PADDLE_TRAINING_ROLE` exits
trainer_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
trainer_count = int(os.getenv("PADDLE_TRAINERS", "1"))
trainer_count = int(os.getenv("PADDLE_TRAINERS_NUM", "1"))
per_node_lines = len(full_lines) // trainer_count
lines = full_lines[trainer_id * per_node_lines:(trainer_id + 1)
* per_node_lines]
......@@ -166,7 +169,7 @@ def _reader_creator(file_list,
return paddle.reader.xmap_readers(mapper, reader, THREAD, BUF_SIZE)
def train(data_dir=DATA_DIR):
def train(data_dir=DATA_DIR, pass_id_as_seed=0):
file_list = os.path.join(data_dir, 'train_list.txt')
return _reader_creator(
file_list,
......@@ -174,7 +177,8 @@ def train(data_dir=DATA_DIR):
shuffle=True,
color_jitter=False,
rotate=False,
data_dir=data_dir)
data_dir=data_dir,
pass_id_as_seed=pass_id_as_seed)
def val(data_dir=DATA_DIR):
......
fluid/PaddleCV/image_classification/reader_cv2.py
浏览文件 @ e6ed31ef
......@@ -101,8 +101,6 @@ def process_image(sample,
std = [0.229, 0.224, 0.225] if std is None else std
img_path = sample[0]
print('&' * 80)
print(img_path)
img = cv2.imread(img_path)
if mode == 'train':
......
fluid/PaddleCV/image_classification/run.sh
浏览文件 @ e6ed31ef
......@@ -78,3 +78,58 @@ python train.py \
# --num_epochs=120 \
# --lr=0.1
#ResNet152:
#python train.py \
# --model=ResNet152 \
# --batch_size=256 \
# --total_images=1281167 \
# --image_shape=3,224,224 \
# --lr_strategy=piecewise_decay \
# --lr=0.1 \
# --num_epochs=120 \
# --l2_decay=1e-4 \(TODO)
#SE_ResNeXt50:
#python train.py \
# --model=SE_ResNeXt50 \
# --batch_size=400 \
# --total_images=1281167 \
# --image_shape=3,224,224 \
# --lr_strategy=cosine_decay \
# --lr=0.1 \
# --num_epochs=200 \
# --l2_decay=12e-5 \(TODO)
#SE_ResNeXt101:
#python train.py \
# --model=SE_ResNeXt101 \
# --batch_size=400 \
# --total_images=1281167 \
# --image_shape=3,224,224 \
# --lr_strategy=cosine_decay \
# --lr=0.1 \
# --num_epochs=200 \
# --l2_decay=15e-5 \(TODO)
#VGG11:
#python train.py \
# --model=VGG11 \
# --batch_size=512 \
# --total_images=1281167 \
# --image_shape=3,224,224 \
# --lr_strategy=cosine_decay \
# --lr=0.1 \
# --num_epochs=90 \
# --l2_decay=2e-4 \(TODO)
#VGG13:
#python train.py
# --model=VGG13 \
# --batch_size=256 \
# --total_images=1281167 \
# --image_shape=3,224,224 \
# --lr_strategy=cosine_decay \
# --lr=0.01 \
# --num_epochs=90 \
# --l2_decay=3e-4 \(TODO)
fluid/PaddleCV/image_classification/train.py
浏览文件 @ e6ed31ef
......@@ -17,6 +17,7 @@ import functools
import subprocess
import utils
from utils.learning_rate import cosine_decay
from utils.fp16_utils import create_master_params_grads, master_param_to_train_param
from utility import add_arguments, print_arguments
import models
import models_name
......@@ -40,7 +41,9 @@ add_arg('model', str, "SE_ResNeXt50_32x4d", "Set the network to use
add_arg('enable_ce', bool, False, "If set True, enable continuous evaluation job.")
add_arg('data_dir', str, "./data/ILSVRC2012", "The ImageNet dataset root dir.")
add_arg('model_category', str, "models", "Whether to use models_name or not, valid value:'models','models_name'" )
# yapf: enabl
add_arg('fp16', bool, False, "Enable half precision training with fp16." )
add_arg('scale_loss', float, 1.0, "Scale loss for fp16." )
# yapf: enable
def set_models(model):
......@@ -146,11 +149,14 @@ def net_config(image, label, model, args):
acc_top5 = fluid.layers.accuracy(input=out0, label=label, k=5)
else:
out = model.net(input=image, class_dim=class_dim)
cost = fluid.layers.cross_entropy(input=out, label=label)
cost, pred = fluid.layers.softmax_with_cross_entropy(out, label, return_softmax=True)
if args.scale_loss > 1:
avg_cost = fluid.layers.mean(x=cost) * float(args.scale_loss)
else:
avg_cost = fluid.layers.mean(x=cost)
acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
acc_top1 = fluid.layers.accuracy(input=pred, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=pred, label=label, k=5)
return avg_cost, acc_top1, acc_top5
......@@ -171,6 +177,8 @@ def build_program(is_train, main_prog, startup_prog, args):
use_double_buffer=True)
with fluid.unique_name.guard():
image, label = fluid.layers.read_file(py_reader)
if args.fp16:
image = fluid.layers.cast(image, "float16")
avg_cost, acc_top1, acc_top5 = net_config(image, label, model, args)
avg_cost.persistable = True
acc_top1.persistable = True
......@@ -184,6 +192,14 @@ def build_program(is_train, main_prog, startup_prog, args):
params["learning_strategy"]["name"] = args.lr_strategy
optimizer = optimizer_setting(params)
if args.fp16:
params_grads = optimizer.backward(avg_cost)
master_params_grads = create_master_params_grads(
params_grads, main_prog, startup_prog, args.scale_loss)
optimizer.apply_gradients(master_params_grads)
master_param_to_train_param(master_params_grads, params_grads, main_prog)
else:
optimizer.minimize(avg_cost)
return py_reader, avg_cost, acc_top1, acc_top5
......@@ -200,7 +216,6 @@ def train(args):
startup_prog = fluid.Program()
train_prog = fluid.Program()
test_prog = fluid.Program()
if args.enable_ce:
startup_prog.random_seed = 1000
train_prog.random_seed = 1000
......@@ -240,10 +255,10 @@ def train(args):
if visible_device:
device_num = len(visible_device.split(','))
else:
device_num = subprocess.check_output(['nvidia-smi', '-L']).count('\n')
device_num = subprocess.check_output(['nvidia-smi', '-L']).decode().count('\n')
train_batch_size = args.batch_size / device_num
test_batch_size = 8
test_batch_size = 16
if not args.enable_ce:
train_reader = paddle.batch(
reader.train(), batch_size=train_batch_size, drop_last=True)
......@@ -307,7 +322,7 @@ def train(args):
train_loss = np.array(train_info[0]).mean()
train_acc1 = np.array(train_info[1]).mean()
train_acc5 = np.array(train_info[2]).mean()
train_speed = np.array(train_time).mean() / train_batch_size
train_speed = np.array(train_time).mean() / (train_batch_size * device_num)
test_py_reader.start()
......
fluid/PaddleCV/image_classification/utils/__init__.py
浏览文件 @ e6ed31ef
from .learning_rate import cosine_decay, lr_warmup
from .fp16_utils import create_master_params_grads, master_param_to_train_param
fluid/PaddleCV/image_classification/utils/fp16_utils.py 0 → 100644
浏览文件 @ e6ed31ef
from __future__ import print_function
import paddle
import paddle.fluid as fluid
def cast_fp16_to_fp32(i, o, prog):
prog.global_block().append_op(
type="cast",
inputs={"X": i},
outputs={"Out": o},
attrs={
"in_dtype": fluid.core.VarDesc.VarType.FP16,
"out_dtype": fluid.core.VarDesc.VarType.FP32
}
)
def cast_fp32_to_fp16(i, o, prog):
prog.global_block().append_op(
type="cast",
inputs={"X": i},
outputs={"Out": o},
attrs={
"in_dtype": fluid.core.VarDesc.VarType.FP32,
"out_dtype": fluid.core.VarDesc.VarType.FP16
}
)
def copy_to_master_param(p, block):
v = block.vars.get(p.name, None)
if v is None:
raise ValueError("no param name %s found!" % p.name)
new_p = fluid.framework.Parameter(
block=block,
shape=v.shape,
dtype=fluid.core.VarDesc.VarType.FP32,
type=v.type,
lod_level=v.lod_level,
stop_gradient=p.stop_gradient,
trainable=p.trainable,
optimize_attr=p.optimize_attr,
regularizer=p.regularizer,
gradient_clip_attr=p.gradient_clip_attr,
error_clip=p.error_clip,
name=v.name + ".master")
return new_p
def create_master_params_grads(params_grads, main_prog, startup_prog, scale_loss):
master_params_grads = []
tmp_role = main_prog._current_role
OpRole = fluid.core.op_proto_and_checker_maker.OpRole
main_prog._current_role = OpRole.Backward
for p, g in params_grads:
# create master parameters
master_param = copy_to_master_param(p, main_prog.global_block())
startup_master_param = startup_prog.global_block()._clone_variable(master_param)
startup_p = startup_prog.global_block().var(p.name)
cast_fp16_to_fp32(startup_p, startup_master_param, startup_prog)
# cast fp16 gradients to fp32 before apply gradients
if g.name.startswith("batch_norm"):
if scale_loss > 1:
scaled_g = g / float(scale_loss)
else:
scaled_g = g
master_params_grads.append([p, scaled_g])
continue
master_grad = fluid.layers.cast(g, "float32")
if scale_loss > 1:
master_grad = master_grad / float(scale_loss)
master_params_grads.append([master_param, master_grad])
main_prog._current_role = tmp_role
return master_params_grads
def master_param_to_train_param(master_params_grads, params_grads, main_prog):
for idx, m_p_g in enumerate(master_params_grads):
train_p, _ = params_grads[idx]
if train_p.name.startswith("batch_norm"):
continue
with main_prog._optimized_guard([m_p_g[0], m_p_g[1]]):
cast_fp32_to_fp16(m_p_g[0], train_p, main_prog)
fluid/PaddleCV/metric_learning/README.md
浏览文件 @ e6ed31ef
# Deep Metric Learning
Metric learning is a kind of methods to learn discriminative features for each sample, with the purpose that intra-class samples have smaller distances while inter-class samples have larger distances in the learned space. With the develop of deep learning technique, metric learning methods are combined with deep neural networks to boost the performance of traditional tasks, such as face recognition/verification, human re-identification, image retrieval and so on. In this page, we introduce the way to implement deep metric learning using PaddlePaddle Fluid, including [data preparation](#data-preparation), [training](#training-a-model), [finetuning](#finetuning), [evaluation](#evaluation) and [inference](#inference).
Metric learning is a kind of methods to learn discriminative features for each sample, with the purpose that intra-class samples have smaller distances while inter-class samples have larger distances in the learned space. With the develop of deep learning technique, metric learning methods are combined with deep neural networks to boost the performance of traditional tasks, such as face recognition/verification, human re-identification, image retrieval and so on. In this page, we introduce the way to implement deep metric learning using PaddlePaddle Fluid, including [data preparation](#data-preparation), [training](#training-metric-learning-models), [finetuning](#finetuning), [evaluation](#evaluation), [inference](#inference) and [Performances](#performances).
---
## Table of Contents
- [Installation](#installation)
- [Data preparation](#data-preparation)
- [Training metric learning models](#training-a-model)
- [Training metric learning models](#training-metric-learning-models)
- [Finetuning](#finetuning)
- [Evaluation](#evaluation)
- [Inference](#inference)
- [Performances](#supported-models)
- [Performances](#performances)
## Installation
......@@ -17,7 +17,7 @@ Running sample code in this directory requires PaddelPaddle Fluid v0.14.0 and la
## Data preparation
Stanford Online Product(SOP) dataset contains 120,053 images of 22,634 products downloaded from eBay.com. We use it to conduct the metric learning experiments. For training, 59,5511 out of 11,318 classes are used, and 11,316 classes(60,502 images) are held out for testing. First of all, preparation of SOP data can be done as:
Stanford Online Product(SOP) dataset contains 120,053 images of 22,634 products downloaded from eBay.com. We use it to conduct the metric learning experiments. For training, 59,551 out of 11,318 classes are used, and 11,316 classes(60,502 images) are held out for testing. First of all, preparation of SOP data can be done as:
```
cd data/
sh download_sop.sh
......@@ -25,7 +25,7 @@ sh download_sop.sh
## Training metric learning models
To train a metric learning model, one need to set the neural network as backbone and the metric loss function to optimize. We train meiric learning model using softmax or [arcmargin](https://arxiv.org/abs/1801.07698) loss firstly, and then fine-turned the model using other metric learning loss, such as triplet, [quadruplet](https://arxiv.org/abs/1710.00478) and [eml](https://arxiv.org/abs/1212.6094) loss. One example of training using arcmargin loss is shown below:
To train a metric learning model, one need to set the neural network as backbone and the metric loss function to optimize. We train meiric learning model using softmax or arcmargin loss firstly, and then fine-turned the model using other metric learning loss, such as triplet, quadruplet and eml loss. One example of training using arcmargin loss is shown below:
```
......@@ -52,7 +52,7 @@ python train_elem.py \
* **use_gpu**: whether to use GPU or not. Default: True.
* **pretrained_model**: model path for pretraining. Default: None.
* **model_save_dir**: the directory to save trained model. Default: "output".
* **loss_name**: loss fortraining model. Default: "softmax".
* **loss_name**: loss for training model. Default: "softmax".
* **arc_scale**: parameter of arcmargin loss. Default: 80.0.
* **arc_margin**: parameter of arcmargin loss. Default: 0.15.
* **arc_easy_margin**: parameter of arcmargin loss. Default: False.
......@@ -103,3 +103,9 @@ For comparation, many metric learning models with different neural networks and
|fine-tuned with triplet | 78.37% | 79.21%
|fine-tuned with quadruplet | 78.10% | 79.59%
|fine-tuned with eml | 79.32% | 80.11%
## Reference
- ArcFace: Additive Angular Margin Loss for Deep Face Recognition [link](https://arxiv.org/abs/1801.07698)
- Margin Sample Mining Loss: A Deep Learning Based Method for Person Re-identification [link](https://arxiv.org/abs/1710.00478)
- Large Scale Strongly Supervised Ensemble Metric Learning, with Applications to Face Verification and Retrieval [link](https://arxiv.org/abs/1212.6094)
fluid/PaddleCV/metric_learning/README_cn.md 0 → 100644
浏览文件 @ e6ed31ef
# 深度度量学习
度量学习是一种为样本对学习具有区分性特征的方法，目的是在特征空间中，让同一个类别的样本具有较小的特征距离，不同类的样本具有较大的特征距离。随着深度学习技术的发展，基于深度神经网络的度量学习方法已经在许多视觉任务上提升了很大的性能，例如：人脸识别、人脸校验、行人重识别和图像检索等等。在本章节，介绍在PaddlePaddle Fluid里实现的几种度量学习方法和使用方法，具体包括[数据准备](#数据准备)，[模型训练](#模型训练)，[模型微调](#模型微调)，[模型评估](#模型评估)，[模型预测](#模型预测)。
---
## 简介
- [安装](#安装)
- [数据准备](#数据准备)
- [模型训练](#模型训练)
- [模型微调](#模型微调)
- [模型评估](#模型评估)
- [模型预测](#模型预测)
- [模型性能](#模型性能)
## 安装
运行本章节代码需要在PaddlePaddle Fluid v0.14.0 或更高的版本环境。如果你的设备上的PaddlePaddle版本低于v0.14.0，请按照此[安装文档](http://www.paddlepaddle.org/docs/develop/documentation/zh/build_and_install/pip_install_cn.html)进行安装和跟新。
## 数据准备
Stanford Online Product(SOP) 数据集下载自eBay，包含120053张商品图片，有22634个类别。我们使用该数据集进行实验。训练时，使用59551张图片，11318个类别的数据；测试时，使用60502张图片，11316个类别。首先，SOP数据集可以使用以下脚本下载：
```
cd data/
sh download_sop.sh
```
## 模型训练
为了训练度量学习模型，我们需要一个神经网络模型作为骨架模型（如ResNet50）和度量学习代价函数来进行优化。我们首先使用 softmax 或者 arcmargin 来进行训练，然后使用其它的代价函数来进行微调，例如：triplet，quadruplet和eml。下面是一个使用arcmargin训练的例子：
```
python train_elem.py \
--model=ResNet50 \
--train_batch_size=256 \
--test_batch_size=50 \
--lr=0.01 \
--total_iter_num=30000 \
--use_gpu=True \
--pretrained_model=${path_to_pretrain_imagenet_model} \
--model_save_dir=${output_model_path} \
--loss_name=arcmargin \
--arc_scale=80.0 \
--arc_margin=0.15 \
--arc_easy_margin=False
```
**参数介绍:**
* **model**: 使用的模型名字. 默认: "ResNet50".
* **train_batch_size**: 训练的 mini-batch大小. 默认: 256.
* **test_batch_size**: 测试的 mini-batch大小. 默认: 50.
* **lr**: 初始学习率. 默认: 0.01.
* **total_iter_num**: 总的训练迭代轮数. 默认: 30000.
* **use_gpu**: 是否使用GPU. 默认: True.
* **pretrained_model**: 预训练模型的路径. 默认: None.
* **model_save_dir**: 保存模型的路径. 默认: "output".
* **loss_name**: 优化的代价函数. 默认: "softmax".
* **arc_scale**: arcmargin的参数. 默认: 80.0.
* **arc_margin**: arcmargin的参数. 默认: 0.15.
* **arc_easy_margin**: arcmargin的参数. 默认: False.
## 模型微调
网络微调是在指定的任务上加载已有的模型来微调网络。在用softmax和arcmargin训完网络后，可以继续使用triplet，quadruplet或eml来微调网络。下面是一个使用eml来微调网络的例子：
```
python train_pair.py \
--model=ResNet50 \
--train_batch_size=160 \
--test_batch_size=50 \
--lr=0.0001 \
--total_iter_num=100000 \
--use_gpu=True \
--pretrained_model=${path_to_pretrain_arcmargin_model} \
--model_save_dir=${output_model_path} \
--loss_name=eml \
--samples_each_class=2
```
## 模型评估
模型评估主要是评估模型的检索性能。这里需要设置```path_to_pretrain_model```。可以使用下面命令来计算Recall@Rank-1。
```
python eval.py \
--model=ResNet50 \
--batch_size=50 \
--pretrained_model=${path_to_pretrain_model} \
```
## 模型预测
模型预测主要是基于训练好的网络来获取图像数据的特征，下面是模型预测的例子：
```
python infer.py \
--model=ResNet50 \
--batch_size=1 \
--pretrained_model=${path_to_pretrain_model}
```
## 模型性能
下面列举了几种度量学习的代价函数在SOP数据集上的检索效果，这里使用Recall@Rank-1来进行评估。
|预训练模型 | softmax | arcmargin
|- | - | -:
|未微调 | 77.42% | 78.11%
|使用triplet微调 | 78.37% | 79.21%
|使用quadruplet微调 | 78.10% | 79.59%
|使用eml微调 | 79.32% | 80.11%
## 引用
- ArcFace: Additive Angular Margin Loss for Deep Face Recognition [链接](https://arxiv.org/abs/1801.07698)
- Margin Sample Mining Loss: A Deep Learning Based Method for Person Re-identification [链接](https://arxiv.org/abs/1710.00478)
- Large Scale Strongly Supervised Ensemble Metric Learning, with Applications to Face Verification and Retrieval [链接](https://arxiv.org/abs/1212.6094)
fluid/PaddleCV/metric_learning/reader.py
浏览文件 @ e6ed31ef
......@@ -63,6 +63,7 @@ def common_iterator(data, settings):
assert (batch_size % samples_each_class == 0)
class_num = batch_size // samples_each_class
def train_iterator():
count = 0
labs = list(data.keys())
lab_num = len(labs)
ind = list(range(0, lab_num))
......@@ -79,6 +80,9 @@ def common_iterator(data, settings):
for anchor_ind_i in anchor_ind:
anchor_path = DATA_DIR + data_list[anchor_ind_i]
yield anchor_path, lab
count += 1
if count >= settings.total_iter_num + 1:
return
return train_iterator
......@@ -86,6 +90,8 @@ def triplet_iterator(data, settings):
batch_size = settings.train_batch_size
assert (batch_size % 3 == 0)
def train_iterator():
total_count = settings.train_batch_size * (settings.total_iter_num + 1)
count = 0
labs = list(data.keys())
lab_num = len(labs)
ind = list(range(0, lab_num))
......@@ -108,16 +114,24 @@ def triplet_iterator(data, settings):
yield pos_path, lab_pos
neg_path = DATA_DIR + neg_data_list[neg_ind]
yield neg_path, lab_neg
count += 3
if count >= total_count:
return
return train_iterator
def arcmargin_iterator(data, settings):
def train_iterator():
total_count = settings.train_batch_size * (settings.total_iter_num + 1)
count = 0
while True:
for items in data:
path, label = items
path = DATA_DIR + path
yield path, label
count += 1
if count >= total_count:
return
return train_iterator
def image_iterator(data, mode):
......
fluid/PaddleCV/object_detection/README.md
浏览文件 @ e6ed31ef
......@@ -21,9 +21,7 @@ SSD is readily pluggable into a wide variant standard convolutional network, suc
### Data Preparation
You can use [PASCAL VOC dataset](http://host.robots.ox.ac.uk/pascal/VOC/) or [MS-COCO dataset](http://cocodataset.org/#download).
If you want to train a model on PASCAL VOC dataset, please download dataset at first, skip this step if you already have one.
Please download [PASCAL VOC dataset](http://host.robots.ox.ac.uk/pascal/VOC/) at first, skip this step if you already have one.
```bash
cd data/pascalvoc
......@@ -32,30 +30,18 @@ cd data/pascalvoc
The command `download.sh` also will create training and testing file lists.
If you want to train a model on MS-COCO dataset, please download dataset at first, skip this step if you already have one.
```
cd data/coco
./download.sh
```
### Train
#### Download the Pre-trained Model.
We provide two pre-trained models. The one is MobileNet-v1 SSD trained on COCO dataset, but removed the convolutional predictors for COCO dataset. This model can be used to initialize the models when training other datasets, like PASCAL VOC. The other pre-trained model is MobileNet-v1 trained on ImageNet 2012 dataset but removed the last weights and bias in the Fully-Connected layer.
Declaration: the MobileNet-v1 SSD model is converted by [TensorFlow model](https://github.com/tensorflow/models/blob/f87a58cd96d45de73c9a8330a06b2ab56749a7fa/research/object_detection/g3doc/detection_model_zoo.md). The MobileNet-v1 model is converted from [Caffe](https://github.com/shicai/MobileNet-Caffe).
We will release the pre-trained models by ourself in the upcoming soon.
We provide two pre-trained models. The one is MobileNet-v1 SSD trained on COCO dataset, but removed the convolutional predictors for COCO dataset. This model can be used to initialize the models when training other datasets, like PASCAL VOC. The other pre-trained model is MobileNet-v1 trained on ImageNet 2012 dataset but removed the last weights and bias in the Fully-Connected layer. Download MobileNet-v1 SSD:
- Download MobileNet-v1 SSD:
```bash
./pretrained/download_coco.sh
```
- Download MobileNet-v1:
```bash
./pretrained/download_imagenet.sh
```
Declaration: the MobileNet-v1 SSD model is converted by [TensorFlow model](https://github.com/tensorflow/models/blob/f87a58cd96d45de73c9a8330a06b2ab56749a7fa/research/object_detection/g3doc/detection_model_zoo.md).
#### Train on PASCAL VOC
......@@ -64,7 +50,6 @@ We will release the pre-trained models by ourself in the upcoming soon.
python -u train.py --batch_size=64 --dataset='pascalvoc' --pretrained_model='pretrained/ssd_mobilenet_v1_coco/'
```
- Set ```export CUDA_VISIBLE_DEVICES=0,1``` to specifiy the number of GPU you want to use.
- Set ```--dataset='coco2014'``` or ```--dataset='coco2017'``` to train model on MS COCO dataset.
- For more help on arguments:
```bash
......@@ -88,19 +73,6 @@ You can evaluate your trained model in different metrics like 11point, integral
python eval.py --dataset='pascalvoc' --model_dir='train_pascal_model/best_model' --data_dir='data/pascalvoc' --test_list='test.txt' --ap_version='11point' --nms_threshold=0.45
```
You can set ```--dataset``` to ```coco2014``` or ```coco2017``` to evaluate COCO dataset. Moreover, we provide `eval_coco_map.py` which uses a COCO-specific mAP metric defined by [COCO committee](http://cocodataset.org/#detections-eval). To use this eval_coco_map.py, [cocoapi](https://github.com/cocodataset/cocoapi) is needed.
Install the cocoapi:
```
# COCOAPI=/path/to/clone/cocoapi
git clone https://github.com/cocodataset/cocoapi.git $COCOAPI
cd $COCOAPI/PythonAPI
# Install into global site-packages
make install
# Alternatively, if you do not have permissions or prefer
# not to install the COCO API into global site-packages
python2 setup.py install --user
```
### Infer and Visualize
`infer.py` is the main caller of the inferring module. Examples of usage are shown below.
```bash
......
fluid/PaddleCV/object_detection/README_cn.md
浏览文件 @ e6ed31ef
......@@ -21,9 +21,8 @@ SSD 可以方便地插入到任何一种标准卷积网络中，比如 VGG、Res
### 数据准备
你可以使用 [PASCAL VOC 数据集](http://host.robots.ox.ac.uk/pascal/VOC/) 或者 [MS-COCO 数据集](http://cocodataset.org/#download)。
如果你想在 PASCAL VOC 数据集上进行训练，请先使用下面的命令下载数据集。
请先使用下面的命令下载 [PASCAL VOC 数据集](http://host.robots.ox.ac.uk/pascal/VOC/)：
```bash
cd data/pascalvoc
......@@ -32,29 +31,19 @@ cd data/pascalvoc
`download.sh` 命令会自动创建训练和测试用的列表文件。
如果你想在 MS-COCO 数据集上进行训练，请先使用下面的命令下载数据集。
```
cd data/coco
./download.sh
```
### 模型训练
#### 下载预训练模型
我们提供了两个预训练模型。第一个模型是在 COCO 数据集上预训练的 MobileNet-v1 SSD，我们将它的预测头移除了以便在 COCO 以外的数据集上进行训练。第二个模型是在 ImageNet 2012 数据集上预训练的 MobileNet-v1，我们也将最后的全连接层移除以便进行目标检测训练。
声明：MobileNet-v1 SSD 模型转换自[TensorFlow model](https://github.com/tensorflow/models/blob/f87a58cd96d45de73c9a8330a06b2ab56749a7fa/research/object_detection/g3doc/detection_model_zoo.md)。MobileNet-v1 模型转换自[Caffe](https://github.com/shicai/MobileNet-Caffe)。我们不久也会发布我们自己预训练的模型。
我们提供了两个预训练模型。第一个模型是在 COCO 数据集上预训练的 MobileNet-v1 SSD，我们将它的预测头移除了以便在 COCO 以外的数据集上进行训练。第二个模型是在 ImageNet 2012 数据集上预训练的 MobileNet-v1，我们也将最后的全连接层移除以便进行目标检测训练。下载 MobileNet-v1 SSD:
- 下载 MobileNet-v1 SSD:
```bash
./pretrained/download_coco.sh
```
- 下载 MobileNet-v1:
```bash
./pretrained/download_imagenet.sh
```
声明：MobileNet-v1 SSD 模型转换自[TensorFlow model](https://github.com/tensorflow/models/blob/f87a58cd96d45de73c9a8330a06b2ab56749a7fa/research/object_detection/g3doc/detection_model_zoo.md)。MobileNet-v1 模型转换自[Caffe](https://github.com/shicai/MobileNet-Caffe)。
#### 训练
......@@ -63,7 +52,6 @@ cd data/coco
python -u train.py --batch_size=64 --dataset='pascalvoc' --pretrained_model='pretrained/ssd_mobilenet_v1_coco/'
```
- 可以通过设置 ```export CUDA_VISIBLE_DEVICES=0,1``` 指定想要使用的GPU数量。
- 可以通过设置 ```--dataset='coco2014'``` 或 ```--dataset='coco2017'``` 指定训练 MS-COCO数据集。
- 更多的可选参数见:
```bash
......@@ -80,25 +68,13 @@ cd data/coco
### 模型评估
你可以使用11point、integral等指标在PASCAL VOC 和 COCO 数据集上评估训练好的模型。不失一般性，我们采用相应数据集的测试列表作为样例代码的默认列表，你也可以通过设置```--test_list```来指定自己的测试样本列表。
你可以使用11point、integral等指标在PASCAL VOC 数据集上评估训练好的模型。不失一般性，我们采用相应数据集的测试列表作为样例代码的默认列表，你也可以通过设置```--test_list```来指定自己的测试样本列表。
`eval.py`是评估模块的主要执行程序，调用示例如下：
```bash
python eval.py --dataset='pascalvoc' --model_dir='train_pascal_model/best_model' --data_dir='data/pascalvoc' --test_list='test.txt' --ap_version='11point' --nms_threshold=0.45
```
你可以设置```--dataset``` 为 ```coco2014``` 或 ```coco2017```来评估 COCO 数据集。我们也提供了`eval_coco_map.py`以进行[COCO官方评估](http://cocodataset.org/#detections-eval)。若要使用 eval_coco_map.py, 需要首先下载[cocoapi](https://github.com/cocodataset/cocoapi)：
```
# COCOAPI=/path/to/clone/cocoapi
git clone https://github.com/cocodataset/cocoapi.git $COCOAPI
cd $COCOAPI/PythonAPI
# Install into global site-packages
make install
# Alternatively, if you do not have permissions or prefer
# not to install the COCO API into global site-packages
python2 setup.py install --user
```
### 模型预测以及可视化
`infer.py`是预测及可视化模块的主要执行程序，调用示例如下：
......
fluid/PaddleCV/object_detection/README_quant.md
浏览文件 @ e6ed31ef
......@@ -2,7 +2,7 @@
### Introduction
The quantization-aware training used in this experiments is introduced in [fixed-point quantization desigin](https://gthub.com/PaddlePaddle/FluidDoc/blob/develop/doc/fluid/design/quantization/fixed_point_quantization.md). Since quantization-aware training is still an active area of research and experimentation,
The quantization-aware training used in this experiments is introduced in [fixed-point quantization desigin](https://github.com/PaddlePaddle/FluidDoc/blob/develop/doc/fluid/design/quantization/fixed_point_quantization.md). Since quantization-aware training is still an active area of research and experimentation,
here, we just give an simple quantization training usage in Fluid based on MobileNet-SSD model, and more other exeperiments are still needed, like how to quantization traning by considering fusing batch normalization and convolution/fully-connected layers, channel-wise quantization of weights and so on.
......@@ -130,6 +130,9 @@ A Python transpiler is used to rewrite Fluid training program or evaluation prog
```
See 002271.jpg for the visualized image with bbouding boxes.
**Note**, if you want to convert model to 8-bit, you should call `fluid.contrib.QuantizeTranspiler.convert_to_int8` to do this. But, now Paddle can't load 8-bit model to do inference.
### Results
Results of MobileNet-v1-SSD 300x300 model on PascalVOC dataset.
......
fluid/PaddleCV/object_detection/_ce.py
浏览文件 @ e6ed31ef
......@@ -9,10 +9,10 @@ from kpi import CostKpi, DurationKpi, AccKpi
train_cost_kpi = CostKpi('train_cost', 0.02, 0, actived=True)
test_acc_kpi = AccKpi('test_acc', 0.01, 0, actived=False)
train_speed_kpi = AccKpi('train_speed', 0.1, 0, actived=True)
train_speed_kpi = DurationKpi('train_speed', 0.1, 0, actived=True, unit_repr="s/epoch")
train_cost_card4_kpi = CostKpi('train_cost_card4', 0.02, 0, actived=True)
test_acc_card4_kpi = AccKpi('test_acc_card4', 0.01, 0, actived=False)
train_speed_card4_kpi = AccKpi('train_speed_card4', 0.1, 0, actived=True)
train_speed_card4_kpi = DurationKpi('train_speed_card4', 0.1, 0, actived=True, unit_repr="s/epoch")
tracking_kpis = [
train_cost_kpi,
......
fluid/PaddleCV/object_detection/data_util.py 已删除 100644 → 0
浏览文件 @ 366d6612
"""
This code is based on https://github.com/fchollet/keras/blob/master/keras/utils/data_utils.py
"""
import time
import numpy as np
import threading
import multiprocessing
try:
import queue
except ImportError:
import Queue as queue
class GeneratorEnqueuer(object):
"""
Builds a queue out of a data generator.
Args:
generator: a generator function which endlessly yields data
use_multiprocessing (bool): use multiprocessing if True,
otherwise use threading.
wait_time (float): time to sleep in-between calls to `put()`.
random_seed (int): Initial seed for workers,
will be incremented by one for each workers.
"""
def __init__(self,
generator,
use_multiprocessing=False,
wait_time=0.05,
random_seed=None):
self.wait_time = wait_time
self._generator = generator
self._use_multiprocessing = use_multiprocessing
self._threads = []
self._stop_event = None
self.queue = None
self._manager = None
self.seed = random_seed
def start(self, workers=1, max_queue_size=10):
"""
Start worker threads which add data from the generator into the queue.
Args:
workers (int): number of worker threads
max_queue_size (int): queue size
(when full, threads could block on `put()`)
"""
def data_generator_task():
"""
Data generator task.
"""
def task():
if (self.queue is not None and
self.queue.qsize() < max_queue_size):
generator_output = next(self._generator)
self.queue.put((generator_output))
else:
time.sleep(self.wait_time)
if not self._use_multiprocessing:
while not self._stop_event.is_set():
with self.genlock:
try:
task()
except Exception:
self._stop_event.set()
break
else:
while not self._stop_event.is_set():
try:
task()
except Exception:
self._stop_event.set()
break
try:
if self._use_multiprocessing:
self._manager = multiprocessing.Manager()
self.queue = self._manager.Queue(maxsize=max_queue_size)
self._stop_event = multiprocessing.Event()
else:
self.genlock = threading.Lock()
self.queue = queue.Queue()
self._stop_event = threading.Event()
for _ in range(workers):
if self._use_multiprocessing:
# Reset random seed else all children processes
# share the same seed
np.random.seed(self.seed)
thread = multiprocessing.Process(target=data_generator_task)
thread.daemon = True
if self.seed is not None:
self.seed += 1
else:
thread = threading.Thread(target=data_generator_task)
self._threads.append(thread)
thread.start()
except:
self.stop()
raise
def is_running(self):
"""
Returns:
bool: Whether the worker theads are running.
"""
return self._stop_event is not None and not self._stop_event.is_set()
def stop(self, timeout=None):
"""
Stops running threads and wait for them to exit, if necessary.
Should be called by the same thread which called `start()`.
Args:
timeout(int|None): maximum time to wait on `thread.join()`.
"""
if self.is_running():
self._stop_event.set()
for thread in self._threads:
if self._use_multiprocessing:
if thread.is_alive():
thread.terminate()
else:
thread.join(timeout)
if self._manager:
self._manager.shutdown()
self._threads = []
self._stop_event = None
self.queue = None
def get(self):
"""
Creates a generator to extract data from the queue.
Skip the data if it is `None`.
# Yields
tuple of data in the queue.
"""
while self.is_running():
if not self.queue.empty():
inputs = self.queue.get()
if inputs is not None:
yield inputs
else:
time.sleep(self.wait_time)
fluid/PaddleCV/object_detection/eval.py
浏览文件 @ e6ed31ef
......@@ -52,7 +52,7 @@ def build_program(main_prog, startup_prog, args, data_args):
nmsed_out = fluid.layers.detection_output(
locs, confs, box, box_var, nms_threshold=args.nms_threshold)
with fluid.program_guard(main_prog):
map = fluid.evaluator.DetectionMAP(
map = fluid.metrics.DetectionMAP(
nmsed_out,
gt_label,
gt_box,
......
fluid/PaddleCV/object_detection/eval_coco_map.py
浏览文件 @ e6ed31ef
......@@ -47,7 +47,7 @@ def eval(args, data_args, test_list, batch_size, model_dir=None):
gt_iscrowd = fluid.layers.data(
name='gt_iscrowd', shape=[1], dtype='int32', lod_level=1)
gt_image_info = fluid.layers.data(
name='gt_image_id', shape=[3], dtype='int32', lod_level=1)
name='gt_image_id', shape=[3], dtype='int32')
locs, confs, box, box_var = mobile_net(num_classes, image, image_shape)
nmsed_out = fluid.layers.detection_output(
......@@ -57,14 +57,14 @@ def eval(args, data_args, test_list, batch_size, model_dir=None):
place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
# yapf: disable
if model_dir:
def if_exist(var):
return os.path.exists(os.path.join(model_dir, var.name))
fluid.io.load_vars(exe, model_dir, predicate=if_exist)
# yapf: enable
test_reader = paddle.batch(
reader.test(data_args, test_list), batch_size=batch_size)
test_reader = reader.test(data_args, test_list, batch_size)
feeder = fluid.DataFeeder(
place=place,
feed_list=[image, gt_box, gt_label, gt_iscrowd, gt_image_info])
......@@ -146,8 +146,7 @@ if __name__ == '__main__':
mean_value=[args.mean_value_B, args.mean_value_G, args.mean_value_R],
apply_distort=False,
apply_expand=False,
ap_version=args.ap_version,
toy=0)
ap_version=args.ap_version)
eval(
args,
data_args=data_args,
......
fluid/PaddleCV/object_detection/main_quant.py
浏览文件 @ e6ed31ef
......@@ -85,7 +85,6 @@ def train(args,
batch_size = train_params['batch_size']
batch_size_per_device = batch_size // devices_num
iters_per_epoc = train_params["train_images"] // batch_size
num_workers = 4
startup_prog = fluid.Program()
......@@ -134,22 +133,22 @@ def train(args,
train_file_list,
batch_size_per_device,
shuffle=is_shuffle,
use_multiprocessing=True,
num_workers=num_workers,
max_queue=24)
num_workers=num_workers)
test_reader = reader.test(data_args, val_file_list, batch_size)
train_py_reader.decorate_paddle_reader(train_reader)
test_py_reader.decorate_paddle_reader(test_reader)
train_py_reader.start()
best_map = 0.
try:
for epoc in range(epoc_num):
if epoc == 0:
# test quantized model without quantization-aware training.
test_map = test(exe, test_prog, map_eval, test_py_reader)
batch = 0
train_py_reader.start()
while True:
try:
# train
for batch in range(iters_per_epoc):
start_time = time.time()
if parallel:
outs = train_exe.run(fetch_list=[loss.name])
......@@ -157,18 +156,19 @@ def train(args,
outs = exe.run(train_prog, fetch_list=[loss])
end_time = time.time()
avg_loss = np.mean(np.array(outs[0]))
if batch % 20 == 0:
if batch % 10 == 0:
print("Epoc {:d}, batch {:d}, loss {:.6f}, time {:.5f}".format(
epoc , batch, avg_loss, end_time - start_time))
end_time = time.time()
except (fluid.core.EOFException, StopIteration):
train_reader().close()
train_py_reader.reset()
break
test_map = test(exe, test_prog, map_eval, test_py_reader)
save_model(exe, train_prog, model_save_dir, str(epoc))
if test_map > best_map:
best_map = test_map
save_model(exe, train_prog, model_save_dir, 'best_map')
print("Best test map {0}".format(best_map))
except (fluid.core.EOFException, StopIteration):
train_py_reader.reset()
def eval(args, data_args, configs, val_file_list):
......@@ -212,6 +212,9 @@ def eval(args, data_args, configs, val_file_list):
test_map = test(exe, test_prog, map_eval, test_py_reader)
print("Test model {0}, map {1}".format(init_model, test_map))
# convert model to 8-bit before saving, but now Paddle can't load
# the 8-bit model to do inference.
# transpiler.convert_to_int8(test_prog, place)
fluid.io.save_inference_model(model_save_dir, [image.name],
[nmsed_out], exe, test_prog)
......
fluid/PaddleCV/object_detection/reader.py
浏览文件 @ e6ed31ef
......@@ -12,17 +12,17 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import image_util
from paddle.utils.image_util import *
from PIL import Image
from PIL import ImageDraw
import numpy as np
import xml.etree.ElementTree
import os
import time
import copy
import six
from data_util import GeneratorEnqueuer
import math
import numpy as np
from PIL import Image
from PIL import ImageDraw
import image_util
import paddle
class Settings(object):
......@@ -162,24 +162,19 @@ def preprocess(img, bbox_labels, mode, settings):
return img, sampled_labels
def coco(settings, file_list, mode, batch_size, shuffle):
# cocoapi
def coco(settings, coco_api, file_list, mode, batch_size, shuffle, data_dir):
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
coco = COCO(file_list)
image_ids = coco.getImgIds()
images = coco.loadImgs(image_ids)
print("{} on {} with {} images".format(mode, settings.dataset, len(images)))
def reader():
if mode == 'train' and shuffle:
np.random.shuffle(images)
np.random.shuffle(file_list)
batch_out = []
for image in images:
for image in file_list:
image_name = image['file_name']
image_path = os.path.join(settings.data_dir, image_name)
image_path = os.path.join(data_dir, image_name)
if not os.path.exists(image_path):
raise ValueError("%s is not exist, you should specify "
"data path correctly." % image_path)
im = Image.open(image_path)
if im.mode == 'L':
im = im.convert('RGB')
......@@ -188,8 +183,8 @@ def coco(settings, file_list, mode, batch_size, shuffle):
# layout: category_id | xmin | ymin | xmax | ymax | iscrowd
bbox_labels = []
annIds = coco.getAnnIds(imgIds=image['id'])
anns = coco.loadAnns(annIds)
annIds = coco_api.getAnnIds(imgIds=image['id'])
anns = coco_api.loadAnns(annIds)
for ann in anns:
bbox_sample = []
# start from 1, leave 0 to background
......@@ -229,20 +224,18 @@ def coco(settings, file_list, mode, batch_size, shuffle):
def pascalvoc(settings, file_list, mode, batch_size, shuffle):
flist = open(file_list)
images = [line.strip() for line in flist]
print("{} on {} with {} images".format(mode, settings.dataset, len(images)))
def reader():
if mode == 'train' and shuffle:
np.random.shuffle(images)
np.random.shuffle(file_list)
batch_out = []
cnt = 0
for image in images:
for image in file_list:
image_path, label_path = image.split()
image_path = os.path.join(settings.data_dir, image_path)
label_path = os.path.join(settings.data_dir, label_path)
if not os.path.exists(image_path):
raise ValueError("%s is not exist, you should specify "
"data path correctly." % image_path)
im = Image.open(image_path)
if im.mode == 'L':
im = im.convert('RGB')
......@@ -290,57 +283,62 @@ def train(settings,
file_list,
batch_size,
shuffle=True,
use_multiprocessing=True,
num_workers=8,
max_queue=24,
enable_ce=False):
file_list = os.path.join(settings.data_dir, file_list)
file_path = os.path.join(settings.data_dir, file_list)
readers = []
if 'coco' in settings.dataset:
generator = coco(settings, file_list, "train", batch_size, shuffle)
# cocoapi
from pycocotools.coco import COCO
coco_api = COCO(file_path)
image_ids = coco_api.getImgIds()
images = coco_api.loadImgs(image_ids)
n = int(math.ceil(len(images) // num_workers))
image_lists = [images[i:i + n] for i in range(0, len(images), n)]
if '2014' in file_list:
sub_dir = "train2014"
elif '2017' in file_list:
sub_dir = "train2017"
data_dir = os.path.join(settings.data_dir, sub_dir)
for l in image_lists:
readers.append(
coco(settings, coco_api, l, 'train', batch_size, shuffle,
data_dir))
else:
generator = pascalvoc(settings, file_list, "train", batch_size, shuffle)
images = [line.strip() for line in open(file_path)]
n = int(math.ceil(len(images) // num_workers))
image_lists = [images[i:i + n] for i in range(0, len(images), n)]
for l in image_lists:
readers.append(pascalvoc(settings, l, 'train', batch_size, shuffle))
def infinite_reader():
while True:
for data in generator():
yield data
def reader():
try:
enqueuer = GeneratorEnqueuer(
infinite_reader(), use_multiprocessing=use_multiprocessing)
enqueuer.start(max_queue_size=max_queue, workers=num_workers)
generator_output = None
while True:
while enqueuer.is_running():
if not enqueuer.queue.empty():
generator_output = enqueuer.queue.get()
break
else:
time.sleep(0.02)
yield generator_output
generator_output = None
finally:
if enqueuer is not None:
enqueuer.stop()
if enable_ce:
return infinite_reader
else:
return reader
return paddle.reader.multiprocess_reader(readers, False)
def test(settings, file_list, batch_size):
file_list = os.path.join(settings.data_dir, file_list)
if 'coco' in settings.dataset:
return coco(settings, file_list, 'test', batch_size, False)
from pycocotools.coco import COCO
coco_api = COCO(file_list)
image_ids = coco_api.getImgIds()
images = coco_api.loadImgs(image_ids)
if '2014' in file_list:
sub_dir = "val2014"
elif '2017' in file_list:
sub_dir = "val2017"
data_dir = os.path.join(settings.data_dir, sub_dir)
return coco(settings, coco_api, images, 'test', batch_size, False,
data_dir)
else:
return pascalvoc(settings, file_list, 'test', batch_size, False)
image_list = [line.strip() for line in open(file_list)]
return pascalvoc(settings, image_list, 'test', batch_size, False)
def infer(settings, image_path):
def reader():
if not os.path.exists(image_path):
raise ValueError("%s is not exist, you should specify "
"data path correctly." % image_path)
img = Image.open(image_path)
if img.mode == 'L':
img = im.convert('RGB')
......
fluid/PaddleCV/object_detection/train.py
浏览文件 @ e6ed31ef
......@@ -105,7 +105,7 @@ def build_program(main_prog, startup_prog, train_params, is_train):
with fluid.unique_name.guard("inference"):
nmsed_out = fluid.layers.detection_output(
locs, confs, box, box_var, nms_threshold=0.45)
map_eval = fluid.evaluator.DetectionMAP(
map_eval = fluid.metrics.DetectionMAP(
nmsed_out,
gt_label,
gt_box,
......@@ -141,7 +141,6 @@ def train(args,
batch_size = train_params['batch_size']
epoc_num = train_params['epoc_num']
batch_size_per_device = batch_size // devices_num
iters_per_epoc = train_params["train_images"] // batch_size
num_workers = 8
startup_prog = fluid.Program()
......@@ -186,9 +185,7 @@ def train(args,
train_file_list,
batch_size_per_device,
shuffle=is_shuffle,
use_multiprocessing=True,
num_workers=num_workers,
max_queue=24,
enable_ce=enable_ce)
test_reader = reader.test(data_args, val_file_list, batch_size)
train_py_reader.decorate_paddle_reader(train_reader)
......@@ -205,7 +202,7 @@ def train(args,
def test(epoc_id, best_map):
_, accum_map = map_eval.get_map_var()
map_eval.reset(exe)
every_epoc_map=[]
every_epoc_map=[] # for CE
test_py_reader.start()
try:
batch_id = 0
......@@ -218,22 +215,23 @@ def train(args,
except fluid.core.EOFException:
test_py_reader.reset()
mean_map = np.mean(every_epoc_map)
print("Epoc {0}, test map {1}".format(epoc_id, test_map))
print("Epoc {0}, test map {1}".format(epoc_id, test_map[0]))
if test_map[0] > best_map:
best_map = test_map[0]
save_model('best_model', test_prog)
return best_map, mean_map
train_py_reader.start()
total_time = 0.0
try:
for epoc_id in range(epoc_num):
epoch_idx = epoc_id + 1
start_time = time.time()
prev_start_time = start_time
every_epoc_loss = []
for batch_id in range(iters_per_epoc):
batch_id = 0
train_py_reader.start()
while True:
try:
prev_start_time = start_time
start_time = time.time()
if parallel:
......@@ -242,18 +240,23 @@ def train(args,
loss_v, = exe.run(train_prog, fetch_list=[loss])
loss_v = np.mean(np.array(loss_v))
every_epoc_loss.append(loss_v)
if batch_id % 20 == 0:
if batch_id % 10 == 0:
print("Epoc {:d}, batch {:d}, loss {:.6f}, time {:.5f}".format(
epoc_id, batch_id, loss_v, start_time - prev_start_time))
batch_id += 1
except (fluid.core.EOFException, StopIteration):
train_reader().close()
train_py_reader.reset()
break
end_time = time.time()
total_time += end_time - start_time
best_map, mean_map = test(epoc_id, best_map)
print("Best test map {0}".format(best_map))
if epoc_id % 10 == 0 or epoc_id == epoc_num - 1:
save_model(str(epoc_id), train_prog)
if enable_ce and epoc_id == epoc_num - 1:
if enable_ce:
train_avg_loss = np.mean(every_epoc_loss)
if devices_num == 1:
print("kpis train_cost %s" % train_avg_loss)
......@@ -267,10 +270,6 @@ def train(args,
print("kpis train_speed_card%s %f" %
(devices_num, total_time / epoch_idx))
except (fluid.core.EOFException, StopIteration):
train_reader().close()
train_py_reader.reset()
if __name__ == '__main__':
args = parser.parse_args()
......
fluid/PaddleCV/ocr_recognition/README.md
浏览文件 @ e6ed31ef
......@@ -80,7 +80,7 @@
在训练时，我们通过选项`--train_images` 和 `--train_list` 分别设置准备好的`train_images` 和`train_list`。
>**注：** 如果`--train_images` 和 `--train_list`都未设置或设置为None， ctc_reader.py会自动下载使用[示例数据](http://paddle-ocr-data.bj.bcebos.com/data.tar.gz)，并将其缓存到`$HOME/.cache/paddle/dataset/ctc_data/data/` 路径下。
>**注：** 如果`--train_images` 和 `--train_list`都未设置或设置为None， reader.py会自动下载使用[示例数据](http://paddle-ocr-data.bj.bcebos.com/data.tar.gz)，并将其缓存到`$HOME/.cache/paddle/dataset/ctc_data/data/` 路径下。
**B. 测试集和评估集**
......@@ -119,17 +119,17 @@ data/test_images/00003.jpg
使用默认数据在GPU单卡上训练:
```
env CUDA_VISIBLE_DEVICES=0 python ctc_train.py
env CUDA_VISIBLE_DEVICES=0 python train.py
```
使用默认数据在CPU上训练:
```
env OMP_NUM_THREADS=<num_of_physical_cores> python ctc_train.py --use_gpu False --parallel=False
env OMP_NUM_THREADS=<num_of_physical_cores> python train.py --use_gpu False --parallel=False
```
使用默认数据在GPU多卡上训练:
```
env CUDA_VISIBLE_DEVICES=0,1,2,3 python ctc_train.py --parallel=True
env CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --parallel=True
```
默认使用的是`CTC model`, 可以通过选项`--model="attention"`切换为`attention model`。
......
fluid/PaddleNLP/chinese_ner/README.md
浏览文件 @ e6ed31ef
......@@ -15,7 +15,14 @@
在data目录下，有两个文件夹，train_files中保存的是训练数据，test_files中保存的是测试数据，作为示例，在目录下我们各放置了两个文件，实际训练时，根据自己的实际需要将数据放置在对应目录，并根据数据格式，修改reader.py中的数据读取函数。
## 训练
修改 [train.py](./train.py) 的 `main` 函数，指定数据路径，运行`python train.py`开始训练。
通过运行
```
python train.py --help
```
来获取命令行参数的帮助，设置正确的数据路径等参数后，运行`train.py`开始训练。
训练记录形如
```txt
......@@ -31,7 +38,7 @@ pass_id:2, time_cost:0.740842103958s
```
## 预测
修改 [infer.py](./infer.py) 的 `infer` 函数，指定：需要测试的模型的路径、测试数据、预测标记文件的路径，运行`python infer.py`开始预测。
类似于训练过程，预测时指定需要测试模型的路径、测试数据、预测标记文件的路径，运行`infer.py`开始预测。
预测结果如下
```txt
......
fluid/PaddleNLP/chinese_ner/infer.py
浏览文件 @ e6ed31ef
......@@ -52,7 +52,7 @@ def parse_args():
def print_arguments(args):
print('----------- Configuration Arguments -----------')
for arg, value in sorted(vars(args).iteritems()):
for arg, value in sorted(vars(args).items()):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
......@@ -61,6 +61,7 @@ def load_reverse_dict(dict_path):
return dict((idx, line.strip().split("\t")[0])
for idx, line in enumerate(open(dict_path, "r").readlines()))
def to_lodtensor(data, place):
seq_lens = [len(seq) for seq in data]
cur_len = 0
......@@ -76,7 +77,6 @@ def to_lodtensor(data, place):
return res
def infer(args):
word = fluid.layers.data(name='word', shape=[1], dtype='int64', lod_level=1)
mention = fluid.layers.data(
......@@ -108,8 +108,8 @@ def infer(args):
profiler.reset_profiler()
iters = 0
for data in test_data():
word = to_lodtensor(map(lambda x: x[0], data), place)
mention = to_lodtensor(map(lambda x: x[1], data), place)
word = to_lodtensor(list(map(lambda x: x[0], data)), place)
mention = to_lodtensor(list(map(lambda x: x[1], data)), place)
start = time.time()
crf_decode = exe.run(inference_program,
......@@ -122,11 +122,11 @@ def infer(args):
np_data = np.array(crf_decode[0])
word_count = 0
assert len(data) == len(lod_info) - 1
for sen_index in xrange(len(data)):
for sen_index in range(len(data)):
assert len(data[sen_index][0]) == lod_info[
sen_index + 1] - lod_info[sen_index]
word_index = 0
for tag_index in xrange(lod_info[sen_index],
for tag_index in range(lod_info[sen_index],
lod_info[sen_index + 1]):
word = str(data[sen_index][0][word_index])
gold_tag = label_reverse_dict[data[sen_index][2][
......
fluid/PaddleNLP/chinese_ner/train.py
浏览文件 @ e6ed31ef
......@@ -12,7 +12,7 @@ import reader
def parse_args():
parser = argparse.ArgumentParser("Run inference.")
parser = argparse.ArgumentParser("Run training.")
parser.add_argument(
'--batch_size',
type=int,
......@@ -65,7 +65,7 @@ def parse_args():
def print_arguments(args):
print('----------- Configuration Arguments -----------')
for arg, value in sorted(vars(args).iteritems()):
for arg, value in sorted(vars(args).items()):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
......@@ -220,9 +220,9 @@ def test2(exe, chunk_evaluator, inference_program, test_data, place,
cur_fetch_list):
chunk_evaluator.reset()
for data in test_data():
word = to_lodtensor(map(lambda x: x[0], data), place)
mention = to_lodtensor(map(lambda x: x[1], data), place)
target = to_lodtensor(map(lambda x: x[2], data), place)
word = to_lodtensor(list(map(lambda x: x[0], data)), place)
mention = to_lodtensor(list(map(lambda x: x[1], data)), place)
target = to_lodtensor(list(map(lambda x: x[2], data)), place)
result_list = exe.run(
inference_program,
feed={"word": word,
......@@ -232,8 +232,9 @@ def test2(exe, chunk_evaluator, inference_program, test_data, place,
number_infer = np.array(result_list[0])
number_label = np.array(result_list[1])
number_correct = np.array(result_list[2])
chunk_evaluator.update(number_infer[0], number_label[0],
number_correct[0])
chunk_evaluator.update(number_infer[0].astype('int64'),
number_label[0].astype('int64'),
number_correct[0].astype('int64'))
return chunk_evaluator.eval()
......@@ -241,9 +242,9 @@ def test(test_exe, chunk_evaluator, inference_program, test_data, place,
cur_fetch_list):
chunk_evaluator.reset()
for data in test_data():
word = to_lodtensor(map(lambda x: x[0], data), place)
mention = to_lodtensor(map(lambda x: x[1], data), place)
target = to_lodtensor(map(lambda x: x[2], data), place)
word = to_lodtensor(list(map(lambda x: x[0], data)), place)
mention = to_lodtensor(list(map(lambda x: x[1], data)), place)
target = to_lodtensor(list(map(lambda x: x[2], data)), place)
result_list = test_exe.run(
fetch_list=cur_fetch_list,
feed={"word": word,
......@@ -252,8 +253,9 @@ def test(test_exe, chunk_evaluator, inference_program, test_data, place,
number_infer = np.array(result_list[0])
number_label = np.array(result_list[1])
number_correct = np.array(result_list[2])
chunk_evaluator.update(number_infer.sum(),
number_label.sum(), number_correct.sum())
chunk_evaluator.update(number_infer.sum().astype('int64'),
number_label.sum().astype('int64'),
number_correct.sum().astype('int64'))
return chunk_evaluator.eval()
......@@ -270,11 +272,6 @@ def main(args):
crf_decode = fluid.layers.crf_decoding(
input=feature_out, param_attr=fluid.ParamAttr(name='crfw'))
inference_program = fluid.default_main_program().clone(for_test=True)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=1e-3)
sgd_optimizer.minimize(avg_cost)
(precision, recall, f1_score, num_infer_chunks, num_label_chunks,
num_correct_chunks) = fluid.layers.chunk_eval(
input=crf_decode,
......@@ -282,6 +279,11 @@ def main(args):
chunk_scheme="IOB",
num_chunk_types=int(math.ceil((args.label_dict_len - 1) / 2.0)))
inference_program = fluid.default_main_program().clone(for_test=True)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=1e-3)
sgd_optimizer.minimize(avg_cost)
chunk_evaluator = fluid.metrics.ChunkEvaluator()
train_reader = paddle.batch(
......@@ -312,7 +314,7 @@ def main(args):
test_exe = exe
batch_id = 0
for pass_id in xrange(args.num_passes):
for pass_id in range(args.num_passes):
chunk_evaluator.reset()
train_reader_iter = train_reader()
start_time = time.time()
......@@ -326,9 +328,9 @@ def main(args):
],
feed=feeder.feed(cur_batch))
chunk_evaluator.update(
np.array(nums_infer).sum(),
np.array(nums_label).sum(),
np.array(nums_correct).sum())
np.array(nums_infer).sum().astype("int64"),
np.array(nums_label).sum().astype("int64"),
np.array(nums_correct).sum().astype("int64"))
cost_list = np.array(cost)
batch_id += 1
except StopIteration:
......
fluid/PaddleNLP/deep_attention_matching_net/_ce.py
浏览文件 @ e6ed31ef
......@@ -7,8 +7,8 @@ from kpi import CostKpi, DurationKpi, AccKpi
#### NOTE kpi.py should shared in models in some way!!!!
train_cost_kpi = CostKpi('train_cost', 0.02, actived=True)
train_duration_kpi = DurationKpi('train_duration', 0.05, actived=True)
train_cost_kpi = CostKpi('train_cost', 0.02, 0, actived=True)
train_duration_kpi = DurationKpi('train_duration', 0.05, 0, actived=True)
tracking_kpis = [
train_cost_kpi,
......
fluid/PaddleNLP/deep_attention_matching_net/train_and_evaluate.py
浏览文件 @ e6ed31ef
......@@ -248,7 +248,8 @@ def train(args):
print("device count %d" % dev_count)
print("theoretical memory usage: ")
print(fluid.contrib.memory_usage(
print(
fluid.contrib.memory_usage(
program=train_program, batch_size=args.batch_size))
exe = fluid.Executor(place)
......@@ -318,7 +319,8 @@ def train(args):
if (args.save_path is not None) and (step % save_step == 0):
save_path = os.path.join(args.save_path, "step_" + str(step))
print("Save model at step %d ... " % step)
print(time.strftime('%Y-%m-%d %H:%M:%S',
print(
time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time())))
fluid.io.save_persistables(exe, save_path, train_program)
......@@ -358,7 +360,8 @@ def train(args):
save_path = os.path.join(args.save_path,
"step_" + str(step))
print("Save model at step %d ... " % step)
print(time.strftime('%Y-%m-%d %H:%M:%S',
print(
time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime(time.time())))
fluid.io.save_persistables(exe, save_path, train_program)
......@@ -389,7 +392,11 @@ def train(args):
global_step, last_cost = train_with_pyreader(global_step)
else:
global_step, last_cost = train_with_feed(global_step)
train_time += time.time() - begin_time
pass_time_cost = time.time() - begin_time
train_time += pass_time_cost
print("Pass {0}, pass_time_cost {1}"
.format(epoch, "%2.2f sec" % pass_time_cost))
# For internal continuous evaluation
if "CE_MODE_X" in os.environ:
print("kpis train_cost %f" % last_cost)
......
fluid/PaddleNLP/machine_reading_comprehension/_ce.py
浏览文件 @ e6ed31ef
......@@ -3,6 +3,7 @@
import os
import sys
#sys.path.insert(0, os.environ['ceroot'])
sys.path.append(os.environ['ceroot'])
from kpi import CostKpi, DurationKpi, AccKpi
#### NOTE kpi.py should shared in models in some way!!!!
......
fluid/PaddleNLP/machine_reading_comprehension/dataset.py
浏览文件 @ e6ed31ef
......@@ -23,6 +23,7 @@ import json
import logging
import numpy as np
from collections import Counter
import io
class BRCDataset(object):
......@@ -67,7 +68,7 @@ class BRCDataset(object):
Args:
data_path: the data file to load
"""
with open(data_path) as fin:
with io.open(data_path, 'r', encoding='utf-8') as fin:
data_set = []
for lidx, line in enumerate(fin):
sample = json.loads(line.strip())
......
fluid/PaddleNLP/machine_reading_comprehension/run.py
浏览文件 @ e6ed31ef
......@@ -22,6 +22,7 @@ import os
import random
import json
import six
import multiprocessing
import paddle
import paddle.fluid as fluid
......@@ -445,7 +446,9 @@ def train(logger, args):
logger.info('Dev eval result: {}'.format(
bleu_rouge))
pass_end_time = time.time()
time_consumed = pass_end_time - pass_start_time
logger.info('epoch: {0}, epoch_time_cost: {1:.2f}'.format(
pass_id, time_consumed))
logger.info('Evaluating the model after epoch {}'.format(
pass_id))
if brc_data.dev_set is not None:
......@@ -458,7 +461,7 @@ def train(logger, args):
else:
logger.warning(
'No dev set is loaded for evaluation in the dataset!')
time_consumed = pass_end_time - pass_start_time
logger.info('Average train loss for epoch {} is {}'.format(
pass_id, "%.10f" % (1.0 * total_loss / total_num)))
......
fluid/PaddleNLP/neural_machine_translation/transformer/train.py
浏览文件 @ e6ed31ef
......@@ -408,10 +408,19 @@ def test_context(exe, train_exe, dev_count):
test_data = prepare_data_generator(
args, is_test=True, count=dev_count, pyreader=pyreader)
exe.run(startup_prog)
exe.run(startup_prog) # to init pyreader for testing
if TrainTaskConfig.ckpt_path:
fluid.io.load_persistables(
exe, TrainTaskConfig.ckpt_path, main_program=test_prog)
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.use_experimental_executor = True
build_strategy = fluid.BuildStrategy()
test_exe = fluid.ParallelExecutor(
use_cuda=TrainTaskConfig.use_gpu,
main_program=test_prog,
build_strategy=build_strategy,
exec_strategy=exec_strategy,
share_vars_from=train_exe)
def test(exe=test_exe, pyreader=pyreader):
......@@ -457,7 +466,11 @@ def train_loop(exe,
nccl2_trainer_id=0):
# Initialize the parameters.
if TrainTaskConfig.ckpt_path:
fluid.io.load_persistables(exe, TrainTaskConfig.ckpt_path)
exe.run(startup_prog) # to init pyreader for training
logging.info("load checkpoint from {}".format(
TrainTaskConfig.ckpt_path))
fluid.io.load_persistables(
exe, TrainTaskConfig.ckpt_path, main_program=train_prog)
else:
logging.info("init fluid.framework.default_startup_program")
exe.run(startup_prog)
......@@ -469,7 +482,7 @@ def train_loop(exe,
# For faster executor
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.use_experimental_executor = True
# exec_strategy.num_iteration_per_drop_scope = 5
exec_strategy.num_iteration_per_drop_scope = int(args.fetch_steps)
build_strategy = fluid.BuildStrategy()
# Since the token number differs among devices, customize gradient scale to
# use token average cost among multi-devices. and the gradient scale is
......@@ -741,6 +754,7 @@ if __name__ == "__main__":
LOG_FORMAT = "[%(asctime)s %(levelname)s %(filename)s:%(lineno)d] %(message)s"
logging.basicConfig(
stream=sys.stdout, level=logging.DEBUG, format=LOG_FORMAT)
logging.getLogger().setLevel(logging.INFO)
args = parse_args()
train(args)
fluid/PaddleNLP/sequence_tagging_for_ner/infer.py
浏览文件 @ e6ed31ef
......@@ -38,12 +38,10 @@ def infer(model_path, batch_size, test_data_file, vocab_file, target_file,
for data in test_data():
word = to_lodtensor([x[0] for x in data], place)
mark = to_lodtensor([x[1] for x in data], place)
target = to_lodtensor([x[2] for x in data], place)
crf_decode = exe.run(
inference_program,
feed={"word": word,
"mark": mark,
"target": target},
"mark": mark},
fetch_list=fetch_targets,
return_numpy=False)
lod_info = (crf_decode[0].lod())[0]
......
fluid/PaddleNLP/sequence_tagging_for_ner/train.py
浏览文件 @ e6ed31ef
......@@ -30,7 +30,9 @@ def test(exe, chunk_evaluator, inference_program, test_data, test_fetch_list,
num_infer = np.array(rets[0])
num_label = np.array(rets[1])
num_correct = np.array(rets[2])
chunk_evaluator.update(num_infer[0], num_label[0], num_correct[0])
chunk_evaluator.update(num_infer[0].astype('int64'),
num_label[0].astype('int64'),
num_correct[0].astype('int64'))
return chunk_evaluator.eval()
......@@ -61,9 +63,6 @@ def main(train_data_file,
avg_cost, feature_out, word, mark, target = ner_net(
word_dict_len, label_dict_len, parallel)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=1e-3)
sgd_optimizer.minimize(avg_cost)
crf_decode = fluid.layers.crf_decoding(
input=feature_out, param_attr=fluid.ParamAttr(name='crfw'))
......@@ -77,6 +76,8 @@ def main(train_data_file,
inference_program = fluid.default_main_program().clone(for_test=True)
test_fetch_list = [num_infer_chunks, num_label_chunks, num_correct_chunks]
sgd_optimizer = fluid.optimizer.SGD(learning_rate=1e-3)
sgd_optimizer.minimize(avg_cost)
if "CE_MODE_X" not in os.environ:
train_reader = paddle.batch(
......@@ -135,7 +136,7 @@ def main(train_data_file,
" pass_f1_score:" + str(test_pass_f1_score))
save_dirname = os.path.join(model_save_dir, "params_pass_%d" % pass_id)
fluid.io.save_inference_model(save_dirname, ['word', 'mark', 'target'],
fluid.io.save_inference_model(save_dirname, ['word', 'mark'],
crf_decode, exe)
if "CE_MODE_X" in os.environ:
......
fluid/PaddleNLP/text_classification/README.md
浏览文件 @ e6ed31ef
......@@ -14,7 +14,7 @@
## 简介，模型详解
在PaddlePaddle v2版本[文本分类](https://github.com/PaddlePaddle/models/blob/develop/text/README.md)中对于文本分类任务有较详细的介绍，在本例中不再重复介绍。
在PaddlePaddle v2版本[文本分类](https://github.com/PaddlePaddle/models/blob/develop/legacy/text_classification/README.md)中对于文本分类任务有较详细的介绍，在本例中不再重复介绍。
在模型上，我们采用了bow, cnn, lstm, gru四种常见的文本分类模型。
## 训练
......
fluid/PaddleNLP/text_classification/async_executor/README.md 0 → 100644
浏览文件 @ e6ed31ef
# 文本分类
以下是本例的简要目录结构及说明：
```text
.
|-- README.md # README
|-- data_generator # IMDB数据集生成工具
| |-- IMDB.py # 在data_generator.py基础上扩展IMDB数据集处理逻辑
| |-- build_raw_data.py # IMDB数据预处理，其产出被splitfile.py读取。格式：word word ... | label
| |-- data_generator.py # 与AsyncExecutor配套的数据生成工具框架
| `-- splitfile.py # 将build_raw_data.py生成的文件切分，其产出被IMDB.py读取
|-- data_generator.sh # IMDB数据集生成工具入口
|-- data_reader.py # 预测脚本使用的数据读取工具
|-- infer.py # 预测脚本
`-- train.py # 训练脚本
```
## 简介
本目录包含用fluid.AsyncExecutor训练文本分类任务的脚本。网络模型定义沿用自父目录nets.py
## 训练
1. 运行命令 `sh data_generator.sh`，下载IMDB数据集，并转化成适合AsyncExecutor读取的训练数据
2. 运行命令 `python train.py bow` 开始训练模型。
```python
python train.py bow # bow指定网络结构，可替换成cnn, lstm, gru
```
3. (可选）想自定义网络结构，需在[nets.py](../nets.py)中自行添加，并设置[train.py](./train.py)中的相应参数。
```python
def train(train_reader, # 训练数据
word_dict, # 数据字典
network, # 模型配置
use_cuda, # 是否用GPU
parallel, # 是否并行
save_dirname, # 保存模型路径
lr=0.2, # 学习率大小
batch_size=128, # 每个batch的样本数
pass_num=30): # 训练的轮数
```
## 训练结果示例
```text
pass_id: 0 pass_time_cost 4.723438
pass_id: 1 pass_time_cost 3.867186
pass_id: 2 pass_time_cost 4.490111
pass_id: 3 pass_time_cost 4.573296
pass_id: 4 pass_time_cost 4.180547
pass_id: 5 pass_time_cost 4.214476
pass_id: 6 pass_time_cost 4.520387
pass_id: 7 pass_time_cost 4.149485
pass_id: 8 pass_time_cost 3.821354
pass_id: 9 pass_time_cost 5.136178
pass_id: 10 pass_time_cost 4.137318
pass_id: 11 pass_time_cost 3.943429
pass_id: 12 pass_time_cost 3.766478
pass_id: 13 pass_time_cost 4.235983
pass_id: 14 pass_time_cost 4.796462
pass_id: 15 pass_time_cost 4.668116
pass_id: 16 pass_time_cost 4.373798
pass_id: 17 pass_time_cost 4.298131
pass_id: 18 pass_time_cost 4.260021
pass_id: 19 pass_time_cost 4.244411
pass_id: 20 pass_time_cost 3.705138
pass_id: 21 pass_time_cost 3.728070
pass_id: 22 pass_time_cost 3.817919
pass_id: 23 pass_time_cost 4.698598
pass_id: 24 pass_time_cost 4.859262
pass_id: 25 pass_time_cost 5.725732
pass_id: 26 pass_time_cost 5.102599
pass_id: 27 pass_time_cost 3.876582
pass_id: 28 pass_time_cost 4.762538
pass_id: 29 pass_time_cost 3.797759
```
与fluid.Executor不同，AsyncExecutor在每个pass结束不会将accuracy打印出来。为了观察训练过程，可以将fluid.AsyncExecutor.run()方法的Debug参数设为True，这样每个pass结束会把参数指定的fetch variable打印出来：
```
async_executor.run(
main_program,
dataset,
filelist,
thread_num,
[acc],
debug=True)
```
## 预测
1. 运行命令 `python infer.py bow_model`, 开始预测。
```python
python infer.py bow_model # bow_model指定需要导入的模型
```
## 预测结果示例
```text
model_path: bow_model/epoch0.model, avg_acc: 0.882600
model_path: bow_model/epoch1.model, avg_acc: 0.887920
model_path: bow_model/epoch2.model, avg_acc: 0.886920
model_path: bow_model/epoch3.model, avg_acc: 0.884720
model_path: bow_model/epoch4.model, avg_acc: 0.879760
model_path: bow_model/epoch5.model, avg_acc: 0.876920
model_path: bow_model/epoch6.model, avg_acc: 0.874160
model_path: bow_model/epoch7.model, avg_acc: 0.872000
model_path: bow_model/epoch8.model, avg_acc: 0.870360
model_path: bow_model/epoch9.model, avg_acc: 0.868480
model_path: bow_model/epoch10.model, avg_acc: 0.867240
model_path: bow_model/epoch11.model, avg_acc: 0.866200
model_path: bow_model/epoch12.model, avg_acc: 0.865560
model_path: bow_model/epoch13.model, avg_acc: 0.865160
model_path: bow_model/epoch14.model, avg_acc: 0.864480
model_path: bow_model/epoch15.model, avg_acc: 0.864240
model_path: bow_model/epoch16.model, avg_acc: 0.863800
model_path: bow_model/epoch17.model, avg_acc: 0.863520
model_path: bow_model/epoch18.model, avg_acc: 0.862760
model_path: bow_model/epoch19.model, avg_acc: 0.862680
model_path: bow_model/epoch20.model, avg_acc: 0.862240
model_path: bow_model/epoch21.model, avg_acc: 0.862280
model_path: bow_model/epoch22.model, avg_acc: 0.862080
model_path: bow_model/epoch23.model, avg_acc: 0.861560
model_path: bow_model/epoch24.model, avg_acc: 0.861280
model_path: bow_model/epoch25.model, avg_acc: 0.861160
model_path: bow_model/epoch26.model, avg_acc: 0.861080
model_path: bow_model/epoch27.model, avg_acc: 0.860920
model_path: bow_model/epoch28.model, avg_acc: 0.860800
model_path: bow_model/epoch29.model, avg_acc: 0.860760
```
注：过拟合导致acc持续下降，请忽略
fluid/PaddleNLP/text_classification/async_executor/data_generator.sh 0 → 100644
浏览文件 @ e6ed31ef
#!/usr/bin/env bash
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
pushd .
cd ./data_generator
# wget "http://ai.stanford.edu/%7Eamaas/data/sentiment/aclImdb_v1.tar.gz"
if [ ! -f aclImdb_v1.tar.gz ]; then
wget "http://10.64.74.104:8080/paddle/dataset/imdb/aclImdb_v1.tar.gz"
fi
tar zxvf aclImdb_v1.tar.gz
mkdir train_data
python build_raw_data.py train | python splitfile.py 12 train_data
mkdir test_data
python build_raw_data.py test | python splitfile.py 12 test_data
/opt/python27/bin/python IMDB.py train_data
/opt/python27/bin/python IMDB.py test_data
mv ./output_dataset/train_data ../
mv ./output_dataset/test_data ../
cp aclImdb/imdb.vocab ../
rm -rf ./output_dataset
rm -rf train_data
rm -rf test_data
rm -rf aclImdb
popd
fluid/PaddleNLP/text_classification/async_executor/data_generator/IMDB.py 0 → 100644
浏览文件 @ e6ed31ef
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import re
import os, sys
sys.path.append(os.path.abspath(os.path.join('..')))
from data_generator import MultiSlotDataGenerator
class IMDbDataGenerator(MultiSlotDataGenerator):
def load_resource(self, dictfile):
self._vocab = {}
wid = 0
with open(dictfile) as f:
for line in f:
self._vocab[line.strip()] = wid
wid += 1
self._unk_id = len(self._vocab)
self._pattern = re.compile(r'(;|,|\.|\?|!|\s|\(|\))')
def process(self, line):
send = '|'.join(line.split('|')[:-1]).lower().replace("<br />",
" ").strip()
label = [int(line.split('|')[-1])]
words = [x for x in self._pattern.split(send) if x and x != " "]
feas = [
self._vocab[x] if x in self._vocab else self._unk_id for x in words
]
return ("words", feas), ("label", label)
imdb = IMDbDataGenerator()
imdb.load_resource("aclImdb/imdb.vocab")
# data from files
file_names = os.listdir(sys.argv[1])
filelist = []
for i in range(0, len(file_names)):
filelist.append(os.path.join(sys.argv[1], file_names[i]))
line_limit = 2500
process_num = 24
imdb.run_from_files(
filelist=filelist,
line_limit=line_limit,
process_num=process_num,
output_dir=('output_dataset/%s' % (sys.argv[1])))
fluid/PaddleNLP/text_classification/async_executor/data_generator/data_generator.py 0 → 100644
浏览文件 @ e6ed31ef
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import os
import sys
import multiprocessing
__all__ = ['MultiSlotDataGenerator']
class DataGenerator(object):
def __init__(self):
self._proto_info = None
def _set_filelist(self, filelist):
if not isinstance(filelist, list) and not isinstance(filelist, tuple):
raise ValueError("filelist%s must be in list or tuple type" %
type(filelist))
if not filelist:
raise ValueError("filelist can not be empty")
self._filelist = filelist
def _set_process_num(self, process_num):
if not isinstance(process_num, int):
raise ValueError("process_num%s must be in int type" %
type(process_num))
if process_num < 1:
raise ValueError("process_num can not less than 1")
self._process_num = process_num
def _set_line_limit(self, line_limit):
if not isinstance(line_limit, int):
raise ValueError("line_limit%s must be in int type" %
type(line_limit))
if line_limit < 1:
raise ValueError("line_limit can not less than 1")
self._line_limit = line_limit
def _set_output_dir(self, output_dir):
if not isinstance(output_dir, str):
raise ValueError("output_dir%s must be in str type" %
type(output_dir))
if not output_dir:
raise ValueError("output_dir can not be empty")
self._output_dir = output_dir
def _set_output_prefix(self, output_prefix):
if not isinstance(output_prefix, str):
raise ValueError("output_prefix%s must be in str type" %
type(output_prefix))
self._output_prefix = output_prefix
def _set_output_fill_digit(self, output_fill_digit):
if not isinstance(output_fill_digit, int):
raise ValueError("output_fill_digit%s must be in int type" %
type(output_fill_digit))
if output_fill_digit < 1:
raise ValueError("output_fill_digit can not less than 1")
self._output_fill_digit = output_fill_digit
def _set_proto_filename(self, proto_filename):
if not isinstance(proto_filename, str):
raise ValueError("proto_filename%s must be in str type" %
type(proto_filename))
if not proto_filename:
raise ValueError("proto_filename can not be empty")
self._proto_filename = proto_filename
def _print_info(self):
'''
Print the configuration information
(Called only in the run_from_stdin function).
'''
sys.stderr.write("=" * 16 + " config " + "=" * 16 + "\n")
sys.stderr.write(" filelist size: %d\n" % len(self._filelist))
sys.stderr.write(" process num: %d\n" % self._process_num)
sys.stderr.write(" line limit: %d\n" % self._line_limit)
sys.stderr.write(" output dir: %s\n" % self._output_dir)
sys.stderr.write(" output prefix: %s\n" % self._output_prefix)
sys.stderr.write(" output fill digit: %d\n" % self._output_fill_digit)
sys.stderr.write(" proto filename: %s\n" % self._proto_filename)
sys.stderr.write("==== This may take a few minutes... ====\n")
def _get_output_filename(self, output_index, lock=None):
'''
This function is used to get the name of the output file and
update output_index.
Args:
output_index(manager.Value(i)): the index of output file.
lock(manager.Lock): The lock for processes safe.
Return:
Return the name(string) of output file.
'''
if lock is not None: lock.acquire()
file_index = output_index.value
output_index.value += 1
if lock is not None: lock.release()
filename = os.path.join(self._output_dir, self._output_prefix) \
+ str(file_index).zfill(self._output_fill_digit)
sys.stderr.write("[%d] write data to file: %s\n" %
(os.getpid(), filename))
return filename
def run_from_stdin(self,
is_local=True,
hadoop_host=None,
hadoop_ugi=None,
proto_path=None,
proto_filename="data_feed.proto"):
'''
This function reads the data row from stdin, parses it with the
process function, and further parses the return value of the
process function with the _gen_str function. The parsed data will
be wrote to stdout and the corresponding protofile will be
generated. If local is set to False, the protofile will be
uploaded to hadoop.
Args:
is_local(bool): Whether to execute locally. If it is False, the
protofile will be uploaded to hadoop. The
default value is True.
hadoop_host(str): The host name of the hadoop. It should be
in this format: "hdfs://${HOST}:${PORT}".
hadoop_ugi(str): The ugi of the hadoop. It should be in this
format: "${USERNAME},${PASSWORD}".
proto_path(str): The hadoop path you want to upload the
protofile to.
proto_filename(str): The name of protofile. The default value
is "data_feed.proto". It is not
recommended to modify it.
'''
if is_local:
print \
'''\033[1;34m=======================================================
Pay attention to that the version of Python in Hadoop
may inconsistent with local version. Please check the
Python version of Hadoop to ensure that it is >= 2.7.
=======================================================\033[0m'''
else:
if hadoop_ugi is None or \
hadoop_host is None or \
proto_path is None:
raise ValueError(
"pls set hadoop_ugi, hadoop_host, and proto_path")
self._set_proto_filename(proto_filename)
for line in sys.stdin:
user_parsed_line = self.process(line)
sys.stdout.write(self._gen_str(user_parsed_line))
if self._proto_info is not None:
# maybe some task do not catch files
with open(self._proto_filename, "w") as f:
f.write(self._get_proto_desc(self._proto_info))
if is_local == False:
cmd = "$HADOOP_HOME/bin/hadoop fs" \
+ " -Dhadoop.job.ugi=" + hadoop_ugi \
+ " -Dfs.default.name=" + hadoop_host \
+ " -put " + self._proto_filename + " " + proto_path
os.system(cmd)
def run_from_files(self,
filelist,
line_limit,
process_num=1,
output_dir="./output_dataset",
output_prefix="part-",
output_fill_digit=8,
proto_filename="data_feed.proto"):
'''
This function will run process_num processes to process the files
in the filelist. It will create the output data folder(output_dir)
in the current directory, and write the processed data into the
output_dir folder(each file line_limit data, the prefix of filename
is output_prefix, the suffix of filename is output_fill_digit
numbers). And the proto_info is generated at the same time. the
name of proto file will be proto_filename.
Args:
filelist(list or tuple): Files that need to be processed.
line_limit(int): Maximum number of data stored per file.
process_num(int): Number of processes running simultaneously.
output_dir(str): The name of the folder where the output
data file is stored.
output_prefix(str): The prefix of output data file.
output_fill_digit(int): The number of suffix numbers of the
output data file.
proto_filename(str): The name of protofile.
'''
self._set_filelist(filelist)
self._set_line_limit(line_limit)
self._set_process_num(min(process_num, len(filelist)))
self._set_output_dir(output_dir)
self._set_output_prefix(output_prefix)
self._set_output_fill_digit(output_fill_digit)
self._set_proto_filename(proto_filename)
self._print_info()
if not os.path.exists(self._output_dir):
os.makedirs(self._output_dir)
elif not os.path.isdir(self._output_dir):
raise ValueError("%s is not a directory" % self._output_dir)
processes = multiprocessing.Pool()
manager = multiprocessing.Manager()
output_index = manager.Value('i', 0)
file_queue = manager.Queue()
lock = manager.Lock()
remaining_queue = manager.Queue()
for file in self._filelist:
file_queue.put(file)
info_result = []
for i in range(self._process_num):
info_result.append(processes.apply_async(subprocess_wrapper, \
(self, file_queue, remaining_queue, output_index, lock, )))
processes.close()
processes.join()
infos = [
result.get() for result in info_result if result.get() is not None
]
proto_info = self._combine_infos(infos)
with open(os.path.join(self._output_dir, self._proto_filename),
"w") as f:
f.write(self._get_proto_desc(proto_info))
while not remaining_queue.empty():
with open(self._get_output_filename(output_index), "w") as f:
for i in range(min(self._line_limit, remaining_queue.qsize())):
f.write(remaining_queue.get(False))
def _subprocess(self, file_queue, remaining_queue, output_index, lock):
'''
This function will be called by multiple processes. It is used to
continuously fetch files from file_queue, using process() function
(defined by user) and _gen_str() function(defined by concrete classes)
to process data in units of rows. Write the processed data to the
file(each file will be self._line_limit line). If the file in the
file_queue has been consumed, but the file is not full, the data
that is less than the self._line_limit line will be stored in the
remaining_queue.
Args:
file_queue(manager.Queue): The queue contains all the file
names to be processed.
remaining_queue(manager.Queue): The queue contains the data that
is less than the self._line_limit
line.
output_index(manager.Value(i)): The index(suffix) of the
output file.
lock(manager.Lock): The lock for processes safe.
Returns:
Return a proto_info which can be translated into a proto string.
'''
buffer = []
while not file_queue.empty():
try:
filename = file_queue.get(False)
except: # file_queue empty
break
with open(filename, 'r') as f:
for line in f:
buffer.append(self._gen_str(self.process(line)))
if len(buffer) == self._line_limit:
with open(
self._get_output_filename(output_index, lock),
"w") as wf:
for x in buffer:
wf.write(x)
buffer = []
if buffer:
for x in buffer:
remaining_queue.put(x)
return self._proto_info
def _gen_str(self, line):
'''
Further processing the output of the process() function rewritten by
user, outputting data that can be directly read by the datafeed,and
updating proto_info infomation.
Args:
line(str): the output of the process() function rewritten by user.
Returns:
Return a string data that can be read directly by the datafeed.
'''
raise NotImplementedError(
"pls use MultiSlotDataGenerator or PairWiseDataGenerator")
def _combine_infos(self, infos):
'''
This function is used to merge proto_info information from different
processes. In general, the proto_info of each process is consistent.
Args:
infos(list): the list of proto_infos from different processes.
Returns:
Return a unified proto_info.
'''
raise NotImplementedError(
"pls use MultiSlotDataGenerator or PairWiseDataGenerator")
def _get_proto_desc(self, proto_info):
'''
This function outputs the string of the proto file(can be directly
written to the file) according to the proto_info information.
Args:
proto_info: The proto information used to generate the proto
string. The type of the variable will be determined
by the subclass. In the MultiSlotDataGenerator,
proto_info variable is a list of tuple.
Returns:
Returns a string of the proto file.
'''
raise NotImplementedError(
"pls use MultiSlotDataGenerator or PairWiseDataGenerator")
def process(self, line):
'''
This function needs to be overridden by the user to process the
original data row into a list or tuple.
Args:
line(str): the original data row
Returns:
Returns the data processed by the user.
The data format is list or tuple:
[(name, [feasign, ...]), ...]
or ((name, [feasign, ...]), ...)
For example:
[("words", [1926, 08, 17]), ("label", [1])]
or (("words", [1926, 08, 17]), ("label", [1]))
Note:
The type of feasigns must be in int or float. Once the float
element appears in the feasign, the type of that slot will be
processed into a float.
'''
raise NotImplementedError(
"pls rewrite this function to return a list or tuple: " +
"[(name, [feasign, ...]), ...] or ((name, [feasign, ...]), ...)")
def subprocess_wrapper(instance, file_queue, remaining_queue, output_index,
lock):
'''
In order to use the class function as a process, you need to wrap it.
'''
return instance._subprocess(file_queue, remaining_queue, output_index, lock)
class MultiSlotDataGenerator(DataGenerator):
def _combine_infos(self, infos):
'''
This function is used to merge proto_info information from different
processes. In general, the proto_info of each process is consistent.
The type of input infos is list, and the type of element of infos is
tuple. The format of element of infos will be (name, type).
Args:
infos(list): the list of proto_infos from different processes.
Returns:
Return a unified proto_info.
Note:
This function is only called by the run_from_files function, so
when using the run_from_stdin function(usually used for hadoop),
the output of the process function(rewritten by the user) does
not allow that the same field to have both float and int type
values.
'''
proto_info = infos[0]
for info in infos:
for index, slot in enumerate(info):
name, type = slot
if name != proto_info[index][0]:
raise ValueError(
"combine infos error, pls contact the maintainer of this code~"
)
if type == "float" and proto_info[index][1] == "uint64":
proto_info[index] = (name, type)
return proto_info
def _get_proto_desc(self, proto_info):
'''
Generate a string of proto file based on the proto_info information.
The proto_info will be a list of tuples:
>>> [(Name, Type), ...]
The string of proto file will be in this format:
>>> name: "MultiSlotDataFeed"
>>> batch_size: 32
>>> multi_slot_desc {
>>> slots {
>>> name: Name
>>> type: Type
>>> is_dense: false
>>> is_used: false
>>> }
>>> }
Args:
proto_info(list): The proto information used to generate the
proto string.
Returns:
Returns a string of the proto file.
'''
proto_str = "name: \"MultiSlotDataFeed\"\n" \
+ "batch_size: 32\nmulti_slot_desc {\n"
for elem in proto_info:
proto_str += " slots {\n" \
+ " name: \"%s\"\n" % elem[0]\
+ " type: \"%s\"\n" % elem[1]\
+ " is_dense: false\n" \
+ " is_used: false\n" \
+ " }\n"
proto_str += "}"
return proto_str
def _gen_str(self, line):
'''
Further processing the output of the process() function rewritten by
user, outputting data that can be directly read by the MultiSlotDataFeed,
and updating proto_info infomation.
The input line will be in this format:
>>> [(name, [feasign, ...]), ...]
>>> or ((name, [feasign, ...]), ...)
The output will be in this format:
>>> [ids_num id1 id2 ...] ...
The proto_info will be in this format:
>>> [(name, type), ...]
For example, if the input is like this:
>>> [("words", [1926, 08, 17]), ("label", [1])]
>>> or (("words", [1926, 08, 17]), ("label", [1]))
the output will be:
>>> 3 1234 2345 3456 1 1
the proto_info will be:
>>> [("words", "uint64"), ("label", "uint64")]
Args:
line(str): the output of the process() function rewritten by user.
Returns:
Return a string data that can be read directly by the MultiSlotDataFeed.
'''
if not isinstance(line, list) and not isinstance(line, tuple):
raise ValueError(
"the output of process() must be in list or tuple type")
output = ""
if self._proto_info is None:
self._proto_info = []
for item in line:
name, elements = item
if not isinstance(name, str):
raise ValueError("name%s must be in str type" % type(name))
if not isinstance(elements, list):
raise ValueError("elements%s must be in list type" %
type(elements))
if not elements:
raise ValueError(
"the elements of each field can not be empty, you need padding it in process()."
)
self._proto_info.append((name, "uint64"))
if output:
output += " "
output += str(len(elements))
for elem in elements:
if isinstance(elem, float):
self._proto_info[-1] = (name, "float")
elif not isinstance(elem, int) and not isinstance(elem,
long):
raise ValueError(
"the type of element%s must be in int or float" %
type(elem))
output += " " + str(elem)
else:
if len(line) != len(self._proto_info):
raise ValueError(
"the complete field set of two given line are inconsistent.")
for index, item in enumerate(line):
name, elements = item
if not isinstance(name, str):
raise ValueError("name%s must be in str type" % type(name))
if not isinstance(elements, list):
raise ValueError("elements%s must be in list type" %
type(elements))
if not elements:
raise ValueError(
"the elements of each field can not be empty, you need padding it in process()."
)
if name != self._proto_info[index][0]:
raise ValueError(
"the field name of two given line are not match: require<%s>, get<%d>."
% (self._proto_info[index][0], name))
if output:
output += " "
output += str(len(elements))
for elem in elements:
if self._proto_info[index][1] != "float":
if isinstance(elem, float):
self._proto_info[index] = (name, "float")
elif not isinstance(elem, int) and not isinstance(elem,
long):
raise ValueError(
"the type of element%s must be in int or float"
% type(elem))
output += " " + str(elem)
return output + "\n"
fluid/PaddleNLP/text_classification/async_executor/data_generator/splitfile.py 0 → 100644
浏览文件 @ e6ed31ef
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
"""
Split file into parts
"""
import sys
import os
block = int(sys.argv[1])
datadir = sys.argv[2]
file_list = []
for i in range(block):
file_list.append(open(datadir + "/part-" + str(i), "w"))
id_ = 0
for line in sys.stdin:
file_list[id_ % block].write(line)
id_ += 1
for f in file_list:
f.close()
fluid/PaddleNLP/text_classification/async_executor/data_reader.py 0 → 100644
浏览文件 @ e6ed31ef
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import sys
import os
import paddle
def parse_fields(fields):
words_width = int(fields[0])
words = fields[1:1 + words_width]
label = fields[-1]
return words, label
def imdb_data_feed_reader(data_dir, batch_size, buf_size):
"""
Data feed reader for IMDB dataset.
This data set has been converted from original format to a format suitable
for AsyncExecutor
See data.proto for data format
"""
def reader():
for file in os.listdir(data_dir):
if file.endswith('.proto'):
continue
with open(os.path.join(data_dir, file), 'r') as f:
for line in f:
fields = line.split(' ')
words, label = parse_fields(fields)
yield words, label
test_reader = paddle.batch(
paddle.reader.shuffle(
reader, buf_size=buf_size), batch_size=batch_size)
return test_reader
fluid/PaddleNLP/text_classification/async_executor/infer.py 0 → 100644
浏览文件 @ e6ed31ef
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import os
import sys
import time
import unittest
import contextlib
import numpy as np
import paddle
import paddle.fluid as fluid
import data_reader
def infer(test_reader, use_cuda, model_path=None):
"""
inference function
"""
if model_path is None:
print(str(model_path) + " cannot be found")
return
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
[inference_program, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(model_path, exe)
total_acc = 0.0
total_count = 0
for data in test_reader():
acc = exe.run(inference_program,
feed=utils.data2tensor(data, place),
fetch_list=fetch_targets,
return_numpy=True)
total_acc += acc[0] * len(data)
total_count += len(data)
avg_acc = total_acc / total_count
print("model_path: %s, avg_acc: %f" % (model_path, avg_acc))
if __name__ == "__main__":
if __package__ is None:
from os import sys, path
sys.path.append(
os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import utils
batch_size = 128
model_path = sys.argv[1]
test_data_dirname = 'test_data'
if len(sys.argv) == 3:
test_data_dirname = sys.argv[2]
test_reader = data_reader.imdb_data_feed_reader(
'test_data', batch_size, buf_size=500000)
models = os.listdir(model_path)
for i in range(0, len(models)):
epoch_path = "epoch" + str(i) + ".model"
epoch_path = os.path.join(model_path, epoch_path)
infer(test_reader, use_cuda=False, model_path=epoch_path)
fluid/PaddleNLP/text_classification/async_executor/train.py 0 → 100644
浏览文件 @ e6ed31ef
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import os
import sys
import time
import multiprocessing
import paddle
import paddle.fluid as fluid
def train(network, dict_dim, lr, save_dirname, training_data_dirname, pass_num,
thread_num, batch_size):
file_names = os.listdir(training_data_dirname)
filelist = []
for i in range(0, len(file_names)):
if file_names[i] == 'data_feed.proto':
continue
filelist.append(os.path.join(training_data_dirname, file_names[i]))
dataset = fluid.DataFeedDesc(
os.path.join(training_data_dirname, 'data_feed.proto'))
dataset.set_batch_size(
batch_size) # datafeed should be assigned a batch size
dataset.set_use_slots(['words', 'label'])
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
avg_cost, acc, prediction = network(data, label, dict_dim)
optimizer = fluid.optimizer.Adagrad(learning_rate=lr)
opt_ops, weight_and_grad = optimizer.minimize(avg_cost)
startup_program = fluid.default_startup_program()
main_program = fluid.default_main_program()
place = fluid.CPUPlace()
executor = fluid.Executor(place)
executor.run(startup_program)
async_executor = fluid.AsyncExecutor(place)
for i in range(pass_num):
pass_start = time.time()
async_executor.run(main_program,
dataset,
filelist,
thread_num, [acc],
debug=False)
print('pass_id: %u pass_time_cost %f' % (i, time.time() - pass_start))
fluid.io.save_inference_model('%s/epoch%d.model' % (save_dirname, i),
[data.name, label.name], [acc], executor)
if __name__ == "__main__":
if __package__ is None:
from os import sys, path
sys.path.append(
os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
from nets import bow_net, cnn_net, lstm_net, gru_net
from utils import load_vocab
batch_size = 4
lr = 0.002
pass_num = 30
save_dirname = ""
thread_num = multiprocessing.cpu_count()
if sys.argv[1] == "bow":
network = bow_net
batch_size = 128
save_dirname = "bow_model"
elif sys.argv[1] == "cnn":
network = cnn_net
lr = 0.01
save_dirname = "cnn_model"
elif sys.argv[1] == "lstm":
network = lstm_net
lr = 0.05
save_dirname = "lstm_model"
elif sys.argv[1] == "gru":
network = gru_net
batch_size = 128
lr = 0.05
save_dirname = "gru_model"
training_data_dirname = 'train_data/'
if len(sys.argv) == 3:
training_data_dirname = sys.argv[2]
if len(sys.argv) == 4:
if thread_num >= int(sys.argv[3]):
thread_num = int(sys.argv[3])
vocab = load_vocab('imdb.vocab')
dict_dim = len(vocab)
train(network, dict_dim, lr, save_dirname, training_data_dirname, pass_num,
thread_num, batch_size)
fluid/PaddleNLP/text_classification/train.py
浏览文件 @ e6ed31ef
......@@ -89,7 +89,7 @@ def train(train_reader,
def train_net():
word_dict, train_reader, test_reader = utils.prepare_data(
"imdb", self_dict=False, batch_size=4, buf_size=50000)
"imdb", self_dict=False, batch_size=128, buf_size=50000)
if sys.argv[1] == "bow":
train(
......
fluid/PaddleNLP/text_matching_on_quora/.run_ce.sh 0 → 100755
浏览文件 @ e6ed31ef
#!/bin/bash
export MKL_NUM_THREADS=1
export OMP_NUM_THREADS=1
cudaid=${text_matching_on_quora:=0} # use 0-th card as default
export CUDA_VISIBLE_DEVICES=$cudaid
FLAGS_benchmark=true python train_and_evaluate.py --model_name=cdssmNet --config=cdssm_base --enable_ce --epoch_num=5 | python _ce.py
cudaid=${text_matching_on_quora_m:=0,1,2,3} # use 0,1,2,3 card as default
export CUDA_VISIBLE_DEVICES=$cudaid
FLAGS_benchmark=true python train_and_evaluate.py --model_name=cdssmNet --config=cdssm_base --enable_ce --epoch_num=5 | python _ce.py
fluid/PaddleNLP/text_matching_on_quora/_ce.py 0 → 100644
浏览文件 @ e6ed31ef
# this file is only used for continuous evaluation test!
import os
import sys
sys.path.append(os.environ['ceroot'])
from kpi import CostKpi
from kpi import DurationKpi
each_pass_duration_card1_kpi = DurationKpi('each_pass_duration_card1', 0.08, 0, actived=True)
train_avg_cost_card1_kpi = CostKpi('train_avg_cost_card1', 0.08, 0)
train_avg_acc_card1_kpi = CostKpi('train_avg_acc_card1', 0.02, 0)
each_pass_duration_card4_kpi = DurationKpi('each_pass_duration_card4', 0.08, 0, actived=True)
train_avg_cost_card4_kpi = CostKpi('train_avg_cost_card4', 0.08, 0)
train_avg_acc_card4_kpi = CostKpi('train_avg_acc_card4', 0.02, 0)
tracking_kpis = [
each_pass_duration_card1_kpi,
train_avg_cost_card1_kpi,
train_avg_acc_card1_kpi,
each_pass_duration_card4_kpi,
train_avg_cost_card4_kpi,
train_avg_acc_card4_kpi,
]
def parse_log(log):
'''
This method should be implemented by model developers.
The suggestion:
each line in the log should be key, value, for example:
"
train_cost\t1.0
test_cost\t1.0
train_cost\t1.0
train_cost\t1.0
train_acc\t1.2
"
'''
for line in log.split('\n'):
fs = line.strip().split('\t')
print(fs)
if len(fs) == 3 and fs[0] == 'kpis':
kpi_name = fs[1]
kpi_value = float(fs[2])
yield kpi_name, kpi_value
def log_to_ce(log):
kpi_tracker = {}
for kpi in tracking_kpis:
kpi_tracker[kpi.name] = kpi
for (kpi_name, kpi_value) in parse_log(log):
print(kpi_name, kpi_value)
kpi_tracker[kpi_name].add_record(kpi_value)
kpi_tracker[kpi_name].persist()
if __name__ == '__main__':
log = sys.stdin.read()
log_to_ce(log)
fluid/PaddleNLP/text_matching_on_quora/pretrained_word2vec.py
浏览文件 @ e6ed31ef
......@@ -21,6 +21,11 @@ import numpy as np
import time, datetime
import os, sys
def maybe_open(filepath):
if sys.version_info <= (3, 0): # for python2
return open(filepath, 'r')
else:
return open(filepath, 'r', encoding="utf-8")
def Glove840B_300D(filepath, keys=None):
"""
......@@ -33,7 +38,7 @@ def Glove840B_300D(filepath, keys=None):
print("please wait for a minute.")
start = time.time()
word2vec = {}
with open(filepath, "r") as f:
with maybe_open(filepath) as f:
for line in f:
if sys.version_info <= (3, 0): # for python2
line = line.decode('utf-8')
......
fluid/PaddleNLP/text_matching_on_quora/quora_question_pairs.py
浏览文件 @ e6ed31ef
......@@ -68,8 +68,10 @@ def maybe_open(file_name):
" |- readme.txt\n"
" |- wordvec.txt\n")
raise RuntimeError(msg)
if sys.version_info <= (3, 0): # for python2
return open(file_name, 'r')
else:
return open(file_name, 'r', encoding="utf-8")
def tokenized_question_pairs(file_name):
......
fluid/PaddleNLP/text_matching_on_quora/train_and_evaluate.py
浏览文件 @ e6ed31ef
......@@ -33,6 +33,8 @@ parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument('--model_name', type=str, default='cdssmNet', help="Which model to train")
parser.add_argument('--config', type=str, default='cdssm_base', help="The global config setting")
parser.add_argument('--enable_ce', action='store_true', help='If set, run the task with continuous evaluation logs.')
parser.add_argument('--epoch_num', type=int, help='Number of epoch')
DATA_DIR = os.path.join(os.path.expanduser('~'), '.cache/paddle/dataset')
......@@ -139,6 +141,13 @@ def train_and_evaluate(train_reader,
else:
feeder = fluid.DataFeeder(feed_list=[q1, q2, mask1, mask2, label], place=place)
# only for ce
args = parser.parse_args()
if args.enable_ce:
SEED = 102
fluid.default_startup_program().random_seed = SEED
fluid.default_main_program().random_seed = SEED
# logging param info
for param in fluid.default_main_program().global_block().all_parameters():
print("param name: %s; param shape: %s" % (param.name, param.shape))
......@@ -167,8 +176,10 @@ def train_and_evaluate(train_reader,
metric_type=global_config.metric_type)
# start training
total_time = 0.0
print("[%s] Start Training" % time.asctime(time.localtime(time.time())))
for epoch_id in range(global_config.epoch_num):
data_size, data_count, total_acc, total_cost = 0, 0, 0.0, 0.0
batch_id = 0
epoch_begin_time = time.time()
......@@ -177,8 +188,8 @@ def train_and_evaluate(train_reader,
feed=feeder.feed(data),
fetch_list=[cost, acc])
data_size = len(data)
total_acc += data_size * avg_acc_np
total_cost += data_size * avg_cost_np
total_acc += data_size * avg_acc_np[0]
total_cost += data_size * avg_cost_np[0]
data_count += data_size
if batch_id % 100 == 0:
print("[%s] epoch_id: %d, batch_id: %d, cost: %f, acc: %f" % (
......@@ -188,9 +199,10 @@ def train_and_evaluate(train_reader,
avg_cost_np,
avg_acc_np))
batch_id += 1
avg_cost = total_cost / data_count
avg_acc = total_acc / data_count
epoch_end_time = time.time()
total_time += epoch_end_time - epoch_begin_time
print("")
print("[%s] epoch_id: %d, train_avg_cost: %f, train_avg_acc: %f, epoch_time_cost: %f" % (
......@@ -198,6 +210,19 @@ def train_and_evaluate(train_reader,
epoch_id, avg_cost, avg_acc,
time.time() - epoch_begin_time))
# only for ce
if epoch_id == global_config.epoch_num - 1 and args.enable_ce:
#Note: The following logs are special for CE monitoring.
#Other situations do not need to care about these logs.
gpu_num = get_cards(args)
print("kpis\teach_pass_duration_card%s\t%s" % \
(gpu_num, total_time / (global_config.epoch_num)))
print("kpis\ttrain_avg_cost_card%s\t%s" %
(gpu_num, avg_cost))
print("kpis\ttrain_avg_acc_card%s\t%s" %
(gpu_num, avg_acc))
epoch_model = global_config.save_dirname + "/" + "epoch" + str(epoch_id)
fluid.io.save_inference_model(epoch_model, ["question1", "question2", "label"], acc, exe)
......@@ -217,6 +242,9 @@ def main():
args = parser.parse_args()
global_config = configs.__dict__[args.config]()
if args.epoch_num != None:
global_config.epoch_num = args.epoch_num
print("net_name: ", args.model_name)
net = models.__dict__[args.model_name](global_config)
......@@ -267,5 +295,15 @@ def main():
use_cuda=global_config.use_cuda,
parallel=False)
def get_cards(args):
if args.enable_ce:
cards = os.environ.get('CUDA_VISIBLE_DEVICES')
num = len(cards.split(","))
return num
else:
return args.num_devices
if __name__ == "__main__":
main()
fluid/PaddleRec/ctr/data/download.sh
浏览文件 @ e6ed31ef
#!/bin/bash
wget --no-check-certificate https://s3-eu-west-1.amazonaws.com/criteo-labs/dac.tar.gz
wget --no-check-certificate https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz
tar zxf dac.tar.gz
rm -f dac.tar.gz
......
fluid/PaddleRec/ctr/network_conf.py
浏览文件 @ e6ed31ef
......@@ -78,7 +78,7 @@ def ctr_deepfm_model(factor_size, sparse_feature_dim, dense_feature_dim, sparse_
param_attr=sparse_fm_param_attr, is_sparse=True)
return fluid.layers.sequence_pool(input=emb, pool_type='average')
sparse_embed_seq = map(embedding_layer, sparse_input_ids)
sparse_embed_seq = list(map(embedding_layer, sparse_input_ids))
concated = fluid.layers.concat(sparse_embed_seq + [dense_input], axis=1)
fc1 = fluid.layers.fc(input=concated, size=400, act='relu',
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(
......@@ -134,7 +134,7 @@ def ctr_dnn_model(embedding_size, sparse_feature_dim):
use_double_buffer=True)
words = fluid.layers.read_file(py_reader)
sparse_embed_seq = map(embedding_layer, words[1:-1])
sparse_embed_seq = list(map(embedding_layer, words[1:-1]))
concated = fluid.layers.concat(sparse_embed_seq + words[0:1], axis=1)
fc1 = fluid.layers.fc(input=concated, size=400, act='relu',
......
fluid/PaddleRec/ctr/preprocess.py
浏览文件 @ e6ed31ef
......@@ -51,7 +51,7 @@ class CategoryDictGenerator:
return res
def dicts_sizes(self):
return map(len, self.dicts)
return list(map(len, self.dicts))
class ContinuousFeatureGenerator:
......@@ -61,8 +61,8 @@ class ContinuousFeatureGenerator:
def __init__(self, num_feature):
self.num_feature = num_feature
self.min = [sys.maxint] * num_feature
self.max = [-sys.maxint] * num_feature
self.min = [sys.maxsize] * num_feature
self.max = [-sys.maxsize] * num_feature
def build(self, datafile, continous_features):
with open(datafile, 'r') as f:
......
fluid/PaddleRec/gru4rec/README.md
浏览文件 @ e6ed31ef
......@@ -32,7 +32,15 @@ GRU4REC模型的介绍可以参阅论文[Session-based Recommendations with Recu
session-based推荐应用场景非常广泛，比如用户的商品浏览、新闻点击、地点签到等序列数据。
支持三种形式的损失函数, 分别是全词表的cross-entropy, 采负样本的Bayesian Pairwise Ranking和采负样本的Cross-entropy.
支持三种形式的损失函数, 分别是全词表的cross-entropy, 负采样的Bayesian Pairwise Ranking和负采样的Cross-entropy.
我们基本复现了论文效果，recall@20的效果分别为
全词表 cross entropy : 0.67
负采样 bpr : 0.606
负采样 cross entropy : 0.605
运行样例程序可跳过'RSC15 数据下载及预处理'部分
......@@ -113,31 +121,42 @@ python text2paddle.py raw_train_data/ raw_test_data/ train_data test_data vocab.
```
## 训练
'--use_cuda 1' 表示使用gpu, 缺省表示使用cpu '--parallel 1' 表示使用多卡，缺省表示使用单卡
具体的参数配置可运行
```
python train.py -h
```
全词表cross entropy 训练代码
GPU 环境
运行命令开始训练模型。
gpu 单机单卡训练
``` bash
CUDA_VISIBLE_DEVICES=0 python train.py --train_dir train_data --use_cuda 1 --batch_size 50 --model_dir model_output
```
CUDA_VISIBLE_DEVICES=0 python train.py --train_dir train_data/ --use_cuda 1
cpu 单机训练
``` bash
python train.py --train_dir train_data --use_cuda 0 --batch_size 50 --model_dir model_output
```
CPU 环境
运行命令开始训练模型。
gpu 单机多卡训练
``` bash
CUDA_VISIBLE_DEVICES=0,1 python train.py --train_dir train_data --use_cuda 1 --parallel 1 --batch_size 50 --model_dir model_output --num_devices 2
```
python train.py --train_dir train_data/
cpu 单机多卡训练
``` bash
CPU_NUM=10 python train.py --train_dir train_data --use_cuda 0 --parallel 1 --batch_size 50 --model_dir model_output --num_devices 10
```
bayesian pairwise ranking loss(bpr loss) 训练
负采样 bayesian pairwise ranking loss(bpr loss) 训练
```
CUDA_VISIBLE_DEVICES=0 python train_sample_neg.py --loss bpr --use_cuda 1
```
请注意CPU环境下运行单机多卡任务（--parallel 1)时，batch_size应大于cpu核数。
负采样 cross entropy 训练
```
CUDA_VISIBLE_DEVICES=0 python train_sample_neg.py --loss ce --use_cuda 1
```
## 自定义网络结构
......
fluid/PaddleRec/gru4rec/infer_sample_neg.py
浏览文件 @ e6ed31ef
......@@ -9,7 +9,6 @@ import six
import paddle.fluid as fluid
import paddle
import net
import utils
......
fluid/PaddleRec/gru4rec/net.py
浏览文件 @ e6ed31ef
......@@ -178,7 +178,7 @@ def train_cross_entropy_network(vocab_size, neg_size, hid_size, drop_out=0.2):
return src, pos_label, label, cost_sum
def infer_bpr_network(vocab_size, batch_size, hid_size, dropout=0.2):
def infer_network(vocab_size, batch_size, hid_size, dropout=0.2):
src = fluid.layers.data(name="src", shape=[1], dtype="int64", lod_level=1)
emb_src = fluid.layers.embedding(
input=src, size=[vocab_size, hid_size], param_attr="emb")
......
fluid/PaddleRec/ssr/README.md
浏览文件 @ e6ed31ef
......@@ -16,18 +16,34 @@ Sequence Semantic Retrieval(SSR) Model shares the similar idea with Multi-Rate D
Dataset preprocessing follows the method of [GRU4Rec Project](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleRec/gru4rec). Note that you should reuse scripts from GRU4Rec project for data preprocessing.
## Training
Before training, you should set PYTHONPATH environment
The command line options for training can be listed by `python train.py -h`
gpu 单机单卡训练
``` bash
CUDA_VISIBLE_DEVICES=0 python train.py --train_dir train_data --use_cuda 1 --batch_size 50 --model_dir model_output
```
export PYTHONPATH=./models/fluid:$PYTHONPATH
cpu 单机训练
``` bash
python train.py --train_dir train_data --use_cuda 0 --batch_size 50 --model_dir model_output
```
The command line options for training can be listed by `python train.py -h`
gpu 单机多卡训练
``` bash
python train.py --train_file rsc15_train_tr_paddle.txt
CUDA_VISIBLE_DEVICES=0,1 python train.py --train_dir train_data --use_cuda 1 --parallel 1 --batch_size 50 --model_dir model_output --num_devices 2
```
## Build Index
TBA
cpu 单机多卡训练
``` bash
CPU_NUM=10 python train.py --train_dir train_data --use_cuda 0 --parallel 1 --batch_size 50 --model_dir model_output --num_devices 10
```
多机训练 参考fluid/PaddleRec/gru4rec下的配置
## Retrieval
TBA
## Inference
gpu 预测
``` bash
CUDA_VISIBLE_DEVICES=0 python infer.py --test_dir test_data --use_cuda 1 --batch_size 50 --model_dir model_output
```
fluid/PaddleRec/ssr/infer.py 0 → 100644
浏览文件 @ e6ed31ef
import sys
import argparse
import time
import math
import unittest
import contextlib
import numpy as np
import six
import paddle.fluid as fluid
import paddle
import utils
import nets as net
def parse_args():
parser = argparse.ArgumentParser("ssr benchmark.")
parser.add_argument(
'--test_dir', type=str, default='test_data', help='test file address')
parser.add_argument(
'--vocab_path', type=str, default='vocab.txt', help='vocab path')
parser.add_argument(
'--start_index', type=int, default='1', help='start index')
parser.add_argument(
'--last_index', type=int, default='10', help='end index')
parser.add_argument(
'--model_dir', type=str, default='model_output', help='model dir')
parser.add_argument(
'--use_cuda', type=int, default='0', help='whether use cuda')
parser.add_argument(
'--batch_size', type=int, default='50', help='batch_size')
parser.add_argument(
'--hid_size', type=int, default='128', help='hidden size')
parser.add_argument(
'--emb_size', type=int, default='128', help='embedding size')
args = parser.parse_args()
return args
def model(vocab_size, emb_size, hidden_size):
user_data = fluid.layers.data(
name="user", shape=[1], dtype="int64", lod_level=1)
all_item_data = fluid.layers.data(
name="all_item", shape=[vocab_size, 1], dtype="int64")
user_emb = fluid.layers.embedding(
input=user_data, size=[vocab_size, emb_size], param_attr="emb.item")
all_item_emb = fluid.layers.embedding(
input=all_item_data, size=[vocab_size, emb_size], param_attr="emb.item")
all_item_emb_re = fluid.layers.reshape(x=all_item_emb, shape=[-1, emb_size])
user_encoder = net.GrnnEncoder(hidden_size=hidden_size)
user_enc = user_encoder.forward(user_emb)
user_hid = fluid.layers.fc(input=user_enc,
size=hidden_size,
param_attr='user.w',
bias_attr="user.b")
user_exp = fluid.layers.expand(x=user_hid, expand_times=[1, vocab_size])
user_re = fluid.layers.reshape(x=user_exp, shape=[-1, hidden_size])
all_item_hid = fluid.layers.fc(input=all_item_emb_re,
size=hidden_size,
param_attr='item.w',
bias_attr="item.b")
cos_item = fluid.layers.cos_sim(X=all_item_hid, Y=user_re)
all_pre_ = fluid.layers.reshape(x=cos_item, shape=[-1, vocab_size])
pos_label = fluid.layers.data(name="pos_label", shape=[1], dtype="int64")
acc = fluid.layers.accuracy(input=all_pre_, label=pos_label, k=20)
return acc
def infer(args, vocab_size, test_reader):
""" inference function """
place = fluid.CUDAPlace(0) if args.use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
emb_size = args.emb_size
hid_size = args.hid_size
batch_size = args.batch_size
model_path = args.model_dir
with fluid.scope_guard(fluid.core.Scope()):
main_program = fluid.Program()
start_up_program = fluid.Program()
with fluid.program_guard(main_program, start_up_program):
acc = model(vocab_size, emb_size, hid_size)
for epoch in xrange(start_index, last_index + 1):
copy_program = main_program.clone()
model_path = model_dir + "/epoch_" + str(epoch)
fluid.io.load_params(
executor=exe, dirname=model_path, main_program=copy_program)
accum_num_recall = 0.0
accum_num_sum = 0.0
t0 = time.time()
step_id = 0
for data in test_reader():
step_id += 1
user_data, pos_label = utils.infer_data(data, place)
all_item_numpy = np.tile(
np.arange(vocab_size), len(pos_label)).reshape(
len(pos_label), vocab_size, 1)
para = exe.run(copy_program,
feed={
"user": user_data,
"all_item": all_item_numpy,
"pos_label": pos_label
},
fetch_list=[acc.name],
return_numpy=False)
acc_ = para[0]._get_float_element(0)
data_length = len(
np.concatenate(
pos_label, axis=0).astype("int64"))
accum_num_sum += (data_length)
accum_num_recall += (data_length * acc_)
if step_id % 1 == 0:
print("step:%d " % (step_id),
accum_num_recall / accum_num_sum)
t1 = time.time()
print("model:%s recall@20:%.3f time_cost(s):%.2f" %
(model_path, accum_num_recall / accum_num_sum, t1 - t0))
if __name__ == "__main__":
args = parse_args()
start_index = args.start_index
last_index = args.last_index
test_dir = args.test_dir
model_dir = args.model_dir
batch_size = args.batch_size
vocab_path = args.vocab_path
use_cuda = True if args.use_cuda else False
print("start index: ", start_index, " last_index:", last_index)
test_reader, vocab_size = utils.construct_test_data(
test_dir, vocab_path, batch_size=args.batch_size)
infer(args, vocab_size, test_reader=test_reader)
fluid/PaddleRec/ssr/nets.py
浏览文件 @ e6ed31ef
......@@ -17,28 +17,53 @@ import paddle.fluid.layers.nn as nn
import paddle.fluid.layers.tensor as tensor
import paddle.fluid.layers.control_flow as cf
import paddle.fluid.layers.io as io
from PaddleRec.multiview_simnet.nets import BowEncoder
from PaddleRec.multiview_simnet.nets import GrnnEncoder
class BowEncoder(object):
""" bow-encoder """
def __init__(self):
self.param_name = ""
def forward(self, emb):
return nn.sequence_pool(input=emb, pool_type='sum')
class GrnnEncoder(object):
""" grnn-encoder """
def __init__(self, param_name="grnn", hidden_size=128):
self.param_name = param_name
self.hidden_size = hidden_size
def forward(self, emb):
fc0 = nn.fc(input=emb,
size=self.hidden_size * 3,
param_attr=self.param_name + "_fc.w",
bias_attr=False)
gru_h = nn.dynamic_gru(
input=fc0,
size=self.hidden_size,
is_reverse=False,
param_attr=self.param_name + ".param",
bias_attr=self.param_name + ".bias")
return nn.sequence_pool(input=gru_h, pool_type='max')
class PairwiseHingeLoss(object):
def __init__(self, margin=0.8):
self.margin = margin
def forward(self, pos, neg):
loss_part1 = nn.elementwise_sub(
tensor.fill_constant_batch_size_like(
input=pos,
shape=[-1, 1],
value=self.margin,
dtype='float32'),
input=pos, shape=[-1, 1], value=self.margin, dtype='float32'),
pos)
loss_part2 = nn.elementwise_add(loss_part1, neg)
loss_part3 = nn.elementwise_max(
tensor.fill_constant_batch_size_like(
input=loss_part2,
shape=[-1, 1],
value=0.0,
dtype='float32'),
input=loss_part2, shape=[-1, 1], value=0.0, dtype='float32'),
loss_part2)
return loss_part3
......@@ -61,41 +86,37 @@ class SequenceSemanticRetrieval(object):
return correct
def train(self):
user_data = io.data(
name="user", shape=[1], dtype="int64", lod_level=1
)
user_data = io.data(name="user", shape=[1], dtype="int64", lod_level=1)
pos_item_data = io.data(
name="p_item", shape=[1], dtype="int64", lod_level=1
)
name="p_item", shape=[1], dtype="int64", lod_level=1)
neg_item_data = io.data(
name="n_item", shape=[1], dtype="int64", lod_level=1
)
name="n_item", shape=[1], dtype="int64", lod_level=1)
user_emb = nn.embedding(
input=user_data, size=self.emb_shape, param_attr="emb.item"
)
input=user_data, size=self.emb_shape, param_attr="emb.item")
pos_item_emb = nn.embedding(
input=pos_item_data, size=self.emb_shape, param_attr="emb.item"
)
input=pos_item_data, size=self.emb_shape, param_attr="emb.item")
neg_item_emb = nn.embedding(
input=neg_item_data, size=self.emb_shape, param_attr="emb.item"
)
input=neg_item_data, size=self.emb_shape, param_attr="emb.item")
user_enc = self.user_encoder.forward(user_emb)
pos_item_enc = self.item_encoder.forward(pos_item_emb)
neg_item_enc = self.item_encoder.forward(neg_item_emb)
user_hid = nn.fc(
input=user_enc, size=self.hidden_size, param_attr='user.w', bias_attr="user.b"
)
pos_item_hid = nn.fc(
input=pos_item_enc, size=self.hidden_size, param_attr='item.w', bias_attr="item.b"
)
neg_item_hid = nn.fc(
input=neg_item_enc, size=self.hidden_size, param_attr='item.w', bias_attr="item.b"
)
user_hid = nn.fc(input=user_enc,
size=self.hidden_size,
param_attr='user.w',
bias_attr="user.b")
pos_item_hid = nn.fc(input=pos_item_enc,
size=self.hidden_size,
param_attr='item.w',
bias_attr="item.b")
neg_item_hid = nn.fc(input=neg_item_enc,
size=self.hidden_size,
param_attr='item.w',
bias_attr="item.b")
cos_pos = nn.cos_sim(user_hid, pos_item_hid)
cos_neg = nn.cos_sim(user_hid, neg_item_hid)
hinge_loss = self.pairwise_hinge_loss.forward(cos_pos, cos_neg)
avg_cost = nn.mean(hinge_loss)
correct = self.get_correct(cos_neg, cos_pos)
return [user_data, pos_item_data, neg_item_data], \
pos_item_hid, neg_item_hid, avg_cost, correct
return [user_data, pos_item_data,
neg_item_data], cos_pos, avg_cost, correct
fluid/PaddleRec/ssr/reader.py
浏览文件 @ e6ed31ef
......@@ -14,14 +14,17 @@
import random
class Dataset:
def __init__(self):
pass
class Vocab:
def __init__(self):
pass
class YoochooseVocab(Vocab):
def __init__(self):
self.vocab = {}
......@@ -47,11 +50,10 @@ class YoochooseVocab(Vocab):
def _get_word_array(self):
return self.word_array
class YoochooseDataset(Dataset):
def __init__(self, y_vocab):
self.vocab_size = len(y_vocab.get_vocab())
self.word_array = y_vocab._get_word_array()
self.vocab = y_vocab.get_vocab()
def __init__(self, vocab_size):
self.vocab_size = vocab_size
def sample_neg(self):
return random.randint(0, self.vocab_size - 1)
......@@ -59,10 +61,6 @@ class YoochooseDataset(Dataset):
def sample_neg_from_seq(self, seq):
return seq[random.randint(0, len(seq) - 1)]
# TODO(guru4elephant): wait memory, should be improved
def sample_from_word_freq(self):
return self.word_array[random.randint(0, len(self.word_array) - 1)]
def _reader_creator(self, filelist, is_train):
def reader():
for f in filelist:
......@@ -72,18 +70,16 @@ class YoochooseDataset(Dataset):
ids = line.strip().split()
if len(ids) <= 1:
continue
conv_ids = [self.vocab[i] if i in self.vocab else 0 for i in ids]
# random select an index as boundary
# make ids before boundary as sequence
# make id next to boundary right as target
boundary = random.randint(1, len(ids) - 1)
conv_ids = [i for i in ids]
boundary = len(ids) - 1
src = conv_ids[:boundary]
pos_tgt = [conv_ids[boundary]]
if is_train:
neg_tgt = [self.sample_from_word_freq()]
neg_tgt = [self.sample_neg()]
yield [src, pos_tgt, neg_tgt]
else:
yield [src, pos_tgt]
return reader
def train(self, file_list):
......@@ -91,4 +87,3 @@ class YoochooseDataset(Dataset):
def test(self, file_list):
return self._reader_creator(file_list, False)
fluid/PaddleRec/ssr/test_data/small_test.txt 0 → 100644
浏览文件 @ e6ed31ef
0 16
475 473 155
491 21
96 185 96
29 14 13
5 481 11 21 470
70 5 70 11
167 42 167 217
72 15 73 161 172
82 82
97 297 97
193 182 186 183 184 177 214
152 152
163 298 7
39 73 71
490 23 23 496 488 74 23 74 486 23 23 74
17 17
170 170 483 444 443 234
25 472
5 5 11 70 69
149 149 455
356 68 477 468 17 479 66
159 172 6 71 6 6 158 13 494 169
155 44 438 144 500
156 9 9
146 146
173 10 10 461
7 6 6
269 48 268
50 100
323 174 18
69 69 22 98
38 171
22 29 489 10
0 0
11 5
29 13 14 232 231 451 289 452 229
260 11 156
166 160 166 39
223 134 134 420
66 401 68 132 17 84 287 5
39 304
65 84 132
400 211
145 144
16 28 254 48 50 100 42 154 262 133 17
0 0
28 28
11 476 464
61 61 86 86
38 38
463 478
437 265
22 39 485 171 98
434 51 344
16 16
67 67 67 448
22 12 161
15 377 147 147 374
119 317 0
38 484
403 499
432 442
28 0 16 50 465 42
163 487 7 162
99 99 325 423 83 83
154 133
5 37 492 235 160 279
10 10 457 493 10 460
441 4 4 4 4 4 4 4
153 153
159 164 164
328 37
65 65 404 347 431 459
80 80 44 44
61 446
162 495 7 453
157 21 204 68 37 66 469 145
37 151 230 206 240 205 264 87 409 87 288 270 280 329 157 296 454 474
430 445 433
449 14
9 9 9 9
440 238 226
148 148
266 267 181
48 498
263 255 256
458 158 7
72 168 12 165 71 73 173 49
0 0
7 7 6
14 29 13 6 15 14 15 13
480 439 21
450 21 151
12 12 49 14 13 165 12 169 72 15 15
91 91
22 12 49 168
497 101 30 411 30 482 30 53 30 101 176 415 53 447
462 150 150
471 456 131 435 131 467 436 412 227 218 190 466 429 213 326
fluid/PaddleRec/ssr/train.py
浏览文件 @ e6ed31ef
......@@ -13,87 +13,108 @@
# limitations under the License.
import os
import sys
import time
import argparse
import logging
import paddle.fluid as fluid
import paddle
import reader as reader
import utils
import numpy as np
from nets import SequenceSemanticRetrieval
logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger("fluid")
logger.setLevel(logging.INFO)
def parse_args():
parser = argparse.ArgumentParser("sequence semantic retrieval")
parser.add_argument("--train_file", type=str, help="Training file")
parser.add_argument("--valid_file", type=str, help="Validation file")
parser.add_argument(
"--epochs", type=int, default=10, help="Number of epochs for training")
"--train_dir", type=str, default='train_data', help="Training file")
parser.add_argument(
"--base_lr", type=float, default=0.01, help="learning rate")
parser.add_argument(
'--vocab_path', type=str, default='vocab.txt', help='vocab file')
parser.add_argument(
"--epochs", type=int, default=10, help="Number of epochs")
parser.add_argument(
'--parallel', type=int, default=0, help='whether parallel')
parser.add_argument(
'--use_cuda', type=int, default=0, help='whether use gpu')
parser.add_argument(
'--print_batch', type=int, default=10, help='num of print batch')
parser.add_argument(
"--model_output_dir",
type=str,
default='model_output',
help="Model output folder")
'--model_dir', type=str, default='model_output', help='model dir')
parser.add_argument(
"--sequence_encode_dim",
type=int,
default=128,
help="Dimension of sequence encoder output")
"--hidden_size", type=int, default=128, help="hidden size")
parser.add_argument(
"--matching_dim",
type=int,
default=128,
help="Dimension of hidden layer")
"--batch_size", type=int, default=50, help="number of batch")
parser.add_argument(
"--batch_size", type=int, default=128, help="Batch size for training")
"--embedding_dim", type=int, default=128, help="embedding dim")
parser.add_argument(
"--embedding_dim",
type=int,
default=128,
help="Default Dimension of Embedding")
'--num_devices', type=int, default=1, help='Number of GPU devices')
return parser.parse_args()
def start_train(args):
y_vocab = reader.YoochooseVocab()
y_vocab.load([args.train_file])
logger.info("Load yoochoose vocabulary size: {}".format(len(y_vocab.get_vocab())))
y_data = reader.YoochooseDataset(y_vocab)
train_reader = paddle.batch(
paddle.reader.shuffle(
y_data.train([args.train_file]), buf_size=args.batch_size * 100),
batch_size=args.batch_size)
place = fluid.CPUPlace()
ssr = SequenceSemanticRetrieval(
len(y_vocab.get_vocab()), args.embedding_dim, args.matching_dim
)
input_data, user_rep, item_rep, avg_cost, acc = ssr.train()
optimizer = fluid.optimizer.Adam(learning_rate=1e-4)
def get_cards(args):
return args.num_devices
def train(args):
use_cuda = True if args.use_cuda else False
parallel = True if args.parallel else False
print("use_cuda:", use_cuda, "parallel:", parallel)
train_reader, vocab_size = utils.construct_train_data(
args.train_dir, args.vocab_path, args.batch_size * get_cards(args))
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
ssr = SequenceSemanticRetrieval(vocab_size, args.embedding_dim,
args.hidden_size)
# Train program
train_input_data, cos_pos, avg_cost, acc = ssr.train()
# Optimization to minimize lost
optimizer = fluid.optimizer.Adagrad(learning_rate=args.base_lr)
optimizer.minimize(avg_cost)
startup_program = fluid.default_startup_program()
loop_program = fluid.default_main_program()
data_list = [var.name for var in input_data]
data_list = [var.name for var in train_input_data]
feeder = fluid.DataFeeder(feed_list=data_list, place=place)
exe = fluid.Executor(place)
exe.run(startup_program)
exe.run(fluid.default_startup_program())
if parallel:
train_exe = fluid.ParallelExecutor(
use_cuda=use_cuda, loss_name=avg_cost.name)
else:
train_exe = exe
total_time = 0.0
for pass_id in range(args.epochs):
epoch_idx = pass_id + 1
print("epoch_%d start" % epoch_idx)
t0 = time.time()
i = 0
for batch_id, data in enumerate(train_reader()):
loss_val, correct_val = exe.run(loop_program,
feed=feeder.feed(data),
fetch_list=[avg_cost, acc])
logger.info("Train --> pass: {} batch_id: {} avg_cost: {}, acc: {}".
format(pass_id, batch_id, loss_val,
float(correct_val) / args.batch_size))
fluid.io.save_inference_model(args.model_output_dir,
[var.name for val in input_data],
[user_rep, item_rep, avg_cost, acc], exe)
i += 1
loss_val, correct_val = train_exe.run(
feed=feeder.feed(data), fetch_list=[avg_cost.name, acc.name])
if i % args.print_batch == 0:
logger.info(
"Train --> pass: {} batch_id: {} avg_cost: {}, acc: {}".
format(pass_id, batch_id,
np.mean(loss_val),
float(np.mean(correct_val)) / args.batch_size))
t1 = time.time()
total_time += t1 - t0
print("epoch:%d num_steps:%d time_cost(s):%f" %
(epoch_idx, i, total_time / epoch_idx))
save_dir = "%s/epoch_%d" % (args.model_dir, epoch_idx)
fluid.io.save_params(executor=exe, dirname=save_dir)
print("model saved in %s" % save_dir)
def main():
args = parse_args()
start_train(args)
train(args)
if __name__ == "__main__":
main()
fluid/PaddleRec/ssr/train_data/small_train.txt 0 → 100644
浏览文件 @ e6ed31ef
197 196 198 236
93 93 384 362 363 43
336 364 407
421 322
314 388
128 58
138 138
46 46 46
34 34 57 57 57 342 228 321 346 357 59 376
110 110
135 94 135
27 250 27
129 118
18 18 18
81 81 89 89
27 27
20 20 20 20 20 212
33 33 33 33
62 62 62 63 63 55 248 124 381 428 383 382 43 43 261 63
90 90 78 78
399 397 202 141 104 104 245 192 191 271
239 332 283 88
187 313
136 136 324
41 41
352 128
413 414
410 45 45 45 1 1 1 1 1 1 1 1 31 31 31 31
92 334 92
95 285
215 249
390 41
116 116
300 252
2 2 2 2 2
8 8 8 8 8 8
53 241 259
118 129 126 94 137 208 216 299
209 368 139 418 419
311 180
303 302 203 284
369 32 32 32 32 337
207 47 47 47
106 107
143 143
179 178
109 109
405 79 79 371 246
251 417 427
333 88 387 358 123 348 394 360 36 365
3 3 3 3 3
189 188
398 425
107 406
281 201 141
2 2 2
359 54
395 385 293
60 60 60 121 121 233 58 58
24 199 175 24 24 24 351 386 106
115 294
122 122 127 127
35 35
282 393
277 140 140 343 225 123 36 36 36 221 114 114 59 59 117 117 247 367 219 258 222 301 375 350 353 111 111
275 272 273 274 331 330 305 108 76 76 108
26 26 26 408 26
290 18 210 291
372 139 424 113
341 340 335
120 370
224 200
426 416
137 319
402 55
54 54
327 119
125 125
391 396 354 355 389
142 142
295 320
113 366
253 85 85
56 56 310 309 308 307 278 25 25 19 19 3 312 19 19 19 3 25
220 338
34 130
130 120 380 315
339 422
379 378
95 56 392 115
55 124
126 34
349 373 361
195 194
75 75
64 64 64
35 35
40 40 40 242 77 244 77 243
257 316
103 306 102 51 52 103 105 52 52 292 318 112 286 345 237 276 112 51 102 105
fluid/PaddleRec/ssr/utils.py 0 → 100644
浏览文件 @ e6ed31ef
import numpy as np
import reader as reader
import os
import logging
import paddle.fluid as fluid
import paddle
def get_vocab_size(vocab_path):
with open(vocab_path, "r") as rf:
line = rf.readline()
return int(line.strip())
def construct_train_data(file_dir, vocab_path, batch_size):
vocab_size = get_vocab_size(vocab_path)
files = [file_dir + '/' + f for f in os.listdir(file_dir)]
y_data = reader.YoochooseDataset(vocab_size)
train_reader = paddle.batch(
paddle.reader.shuffle(
y_data.train(files), buf_size=batch_size * 100),
batch_size=batch_size)
return train_reader, vocab_size
def construct_test_data(file_dir, vocab_path, batch_size):
vocab_size = get_vocab_size(vocab_path)
files = [file_dir + '/' + f for f in os.listdir(file_dir)]
y_data = reader.YoochooseDataset(vocab_size)
test_reader = paddle.batch(y_data.test(files), batch_size=batch_size)
return test_reader, vocab_size
def infer_data(raw_data, place):
data = [dat[0] for dat in raw_data]
seq_lens = [len(seq) for seq in data]
cur_len = 0
lod = [cur_len]
for l in seq_lens:
cur_len += l
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = fluid.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
p_label = [dat[1] for dat in raw_data]
pos_label = np.array(p_label).astype("int64").reshape(len(p_label), 1)
return res, pos_label
fluid/PaddleRec/tagspace/train.py
浏览文件 @ e6ed31ef
......@@ -21,15 +21,9 @@ def parse_args():
parser.add_argument(
'--train_dir', type=str, default='train_data', help='train file')
parser.add_argument(
'--vocab_text_path',
type=str,
default='vocab_text.txt',
help='vocab_text file')
'--vocab_text_path', type=str, default='vocab_text.txt', help='text')
parser.add_argument(
'--vocab_tag_path',
type=str,
default='vocab_tag.txt',
help='vocab_text file')
'--vocab_tag_path', type=str, default='vocab_tag.txt', help='tag')
parser.add_argument(
'--model_dir', type=str, default='model_', help='model dir')
parser.add_argument(
......
fluid/PaddleRec/word2vec/README.cn.md
浏览文件 @ e6ed31ef
......@@ -25,6 +25,14 @@ cd data && ./download.sh && cd ..
```bash
python preprocess.py --data_path ./data/1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled --dict_path data/1-billion_dict
```
如果您想使用自定义的词典形如：
```bash
<UNK>
a
b
c
```
请将--other_dict_path设置为您存放将使用的词典的目录，并设置--with_other_dict使用它
## 训练
训练的命令行选项可以通过`python train.py -h`列出。
......@@ -32,11 +40,21 @@ python preprocess.py --data_path ./data/1-billion-word-language-modeling-benchma
### 单机训练：
```bash
export CPU_NUM=1
python train.py \
--train_data_path ./data/1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled \
--dict_path data/1-billion_dict \
--with_hs --with_nce --is_local \
2>&1 | tee train.log
```
如果您想使用自定义的词典形如：
```bash
<UNK>
a
b
c
```
请将--other_dict_path设置为您存放将使用的词典的目录，并设置--with_other_dict使用它
### 分布式训练
......@@ -59,6 +77,11 @@ sh cluster_train.sh
您也可以在`build_test_case`方法中模仿给出的例子增加自己的测试
要从测试文件运行测试用例，请将测试文件下载到“test”目录中
我们为每个案例提供以下结构的测试：
`word1 word2 word3 word4`
所以我们可以将它构建成`word1 - word2 + word3 = word4`
训练中预测：
```bash
......
fluid/PaddleRec/word2vec/README.md
浏览文件 @ e6ed31ef
......@@ -14,6 +14,11 @@ Download dataset:
```bash
cd data && ./download.sh && cd ..
```
if you would like to use our supported third party vocab, please run:
```bash
wget http://download.tensorflow.org/models/LM_LSTM_CNN/vocab-2016-09-10.txt
```
## Model
This model implement a skip-gram model of word2vector.
......@@ -26,18 +31,31 @@ Preprocess the training data to generate a word dict.
```bash
python preprocess.py --data_path ./data/1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled --dict_path data/1-billion_dict
```
if you would like to use your own vocab follow the format below:
```bash
<UNK>
a
b
c
```
Then, please set --other_dict_path as the directory of where you
save the vocab you will use and set --with_other_dict flag on to using it.
## Train
The command line options for training can be listed by `python train.py -h`.
### Local Train:
we set CPU_NUM=1 as default CPU_NUM to execute
```bash
export CPU_NUM=1 && \
python train.py \
--train_data_path ./data/1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled \
--dict_path data/1-billion_dict \
--with_hs --with_nce --is_local \
2>&1 | tee train.log
```
if you would like to use our supported third party vocab, please set --other_dict_path as the directory of where you
save the vocab you will use and set --with_other_dict flag on to using it.
### Distributed Train
Run a 2 pserver 2 trainer distribute training on a single machine.
......@@ -62,6 +80,11 @@ For: boy - girl + aunt = uncle
You can also add your own tests by mimicking the examples given in the `build_test_case` method.
To running test case from test files, please download the test files into 'test' directory
we provide test for each case with the following structure:
`word1 word2 word3 word4`
so we can build it into `word1 - word2 + word3 = word4`
Forecast in training:
```bash
......
fluid/PaddleRec/word2vec/data/download.sh
浏览文件 @ e6ed31ef
......@@ -2,4 +2,3 @@
wget http://www.statmt.org/lm-benchmark/1-billion-word-language-modeling-benchmark-r13output.tar.gz
tar -zxvf 1-billion-word-language-modeling-benchmark-r13output.tar.gz
fluid/PaddleRec/word2vec/infer.py
浏览文件 @ e6ed31ef
import paddle
import time
import os
import paddle.fluid as fluid
import numpy as np
from Queue import PriorityQueue
import logging
import argparse
from sklearn.metrics.pairwise import cosine_similarity
import preprocess
word_to_id = dict()
id_to_word = dict()
......@@ -47,6 +45,22 @@ def parse_args():
required=False,
default=True,
help='if using infer_during_train, (default: True)')
parser.add_argument(
'--test_acc',
action='store_true',
required=False,
default=False,
help='if using test_files , (default: False)')
parser.add_argument(
'--test_files_dir',
type=str,
default='test',
help="The path for test_files) (default: test)")
parser.add_argument(
'--test_batch_size',
type=int,
default=1000,
help="test used batch size (default: 1000)")
return parser.parse_args()
......@@ -58,75 +72,168 @@ def BuildWord_IdMap(dict_path):
id_to_word[int(line.split(' ')[1])] = line.split(' ')[0]
def inference_prog():
def inference_prog(): # just to create program for test
fluid.layers.create_parameter(
shape=[1, 1], dtype='float32', name="embeding")
def build_test_case(emb):
def build_test_case_from_file(args, emb):
logger.info("test files dir: {}".format(args.test_files_dir))
current_list = os.listdir(args.test_files_dir)
logger.info("test files list: {}".format(current_list))
test_cases = list()
test_labels = list()
test_case_descs = list()
exclude_lists = list()
for file_dir in current_list:
with open(args.test_files_dir + "/" + file_dir, 'r') as f:
for line in f:
if ':' in line:
logger.info("{}".format(line))
pass
else:
line = preprocess.strip_lines(line, word_to_id)
test_case = emb[word_to_id[line.split()[0]]] - emb[
word_to_id[line.split()[1]]] + emb[word_to_id[
line.split()[2]]]
test_case_desc = line.split()[0] + " - " + line.split()[
1] + " + " + line.split()[2] + " = " + line.split()[3]
test_cases.append(test_case)
test_case_descs.append(test_case_desc)
test_labels.append(word_to_id[line.split()[3]])
exclude_lists.append([
word_to_id[line.split()[0]],
word_to_id[line.split()[1]], word_to_id[line.split()[2]]
])
test_cases = norm(np.array(test_cases))
return test_cases, test_case_descs, test_labels, exclude_lists
def build_small_test_case(emb):
emb1 = emb[word_to_id['boy']] - emb[word_to_id['girl']] + emb[word_to_id[
'aunt']]
desc1 = "boy - girl + aunt = uncle"
label1 = word_to_id["uncle"]
emb2 = emb[word_to_id['brother']] - emb[word_to_id['sister']] + emb[
word_to_id['sisters']]
desc2 = "brother - sister + sisters = brothers"
label2 = word_to_id["brothers"]
emb3 = emb[word_to_id['king']] - emb[word_to_id['queen']] + emb[word_to_id[
'woman']]
desc3 = "king - queen + woman = man"
label3 = word_to_id["man"]
emb4 = emb[word_to_id['reluctant']] - emb[word_to_id['reluctantly']] + emb[
word_to_id['slowly']]
desc4 = "reluctant - reluctantly + slowly = slow"
label4 = word_to_id["slow"]
emb5 = emb[word_to_id['old']] - emb[word_to_id['older']] + emb[word_to_id[
'deeper']]
desc5 = "old - older + deeper = deep"
return [[emb1, desc1], [emb2, desc2], [emb3, desc3], [emb4, desc4],
[emb5, desc5]]
label5 = word_to_id["deep"]
emb6 = emb[word_to_id['boy']]
desc6 = "boy"
label6 = word_to_id["boy"]
emb7 = emb[word_to_id['king']]
desc7 = "king"
label7 = word_to_id["king"]
emb8 = emb[word_to_id['sun']]
desc8 = "sun"
label8 = word_to_id["sun"]
emb9 = emb[word_to_id['key']]
desc9 = "key"
label9 = word_to_id["key"]
test_cases = [emb1, emb2, emb3, emb4, emb5, emb6, emb7, emb8, emb9]
test_case_desc = [
desc1, desc2, desc3, desc4, desc5, desc6, desc7, desc8, desc9
]
test_labels = [
label1, label2, label3, label4, label5, label6, label7, label8, label9
]
return norm(np.array(test_cases)), test_case_desc, test_labels
def build_test_case(args, emb):
if args.test_acc:
return build_test_case_from_file(args, emb)
else:
return build_small_test_case(emb)
def norm(x):
y = np.linalg.norm(x, axis=1, keepdims=True)
return x / y
def inference_test(scope, model_dir, args):
BuildWord_IdMap(args.dict_path)
logger.info("model_dir is: {}".format(model_dir + "/"))
emb = np.array(scope.find_var("embeding").get_tensor())
test_cases = build_test_case(emb)
x = norm(emb)
logger.info("inference result: ====================")
for case in test_cases:
pq = topK(args.rank_num, emb, case[0])
logger.info("Test result for {}".format(case[1]))
pq_tmps = list()
for i in range(args.rank_num):
pq_tmps.append(pq.get())
for i in range(len(pq_tmps)):
logger.info("{} nearest is {}, rate is {}".format(i, id_to_word[
pq_tmps[len(pq_tmps) - 1 - i].id], pq_tmps[len(pq_tmps) - 1 - i]
.priority))
del pq_tmps[:]
class PQ_Entry(object):
def __init__(self, cos_similarity, id):
self.priority = cos_similarity
self.id = id
def __cmp__(self, other):
return cmp(self.priority, other.priority)
def topK(k, emb, test_emb):
pq = PriorityQueue(k + 1)
if len(emb) <= k:
for i in range(len(emb)):
x = cosine_similarity([emb[i]], [test_emb])
pq.put(PQ_Entry(x, i))
return pq
for i in range(len(emb)):
x = cosine_similarity([emb[i]], [test_emb])
pq_e = PQ_Entry(x, i)
if pq.full():
pq.get()
pq.put(pq_e)
pq.get()
return pq
test_cases = None
test_case_desc = list()
test_labels = list()
exclude_lists = list()
if args.test_acc:
test_cases, test_case_desc, test_labels, exclude_lists = build_test_case(
args, emb)
else:
test_cases, test_case_desc, test_labels = build_test_case(args, emb)
exclude_lists = [[-1]]
accual_rank = 1 if args.test_acc else args.rank_num
correct_num = 0
cosine_similarity_matrix = np.dot(test_cases, x.T)
results = topKs(accual_rank, cosine_similarity_matrix, exclude_lists,
args.test_acc)
for i in range(len(test_labels)):
logger.info("Test result for {}".format(test_case_desc[i]))
result = results[i]
for j in range(accual_rank):
if result[j][1] == test_labels[
i]: # if the nearest word is what we want
correct_num += 1
logger.info("{} nearest is {}, rate is {}".format(j, id_to_word[
result[j][1]], result[j][0]))
logger.info("Test acc is: {}, there are {} / {}".format(correct_num / len(
test_labels), correct_num, len(test_labels)))
def topK(k, cosine_similarity_list, exclude_list, is_acc=False):
if k == 1 and is_acc: # accelerate acc calculate
max = cosine_similarity_list[0]
id = 0
for i in range(len(cosine_similarity_list)):
if cosine_similarity_list[i] >= max and (i not in exclude_list):
max = cosine_similarity_list[i]
id = i
else:
pass
return [[max, id]]
else:
result = list()
result_index = np.argpartition(cosine_similarity_list, -k)[-k:]
for index in result_index:
result.append([cosine_similarity_list[index], index])
result.sort(reverse=True)
return result
def topKs(k, cosine_similarity_matrix, exclude_lists, is_acc=False):
results = list()
result_queues = list()
correct_num = 0
for i in range(cosine_similarity_matrix.shape[0]):
tmp_pq = None
if is_acc:
tmp_pq = topK(k, cosine_similarity_matrix[i], exclude_lists[i],
is_acc)
else:
tmp_pq = topK(k, cosine_similarity_matrix[i], exclude_lists[0],
is_acc)
result_queues.append(tmp_pq)
return result_queues
def infer_during_train(args):
......@@ -138,8 +245,6 @@ def infer_during_train(args):
while True:
time.sleep(60)
current_list = os.listdir(args.model_output_dir)
# logger.info("current_list is : {}".format(current_list))
# logger.info("model_file_list is : {}".format(model_file_list))
if set(model_file_list) == set(current_list):
if solved_new:
solved_new = False
......@@ -174,6 +279,8 @@ def infer_once(args):
fluid.io.load_persistables(
executor=exe, dirname=args.model_output_dir + "/")
inference_test(Scope, args.model_output_dir, args)
else:
logger.info("Wrong Directory or save model failed!")
if __name__ == '__main__':
......@@ -181,5 +288,7 @@ if __name__ == '__main__':
# while setting infer_once please specify the dir to models file with --model_output_dir
if args.infer_once:
infer_once(args)
if args.infer_during_train:
elif args.infer_during_train:
infer_during_train(args)
else:
pass
fluid/PaddleRec/word2vec/network_conf.py
浏览文件 @ e6ed31ef
......@@ -95,8 +95,7 @@ def skip_gram_word2vec(dict_size,
capacity=64, feed_list=datas, name='py_reader', use_double_buffer=True)
words = fluid.layers.read_file(py_reader)
emb = fluid.layers.embedding(
target_emb = fluid.layers.embedding(
input=words[0],
is_sparse=is_sparse,
size=[dict_size, embedding_size],
......@@ -104,16 +103,23 @@ def skip_gram_word2vec(dict_size,
name='embeding',
initializer=fluid.initializer.Normal(scale=1 /
math.sqrt(dict_size))))
context_emb = fluid.layers.embedding(
input=words[1],
is_sparse=is_sparse,
size=[dict_size, embedding_size],
param_attr=fluid.ParamAttr(
name='embeding',
initializer=fluid.initializer.Normal(scale=1 /
math.sqrt(dict_size))))
cost, cost_nce, cost_hs = None, None, None
if with_nce:
cost_nce = nce_layer(emb, words[1], embedding_size, dict_size, 5,
cost_nce = nce_layer(target_emb, words[1], embedding_size, dict_size, 5,
"uniform", word_frequencys, None)
cost = cost_nce
if with_hsigmoid:
cost_hs = hsigmoid_layer(emb, words[1], words[2], words[3], dict_size,
is_sparse)
cost_hs = hsigmoid_layer(context_emb, words[0], words[2], words[3],
dict_size, is_sparse)
cost = cost_hs
if with_nce and with_hsigmoid:
cost = fluid.layers.elementwise_add(cost_nce, cost_hs)
......
fluid/PaddleRec/word2vec/preprocess.py
浏览文件 @ e6ed31ef
# -*- coding: utf-8 -*
import re
import six
import argparse
import io
prog = re.compile("[^a-z ]", flags=0)
word_count = dict()
def parse_args():
......@@ -22,18 +27,75 @@ def parse_args():
type=int,
default=5,
help="If the word count is less then freq, it will be removed from dict")
parser.add_argument(
'--is_local',
'--with_other_dict',
action='store_true',
required=False,
default=False,
help='Local train or not, (default: False)')
help='Using third party provided dict , (default: False)')
parser.add_argument(
'--other_dict_path',
type=str,
default='',
help='The path for third party provided dict (default: '
')')
return parser.parse_args()
def text_strip(text):
return re.sub("[^a-z ]", "", text)
return prog.sub("", text)
# users can self-define their own strip rules by modifing this method
def strip_lines(line, vocab=word_count):
return _replace_oov(vocab, native_to_unicode(line))
# Shameless copy from Tensorflow https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/data_generators/text_encoder.py
def _replace_oov(original_vocab, line):
"""Replace out-of-vocab words with "<UNK>".
This maintains compatibility with published results.
Args:
original_vocab: a set of strings (The standard vocabulary for the dataset)
line: a unicode string - a space-delimited sequence of words.
Returns:
a unicode string - a space-delimited sequence of words.
"""
return u" ".join([
word if word in original_vocab else u"<UNK>" for word in line.split()
])
# Shameless copy from Tensorflow https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/data_generators/text_encoder.py
# Unicode utility functions that work with Python 2 and 3
def native_to_unicode(s):
if _is_unicode(s):
return s
try:
return _to_unicode(s)
except UnicodeDecodeError:
res = _to_unicode(s, ignore_errors=True)
return res
def _is_unicode(s):
if six.PY2:
if isinstance(s, unicode):
return True
else:
if isinstance(s, str):
return True
return False
def _to_unicode(s, ignore_errors=False):
if _is_unicode(s):
return s
error_mode = "ignore" if ignore_errors else "strict"
return s.decode("utf-8", errors=error_mode)
def build_Huffman(word_count, max_code_length):
......@@ -120,7 +182,7 @@ def build_Huffman(word_count, max_code_length):
return word_point, word_code, word_code_len
def preprocess(data_path, dict_path, freq, is_local):
def preprocess(args):
"""
proprocess the data, generate dictionary and save into dict_path.
:param data_path: the input data path.
......@@ -129,14 +191,26 @@ def preprocess(data_path, dict_path, freq, is_local):
:return:
"""
# word to count
word_count = dict()
if is_local:
if args.with_other_dict:
with io.open(args.other_dict_path, 'r', encoding='utf-8') as f:
for line in f:
word_count[native_to_unicode(line.strip())] = 1
for i in range(1, 100):
with open(data_path + "/news.en-000{:0>2d}-of-00100".format(
i)) as f:
with io.open(
args.data_path + "/news.en-000{:0>2d}-of-00100".format(i),
encoding='utf-8') as f:
for line in f:
line = line.lower()
if args.with_other_dict:
line = strip_lines(line)
words = line.split()
for item in words:
if item in word_count:
word_count[item] = word_count[item] + 1
else:
word_count[native_to_unicode('<UNK>')] += 1
else:
line = text_strip(line)
words = line.split()
for item in words:
......@@ -146,26 +220,25 @@ def preprocess(data_path, dict_path, freq, is_local):
word_count[item] = 1
item_to_remove = []
for item in word_count:
if word_count[item] <= freq:
if word_count[item] <= args.freq:
item_to_remove.append(item)
for item in item_to_remove:
del word_count[item]
path_table, path_code, word_code_len = build_Huffman(word_count, 40)
with open(dict_path, 'w+') as f:
with io.open(args.dict_path, 'w+', encoding='utf-8') as f:
for k, v in word_count.items():
f.write(str(k) + " " + str(v) + '\n')
f.write(k + " " + str(v) + '\n')
with open(dict_path + "_ptable", 'w+') as f2:
with io.open(args.dict_path + "_ptable", 'w+', encoding='utf-8') as f2:
for pk, pv in path_table.items():
f2.write(str(pk) + ":" + ' '.join((str(x) for x in pv)) + '\n')
f2.write(pk + '\t' + ' '.join((str(x) for x in pv)) + '\n')
with open(dict_path + "_pcode", 'w+') as f3:
for pck, pcv in path_table.items():
f3.write(str(pck) + ":" + ' '.join((str(x) for x in pcv)) + '\n')
with io.open(args.dict_path + "_pcode", 'w+', encoding='utf-8') as f3:
for pck, pcv in path_code.items():
f3.write(pck + '\t' + ' '.join((str(x) for x in pcv)) + '\n')
if __name__ == "__main__":
args = parse_args()
preprocess(args.data_path, args.dict_path, args.freq, args.is_local)
preprocess(parse_args())
fluid/PaddleRec/word2vec/reader.py
浏览文件 @ e6ed31ef
......@@ -2,14 +2,32 @@
import numpy as np
import preprocess
import logging
import io
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger("fluid")
logger.setLevel(logging.INFO)
class NumpyRandomInt(object):
def __init__(self, a, b, buf_size=1000):
self.idx = 0
self.buffer = np.random.random_integers(a, b, buf_size)
self.a = a
self.b = b
def __call__(self):
if self.idx == len(self.buffer):
self.buffer = np.random.random_integers(self.a, self.b,
len(self.buffer))
self.idx = 0
result = self.buffer[self.idx]
self.idx += 1
return result
class Word2VecReader(object):
def __init__(self,
dict_path,
......@@ -24,6 +42,7 @@ class Word2VecReader(object):
self.num_non_leaf = 0
self.word_to_id_ = dict()
self.id_to_word = dict()
self.word_count = dict()
self.word_to_path = dict()
self.word_to_code = dict()
self.trainer_id = trainer_id
......@@ -33,41 +52,43 @@ class Word2VecReader(object):
word_counts = []
word_id = 0
with open(dict_path, 'r') as f:
with io.open(dict_path, 'r', encoding='utf-8') as f:
for line in f:
word, count = line.split()[0], int(line.split()[1])
self.word_count[word] = count
self.word_to_id_[word] = word_id
self.id_to_word[word_id] = word #build id to word dict
word_id += 1
word_counts.append(count)
word_all_count += count
with open(dict_path + "_word_to_id_", 'w+') as f6:
with io.open(dict_path + "_word_to_id_", 'w+', encoding='utf-8') as f6:
for k, v in self.word_to_id_.items():
f6.write(str(k) + " " + str(v) + '\n')
f6.write(k + " " + str(v) + '\n')
self.dict_size = len(self.word_to_id_)
self.word_frequencys = [
float(count) / word_all_count for count in word_counts
]
print("dict_size = " + str(
self.dict_size)) + " word_all_count = " + str(word_all_count)
print("dict_size = " + str(self.dict_size) + " word_all_count = " + str(
word_all_count))
with open(dict_path + "_ptable", 'r') as f2:
with io.open(dict_path + "_ptable", 'r', encoding='utf-8') as f2:
for line in f2:
self.word_to_path[line.split(":")[0]] = np.fromstring(
line.split(':')[1], dtype=int, sep=' ')
self.word_to_path[line.split('\t')[0]] = np.fromstring(
line.split('\t')[1], dtype=int, sep=' ')
self.num_non_leaf = np.fromstring(
line.split(':')[1], dtype=int, sep=' ')[0]
line.split('\t')[1], dtype=int, sep=' ')[0]
print("word_ptable dict_size = " + str(len(self.word_to_path)))
with open(dict_path + "_pcode", 'r') as f3:
with io.open(dict_path + "_pcode", 'r', encoding='utf-8') as f3:
for line in f3:
self.word_to_code[line.split(":")[0]] = np.fromstring(
line.split(':')[1], dtype=int, sep=' ')
self.word_to_code[line.split('\t')[0]] = np.fromstring(
line.split('\t')[1], dtype=int, sep=' ')
print("word_pcode dict_size = " + str(len(self.word_to_code)))
self.random_generator = NumpyRandomInt(1, self.window_size_ + 1)
def get_context_words(self, words, idx, window_size):
def get_context_words(self, words, idx):
"""
Get the context word list of target word.
......@@ -75,23 +96,30 @@ class Word2VecReader(object):
idx: input word index
window_size: window size
"""
target_window = np.random.randint(1, window_size + 1)
# need to keep in mind that maybe there are no enough words before the target word.
start_point = idx - target_window if (idx - target_window) > 0 else 0
target_window = self.random_generator()
start_point = idx - target_window # if (idx - target_window) > 0 else 0
if start_point < 0:
start_point = 0
end_point = idx + target_window
# context words of the target word
targets = set(words[start_point:idx] + words[idx + 1:end_point + 1])
return list(targets)
targets = words[start_point:idx] + words[idx + 1:end_point + 1]
return set(targets)
def train(self, with_hs):
def train(self, with_hs, with_other_dict):
def _reader():
for file in self.filelist:
with open(self.data_path_ + "/" + file, 'r') as f:
with io.open(
self.data_path_ + "/" + file, 'r',
encoding='utf-8') as f:
logger.info("running data in {}".format(self.data_path_ +
"/" + file))
count = 1
for line in f:
if self.trainer_id == count % self.trainer_num:
if with_other_dict:
line = preprocess.strip_lines(line,
self.word_count)
else:
line = preprocess.text_strip(line)
word_ids = [
self.word_to_id_[word] for word in line.split()
......@@ -99,7 +127,7 @@ class Word2VecReader(object):
]
for idx, target_id in enumerate(word_ids):
context_word_ids = self.get_context_words(
word_ids, idx, self.window_size_)
word_ids, idx)
for context_id in context_word_ids:
yield [target_id], [context_id]
else:
......@@ -108,12 +136,18 @@ class Word2VecReader(object):
def _reader_hs():
for file in self.filelist:
with open(self.data_path_ + "/" + file, 'r') as f:
with io.open(
self.data_path_ + "/" + file, 'r',
encoding='utf-8') as f:
logger.info("running data in {}".format(self.data_path_ +
"/" + file))
count = 1
for line in f:
if self.trainer_id == count % self.trainer_num:
if with_other_dict:
line = preprocess.strip_lines(line,
self.word_count)
else:
line = preprocess.text_strip(line)
word_ids = [
self.word_to_id_[word] for word in line.split()
......@@ -121,14 +155,14 @@ class Word2VecReader(object):
]
for idx, target_id in enumerate(word_ids):
context_word_ids = self.get_context_words(
word_ids, idx, self.window_size_)
word_ids, idx)
for context_id in context_word_ids:
yield [target_id], [context_id], [
self.word_to_code[self.id_to_word[
context_id]]
], [
self.word_to_path[self.id_to_word[
context_id]]
target_id]]
], [
self.word_to_code[self.id_to_word[
target_id]]
]
else:
pass
......@@ -141,13 +175,20 @@ class Word2VecReader(object):
if __name__ == "__main__":
window_size = 10
window_size = 5
reader = Word2VecReader(
"./data/1-billion_dict",
"./data/1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled/",
["news.en-00001-of-00100"], 0, 1)
reader = Word2VecReader("data/enwik9_dict", "data/enwik9", window_size)
i = 0
for x, y in reader.train()():
# print(reader.train(True))
for x, y, z, f in reader.train(True)():
print("x: " + str(x))
print("y: " + str(y))
print("path: " + str(z))
print("code: " + str(f))
print("\n")
if i == 10:
exit(0)
......
fluid/PaddleRec/word2vec/train.py
浏览文件 @ e6ed31ef
from __future__ import print_function
import argparse
import logging
import os
......@@ -13,7 +12,7 @@ os.environ["CUDA_VISIBLE_DEVICES"] = ""
import paddle
import paddle.fluid as fluid
from paddle.fluid.executor import global_scope
import six
import reader
from network_conf import skip_gram_word2vec
from infer import inference_test
......@@ -30,7 +29,7 @@ def parse_args():
'--train_data_path',
type=str,
default='./data/1-billion-word-language-modeling-benchmark-r13output/training-monolingual.tokenized.shuffled',
help="The path of training dataset")
help="The path of taining dataset")
parser.add_argument(
'--dict_path',
type=str,
......@@ -44,7 +43,7 @@ def parse_args():
parser.add_argument(
'--batch_size',
type=int,
default=100,
default=1000,
help="The size of mini-batch (default:100)")
parser.add_argument(
'--num_passes',
......@@ -117,6 +116,13 @@ def parse_args():
default=False,
help='Do inference every 100 batches , (default: False)')
parser.add_argument(
'--with_other_dict',
action='store_true',
required=False,
default=False,
help='if use other dict , (default: False)')
parser.add_argument(
'--rank_num',
type=int,
......@@ -126,14 +132,44 @@ def parse_args():
return parser.parse_args()
def convert_python_to_tensor(batch_size, sample_reader, is_hs):
def __reader__():
result = None
if is_hs:
result = [[], [], [], []]
else:
result = [[], []]
for sample in sample_reader():
for i, fea in enumerate(sample):
result[i].append(fea)
if len(result[0]) == batch_size:
tensor_result = []
for tensor in result:
t = fluid.Tensor()
dat = np.array(tensor, dtype='int64')
if len(dat.shape) > 2:
dat = dat.reshape((dat.shape[0], dat.shape[2]))
elif len(dat.shape) == 1:
dat = dat.reshape((-1, 1))
t.set(dat, fluid.CPUPlace())
tensor_result.append(t)
yield tensor_result
if is_hs:
result = [[], [], [], []]
else:
result = [[], []]
return __reader__
def train_loop(args, train_program, reader, py_reader, loss, trainer_id):
train_reader = paddle.batch(
paddle.reader.shuffle(
reader.train((args.with_hs or (not args.with_nce))),
buf_size=args.batch_size * 100),
batch_size=args.batch_size)
py_reader.decorate_paddle_reader(train_reader)
py_reader.decorate_tensor_provider(
convert_python_to_tensor(args.batch_size,
reader.train((args.with_hs or (
not args.with_nce)), args.with_other_dict),
(args.with_hs or (not args.with_nce))))
place = fluid.CPUPlace()
......@@ -141,6 +177,7 @@ def train_loop(args, train_program, reader, py_reader, loss, trainer_id):
exe.run(fluid.default_startup_program())
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.use_experimental_executor = True
print("CPU_NUM:" + str(os.getenv("CPU_NUM")))
exec_strategy.num_threads = int(os.getenv("CPU_NUM"))
......@@ -162,36 +199,27 @@ def train_loop(args, train_program, reader, py_reader, loss, trainer_id):
profiler_step_end = 30
for pass_id in range(args.num_passes):
epoch_start = time.time()
py_reader.start()
time.sleep(10)
epoch_start = time.time()
batch_id = 0
start = time.clock()
start = time.time()
try:
while True:
if profiler_step == profiler_step_start:
fluid.profiler.start_profiler(profile_state)
loss_val = train_exe.run(fetch_list=[loss.name])
loss_val = np.mean(loss_val)
if profiler_step == profiler_step_end:
fluid.profiler.stop_profiler('total', 'trainer_profile.log')
profiler_step += 1
else:
profiler_step += 1
if batch_id % 50 == 0:
logger.info(
"TRAIN --> pass: {} batch: {} loss: {} reader queue:{}".
format(pass_id, batch_id,
loss_val.mean() / args.batch_size,
py_reader.queue.size()))
loss_val.mean(), py_reader.queue.size()))
if args.with_speed:
if batch_id % 1000 == 0 and batch_id != 0:
elapsed = (time.clock() - start)
start = time.clock()
elapsed = (time.time() - start)
start = time.time()
samples = 1001 * args.batch_size * int(
os.getenv("CPU_NUM"))
logger.info("Time used: {}, Samples/Sec: {}".format(
......@@ -240,7 +268,7 @@ def train(args):
args.dict_path, args.train_data_path, filelist, 0, 1)
else:
trainer_id = int(os.environ["PADDLE_TRAINER_ID"])
trainers = int(os.environ["PADDLE_TRAINERS"])
trainer_num = int(os.environ["PADDLE_TRAINERS"])
word2vec_reader = reader.Word2VecReader(args.dict_path,
args.train_data_path, filelist,
trainer_id, trainer_num)
......@@ -257,9 +285,9 @@ def train(args):
optimizer = None
if args.with_Adam:
optimizer = fluid.optimizer.Adam(learning_rate=1e-3)
optimizer = fluid.optimizer.Adam(learning_rate=1e-4, lazy_mode=True)
else:
optimizer = fluid.optimizer.SGD(learning_rate=1e-3)
optimizer = fluid.optimizer.SGD(learning_rate=1e-4)
optimizer.minimize(loss)
......
legacy/README.md
浏览文件 @ e6ed31ef
# 该目录的模型已经不再维护，不推荐使用。建议使用Fluid目录下的模型。
# Introduction to models
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](https://github.com/PaddlePaddle/models)
......
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