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PaddleDetection
“eca0509213255ef714d350a5af897b381f9244ea”上不存在“plugins/obagent/1.3.0/stop.py”
提交 c1455e60 编写于 作者: D dzhwinter
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# Benchmark
Machine:
- Server: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz, 2 Sockets, 20 Cores per socket
- Laptop: TBD
System: CentOS release 6.3 (Final), Docker 1.12.1.
PaddlePaddle:
- paddlepaddle/paddle:0.11.0 (for MKLML and MKL-DNN)
- MKL-DNN tag v0.11
- MKLML 2018.0.1.20171007
- paddlepaddle/paddle:0.11.0-openblas (for OpenBLAS)
- OpenBLAS v0.2.20
On each machine, we will test and compare the performance of training on single node using MKL-DNN / MKLML / OpenBLAS respectively.
## Benchmark Model
### Server
#### Training
Test on batch size 64, 128, 256 on Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
Pay attetion that the speed below includes forward, backward and parameter update time. So we can not directly compare the data with the benchmark of caffe `time` [command](https://github.com/PaddlePaddle/Paddle/blob/develop/benchmark/caffe/image/run.sh#L9), which only contain forward and backward. The updating time of parameter would become very heavy when the weight size are large, especially on alexnet.
Input image size - 3 * 224 * 224, Time: images/second
- VGG-19
| BatchSize | 64 | 128 | 256 |
|--------------|-------| -----| --------|
| OpenBLAS | 7.80 | 9.00 | 10.80 |
| MKLML | 12.12 | 13.70 | 16.18 |
| MKL-DNN | 28.46 | 29.83 | 30.44 |
<img src="figs/vgg-cpu-train.png" width="500">
- ResNet-50
| BatchSize | 64 | 128 | 256 |
|--------------|-------| ------| -------|
| OpenBLAS | 25.22 | 25.68 | 27.12 |
| MKLML | 32.52 | 31.89 | 33.12 |
| MKL-DNN | 81.69 | 82.35 | 84.08 |
<img src="figs/resnet-cpu-train.png" width="500">
- GoogLeNet
| BatchSize | 64 | 128 | 256 |
|--------------|-------| ------| -------|
| OpenBLAS | 89.52 | 96.97 | 108.25 |
| MKLML | 128.46| 137.89| 158.63 |
| MKL-DNN     | 250.46| 264.83| 269.50 |
<img src="figs/googlenet-cpu-train.png" width="500">
- AlexNet
| BatchSize | 64 | 128 | 256 |
|--------------|--------| ------ | -------|
| OpenBLAS | 45.62 | 72.79 | 107.22 |
| MKLML | 66.37 | 105.60 | 144.04 |
| MKL-DNN | 399.00 | 498.94 | 626.53 |
<img src="figs/alexnet-cpu-train.png" width="500">
#### Inference
Test on batch size 1, 2, 4, 8, 16 on Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
- VGG-19
| BatchSize | 1 | 2 | 4 | 8 | 16 |
|-----------|-------|-------|-------|-------|-------|
| OpenBLAS | 1.10 | 1.96 | 3.62 | 3.63 | 2.25 |
| MKLML | 5.58 | 9.80 | 15.15 | 21.21 | 28.67 |
| MKL-DNN | 75.07 | 88.64 | 82.58 | 92.29 | 96.75 |
<img src="figs/vgg-cpu-infer.png" width="500">
- ResNet-50
| BatchSize | 1 | 2 | 4 | 8 | 16 |
|-----------|-------|--------|--------|--------|--------|
| OpenBLAS | 3.31 | 6.72 | 11.59 | 13.17 | 9.27 |
| MKLML | 6.33 | 12.02 | 22.88 | 40.53 | 63.09 |
| MKL-DNN | 107.83| 148.84 | 177.78 | 189.35 | 217.69 |
<img src="figs/resnet-cpu-infer.png" width="500">
- GoogLeNet
| BatchSize | 1 | 2 | 4 | 8 | 16 |
|-----------|--------|--------|--------|--------|--------|
| OpenBLAS | 12.06 | 23.56 | 34.48 | 36.45 | 23.12 |
| MKLML | 22.74 | 41.56 | 81.22 | 133.47 | 210.53 |
| MKL-DNN | 175.10 | 272.92 | 450.70 | 512.00 | 600.94 |
<img src="figs/googlenet-cpu-infer.png" width="500">
- AlexNet
| BatchSize | 1 | 2 | 4 | 8 | 16 |
|-----------|--------|--------|--------|--------|--------|
| OpenBLAS | 3.53 | 6.23 | 15.04 | 26.06 | 31.62 |
| MKLML | 21.32 | 36.55 | 73.06 | 131.15 | 192.77 |
| MKL-DNN | 442.91 | 656.41 | 719.10 | 847.68 | 850.51 |
<img src="figs/alexnet-cpu-infer.png" width="500">
### Laptop
TBD
benchmark/README.md 已删除 100644 → 0
浏览文件 @ d376cf71
# Benchmark
Machine:
- CPU: 12-core Intel(R) Xeon(R) CPU E5-2620 v2 @2.10GHz
- GPU: Tesla K40m
- cuDNN: v5.1
- system: Docker 1.12.1, all platforms are tested in docker environment.
Platforms:
- PaddlePaddle: paddledev/paddle:gpu-devel-v0.9.0a0
- Tensorflow: gcr.io/tensorflow/tensorflow:0.11.0rc0-gpu
- Caffe: kaixhin/cuda-caffe
Several convolutional neural networks and recurrent neural networks are used to test.
## Image
### Benchmark Model
AlexNet, GoogleNet and a small network used in Caffe.
- [AlexNet](https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet): but the group size is one.
- [GoogleNet](https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet): but remove loss1 and loss2 when testing benchmark.
- [SmallNet](https://github.com/BVLC/caffe/blob/master/examples/cifar10/cifar10\_quick\_train\_test.prototxt)
### Single-GPU
- AlexNet: input - 3 * 227 * 227, Time: ms/batch
| BatchSize | 64 | 128 | 256 | 512 |
|--------------|-----| -----| ------| -----|
| PaddlePaddle | 195 | 334 | 602 | 1629 |
| TensorFlow | 223 | 364 | 645 | 1235 |
| Caffe | 324 | 627 | 1232 | 2513 |
**Notation**
All platforms use cuDNN-v5.1. We see that caffe is slower in this experiment, because its workspace limit size of cuDNN-conv interface is 8 * 1024 * 1024, which is smaller in PaddlePaddle and TensorFlow. Note that Caffe will be faster if increasing the workspace limit size.
- GoogletNet: input - 3 * 224 * 224, Time: ms/batch
| BatchSize | 64 | 128 | 256 |
|--------------|-------| -------| --------|
| PaddlePaddle | 613 | 1149 | 2348 |
| TensorFlow | 644 | 1176 | 2219 |
| Caffe | 694 | 1364 | out of memory |
- SmallNet: input - 3 * 32 * 32, Time ms/batch
| BatchSize | 64 | 128 | 256 | 512 |
|--------------|--------| -------- | --------|---------|
| PaddlePaddle | 10.463 | 18.184 | 33.113 | 63.039 |
| TensorFlow | 9 | 15 | 28 | 59 |
| Caffe | 9.373 | 16.6606 | 31.4797 | 59.719 |
**Notation**
All the single-GPU experiments in caffe use `caffe time` to calculate elapsed time, which does not include parameter updating time. However, both PaddlePaddle and TensorFlow experiments contain the parameter updating time. As compared with the total time, this part is relatively little on single machine, we can ignore it.
In Tensorflow, they implement algorithm searching method instead of using the algorithm searching interface in cuDNN.
### Multi-GPU: 4 GPUs
- AlexNet, ms / batch
| total-BatchSize | 128 * 4 | 256 * 4 |
|------------------|----------| -----------|
| PaddlePaddle | 347 | 622 |
| TensorFlow | 377 | 675 |
| Caffe | 1229 | 2435 |
For example, if `total-BatchSize = 128 * 4`, the speedup ratio is calculated by
```
time_at_1gpu_batch_128 * 4 / time_at_4gpu_total_batch_512
= (334 * 4)/347
= 3.85
```
<img src="figs/alexnet-4gpu.png" width="420">
- GoogleNet, ms / batch
| total-BatchSize | 128 * 4 | 256 * 4 |
|-------------------|--------------| ----------- |
| PaddlePaddle | 1178 | 2367 |
| TensorFlow | 1210 | 2292 |
| Caffe | 2007 | out of memory |
<img src="figs/googlenet-4gpu.png" width="420">
## RNN
We use lstm network for text classfication to test benchmark.
### Dataset
- [IMDB](http://www.iro.umontreal.ca/~lisa/deep/data/imdb.pkl)
- Sequence length is 100. In fact, PaddlePaddle supports training with variable-length sequence, but TensorFlow needs to pad. Thus, we also pad sequence length to 100 in PaddlePaddle in order to compare.
- Dictionary size=30000
- Peephole connection is used in `lstmemory` by default in PaddlePaddle. It is also configured in TensorFlow.
### Single-GPU
#### LSTM in Text Classification
Testing `2 lstm layer + fc` network with different hidden size and batch size.
- Batch size = 64, ms / batch
| hidden_size | 256 | 512 | 1280 |
|--------------|-------| -------| --------|
| PaddlePaddle | 83 | 184 | 641 |
| TensorFlow | 175 | 280 | 818 |
- Batch size = 128, ms / batch
| hidden_size | 256 | 512 | 1280 |
|--------------|------- | -------| --------|
| PaddlePaddle | 110 | 261 | 1007 |
| TensorFlow | 181 | 361 | 1237 |
- Batch size = 256, ms / batch
| hidden_size | 256 | 512 | 1280 |
|--------------|-------| -------| --------|
| PaddlePaddle | 170 | 414 | 1655 |
| TensorFlow | 238 | 536 | 1905 |
<img src="figs/rnn_lstm_cls.png" width="600">
#### Seq2Seq
The benchmark of sequence-to-sequence network will be added later.
### Multi GPU: 4 GPUs
#### LSTM in Text Classification
- hidden_size = 256, ms / batch
| batch_size | 256 | 512 |
|--------------| -------| --------|
| PaddlePaddle | 90 | 118 |
| TensorFlow | 226 | 118 |
- hidden_size = 512, ms / batch
| batch_size | 256 | 512 |
|--------------| -------| --------|
| PaddlePaddle | 189 | 268 |
| TensorFlow | 297 | 383 |
<img src="figs/rnn_lstm_4gpus.png" width="420">
#### Seq2Seq
The benchmark of sequence-to-sequence network will be added later.
benchmark/fluid/Dockerfile
浏览文件 @ c1455e60
......@@ -15,9 +15,6 @@ RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.7 /usr/lib/libcudnn.so && ln -s
RUN pip install -U pip
RUN pip install -U kubernetes paddlepaddle
RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.cifar.train10()\npaddle.dataset.flowers.fetch()" | python'
RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.mnist.train()\npaddle.dataset.mnist.test()\npaddle.dataset.imdb.fetch()" | python'
RUN sh -c 'echo "import paddle.v2 as paddle\npaddle.dataset.imikolov.fetch()" | python'
RUN pip uninstall -y paddlepaddle && mkdir /workspace
ADD https://raw.githubusercontent.com/PaddlePaddle/cloud/develop/docker/paddle_k8s /usr/bin
......
benchmark/paddle/image/alexnet.py 已删除 100644 → 0
浏览文件 @ d376cf71
# 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.
from paddle.trainer_config_helpers import *
height = 227
width = 227
num_class = 1000
batch_size = get_config_arg('batch_size', int, 128)
gp = get_config_arg('layer_num', int, 1)
is_infer = get_config_arg("is_infer", bool, False)
num_samples = get_config_arg('num_samples', int, 2560)
args = {
'height': height,
'width': width,
'color': True,
'num_class': num_class,
'is_infer': is_infer,
'num_samples': num_samples
}
define_py_data_sources2(
"train.list" if not is_infer else None,
"test.list" if is_infer else None,
module="provider",
obj="process",
args=args)
settings(
batch_size=batch_size,
learning_rate=0.01 / batch_size,
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * batch_size))
# conv1
net = data_layer('data', size=height * width * 3)
net = img_conv_layer(
input=net,
filter_size=11,
num_channels=3,
num_filters=96,
stride=4,
padding=1)
net = img_cmrnorm_layer(input=net, size=5, scale=0.0001, power=0.75)
net = img_pool_layer(input=net, pool_size=3, stride=2)
# conv2
net = img_conv_layer(
input=net, filter_size=5, num_filters=256, stride=1, padding=2, groups=gp)
net = img_cmrnorm_layer(input=net, size=5, scale=0.0001, power=0.75)
net = img_pool_layer(input=net, pool_size=3, stride=2)
# conv3
net = img_conv_layer(
input=net, filter_size=3, num_filters=384, stride=1, padding=1)
# conv4
net = img_conv_layer(
input=net, filter_size=3, num_filters=384, stride=1, padding=1, groups=gp)
# conv5
net = img_conv_layer(
input=net, filter_size=3, num_filters=256, stride=1, padding=1, groups=gp)
net = img_pool_layer(input=net, pool_size=3, stride=2)
net = fc_layer(
input=net,
size=4096,
act=ReluActivation(),
layer_attr=ExtraAttr(drop_rate=0.5))
net = fc_layer(
input=net,
size=4096,
act=ReluActivation(),
layer_attr=ExtraAttr(drop_rate=0.5))
net = fc_layer(input=net, size=1000, act=SoftmaxActivation())
if is_infer:
outputs(net)
else:
lab = data_layer('label', num_class)
loss = cross_entropy(input=net, label=lab)
outputs(loss)
benchmark/paddle/image/googlenet.py 已删除 100644 → 0
浏览文件 @ d376cf71
#!/usr/bin/env python
from paddle.trainer_config_helpers import *
height = 224
width = 224
num_class = 1000
batch_size = get_config_arg('batch_size', int, 128)
use_gpu = get_config_arg('use_gpu', bool, True)
is_infer = get_config_arg("is_infer", bool, False)
num_samples = get_config_arg('num_samples', int, 2560)
args = {
'height': height,
'width': width,
'color': True,
'num_class': num_class,
'is_infer': is_infer,
'num_samples': num_samples
}
define_py_data_sources2(
"train.list" if not is_infer else None,
"test.list" if is_infer else None,
module="provider",
obj="process",
args=args)
settings(
batch_size=batch_size,
learning_rate=0.01 / batch_size,
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * batch_size))
conv_projection = conv_projection if use_gpu else img_conv_layer
def inception2(name, input, channels, \
filter1,
filter3R, filter3,
filter5R, filter5,
proj):
conv1 = name + '_1'
conv3r = name + '_3r'
conv3 = name + '_3'
conv5r = name + '_5r'
conv5 = name + '_5'
maxpool = name + '_max'
convproj = name + '_proj'
cov1 = img_conv_layer(
name=conv1,
input=input,
filter_size=1,
num_channels=channels,
num_filters=filter1,
stride=1,
padding=0)
cov3r = img_conv_layer(
name=conv3r,
input=input,
filter_size=1,
num_channels=channels,
num_filters=filter3R,
stride=1,
padding=0)
cov3 = img_conv_layer(
name=conv3,
input=cov3r,
filter_size=3,
num_filters=filter3,
stride=1,
padding=1)
cov5r = img_conv_layer(
name=conv5r,
input=input,
filter_size=1,
num_channels=channels,
num_filters=filter5R,
stride=1,
padding=0)
cov5 = img_conv_layer(
name=conv5,
input=cov5r,
filter_size=5,
num_filters=filter5,
stride=1,
padding=2)
pool1 = img_pool_layer(
name=maxpool,
input=input,
pool_size=3,
num_channels=channels,
stride=1,
padding=1)
covprj = img_conv_layer(
name=convproj,
input=pool1,
filter_size=1,
num_filters=proj,
stride=1,
padding=0)
cat = concat_layer(name=name, input=[cov1, cov3, cov5, covprj])
return cat
def inception(name, input, channels, \
filter1,
filter3R, filter3,
filter5R, filter5,
proj):
cov1 = conv_projection(
input=input,
filter_size=1,
num_channels=channels,
num_filters=filter1,
stride=1,
padding=0)
cov3r = img_conv_layer(
name=name + '_3r',
input=input,
filter_size=1,
num_channels=channels,
num_filters=filter3R,
stride=1,
padding=0)
cov3 = conv_projection(
input=cov3r, filter_size=3, num_filters=filter3, stride=1, padding=1)
cov5r = img_conv_layer(
name=name + '_5r',
input=input,
filter_size=1,
num_channels=channels,
num_filters=filter5R,
stride=1,
padding=0)
cov5 = conv_projection(
input=cov5r, filter_size=5, num_filters=filter5, stride=1, padding=2)
pool1 = img_pool_layer(
name=name + '_max',
input=input,
pool_size=3,
num_channels=channels,
stride=1,
padding=1)
covprj = conv_projection(
input=pool1, filter_size=1, num_filters=proj, stride=1, padding=0)
cat = concat_layer(
name=name,
input=[cov1, cov3, cov5, covprj],
bias_attr=True if use_gpu else False,
act=ReluActivation())
return cat
data = data_layer(name="input", size=3 * height * width)
# stage 1
conv1 = img_conv_layer(
name="conv1",
input=data,
filter_size=7,
num_channels=3,
num_filters=64,
stride=2,
padding=3)
pool1 = img_pool_layer(
name="pool1", input=conv1, pool_size=3, num_channels=64, stride=2)
# stage 2
conv2_1 = img_conv_layer(
name="conv2_1",
input=pool1,
filter_size=1,
num_filters=64,
stride=1,
padding=0)
conv2_2 = img_conv_layer(
name="conv2_2",
input=conv2_1,
filter_size=3,
num_filters=192,
stride=1,
padding=1)
pool2 = img_pool_layer(
name="pool2", input=conv2_2, pool_size=3, num_channels=192, stride=2)
# stage 3
ince3a = inception("ince3a", pool2, 192, 64, 96, 128, 16, 32, 32)
ince3b = inception("ince3b", ince3a, 256, 128, 128, 192, 32, 96, 64)
pool3 = img_pool_layer(
name="pool3", input=ince3b, num_channels=480, pool_size=3, stride=2)
# stage 4
ince4a = inception("ince4a", pool3, 480, 192, 96, 208, 16, 48, 64)
ince4b = inception("ince4b", ince4a, 512, 160, 112, 224, 24, 64, 64)
ince4c = inception("ince4c", ince4b, 512, 128, 128, 256, 24, 64, 64)
ince4d = inception("ince4d", ince4c, 512, 112, 144, 288, 32, 64, 64)
ince4e = inception("ince4e", ince4d, 528, 256, 160, 320, 32, 128, 128)
pool4 = img_pool_layer(
name="pool4", input=ince4e, num_channels=832, pool_size=3, stride=2)
# stage 5
ince5a = inception("ince5a", pool4, 832, 256, 160, 320, 32, 128, 128)
ince5b = inception("ince5b", ince5a, 832, 384, 192, 384, 48, 128, 128)
pool5 = img_pool_layer(
name="pool5",
input=ince5b,
num_channels=1024,
pool_size=7,
stride=7,
pool_type=AvgPooling())
# We remove loss1 and loss2 for all system when testing benchmark
# output 1
# pool_o1 = img_pool_layer(name="pool_o1", input=ince4a, num_channels=512, pool_size=5, stride=3, pool_type=AvgPooling())
# conv_o1 = img_conv_layer(name="conv_o1", input=pool_o1, filter_size=1, num_filters=128, stride=1, padding=0)
# fc_o1 = fc_layer(name="fc_o1", input=conv_o1, size=1024, layer_attr=ExtraAttr(drop_rate=0.7), act=ReluActivation())
# out1 = fc_layer(name="output1", input=fc_o1, size=1000, act=SoftmaxActivation())
# loss1 = cross_entropy(name='loss1', input=out1, label=lab, coeff=0.3)
# output 2
#pool_o2 = img_pool_layer(name="pool_o2", input=ince4d, num_channels=528, pool_size=5, stride=3, pool_type=AvgPooling())
#conv_o2 = img_conv_layer(name="conv_o2", input=pool_o2, filter_size=1, num_filters=128, stride=1, padding=0)
#fc_o2 = fc_layer(name="fc_o2", input=conv_o2, size=1024, layer_attr=ExtraAttr(drop_rate=0.7), act=ReluActivation())
#out2 = fc_layer(name="output2", input=fc_o2, size=1000, act=SoftmaxActivation())
#loss2 = cross_entropy(name='loss2', input=out2, label=lab, coeff=0.3)
# output 3
dropout = dropout_layer(name="dropout", input=pool5, dropout_rate=0.4)
out3 = fc_layer(
name="output3", input=dropout, size=1000, act=SoftmaxActivation())
if is_infer:
outputs(out3)
else:
lab = data_layer(name="label", size=num_class)
loss3 = cross_entropy(name='loss3', input=out3, label=lab)
outputs(loss3)
benchmark/paddle/image/plotlog.py 已删除 100644 → 0
浏览文件 @ d376cf71
# Copyright (c) 2016 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 argparse
import matplotlib.pyplot as plt
def parse_args():
parser = argparse.ArgumentParser('Parse Log')
parser.add_argument(
'--file_path', '-f', type=str, help='the path of the log file')
parser.add_argument(
'--sample_rate',
'-s',
type=float,
default=1.0,
help='the rate to take samples from log')
parser.add_argument(
'--log_period', '-p', type=int, default=1, help='the period of log')
args = parser.parse_args()
return args
def parse_file(file_name):
loss = []
error = []
with open(file_name) as f:
for i, line in enumerate(f):
line = line.strip()
if not line.startswith('pass'):
continue
line_split = line.split(' ')
if len(line_split) != 5:
continue
loss_str = line_split[2][:-1]
cur_loss = float(loss_str.split('=')[-1])
loss.append(cur_loss)
err_str = line_split[3][:-1]
cur_err = float(err_str.split('=')[-1])
error.append(cur_err)
accuracy = [1.0 - err for err in error]
return loss, accuracy
def sample(metric, sample_rate):
interval = int(1.0 / sample_rate)
if interval > len(metric):
return metric[:1]
num = len(metric) / interval
idx = [interval * i for i in range(num)]
metric_sample = [metric[id] for id in idx]
return metric_sample
def plot_metric(metric,
batch_id,
graph_title,
line_style='b-',
line_label='y',
line_num=1):
plt.figure()
plt.title(graph_title)
if line_num == 1:
plt.plot(batch_id, metric, line_style, label=line_label)
else:
for i in range(line_num):
plt.plot(batch_id, metric[i], line_style[i], label=line_label[i])
plt.xlabel('batch')
plt.ylabel(graph_title)
plt.legend()
plt.savefig(graph_title + '.jpg')
plt.close()
def main():
args = parse_args()
assert args.sample_rate > 0. and args.sample_rate <= 1.0, "The sample rate should in the range (0, 1]."
loss, accuracy = parse_file(args.file_path)
batch = [args.log_period * i for i in range(len(loss))]
batch_sample = sample(batch, args.sample_rate)
loss_sample = sample(loss, args.sample_rate)
accuracy_sample = sample(accuracy, args.sample_rate)
plot_metric(loss_sample, batch_sample, 'loss', line_label='loss')
plot_metric(
accuracy_sample,
batch_sample,
'accuracy',
line_style='g-',
line_label='accuracy')
if __name__ == '__main__':
main()
benchmark/paddle/image/provider.py 已删除 100644 → 0
浏览文件 @ d376cf71
# 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 io, os
import random
import numpy as np
from paddle.trainer.PyDataProvider2 import *
def initHook(settings, height, width, color, num_class, **kwargs):
settings.height = height
settings.width = width
settings.color = color
settings.num_class = num_class
if settings.color:
settings.data_size = settings.height * settings.width * 3
else:
settings.data_size = settings.height * settings.width
settings.is_infer = kwargs.get('is_infer', False)
settings.num_samples = kwargs.get('num_samples', 2560)
if settings.is_infer:
settings.slots = [dense_vector(settings.data_size)]
else:
settings.slots = [dense_vector(settings.data_size), integer_value(1)]
@provider(
init_hook=initHook, min_pool_size=-1, cache=CacheType.CACHE_PASS_IN_MEM)
def process(settings, file_list):
for i in xrange(settings.num_samples):
img = np.random.rand(1, settings.data_size).reshape(-1, 1).flatten()
if settings.is_infer:
yield img.astype('float32')
else:
lab = random.randint(0, settings.num_class - 1)
yield img.astype('float32'), int(lab)
benchmark/paddle/image/resnet.py 已删除 100644 → 0
浏览文件 @ d376cf71
#!/usr/bin/env python
from paddle.trainer_config_helpers import *
height = 224
width = 224
num_class = 1000
batch_size = get_config_arg('batch_size', int, 64)
layer_num = get_config_arg("layer_num", int, 50)
is_infer = get_config_arg("is_infer", bool, False)
num_samples = get_config_arg('num_samples', int, 2560)
args = {
'height': height,
'width': width,
'color': True,
'num_class': num_class,
'is_infer': is_infer,
'num_samples': num_samples
}
define_py_data_sources2(
"train.list" if not is_infer else None,
"test.list" if is_infer else None,
module="provider",
obj="process",
args=args)
settings(
batch_size=batch_size,
learning_rate=0.01 / batch_size,
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * batch_size))
#######################Network Configuration #############
def conv_bn_layer(name,
input,
filter_size,
num_filters,
stride,
padding,
channels=None,
active_type=ReluActivation()):
"""
A wrapper for conv layer with batch normalization layers.
Note:
conv layer has no activation.
"""
tmp = img_conv_layer(
name=name + "_conv",
input=input,
filter_size=filter_size,
num_channels=channels,
num_filters=num_filters,
stride=stride,
padding=padding,
act=LinearActivation(),
bias_attr=False)
return batch_norm_layer(
name=name + "_bn",
input=tmp,
act=active_type,
use_global_stats=is_infer)
def bottleneck_block(name, input, num_filters1, num_filters2):
"""
A wrapper for bottlenect building block in ResNet.
Last conv_bn_layer has no activation.
Addto layer has activation of relu.
"""
last_name = conv_bn_layer(
name=name + '_branch2a',
input=input,
filter_size=1,
num_filters=num_filters1,
stride=1,
padding=0)
last_name = conv_bn_layer(
name=name + '_branch2b',
input=last_name,
filter_size=3,
num_filters=num_filters1,
stride=1,
padding=1)
last_name = conv_bn_layer(
name=name + '_branch2c',
input=last_name,
filter_size=1,
num_filters=num_filters2,
stride=1,
padding=0,
active_type=LinearActivation())
return addto_layer(
name=name + "_addto", input=[input, last_name], act=ReluActivation())
def mid_projection(name, input, num_filters1, num_filters2, stride=2):
"""
A wrapper for middile projection in ResNet.
projection shortcuts are used for increasing dimensions,
and other shortcuts are identity
branch1: projection shortcuts are used for increasing
dimensions, has no activation.
branch2x: bottleneck building block, shortcuts are identity.
"""
# stride = 2
branch1 = conv_bn_layer(
name=name + '_branch1',
input=input,
filter_size=1,
num_filters=num_filters2,
stride=stride,
padding=0,
active_type=LinearActivation())
last_name = conv_bn_layer(
name=name + '_branch2a',
input=input,
filter_size=1,
num_filters=num_filters1,
stride=stride,
padding=0)
last_name = conv_bn_layer(
name=name + '_branch2b',
input=last_name,
filter_size=3,
num_filters=num_filters1,
stride=1,
padding=1)
last_name = conv_bn_layer(
name=name + '_branch2c',
input=last_name,
filter_size=1,
num_filters=num_filters2,
stride=1,
padding=0,
active_type=LinearActivation())
return addto_layer(
name=name + "_addto", input=[branch1, last_name], act=ReluActivation())
img = data_layer(name='image', size=height * width * 3)
def deep_res_net(res2_num=3, res3_num=4, res4_num=6, res5_num=3):
"""
A wrapper for 50,101,152 layers of ResNet.
res2_num: number of blocks stacked in conv2_x
res3_num: number of blocks stacked in conv3_x
res4_num: number of blocks stacked in conv4_x
res5_num: number of blocks stacked in conv5_x
"""
# For ImageNet
# conv1: 112x112
tmp = conv_bn_layer(
"conv1",
input=img,
filter_size=7,
channels=3,
num_filters=64,
stride=2,
padding=3)
tmp = img_pool_layer(name="pool1", input=tmp, pool_size=3, stride=2)
# conv2_x: 56x56
tmp = mid_projection(
name="res2_1", input=tmp, num_filters1=64, num_filters2=256, stride=1)
for i in xrange(2, res2_num + 1, 1):
tmp = bottleneck_block(
name="res2_" + str(i), input=tmp, num_filters1=64, num_filters2=256)
# conv3_x: 28x28
tmp = mid_projection(
name="res3_1", input=tmp, num_filters1=128, num_filters2=512)
for i in xrange(2, res3_num + 1, 1):
tmp = bottleneck_block(
name="res3_" + str(i),
input=tmp,
num_filters1=128,
num_filters2=512)
# conv4_x: 14x14
tmp = mid_projection(
name="res4_1", input=tmp, num_filters1=256, num_filters2=1024)
for i in xrange(2, res4_num + 1, 1):
tmp = bottleneck_block(
name="res4_" + str(i),
input=tmp,
num_filters1=256,
num_filters2=1024)
# conv5_x: 7x7
tmp = mid_projection(
name="res5_1", input=tmp, num_filters1=512, num_filters2=2048)
for i in xrange(2, res5_num + 1, 1):
tmp = bottleneck_block(
name="res5_" + str(i),
input=tmp,
num_filters1=512,
num_filters2=2048)
tmp = img_pool_layer(
name='avgpool',
input=tmp,
pool_size=7,
stride=1,
pool_type=AvgPooling())
return fc_layer(input=tmp, size=num_class, act=SoftmaxActivation())
if layer_num == 50:
resnet = deep_res_net(3, 4, 6, 3)
elif layer_num == 101:
resnet = deep_res_net(3, 4, 23, 3)
elif layer_num == 152:
resnet = deep_res_net(3, 8, 36, 3)
else:
print("Wrong layer number.")
if is_infer:
outputs(resnet)
else:
lbl = data_layer(name="label", size=num_class)
loss = cross_entropy(name='loss', input=resnet, label=lbl)
outputs(loss)
benchmark/paddle/image/run.sh 已删除 100755 → 0
浏览文件 @ d376cf71
#!/bin/bash
set -e
function train() {
cfg=$1
thread=$2
bz=$3
args="batch_size=$3"
prefix=$4
paddle train --job=time \
--config=$cfg \
--use_gpu=True \
--trainer_count=$thread \
--log_period=10 \
--test_period=100 \
--config_args=$args \
> logs/$prefix-${thread}gpu-$bz.log 2>&1
}
if [ ! -d "train.list" ]; then
echo " " > train.list
fi
if [ ! -d "logs" ]; then
mkdir logs
fi
#========single-gpu=========#
# alexnet
train alexnet.py 1 64 alexnet
train alexnet.py 1 128 alexnet
train alexnet.py 1 256 alexnet
train alexnet.py 1 512 alexnet
# googlenet
train googlenet.py 1 64 googlenet
train googlenet.py 1 128 googlenet
train googlenet.py 1 256 googlenet
# smallnet
train smallnet_mnist_cifar.py 1 64 smallnet
train smallnet_mnist_cifar.py 1 128 smallnet
train smallnet_mnist_cifar.py 1 256 smallnet
train smallnet_mnist_cifar.py 1 512 smallnet
############################
#========multi-gpus=========#
train alexnet.py 4 512 alexnet
train alexnet.py 4 1024 alexnet
train googlenet.py 4 512 googlenet
train googlenet.py 4 1024 googlenet
benchmark/paddle/image/run_mkl_infer.sh 已删除 100755 → 0
浏览文件 @ d376cf71
#!/bin/bash
set -e
function clock_to_seconds() {
hours=`echo $1 | awk -F ':' '{print $1}'`
mins=`echo $1 | awk -F ':' '{print $2}'`
secs=`echo $1 | awk -F ':' '{print $3}'`
echo `awk 'BEGIN{printf "%.2f",('$secs' + '$mins' * 60 + '$hours' * 3600)}'`
}
function infer() {
unset OMP_NUM_THREADS MKL_NUM_THREADS OMP_DYNAMIC KMP_AFFINITY
topology=$1
layer_num=$2
bs=$3
use_mkldnn=$4
if [ $4 == "True" ]; then
thread=1
log="logs/infer-${topology}-${layer_num}-mkldnn-${bs}.log"
elif [ $4 == "False" ]; then
thread=`nproc`
if [ $thread -gt $bs ]; then
thread=$bs
fi
log="logs/infer-${topology}-${layer_num}-${thread}mklml-${bs}.log"
else
echo "Wrong input $4, use True or False."
exit 0
fi
models_in="models/${topology}-${layer_num}/pass-00000/"
if [ ! -d $models_in ]; then
echo "Training model ${topology}_${layer_num}"
paddle train --job=train \
--config="${topology}.py" \
--use_mkldnn=True \
--use_gpu=False \
--trainer_count=1 \
--num_passes=1 \
--save_dir="models/${topology}-${layer_num}" \
--config_args="batch_size=128,layer_num=${layer_num},num_samples=256" \
> /dev/null 2>&1
echo "Done"
fi
log_period=$((256 / bs))
paddle train --job=test \
--config="${topology}.py" \
--use_mkldnn=$use_mkldnn \
--use_gpu=False \
--trainer_count=$thread \
--log_period=$log_period \
--config_args="batch_size=${bs},layer_num=${layer_num},is_infer=True" \
--init_model_path=$models_in \
2>&1 | tee ${log}
# calculate the last 5 logs period time of 1280 samples,
# the time before are burning time.
start=`tail ${log} -n 7 | head -n 1 | awk -F ' ' '{print $2}' | xargs`
end=`tail ${log} -n 2 | head -n 1 | awk -F ' ' '{print $2}' | xargs`
start_sec=`clock_to_seconds $start`
end_sec=`clock_to_seconds $end`
fps=`awk 'BEGIN{printf "%.2f",(1280 / ('$end_sec' - '$start_sec'))}'`
echo "Last 1280 samples start: ${start}(${start_sec} sec), end: ${end}(${end_sec} sec;" >> ${log}
echo "FPS: $fps images/sec" 2>&1 | tee -a ${log}
}
if [ ! -f "train.list" ]; then
echo " " > train.list
fi
if [ ! -f "test.list" ]; then
echo " " > test.list
fi
if [ ! -d "logs" ]; then
mkdir logs
fi
if [ ! -d "models" ]; then
mkdir -p models
fi
# inference benchmark
for use_mkldnn in True False; do
for batchsize in 1 2 4 8 16; do
infer vgg 19 $batchsize $use_mkldnn
infer resnet 50 $batchsize $use_mkldnn
infer googlenet v1 $batchsize $use_mkldnn
infer alexnet 2 $batchsize $use_mkldnn
done
done
benchmark/paddle/image/run_mkl_train.sh 已删除 100755 → 0
浏览文件 @ d376cf71
#!/bin/bash
set -e
function train() {
unset OMP_NUM_THREADS MKL_NUM_THREADS OMP_DYNAMIC KMP_AFFINITY
topology=$1
layer_num=$2
bs=$3
use_mkldnn=$4
if [ $4 == "True" ]; then
thread=1
log="logs/train-${topology}-${layer_num}-mkldnn-${bs}.log"
elif [ $4 == "False" ]; then
thread=`nproc`
# each trainer_count use only 1 core to avoid conflict
log="logs/train-${topology}-${layer_num}-${thread}mklml-${bs}.log"
else
echo "Wrong input $4, use True or False."
exit 0
fi
args="batch_size=${bs},layer_num=${layer_num}"
config="${topology}.py"
paddle train --job=time \
--config=$config \
--use_mkldnn=$use_mkldnn \
--use_gpu=False \
--trainer_count=$thread \
--log_period=10 \
--test_period=100 \
--config_args=$args \
2>&1 | tee ${log}
avg_time=`tail ${log} -n 1 | awk -F ' ' '{print $8}' | sed 's/avg=//'`
fps=`awk 'BEGIN{printf "%.2f",('$bs' / '$avg_time' * 1000)}'`
echo "FPS: $fps images/sec" 2>&1 | tee -a ${log}
}
if [ ! -f "train.list" ]; then
echo " " > train.list
fi
if [ ! -d "logs" ]; then
mkdir logs
fi
# training benchmark
for use_mkldnn in True False; do
for batchsize in 64 128 256; do
train vgg 19 $batchsize $use_mkldnn
train resnet 50 $batchsize $use_mkldnn
train googlenet v1 $batchsize $use_mkldnn
train alexnet 2 $batchsize $use_mkldnn
done
done
benchmark/paddle/image/run_openblas_infer.sh 已删除 100755 → 0
浏览文件 @ d376cf71
#!/bin/bash
set -e
function clock_to_seconds() {
hours=`echo $1 | awk -F ':' '{print $1}'`
mins=`echo $1 | awk -F ':' '{print $2}'`
secs=`echo $1 | awk -F ':' '{print $3}'`
echo `awk 'BEGIN{printf "%.2f",('$secs' + '$mins' * 60 + '$hours' * 3600)}'`
}
function infer() {
export OPENBLAS_MAIN_FREE=1
topology=$1
layer_num=$2
bs=$3
trainers=`nproc`
if [ $trainers -gt $bs ]; then
trainers=$bs
fi
log="logs/infer-${topology}-${layer_num}-${trainers}openblas-${bs}.log"
threads=$((`nproc` / trainers))
if [ $threads -eq 0 ]; then
threads=1
fi
export OPENBLAS_NUM_THREADS=$threads
models_in="models/${topology}-${layer_num}/pass-00000/"
if [ ! -d $models_in ]; then
echo "./run_mkl_infer.sh to save the model first"
exit 0
fi
log_period=$((32 / bs))
paddle train --job=test \
--config="${topology}.py" \
--use_mkldnn=False \
--use_gpu=False \
--trainer_count=$trainers \
--log_period=$log_period \
--config_args="batch_size=${bs},layer_num=${layer_num},is_infer=True,num_samples=256" \
--init_model_path=$models_in \
2>&1 | tee ${log}
# calculate the last 5 logs period time of 160(=32*5) samples,
# the time before are burning time.
start=`tail ${log} -n 7 | head -n 1 | awk -F ' ' '{print $2}' | xargs`
end=`tail ${log} -n 2 | head -n 1 | awk -F ' ' '{print $2}' | xargs`
start_sec=`clock_to_seconds $start`
end_sec=`clock_to_seconds $end`
fps=`awk 'BEGIN{printf "%.2f",(160 / ('$end_sec' - '$start_sec'))}'`
echo "Last 160 samples start: ${start}(${start_sec} sec), end: ${end}(${end_sec} sec;" >> ${log}
echo "FPS: $fps images/sec" 2>&1 | tee -a ${log}
}
if [ ! -f "train.list" ]; then
echo " " > train.list
fi
if [ ! -f "test.list" ]; then
echo " " > test.list
fi
if [ ! -d "logs" ]; then
mkdir logs
fi
# inference benchmark
for batchsize in 1 2 4 8 16; do
infer vgg 19 $batchsize
infer resnet 50 $batchsize
infer googlenet v1 $batchsize
infer alexnet 2 $batchsize
done
benchmark/paddle/image/run_openblas_train.sh 已删除 100755 → 0
浏览文件 @ d376cf71
#!/bin/bash
set -e
function train() {
export OPENBLAS_NUM_THREADS=1
topology=$1
layer_num=$2
bs=$3
thread=`nproc`
# each trainer_count use only 1 core to avoid conflict
log="logs/train-${topology}-${layer_num}-${thread}openblas-${bs}.log"
args="batch_size=${bs},layer_num=${layer_num}"
config="${topology}.py"
paddle train --job=time \
--config=$config \
--use_mkldnn=False \
--use_gpu=False \
--trainer_count=$thread \
--log_period=3 \
--test_period=30 \
--config_args=$args \
2>&1 | tee ${log}
avg_time=`tail ${log} -n 1 | awk -F ' ' '{print $8}' | sed 's/avg=//'`
fps=`awk 'BEGIN{printf "%.2f",('$bs' / '$avg_time' * 1000)}'`
echo "FPS: $fps images/sec" 2>&1 | tee -a ${log}
}
if [ ! -f "train.list" ]; then
echo " " > train.list
fi
if [ ! -d "logs" ]; then
mkdir logs
fi
# training benchmark
for batchsize in 64 128 256; do
train vgg 19 $batchsize
train resnet 50 $batchsize
train googlenet v1 $batchsize
train alexnet 2 $batchsize
done
benchmark/paddle/image/smallnet_mnist_cifar.py 已删除 100644 → 0
浏览文件 @ d376cf71
#!/usr/bin/env python
from paddle.trainer_config_helpers import *
height = 32
width = 32
num_class = 10
batch_size = get_config_arg('batch_size', int, 128)
args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
define_py_data_sources2(
"train.list", None, module="provider", obj="process", args=args)
settings(
batch_size=batch_size,
learning_rate=0.01 / batch_size,
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * batch_size))
# conv1
net = data_layer('data', size=height * width * 3)
net = img_conv_layer(
input=net,
filter_size=5,
num_channels=3,
num_filters=32,
stride=1,
padding=2)
net = img_pool_layer(input=net, pool_size=3, stride=2, padding=1)
# conv2
net = img_conv_layer(
input=net, filter_size=5, num_filters=32, stride=1, padding=2)
net = img_pool_layer(
input=net, pool_size=3, stride=2, padding=1, pool_type=AvgPooling())
# conv3
net = img_conv_layer(
input=net, filter_size=3, num_filters=64, stride=1, padding=1)
net = img_pool_layer(
input=net, pool_size=3, stride=2, padding=1, pool_type=AvgPooling())
net = fc_layer(input=net, size=64, act=ReluActivation())
net = fc_layer(input=net, size=10, act=SoftmaxActivation())
lab = data_layer('label', num_class)
loss = classification_cost(input=net, label=lab)
outputs(loss)
benchmark/paddle/image/vgg.py 已删除 100644 → 0
浏览文件 @ d376cf71
#!/usr/bin/env python
from paddle.trainer_config_helpers import *
height = 224
width = 224
num_class = 1000
batch_size = get_config_arg('batch_size', int, 64)
layer_num = get_config_arg('layer_num', int, 19)
is_infer = get_config_arg("is_infer", bool, False)
num_samples = get_config_arg('num_samples', int, 2560)
args = {
'height': height,
'width': width,
'color': True,
'num_class': num_class,
'is_infer': is_infer,
'num_samples': num_samples
}
define_py_data_sources2(
"train.list" if not is_infer else None,
"test.list" if is_infer else None,
module="provider",
obj="process",
args=args)
settings(
batch_size=batch_size,
learning_rate=0.001 / batch_size,
learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * batch_size))
img = data_layer(name='image', size=height * width * 3)
def vgg_network(vgg_num=3):
tmp = img_conv_group(
input=img,
num_channels=3,
conv_padding=1,
conv_num_filter=[64, 64],
conv_filter_size=3,
conv_act=ReluActivation(),
pool_size=2,
pool_stride=2,
pool_type=MaxPooling())
tmp = img_conv_group(
input=tmp,
conv_num_filter=[128, 128],
conv_padding=1,
conv_filter_size=3,
conv_act=ReluActivation(),
pool_stride=2,
pool_type=MaxPooling(),
pool_size=2)
channels = []
for i in range(vgg_num):
channels.append(256)
tmp = img_conv_group(
input=tmp,
conv_num_filter=channels,
conv_padding=1,
conv_filter_size=3,
conv_act=ReluActivation(),
pool_stride=2,
pool_type=MaxPooling(),
pool_size=2)
channels = []
for i in range(vgg_num):
channels.append(512)
tmp = img_conv_group(
input=tmp,
conv_num_filter=channels,
conv_padding=1,
conv_filter_size=3,
conv_act=ReluActivation(),
pool_stride=2,
pool_type=MaxPooling(),
pool_size=2)
tmp = img_conv_group(
input=tmp,
conv_num_filter=channels,
conv_padding=1,
conv_filter_size=3,
conv_act=ReluActivation(),
pool_stride=2,
pool_type=MaxPooling(),
pool_size=2)
tmp = fc_layer(
input=tmp,
size=4096,
act=ReluActivation(),
layer_attr=ExtraAttr(drop_rate=0.5))
tmp = fc_layer(
input=tmp,
size=4096,
act=ReluActivation(),
layer_attr=ExtraAttr(drop_rate=0.5))
return fc_layer(input=tmp, size=num_class, act=SoftmaxActivation())
if layer_num == 16:
vgg = vgg_network(3)
elif layer_num == 19:
vgg = vgg_network(4)
else:
print("Wrong layer number.")
if is_infer:
outputs(vgg)
else:
lab = data_layer('label', num_class)
loss = cross_entropy(input=vgg, label=lab)
outputs(loss)
benchmark/paddle/rnn/provider.py 已删除 100644 → 0
浏览文件 @ d376cf71
# 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 io, os
import random
import numpy as np
import six.moves.cPickle as pickle
from paddle.trainer.PyDataProvider2 import *
def remove_unk(x, n_words):
return [[1 if w >= n_words else w for w in sen] for sen in x]
# ==============================================================
# tensorflow uses fixed length, but PaddlePaddle can process
# variable-length. Padding is used in benchmark in order to
# compare with other platform.
# ==============================================================
def pad_sequences(sequences,
maxlen=None,
dtype='int32',
padding='post',
truncating='post',
value=0.):
lengths = [len(s) for s in sequences]
nb_samples = len(sequences)
if maxlen is None:
maxlen = np.max(lengths)
x = (np.ones((nb_samples, maxlen)) * value).astype(dtype)
for idx, s in enumerate(sequences):
if len(s) == 0:
continue # empty list was found
if truncating == 'pre':
trunc = s[-maxlen:]
elif truncating == 'post':
trunc = s[:maxlen]
else:
raise ValueError("Truncating type '%s' not understood" % padding)
if padding == 'post':
x[idx, :len(trunc)] = trunc
elif padding == 'pre':
x[idx, -len(trunc):] = trunc
else:
raise ValueError("Padding type '%s' not understood" % padding)
return x
def initHook(settings, vocab_size, pad_seq, maxlen, **kwargs):
settings.vocab_size = vocab_size
settings.pad_seq = pad_seq
settings.maxlen = maxlen
settings.input_types = [
integer_value_sequence(vocab_size), integer_value(2)
]
@provider(
init_hook=initHook, min_pool_size=-1, cache=CacheType.CACHE_PASS_IN_MEM)
def process(settings, file):
f = open(file, 'rb')
train_set = pickle.load(f)
f.close()
x, y = train_set
# remove unk, namely remove the words out of dictionary
x = remove_unk(x, settings.vocab_size)
if settings.pad_seq:
x = pad_sequences(x, maxlen=settings.maxlen, value=0.)
for i in range(len(y)):
yield map(int, x[i]), int(y[i])
benchmark/paddle/rnn/rnn.py 已删除 100755 → 0
浏览文件 @ d376cf71
#!/usr/bin/env python
from paddle.trainer_config_helpers import *
import imdb
num_class = 2
vocab_size = 30000
fixedlen = 100
batch_size = get_config_arg('batch_size', int, 128)
lstm_num = get_config_arg('lstm_num', int, 1)
hidden_size = get_config_arg('hidden_size', int, 128)
# whether to pad sequence into fixed length
pad_seq = get_config_arg('pad_seq', bool, True)
imdb.create_data('imdb.pkl')
args = {'vocab_size': vocab_size, 'pad_seq': pad_seq, 'maxlen': fixedlen}
define_py_data_sources2(
"train.list", None, module="provider", obj="process", args=args)
settings(
batch_size=batch_size,
learning_rate=2e-3,
learning_method=AdamOptimizer(),
regularization=L2Regularization(8e-4),
gradient_clipping_threshold=25)
net = data_layer('data', size=vocab_size)
net = embedding_layer(input=net, size=128)
for i in xrange(lstm_num):
net = simple_lstm(input=net, size=hidden_size)
net = last_seq(input=net)
net = fc_layer(input=net, size=2, act=SoftmaxActivation())
lab = data_layer('label', num_class)
loss = classification_cost(input=net, label=lab)
outputs(loss)
benchmark/paddle/rnn/run.sh 已删除 100755 → 0
浏览文件 @ d376cf71
#!/bin/bash
set -e
function train() {
cfg=$1
thread=$2
args="lstm_num=${3},seq_pad=${4},hidden_size=${5},batch_size=${6}"
paddle train --job=time \
--config=$cfg \
--use_gpu=1 \
--trainer_count=$thread \
--log_period=10 \
--test_period=100 \
--num_passes=1 \
--feed_data=1 \
--config_args=$args \
>logs/rnn-pad${4}-${thread}gpu-lstm${3}-batch${6}-hid${5}.log 2>&1
}
if [ ! -d "logs" ]; then
mkdir logs
fi
## padding, single gpu
#-----config--gpu--lstm_num--padding--hidden_size--batch_size
## lstm_num=2, batch_size=64
train rnn.py 1 2 1 256 64
train rnn.py 1 2 1 512 64
train rnn.py 1 2 1 1280 64
## lstm_num=2, batch_size=128
train rnn.py 1 2 1 256 128
train rnn.py 1 2 1 512 128
train rnn.py 1 2 1 1280 128
## lstm_num=4, batch_size=256
train rnn.py 1 2 1 256 256
train rnn.py 1 2 1 512 256
train rnn.py 1 2 1 1280 256
#==================multi gpus=====================#
# hidden_size=256, lstm_num=2, different batch size
train rnn.py 4 2 1 256 128
train rnn.py 4 2 1 256 256
train rnn.py 4 2 1 256 512
# hidden_size=512, lstm_num=4, different batch size
train rnn.py 4 2 1 512 128
train rnn.py 4 2 1 512 256
train rnn.py 4 2 1 512 512
benchmark/tensorflow/machine_translation.py
浏览文件 @ c1455e60
......@@ -35,8 +35,6 @@ import os
import argparse
import time
import paddle.v2 as paddle
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument(
"--embedding_dim",
......
benchmark/tensorflow/mnist.py
浏览文件 @ c1455e60
......@@ -21,7 +21,6 @@ import time
import numpy as np
import tensorflow as tf
import paddle.v2 as paddle
DTYPE = tf.float32
......
benchmark/tensorflow/resnet.py
浏览文件 @ c1455e60
......@@ -27,7 +27,6 @@ import argparse
import time
import numpy as np
import paddle.v2 as paddle
import tensorflow as tf
DTYPE = tf.float32
......
benchmark/tensorflow/stacked_dynamic_lstm.py
浏览文件 @ c1455e60
......@@ -21,8 +21,6 @@ import argparse
import time
import tensorflow as tf
import paddle.v2 as paddle
def parse_args():
parser = argparse.ArgumentParser("LSTM model benchmark.")
......
benchmark/tensorflow/vgg.py
浏览文件 @ c1455e60
......@@ -13,7 +13,6 @@
# limitations under the License.
"""VGG16 benchmark in TensorFlow"""
import tensorflow as tf
import paddle.v2 as paddle
import numpy as np
import argparse
import time
......
paddle/fluid/framework/async_executor.cc
浏览文件 @ c1455e60
......@@ -244,6 +244,7 @@ void AsyncExecutor::RunFromFile(const ProgramDesc& main_program,
auto& block = main_program.Block(0);
for (auto var_name : fetch_var_names) {
auto var_desc = block.FindVar(var_name);
PADDLE_ENFORCE_NOT_NULL(var_desc, "%s is not found.", var_name);
auto shapes = var_desc->GetShape();
PADDLE_ENFORCE(shapes[shapes.size() - 1] == 1,
"var %s: Fetched var has wrong shape, "
......
paddle/fluid/framework/details/fused_broadcast_op_handle_test.cc
浏览文件 @ c1455e60
......@@ -34,8 +34,8 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
->Var(details::kLocalExecScopeName)
->GetMutable<Scope*>() = &local_scope;
for (size_t j = 0; j < input_scope_idxes.size(); ++j) {
local_scope.Var("out_var" + j);
if (i == j) local_scope.Var("in_var" + j);
local_scope.Var("out_var" + std::to_string(j));
if (i == j) local_scope.Var("in_var" + std::to_string(j));
}
param_scopes_.emplace_back(&local_scope);
}
......@@ -62,20 +62,21 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
// add input var handle
nodes_.emplace_back(
ir::CreateNodeForTest("in_node" + i, ir::Node::Type::kVariable));
VarHandle* in_var_handle =
new VarHandle(nodes_.back().get(), 1, input_scope_idxes[i],
"in_var" + i, place_list_[input_scope_idxes[i]]);
nodes_.emplace_back(ir::CreateNodeForTest("in_node" + std::to_string(i),
ir::Node::Type::kVariable));
VarHandle* in_var_handle = new VarHandle(
nodes_.back().get(), 1, input_scope_idxes[i],
"in_var" + std::to_string(i), place_list_[input_scope_idxes[i]]);
vars_.emplace_back(in_var_handle);
op_handle_->AddInput(in_var_handle);
// add output var handle
for (size_t j = 0; j < place_list_.size(); ++j) {
nodes_.emplace_back(
ir::CreateNodeForTest("out_node" + i, ir::Node::Type::kVariable));
VarHandle* out_var_handle = new VarHandle(
nodes_.back().get(), 2, j, "out_var" + i, place_list_[j]);
nodes_.emplace_back(ir::CreateNodeForTest(
"out_node" + std::to_string(i), ir::Node::Type::kVariable));
VarHandle* out_var_handle =
new VarHandle(nodes_.back().get(), 2, j,
"out_var" + std::to_string(i), place_list_[j]);
vars_.emplace_back(out_var_handle);
op_handle_->AddOutput(out_var_handle);
}
......@@ -86,7 +87,7 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
std::vector<std::vector<float>> send_vec;
f::LoD lod{{0, 10, 20}};
for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
const std::string varname("in_var" + i);
const std::string varname("in_var" + std::to_string(i));
float val_scalar = static_cast<float>(i);
send_vec.push_back(
InitLoDTensor(varname, input_scope_idxes[i], lod, val_scalar));
......@@ -96,7 +97,7 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
WaitAll();
for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
const std::string& varname("out_var" + i);
const std::string& varname("out_var" + std::to_string(i));
for (size_t j = 0; j < place_list_.size(); ++j) {
LoDTensorEqual(varname, send_vec[i], lod, param_scopes_[j]);
}
......@@ -109,7 +110,7 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
2, 4, 6, 3, 1, 1, 1, 1, 3, 7};
int height = static_cast<int>(kDims[0] * 2);
for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
const std::string varname("in_var" + i);
const std::string varname("in_var" + std::to_string(i));
float val_scalar = static_cast<float>(i);
send_vector.push_back(InitSelectedRows(varname, input_scope_idxes[i],
rows, height, val_scalar));
......@@ -119,7 +120,7 @@ struct TestFusedBroadcastOpHandle : TestBroadcastOpHandle {
WaitAll();
for (size_t i = 0; i < input_scope_idxes.size(); ++i) {
const std::string& varname("out_var" + i);
const std::string& varname("out_var" + std::to_string(i));
for (size_t j = 0; j < place_list_.size(); ++j) {
SelectedRowsEqual(varname, input_scope_idxes[i], send_vector[i], rows,
height);
......
paddle/fluid/framework/feed_fetch_method.cc
浏览文件 @ c1455e60
......@@ -44,6 +44,7 @@ LoDTensor& GetFetchVariable(const Scope& scope, const std::string& var_name,
// Since we want to fetch LodTensor from a variable, the variable must
// be created alreadly.
Variable* g_fetch_value = scope.FindVar(var_name);
PADDLE_ENFORCE_NOT_NULL(g_fetch_value, "%s is not found.", var_name);
PADDLE_ENFORCE(g_fetch_value->IsType<FeedFetchList>(),
"Only %s can be invoked by GetFetchVariable",
typeid(FeedFetchList).name());
......
paddle/fluid/framework/ir/infer_clean_graph_pass.cc
浏览文件 @ c1455e60
......@@ -37,6 +37,7 @@ class InferCleanGraphPass : public FusePassBase {
std::unordered_set<const Node*> invalid_nodes;
int valid_op = 0;
for (auto* node : graph->Nodes()) {
PADDLE_ENFORCE_NOT_NULL(node);
if (is_valid_node(node)) {
invalid_nodes.insert(node);
} else if (node->IsOp()) {
......
paddle/fluid/framework/ir/seqpool_concat_fuse_pass_tester.cc
浏览文件 @ c1455e60
......@@ -164,7 +164,7 @@ ProgramDesc BuildProgramDesc(int num_inputs_of_concat) {
};
std::vector<std::string> concat_inputs;
for (int i = 0; i < num_inputs_of_concat; ++i) {
std::string prefix = "seqpool_op_" + i;
std::string prefix = "seqpool_op_" + std::to_string(i);
new_var(prefix + "in");
new_var(prefix + "out");
new_var(prefix + "out_unused");
......
paddle/fluid/framework/operator.cc
浏览文件 @ c1455e60
......@@ -989,10 +989,13 @@ void OperatorWithKernel::TransferInplaceVarsBack(
const Scope& transfer_scope) const {
for (auto& var_name : inplace_vars) {
VLOG(3) << "share inplace var " + var_name + " back to it's original scope";
auto* origin_var = scope.FindVar(var_name);
PADDLE_ENFORCE_NOT_NULL(origin_var, "The var[%s] should not be nullptr.",
var_name);
auto* original_tensor =
GetMutableLoDTensorOrSelectedRowsValueFromVar(scope.FindVar(var_name));
GetMutableLoDTensorOrSelectedRowsValueFromVar(origin_var);
auto* var = transfer_scope.FindVar(var_name);
PADDLE_ENFORCE(var != nullptr, "The var[%s] should not be nullptr",
PADDLE_ENFORCE_NOT_NULL(var, "The var[%s] should not be nullptr.",
var_name);
auto* transformed_tensor = GetLoDTensorOrSelectedRowsValueFromVar(*var);
original_tensor->ShareDataWith(*transformed_tensor);
......
paddle/fluid/imperative/layer.cc
浏览文件 @ c1455e60
......@@ -207,7 +207,7 @@ framework::LoDTensor& VarBase::GradValue() {
std::map<std::string, std::vector<VarBase*>> OpBase::ApplyGrad() {
if (grad_op_descs_.empty() && backward_id_ <= 0) {
LOG(WARNING) << "op with no grad: " << op_desc_->Type();
VLOG(3) << "op with no grad: " << op_desc_->Type();
return {};
}
......
paddle/fluid/inference/api/paddle_api.h
浏览文件 @ c1455e60
......@@ -16,6 +16,12 @@
/*! \file paddle_api.h
*/
/*! \mainpage Paddle Inference APIs
* \section intro_sec Introduction
* The Paddle inference library aims to offer an high performance inference SDK
* for Paddle users.
*/
#include <cassert>
#include <memory>
#include <string>
......@@ -34,26 +40,49 @@ enum PaddleDType {
};
/**
*\brief Memory menager for PaddleTensor.
* \brief Memory manager for `PaddleTensor`.
*
*The PaddleBuf holds a buffer for data input or output. The memory can be
*allocated by user or by PaddleBuf itself, but in any case, the PaddleBuf
*should be reused for better performance.
* The PaddleBuf holds a buffer for data input or output. The memory can be
* allocated by user or by PaddleBuf itself, but in any case, the PaddleBuf
* should be reused for better performance.
*
*For user allocated memory, the following API can be used:
*- PaddleBuf(void* data, size_t length) to set an external memory by
*specifying
* the memory address and length.
*- Reset(void* data, size_t length) to reset the PaddleBuf with an external
* For user allocated memory, the following API can be used:
* - PaddleBuf(void* data, size_t length) to set an external memory by
* specifying the memory address and length.
* - Reset(void* data, size_t length) to reset the PaddleBuf with an external
*memory.
*ATTENTION, for user allocated memory, deallocation should be done by users
* ATTENTION, for user allocated memory, deallocation should be done by users
*externally after the program finished. The PaddleBuf won't do any allocation
*or deallocation.
*
*To have the PaddleBuf allocate and manage the memory:
*- PaddleBuf(size_t length) will allocate a memory of size `length`.
*- Resize(size_t length) resize the memory to no less than `length`, ATTENTION
* To have the PaddleBuf allocate and manage the memory:
* - PaddleBuf(size_t length) will allocate a memory of size `length`.
* - Resize(size_t length) resize the memory to no less than `length`, ATTENTION
* if the allocated memory is larger than `length`, nothing will done.
*
* Usage:
*
* Let PaddleBuf manage the memory internally.
* \code{cpp}
* const int num_elements = 128;
* PaddleBuf buf(num_elements * sizeof(float));
* \endcode
*
* Or
* \code{cpp}
* PaddleBuf buf;
* buf.Resize(num_elements * sizeof(float));
* \endcode
* Works the exactly the same.
*
* One can also make the `PaddleBuf` use the external memory.
* \code{cpp}
* PaddleBuf buf;
* void* external_memory = new float[num_elements];
* buf.Reset(external_memory, num_elements*sizeof(float));
* ...
* delete[] external_memory; // manage the memory lifetime outside.
* \endcode
*/
class PaddleBuf {
public:
......@@ -78,7 +107,7 @@ class PaddleBuf {
/** Tell whether the buffer is empty.
*/
bool empty() const { return length_ == 0; }
/** Get the memory address.
/** Get the data's memory address.
*/
void* data() const { return data_; }
/** Get the memory length.
......@@ -110,7 +139,8 @@ struct PaddleTensor {
};
enum class PaddlePlace { kUNK = -1, kCPU, kGPU };
/** Tensor without copy, currently only supports AnalysisPredictor.
/** Tensor without copy, currently only supports `AnalysisPredictor`.
*/
class ZeroCopyTensor {
public:
......@@ -269,9 +299,11 @@ struct NativeConfig : public PaddlePredictor::Config {
*
* Usage:
*
* \code{.cpp}
* NativeConfig config;
* ... // change the configs.
* auto native_predictor = CreatePaddlePredictor(config);
* \endcode
*
* FOR EXTENSION DEVELOPER:
* Different predictors are designated by config type. Similar configs can be
......
paddle/fluid/inference/api/paddle_pass_builder.cc
浏览文件 @ c1455e60
......@@ -66,8 +66,54 @@ void GpuPassStrategy::EnableMKLDNN() {
LOG(ERROR) << "GPU not support MKLDNN yet";
}
GpuPassStrategy::GpuPassStrategy() : PassStrategy({}) {
passes_.assign({
"infer_clean_graph_pass", //
"identity_scale_op_clean_pass", //
"conv_affine_channel_fuse_pass", //
"conv_eltwiseadd_affine_channel_fuse_pass", //
"conv_bn_fuse_pass", //
#if CUDNN_VERSION >= 7100 // To run conv_fusion, the version of cudnn must be
// guaranteed at least v7
"conv_elementwise_add_act_fuse_pass", //
"conv_elementwise_add2_act_fuse_pass", //
"conv_elementwise_add_fuse_pass", //
#endif
});
for (int i = 6; i >= 3; i--) {
passes_.push_back("transpose_flatten" + std::to_string(i) +
"_concat_fuse_pass");
}
use_gpu_ = true;
}
void PaddlePassBuilder::AppendAnalysisPass(const std::string &pass) {
analysis_passes_.push_back(pass);
}
CpuPassStrategy::CpuPassStrategy() : PassStrategy({}) {
// NOTE the large fusions should be located in the front, so that they will
// not be damaged by smaller ones.
passes_.assign({
"infer_clean_graph_pass", //
"attention_lstm_fuse_pass", //
"seqpool_concat_fuse_pass", //
"seqconv_eltadd_relu_fuse_pass", //
// "embedding_fc_lstm_fuse_pass", //
"fc_lstm_fuse_pass", //
"mul_lstm_fuse_pass", //
"fc_gru_fuse_pass", //
"mul_gru_fuse_pass", //
"seq_concat_fc_fuse_pass", //
"fc_fuse_pass", //
"repeated_fc_relu_fuse_pass", //
"squared_mat_sub_fuse_pass", //
"conv_bn_fuse_pass", //
"conv_eltwiseadd_bn_fuse_pass", //
"is_test_pass", //
"identity_scale_op_clean_pass", //
});
use_gpu_ = false;
}
} // namespace paddle
paddle/fluid/inference/api/paddle_pass_builder.h
浏览文件 @ c1455e60
......@@ -97,30 +97,7 @@ class PassStrategy : public PaddlePassBuilder {
*/
class CpuPassStrategy : public PassStrategy {
public:
CpuPassStrategy() : PassStrategy({}) {
// NOTE the large fusions should be located in the front, so that they will
// not be damaged by smaller ones.
passes_.assign({
"infer_clean_graph_pass", //
"attention_lstm_fuse_pass", //
"seqpool_concat_fuse_pass", //
"seqconv_eltadd_relu_fuse_pass", //
// "embedding_fc_lstm_fuse_pass", //
"fc_lstm_fuse_pass", //
"mul_lstm_fuse_pass", //
"fc_gru_fuse_pass", //
"mul_gru_fuse_pass", //
"seq_concat_fc_fuse_pass", //
"fc_fuse_pass", //
"repeated_fc_relu_fuse_pass", //
"squared_mat_sub_fuse_pass", //
"conv_bn_fuse_pass", //
"conv_eltwiseadd_bn_fuse_pass", //
"is_test_pass", //
"identity_scale_op_clean_pass", //
});
use_gpu_ = false;
}
CpuPassStrategy();
explicit CpuPassStrategy(const CpuPassStrategy &other)
: PassStrategy(other.AllPasses()) {}
......@@ -153,27 +130,7 @@ class CpuPassStrategy : public PassStrategy {
*/
class GpuPassStrategy : public PassStrategy {
public:
GpuPassStrategy() : PassStrategy({}) {
passes_.assign({
"infer_clean_graph_pass", //
"identity_scale_op_clean_pass", //
"conv_affine_channel_fuse_pass", //
"conv_eltwiseadd_affine_channel_fuse_pass", //
"conv_bn_fuse_pass", //
#if CUDNN_VERSION >= 7100 // To run conv_fusion, the version of cudnn must be
// guaranteed at least v7
"conv_elementwise_add_act_fuse_pass", //
"conv_elementwise_add2_act_fuse_pass", //
"conv_elementwise_add_fuse_pass", //
#endif
});
for (int i = 6; i >= 3; i--) {
passes_.push_back("transpose_flatten" + std::to_string(i) +
"_concat_fuse_pass");
}
use_gpu_ = true;
}
GpuPassStrategy();
explicit GpuPassStrategy(const GpuPassStrategy &other)
: PassStrategy(other.AllPasses()) {
......
paddle/fluid/memory/allocation/best_fit_allocator.cc
浏览文件 @ c1455e60
......@@ -111,6 +111,8 @@ size_t BestFitAllocator::NumFreeChunks() const {
}
void BestFitAllocator::Free(Allocation* allocation) {
auto* bf_allocation = dynamic_cast<BestFitAllocation*>(allocation);
PADDLE_ENFORCE_NOT_NULL(bf_allocation,
"The input allocation is not BestFitAllocation.");
auto chunk_it = bf_allocation->ChunkIterator();
PADDLE_ENFORCE(!chunk_it->is_free);
chunk_it->is_free = true;
......
paddle/fluid/operators/activation_op.cc
浏览文件 @ c1455e60
......@@ -37,7 +37,7 @@ using paddle::framework::Tensor;
"(bool, default false) Set to true for inference only, false " \
"for training. Some layers may run faster when this is true.") \
.SetDefault(false); \
AddComment(#OP_COMMENT); \
AddComment(OP_COMMENT); \
} \
}
......@@ -124,7 +124,7 @@ class ActivationOpGrad : public framework::OperatorWithKernel {
UNUSED constexpr char SigmoidDoc[] = R"DOC(
Sigmoid Activation Operator
$$out = \frac{1}{1 + e^{-x}}$$
$$out = \\frac{1}{1 + e^{-x}}$$
)DOC";
......@@ -187,14 +187,14 @@ $out = |x|$
UNUSED constexpr char CeilDoc[] = R"DOC(
Ceil Activation Operator.
$out = ceil(x)$
$out = \left \lceil x \right \rceil$
)DOC";
UNUSED constexpr char FloorDoc[] = R"DOC(
Floor Activation Operator.
$out = floor(x)$
$out = \left \lfloor x \right \rfloor$
)DOC";
......@@ -252,7 +252,7 @@ $out = \ln(1 + e^{x})$
UNUSED constexpr char SoftsignDoc[] = R"DOC(
Softsign Activation Operator.
$$out = \frac{x}{1 + |x|}$$
$$out = \\frac{x}{1 + \|x\|}$$
)DOC";
......
paddle/fluid/operators/conv_op.cc
浏览文件 @ c1455e60
......@@ -222,7 +222,7 @@ void Conv2DOpMaker::Make() {
.SetDefault(4096);
AddAttr<bool>("exhaustive_search",
"(bool, default false) cuDNN has many algorithm to calculation "
"convolution, whether enable exhaustive search ",
"convolution, whether enable exhaustive search "
"for cuDNN convolution or not, defalut is False.")
.SetDefault(false);
AddComment(R"DOC(
......@@ -341,7 +341,7 @@ void Conv3DOpMaker::Make() {
.SetDefault(4096);
AddAttr<bool>("exhaustive_search",
"(bool, default false) cuDNN has many algorithm to calculation "
"convolution, whether enable exhaustive search ",
"convolution, whether enable exhaustive search "
"for cuDNN convolution or not, defalut is False.")
.SetDefault(false);
AddComment(R"DOC(
......
paddle/fluid/operators/detection/density_prior_box_op.h
浏览文件 @ c1455e60
......@@ -72,7 +72,7 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for
#endif
for (int i = 0; i < fixed_ratios.size(); i++) {
for (size_t i = 0; i < fixed_ratios.size(); i++) {
sqrt_fixed_ratios.push_back(sqrt(fixed_ratios[i]));
}
......@@ -115,11 +115,10 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
}
}
if (clip) {
platform::Transform<platform::CPUDeviceContext> trans;
ClipFunctor<T> clip_func;
trans(ctx.template device_context<platform::CPUDeviceContext>(),
boxes->data<T>(), boxes->data<T>() + boxes->numel(),
boxes->data<T>(), clip_func);
T* dt = boxes->data<T>();
std::transform(dt, dt + boxes->numel(), dt, [](T v) -> T {
return std::min<T>(std::max<T>(v, 0.), 1.);
});
}
framework::Tensor var_t;
var_t.mutable_data<T>(
......@@ -141,7 +140,7 @@ class DensityPriorBoxOpKernel : public framework::OpKernel<T> {
#pragma omp parallel for collapse(2)
#endif
for (int i = 0; i < box_num; ++i) {
for (int j = 0; j < variances.size(); ++j) {
for (size_t j = 0; j < variances.size(); ++j) {
e_vars(i, j) = variances[j];
}
}
......
paddle/fluid/operators/detection/prior_box_op.h
浏览文件 @ c1455e60
......@@ -46,13 +46,6 @@ inline void ExpandAspectRatios(const std::vector<float>& input_aspect_ratior,
}
}
template <typename T>
struct ClipFunctor {
HOSTDEVICE inline T operator()(T in) const {
return std::min<T>(std::max<T>(in, 0.), 1.);
}
};
template <typename T>
class PriorBoxOpKernel : public framework::OpKernel<T> {
public:
......@@ -101,31 +94,30 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
boxes->mutable_data<T>(ctx.GetPlace());
vars->mutable_data<T>(ctx.GetPlace());
auto e_boxes = framework::EigenTensor<T, 4>::From(*boxes);
T* b_t = boxes->data<T>();
for (int h = 0; h < feature_height; ++h) {
for (int w = 0; w < feature_width; ++w) {
T center_x = (w + offset) * step_width;
T center_y = (h + offset) * step_height;
T box_width, box_height;
int idx = 0;
for (size_t s = 0; s < min_sizes.size(); ++s) {
auto min_size = min_sizes[s];
if (min_max_aspect_ratios_order) {
box_width = box_height = min_size / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
b_t[0] = (center_x - box_width) / img_width;
b_t[1] = (center_y - box_height) / img_height;
b_t[2] = (center_x + box_width) / img_width;
b_t[3] = (center_y + box_height) / img_height;
b_t += 4;
if (max_sizes.size() > 0) {
auto max_size = max_sizes[s];
// square prior with size sqrt(minSize * maxSize)
box_width = box_height = sqrt(min_size * max_size) / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
b_t[0] = (center_x - box_width) / img_width;
b_t[1] = (center_y - box_height) / img_height;
b_t[2] = (center_x + box_width) / img_width;
b_t[3] = (center_y + box_height) / img_height;
b_t += 4;
}
// priors with different aspect ratios
for (size_t r = 0; r < aspect_ratios.size(); ++r) {
......@@ -135,11 +127,11 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
}
box_width = min_size * sqrt(ar) / 2.;
box_height = min_size / sqrt(ar) / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
b_t[0] = (center_x - box_width) / img_width;
b_t[1] = (center_y - box_height) / img_height;
b_t[2] = (center_x + box_width) / img_width;
b_t[3] = (center_y + box_height) / img_height;
b_t += 4;
}
} else {
// priors with different aspect ratios
......@@ -147,21 +139,21 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
float ar = aspect_ratios[r];
box_width = min_size * sqrt(ar) / 2.;
box_height = min_size / sqrt(ar) / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
b_t[0] = (center_x - box_width) / img_width;
b_t[1] = (center_y - box_height) / img_height;
b_t[2] = (center_x + box_width) / img_width;
b_t[3] = (center_y + box_height) / img_height;
b_t += 4;
}
if (max_sizes.size() > 0) {
auto max_size = max_sizes[s];
// square prior with size sqrt(minSize * maxSize)
box_width = box_height = sqrt(min_size * max_size) / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
b_t[0] = (center_x - box_width) / img_width;
b_t[1] = (center_y - box_height) / img_height;
b_t[2] = (center_x + box_width) / img_width;
b_t[3] = (center_y + box_height) / img_height;
b_t += 4;
}
}
}
......@@ -169,11 +161,10 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
}
if (clip) {
platform::Transform<platform::CPUDeviceContext> trans;
ClipFunctor<T> clip_func;
trans(ctx.template device_context<platform::CPUDeviceContext>(),
boxes->data<T>(), boxes->data<T>() + boxes->numel(),
boxes->data<T>(), clip_func);
T* dt = boxes->data<T>();
std::transform(dt, dt + boxes->numel(), dt, [](T v) -> T {
return std::min<T>(std::max<T>(v, 0.), 1.);
});
}
framework::Tensor var_t;
......
paddle/fluid/operators/expand_op.cc
浏览文件 @ c1455e60
......@@ -146,7 +146,11 @@ REGISTER_OPERATOR(expand, ops::ExpandOp, ops::ExpandOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(expand_grad, ops::ExpandGradOp);
REGISTER_OP_CPU_KERNEL(
expand, ops::ExpandKernel<paddle::platform::CPUDeviceContext, float>);
expand, ops::ExpandKernel<paddle::platform::CPUDeviceContext, float>,
ops::ExpandKernel<paddle::platform::CPUDeviceContext, double>,
ops::ExpandKernel<paddle::platform::CPUDeviceContext, int>,
ops::ExpandKernel<paddle::platform::CPUDeviceContext, bool>);
REGISTER_OP_CPU_KERNEL(
expand_grad,
ops::ExpandGradKernel<paddle::platform::CPUDeviceContext, float>);
ops::ExpandGradKernel<paddle::platform::CPUDeviceContext, float>,
ops::ExpandGradKernel<paddle::platform::CPUDeviceContext, double>);
paddle/fluid/operators/expand_op.cu
浏览文件 @ c1455e60
......@@ -15,7 +15,11 @@ limitations under the License. */
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
expand, ops::ExpandKernel<paddle::platform::CUDADeviceContext, float>);
expand, ops::ExpandKernel<paddle::platform::CUDADeviceContext, float>,
ops::ExpandKernel<paddle::platform::CUDADeviceContext, double>,
ops::ExpandKernel<paddle::platform::CUDADeviceContext, int>,
ops::ExpandKernel<paddle::platform::CUDADeviceContext, bool>);
REGISTER_OP_CUDA_KERNEL(
expand_grad,
ops::ExpandGradKernel<paddle::platform::CUDADeviceContext, float>);
ops::ExpandGradKernel<paddle::platform::CUDADeviceContext, float>,
ops::ExpandGradKernel<paddle::platform::CUDADeviceContext, double>);
paddle/fluid/operators/fake_quantize_op.cc
浏览文件 @ c1455e60
......@@ -21,26 +21,17 @@ limitations under the License. */
namespace paddle {
namespace operators {
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVectorArrayMap =
Eigen::TensorMap<Eigen::Tensor<T, 1, MajorType, IndexType>>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using ConstEigenVectorArrayMap =
Eigen::TensorMap<const Eigen::Tensor<T, 1, MajorType, IndexType>>;
template <typename T>
struct Compare {
public:
bool operator()(const T a, const T b) { return (std::abs(a) < std::abs(b)); }
};
template <typename T>
struct FindAbsMaxFunctor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& ctx, const T* in,
const int num, T* out) {
Eigen::DSizes<Eigen::DenseIndex, 1> idim(num);
Eigen::DSizes<Eigen::DenseIndex, 1> odim(1);
Eigen::TensorMap<Eigen::Tensor<const T, 1, Eigen::RowMajor>> in_e(in, idim);
Eigen::TensorMap<Eigen::Tensor<T, 1, Eigen::RowMajor>> out_e(out, odim);
out_e = in_e.abs().maximum();
*out = *(std::max_element(in + 0, in + num, Compare<T>()));
}
};
......
paddle/fluid/operators/jit/gen/act.h
浏览文件 @ c1455e60
......@@ -268,7 +268,7 @@ class VActJitCode : public VActFunc {
this->genCode();
}
const char* name() const override {
std::string name() const override {
std::string base = "VActJitCode";
switch (type_) {
case operand_type::RELU:
......@@ -292,7 +292,7 @@ class VActJitCode : public VActFunc {
default:
break;
}
return base.c_str();
return base;
}
void genCode() override;
......
paddle/fluid/operators/jit/gen/blas.h
浏览文件 @ c1455e60
......@@ -41,7 +41,7 @@ class VXXJitCode : public JitCode {
this->genCode();
}
virtual const char* name() const override {
std::string name() const override {
std::string base = "VXXJitCode";
if (scalar_index_ == 1) {
base += "_Scalar";
......@@ -62,7 +62,7 @@ class VXXJitCode : public JitCode {
}
base += (with_relu_ ? "_Relu" : "");
base += "_D" + std::to_string(num_);
return base.c_str();
return base;
}
void genCode() override;
......
paddle/fluid/operators/jit/gen/gru.h
浏览文件 @ c1455e60
......@@ -49,7 +49,7 @@ class GRUJitCode : public VActFunc {
this->genCode();
}
const char* name() const override {
std::string name() const override {
std::string base = "GRUJitCode";
if (id_ == 0) {
base += "_H1";
......@@ -81,7 +81,7 @@ class GRUJitCode : public VActFunc {
};
AddTypeStr(act_gate_);
AddTypeStr(act_cand_);
return base.c_str();
return base;
}
void genCode() override;
......
paddle/fluid/operators/jit/gen/hopv.h
浏览文件 @ c1455e60
......@@ -35,14 +35,14 @@ class HOPVJitCode : public JitCode {
this->genCode();
}
virtual const char* name() const override {
std::string name() const override {
std::string base = "VXXJitCode";
if (type_ == operand_type::MAX) {
base += "_MAX";
} else {
base += "_SUM";
}
return base.c_str();
return base;
}
void genCode() override;
......
paddle/fluid/operators/jit/gen/jitcode.h
浏览文件 @ c1455e60
......@@ -14,6 +14,7 @@
#pragma once
#include <string>
#include <type_traits>
#include "paddle/fluid/operators/jit/gen_base.h"
#include "paddle/fluid/platform/cpu_info.h"
......@@ -59,7 +60,7 @@ typedef enum {
} operand_type;
#define DECLARE_JIT_CODE(codename) \
const char* name() const override { return #codename; }
std::string name() const override { return #codename; }
class JitCode : public GenBase, public Xbyak::CodeGenerator {
public:
......
paddle/fluid/operators/jit/gen/lstm.h
浏览文件 @ c1455e60
......@@ -53,7 +53,7 @@ class LSTMJitCode : public VActFunc {
this->genCode();
}
const char* name() const override {
std::string name() const override {
std::string base = "LSTMJitCode";
if (use_peephole_) {
base += "_Peephole";
......@@ -85,7 +85,7 @@ class LSTMJitCode : public VActFunc {
AddTypeStr(act_gate_);
AddTypeStr(act_cand_);
AddTypeStr(act_cell_);
return base.c_str();
return base;
}
void genCode() override;
......
paddle/fluid/operators/jit/gen/matmul.h
浏览文件 @ c1455e60
......@@ -36,11 +36,11 @@ class MatMulJitCode : public JitCode {
this->genCode();
}
virtual const char* name() const override {
std::string name() const override {
std::string base = "MatMulJitCode";
base = base + "_M" + std::to_string(m_) + "_N" + std::to_string(n_) + "_K" +
std::to_string(k_);
return base.c_str();
return base;
}
void genCode() override;
......
paddle/fluid/operators/jit/gen/seqpool.h
浏览文件 @ c1455e60
......@@ -38,7 +38,7 @@ class SeqPoolJitCode : public JitCode {
this->genCode();
}
virtual const char* name() const override {
std::string name() const override {
std::string base = "SeqPoolJitCode";
if (type_ == SeqPoolType::kSum) {
base += "_Sum";
......@@ -48,7 +48,7 @@ class SeqPoolJitCode : public JitCode {
base += "_Sqrt";
}
base += ("_W" + std::to_string(w_));
return base.c_str();
return base;
}
void genCode() override;
......
paddle/fluid/operators/jit/gen_base.cc
浏览文件 @ c1455e60
......@@ -17,7 +17,13 @@
#include <iostream>
#include <sstream>
#include <vector>
#include "paddle/fluid/memory/allocation/cpu_allocator.h" // for posix_memalign
#include "paddle/fluid/platform/cpu_info.h"
#include "paddle/fluid/platform/enforce.h"
#ifndef _WIN32
#define posix_memalign_free free
#endif
DEFINE_bool(dump_jitcode, false, "Whether to dump the jitcode to file");
......@@ -40,6 +46,17 @@ void GenBase::dumpCode(const unsigned char* code) const {
}
}
void* GenBase::operator new(size_t size) {
void* ptr;
constexpr size_t alignment = 32ul;
PADDLE_ENFORCE_EQ(posix_memalign(&ptr, alignment, size), 0,
"GenBase Alloc %ld error!", size);
PADDLE_ENFORCE(ptr, "Fail to allocate GenBase CPU memory: size = %d .", size);
return ptr;
}
void GenBase::operator delete(void* ptr) { posix_memalign_free(ptr); }
std::vector<int> packed_groups(int n, int k, int* block_out, int* rest_out) {
int block;
int max_num_regs;
......
paddle/fluid/operators/jit/gen_base.h
浏览文件 @ c1455e60
......@@ -16,6 +16,7 @@
#include <gflags/gflags.h>
#include <memory> // for unique_ptr
#include <string>
#include <vector>
#include "paddle/fluid/operators/jit/kernel_base.h"
......@@ -28,7 +29,7 @@ namespace jit {
class GenBase : public Kernel {
public:
virtual ~GenBase() = default;
virtual const char* name() const = 0;
virtual std::string name() const = 0;
virtual size_t getSize() const = 0;
virtual const unsigned char* getCodeInternal() = 0;
template <typename Func>
......@@ -42,6 +43,11 @@ class GenBase : public Kernel {
return reinterpret_cast<Func>(const_cast<unsigned char*>(code));
}
void* operator new(size_t size);
void operator delete(void* ptr);
void* operator new[](size_t size) { return operator new(size); }
void operator delete[](void* ptr) { operator delete(ptr); }
protected:
void dumpCode(const unsigned char* code) const;
};
......
paddle/fluid/operators/load_combine_op.cc
浏览文件 @ c1455e60
......@@ -64,7 +64,7 @@ class LoadCombineOp : public framework::OperatorBase {
auto *tensor = out_var->GetMutable<framework::LoDTensor>();
// Error checking
PADDLE_ENFORCE(static_cast<bool>(buffer), "Cannot read more");
PADDLE_ENFORCE(static_cast<bool>(*buffer), "Cannot read more");
// Get data from fin to tensor
DeserializeFromStream(*buffer, tensor, dev_ctx);
......@@ -90,6 +90,10 @@ class LoadCombineOp : public framework::OperatorBase {
tensor->ShareDataWith(fp16_tensor);
}
}
buffer->peek();
PADDLE_ENFORCE(buffer->eof(),
"You are not allowed to load partial data via "
"load_combine_op, use load_op instead.");
}
};
......
paddle/fluid/operators/lookup_table_op.h
浏览文件 @ c1455e60
......@@ -129,6 +129,7 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
"must be either LoDTensor or SelectedRows");
}
int64_t padding_idx = context.Attr<int64_t>("padding_idx");
bool is_sparse = context.Attr<bool>("is_sparse");
// Since paddings are not trainable and fixed in forward, the gradient of
// paddings makes no sense and we don't deal with it in backward.
......@@ -187,6 +188,10 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
memset(d_table_data, 0, d_table->numel() * sizeof(T));
for (int64_t i = 0; i < ids->numel(); ++i) {
if (padding_idx != kNoPadding && ids_data[i] == padding_idx) {
// the gradient of padding_idx should be 0, already done by memset, so
// do nothing.
} else {
PADDLE_ENFORCE_LT(ids_data[i], N);
PADDLE_ENFORCE_GE(ids_data[i], 0);
for (int j = 0; j < D; ++j) {
......@@ -195,6 +200,7 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
}
}
}
}
};
} // namespace operators
......
paddle/fluid/operators/mkldnn/fc_mkldnn_op.cc
浏览文件 @ c1455e60
......@@ -282,7 +282,7 @@ class FCMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
? mkldnn::inner_product_backward_weights::desc(
src, diff_weights, bias, diff_dst)
: mkldnn::inner_product_backward_weights::desc(
src, diff_weights, bias, diff_dst);
src, diff_weights, diff_dst);
return mkldnn::inner_product_backward_weights::primitive_desc(
bwd_weight_desc, engine, pd);
......
paddle/fluid/operators/ngraph/ngraph_bridge.cc
浏览文件 @ c1455e60
......@@ -34,6 +34,8 @@ std::map<std::string,
{"accuracy", NG_OPS::BuildAccuracyNode},
{"conv2d", NG_OPS::BuildConv2dNode},
{"conv2d_grad", NG_OPS::BuildConv2dGradNode},
{"batch_norm", NG_OPS::BuildBatchNormNode},
{"batch_norm_grad", NG_OPS::BuildBatchNormGradNode},
{"elementwise_add", NG_OPS::BuildElementwiseAddNode},
{"elementwise_add_grad", NG_OPS::BuildElementwiseAddGradNode},
{"fill_constant", NG_OPS::BuildFillConstantNode},
......@@ -46,8 +48,12 @@ std::map<std::string,
{"softmax", NG_OPS::BuildSoftmaxNode},
{"softmax_grad", NG_OPS::BuildSoftmaxGradNode},
{"scale", NG_OPS::BuildScaleNode},
{"sigmoid", NG_OPS::BuildUnaryNode<ngraph::op::Sigmoid>},
{"sum", NG_OPS::BuildSumNode},
{"relu", NG_OPS::BuildUnaryNode<ngraph::op::Relu>},
{"relu_grad", NG_OPS::BuildReluGradNode},
{"tanh", NG_OPS::BuildUnaryNode<ngraph::op::Tanh>},
{"tanh_grad", NG_OPS::BuildTanhGradNode},
{"top_k", NG_OPS::BuildTopKNode}};
void NgraphBridge::BuildNgNode(
......
paddle/fluid/operators/ngraph/ngraph_ops.h
浏览文件 @ c1455e60
......@@ -22,6 +22,8 @@ limitations under the License. */
#pragma once
#include "ops/accuracy_op.h"
#include "ops/activation_op.h"
#include "ops/batch_norm_op.h"
#include "ops/binary_unary_op.h"
#include "ops/conv2d_op.h"
#include "ops/elementwise_add_op.h"
......@@ -31,4 +33,5 @@ limitations under the License. */
#include "ops/pool2d_op.h"
#include "ops/scale_op.h"
#include "ops/softmax_op.h"
#include "ops/sum_op.h"
#include "ops/top_k_op.h"
paddle/fluid/operators/ngraph/ops/activation_op.h 0 → 100644
浏览文件 @ c1455e60
/*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. */
#pragma once
#include <string>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
namespace operators {
namespace ngraphs {
void BuildReluGradNode(
const std::shared_ptr<framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto out = platform::GetInputNode(op, "Out", ngb_node_map);
auto dout = platform::GetInputNode(op, "Out@GRAD", ngb_node_map);
auto relu_grad = std::make_shared<ngraph::op::ReluBackprop>(out, dout);
platform::SetOutputNode(op, "X@GRAD", relu_grad, ngb_node_map);
}
void BuildTanhGradNode(
const std::shared_ptr<framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto out = platform::GetInputNode(op, "Out", ngb_node_map);
auto dout = platform::GetInputNode(op, "Out@GRAD", ngb_node_map);
auto shape = out->get_shape();
auto node_const =
ngraph::op::Constant::create(ngraph::element::f32, shape, {1});
auto result = dout * (node_const - out * out);
platform::SetOutputNode(op, "X@GRAD", result, ngb_node_map);
}
} // namespace ngraphs
} // namespace operators
} // namespace paddle
paddle/fluid/operators/ngraph/ops/batch_norm_op.h 0 → 100644
浏览文件 @ c1455e60
/*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. */
#pragma once
#include <string>
#include <vector>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/operators/ngraph/ops/elementwise_node.h"
#include "paddle/fluid/operators/ngraph/ops/elementwise_scalar_op.h"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
namespace operators {
namespace ngraphs {
void BuildBatchNormNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto op_attrs = paddle::framework::AttrReader(op->Attrs());
auto& data_layout = op_attrs.Get<std::string>("data_layout");
auto bias = paddle::platform::GetInputNode(op, "Bias", ngb_node_map);
auto mean = paddle::platform::GetInputNode(op, "Mean", ngb_node_map);
auto variance = paddle::platform::GetInputNode(op, "Variance", ngb_node_map);
auto scale = paddle::platform::GetInputNode(op, "Scale", ngb_node_map);
auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
const bool is_test = op_attrs.Get<bool>("is_test");
const float epsilon = op_attrs.Get<float>("epsilon");
const float momentum = op_attrs.Get<float>("momentum");
if (data_layout == "NHWC") {
x = paddle::platform::Nhwc2Nchw(x);
}
std::shared_ptr<ngraph::Node> mean_out, saved_mean, saved_variance,
variance_out, y;
if (!is_test) {
auto BN = std::make_shared<ngraph::op::BatchNormTraining>(epsilon, scale,
bias, x);
y = std::make_shared<ngraph::op::GetOutputElement>(BN, 0);
saved_mean = std::make_shared<ngraph::op::GetOutputElement>(BN, 1);
saved_variance = std::make_shared<ngraph::op::GetOutputElement>(BN, 2);
mean_out = std::make_shared<ngraph::op::Add>(
paddle::operators::ngraphs::ElementwiseScalar<ngraph::op::Multiply>(
momentum, mean),
paddle::operators::ngraphs::ElementwiseScalar<ngraph::op::Multiply>(
1. - momentum, saved_mean));
variance_out = std::make_shared<ngraph::op::Add>(
paddle::operators::ngraphs::ElementwiseScalar<ngraph::op::Multiply>(
momentum, variance),
paddle::operators::ngraphs::ElementwiseScalar<ngraph::op::Multiply>(
1. - momentum, saved_variance));
if (data_layout == "NHWC") {
y = paddle::platform::Nchw2Nhwc(y);
}
paddle::platform::SetOutputNode(op, "MeanOut", mean_out, ngb_node_map);
paddle::platform::SetOutputNode(op, "VarianceOut", variance_out,
ngb_node_map);
paddle::platform::SetOutputNode(op, "SavedMean", saved_mean, ngb_node_map);
paddle::platform::SetOutputNode(op, "SavedVariance", saved_variance,
ngb_node_map);
paddle::platform::SetOutputNode(op, "Y", y, ngb_node_map);
} else {
y = std::make_shared<ngraph::op::BatchNormInference>(epsilon, scale, bias,
x, mean, variance);
paddle::platform::SetOutputNode(op, "Y", y, ngb_node_map);
}
}
void BuildBatchNormGradNode(
const std::shared_ptr<paddle::framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
auto op_attrs = paddle::framework::AttrReader(op->Attrs());
auto& data_layout = op_attrs.Get<std::string>("data_layout");
auto bias = paddle::platform::GetInputNode(op, "Bias", ngb_node_map);
auto saved_mean =
paddle::platform::GetInputNode(op, "SavedMean", ngb_node_map);
auto saved_variance =
paddle::platform::GetInputNode(op, "SavedVariance", ngb_node_map);
auto scale = paddle::platform::GetInputNode(op, "Scale", ngb_node_map);
auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
auto dy = paddle::platform::GetInputNode(op, "Y@GRAD", ngb_node_map);
auto x_shape = x->get_shape();
auto dy_shape = dy->get_shape();
PADDLE_ENFORCE(x_shape.size() == 2 || x_shape.size() == 4,
"BN grap input size needs to be 2 or 4");
PADDLE_ENFORCE_EQ(x_shape.size(), dy_shape.size(),
"BN grap input and delta size needs to be equal");
if (x_shape.size() == 2) {
x = std::make_shared<ngraph::op::Reshape>(
x, ngraph::AxisVector{0, 1},
ngraph::Shape{x_shape.at(0), x_shape.at(1), 1, 1});
dy = std::make_shared<ngraph::op::Reshape>(
dy, ngraph::AxisVector{0, 1},
ngraph::Shape{dy_shape.at(0), dy_shape.at(1), 1, 1});
}
if (data_layout == "NHWC") {
x = paddle::platform::Nhwc2Nchw(dy);
dy = paddle::platform::Nhwc2Nchw(dy);
}
const float epsilon = op_attrs.Get<float>("epsilon");
auto bn_bprop = std::make_shared<ngraph::op::BatchNormTrainingBackprop>(
epsilon, scale, bias, x, saved_mean, saved_variance, dy);
std::shared_ptr<ngraph::Node> dx =
std::make_shared<ngraph::op::GetOutputElement>(bn_bprop, 0);
auto dscale = std::make_shared<ngraph::op::GetOutputElement>(bn_bprop, 1);
auto dbias = std::make_shared<ngraph::op::GetOutputElement>(bn_bprop, 2);
paddle::platform::SetOutputNode(op, "Bias@GRAD", dbias, ngb_node_map);
paddle::platform::SetOutputNode(op, "Scale@GRAD", dscale, ngb_node_map);
if (x_shape.size() == 2) {
paddle::platform::SetOutputNode(
op, "X@GRAD", paddle::platform::NgReshaper(dx, x_shape), ngb_node_map);
} else {
if (data_layout == "NHWC") {
dx = paddle::platform::Nchw2Nhwc(dx);
}
paddle::platform::SetOutputNode(op, "X@GRAD", dx, ngb_node_map);
}
}
} // namespace ngraphs
} // namespace operators
} // namespace paddle
paddle/fluid/operators/ngraph/ops/sum_op.h 0 → 100644
浏览文件 @ c1455e60
/*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. */
#pragma once
#include <string>
#include <vector>
#include "ngraph/ngraph.hpp"
#include "paddle/fluid/platform/ngraph_helper.h"
namespace paddle {
namespace operators {
namespace ngraphs {
void BuildSumNode(
const std::shared_ptr<framework::OperatorBase>& op,
std::shared_ptr<
std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
ngb_node_map) {
std::vector<std::string> op_inputs;
for (auto& var_name_item : op->Inputs()) {
for (auto& var_name : var_name_item.second) {
op_inputs.push_back(var_name);
if (ngb_node_map->find(var_name) == ngb_node_map->end()) {
PADDLE_THROW("op % input varname %s is not found in var_node_map",
op->Type(), var_name);
}
}
}
std::shared_ptr<ngraph::Node>& sum = ngb_node_map->at(op_inputs[0]);
for (size_t k = 1; k < op_inputs.size(); ++k) {
std::shared_ptr<ngraph::Node>& nodek = ngb_node_map->at(op_inputs[k]);
if (nodek->get_element_type() != sum->get_element_type()) {
nodek =
std::make_shared<ngraph::op::Convert>(nodek, sum->get_element_type());
}
sum = sum + nodek;
}
platform::SetOutputNode(op, "Out", sum, ngb_node_map);
}
} // namespace ngraphs
} // namespace operators
} // namespace paddle
paddle/fluid/operators/random_crop_op.h
浏览文件 @ c1455e60
......@@ -121,7 +121,7 @@ struct RandomCropFunctor {
HOSTDEVICE void operator()(size_t ins_idx) {
typename Random<DeviceContext>::Engine engine(seed_);
engine.discard(ins_idx * (rank_ - num_batchsize_dims_));
size_t offsets[9];
size_t offsets[9] = {};
for (int i = num_batchsize_dims_; i < rank_; ++i) {
typename Random<DeviceContext>::template UniformIntDist<size_t> dist(
0, x_dims_[i] - out_dims_[i]);
......
paddle/fluid/operators/row_conv_op.cc
浏览文件 @ c1455e60
......@@ -109,23 +109,23 @@ from future subsequences in a computationally efficient manner to improve
unidirectional recurrent neural networks. The row convolution operator is
different from the 1D sequence convolution, and is computed as follows:
Given an input sequence $in$ of length $t$ and input dimension $d$,
and a filter ($W$) of size $context \times d$,
Given an input sequence $X$ of length $t$ and input dimension $D$,
and a filter ($W$) of size $context \times D$,
the output sequence is convolved as:
$$
out_{i, :} = \\sum_{j=i}^{i + context} in_{j,:} \\cdot W_{i-j, :}
out_{i} = \\sum_{j=i}^{i + context - 1} X_{j} \\cdot W_{j-i}
$$
In the above equation:
* $Out_{i}$: The i-th row of output variable with shape [1, D].
* $\\tau$: Future context size.
* $context$: Future context size.
* $X_{j}$: The j-th row of input variable with shape [1, D].
* $W_{i-j}$: The (i-j)-th row of parameters with shape [1, D].
* $W_{j-i}$: The (j-i)-th row of parameters with shape [1, D].
More details about row_conv please refer to
the design document
......
paddle/fluid/platform/ngraph_helper.h
浏览文件 @ c1455e60
......@@ -23,6 +23,26 @@ limitations under the License. */
namespace paddle {
namespace platform {
std::shared_ptr<ngraph::Node> Nhwc2Nchw(std::shared_ptr<ngraph::Node> in) {
auto in_shape = in->get_shape();
in_shape[0] = in->get_shape()[0];
in_shape[1] = in->get_shape()[3];
in_shape[2] = in->get_shape()[1];
in_shape[3] = in->get_shape()[2];
ngraph::AxisVector axis_vec = {0, 3, 1, 2};
return std::make_shared<ngraph::op::Reshape>(in, axis_vec, in_shape);
}
std::shared_ptr<ngraph::Node> Nchw2Nhwc(std::shared_ptr<ngraph::Node> in) {
auto in_shape = in->get_shape();
in_shape[0] = in->get_shape()[0];
in_shape[1] = in->get_shape()[2];
in_shape[2] = in->get_shape()[3];
in_shape[3] = in->get_shape()[1];
ngraph::AxisVector axis_vec = {0, 2, 3, 1};
return std::make_shared<ngraph::op::Reshape>(in, axis_vec, in_shape);
}
ngraph::Shape FlattenTo2d(ngraph::Shape sh, int num) {
auto x1 = std::accumulate(std::begin(sh), std::begin(sh) + num, 1,
std::multiplies<size_t>());
......
paddle/fluid/pybind/ir.cc
浏览文件 @ c1455e60
......@@ -13,10 +13,12 @@
// limitations under the License.
#include "paddle/fluid/pybind/ir.h"
#include <algorithm>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
#include "paddle/fluid/framework/ir/node.h"
#include "paddle/fluid/framework/op_desc.h"
......@@ -27,6 +29,10 @@ namespace py = pybind11;
using paddle::framework::ir::Graph;
using paddle::framework::ir::Node;
using paddle::framework::ir::GraphSafeRemoveNodes;
using paddle::framework::ir::HasCircle;
using paddle::framework::ir::GraphNum;
using paddle::framework::ir::TopologySortOperations;
using paddle::framework::ir::BuildOperationAdjList;
using paddle::framework::OpDesc;
using paddle::framework::ProgramDesc;
using paddle::framework::VarDesc;
......@@ -36,6 +42,12 @@ namespace paddle {
namespace pybind {
void BindGraph(py::module *m) {
m->def("graph_safe_remove_nodes", GraphSafeRemoveNodes);
m->def("has_circle", HasCircle);
m->def("graph_num", GraphNum);
m->def("topology_sort", TopologySortOperations,
return_value_policy::reference);
m->def("build_adjacency_list", BuildOperationAdjList,
return_value_policy::reference);
py::class_<Graph, std::shared_ptr<Graph>>(
*m, "Graph",
"The graph is a Directed Acyclic Single Static Assignment Graph, see "
......@@ -46,7 +58,6 @@ void BindGraph(py::module *m) {
.def("get_float", &Graph::Get<float>)
.def("get_double", &Graph::Get<double>)
.def("get_string", &Graph::Get<std::string>)
.def("get_program", &Graph::Get<ProgramDesc>)
.def("get_marked_nodes", &Graph::Get<std::unordered_set<const Node *>>)
.def("set", [](Graph &self, const std::string &attr_name,
int attr) { return self.Set(attr_name, new int(attr)); })
......@@ -63,11 +74,6 @@ void BindGraph(py::module *m) {
[](Graph &self, const std::string &attr_name, double attr) {
return self.Set(attr_name, new double(attr));
})
.def("set",
[](Graph &self, const std::string &attr_name,
const ProgramDesc &attr) {
return self.Set(attr_name, new ProgramDesc(attr));
})
.def("set",
[](Graph &self, const std::string &attr_name,
const std::unordered_set<const Node *> &attr) {
......@@ -108,42 +114,42 @@ void BindNode(py::module *m) {
.def("is_op", &Node::IsOp)
.def("is_var", &Node::IsVar)
.def("is_ctrl_var", &Node::IsCtrlVar)
.def("clear_inputs", [](Node &self) { self.inputs.clear(); })
.def("inputs_remove",
[](Node &self, int node_id) {
for (auto it = self.inputs.begin(); it != self.inputs.end();
it++) {
if ((*it)->id() == node_id) {
self.inputs.erase(it);
}
auto pos = std::find_if(
self.inputs.begin(), self.inputs.end(),
[&node_id](const Node *n) { return n->id() == node_id; });
if (pos != self.inputs.end()) {
self.inputs.erase(pos);
}
})
.def("inputs_remove",
[](Node &self, Node &node) {
for (auto it = self.inputs.begin(); it != self.inputs.end();
it++) {
if (*it == &node) {
self.inputs.erase(it);
}
auto pos =
std::find(self.inputs.begin(), self.inputs.end(), &node);
if (pos != self.inputs.end()) {
self.inputs.erase(pos);
}
})
.def("inputs_append",
[](Node &self, Node &node) { self.inputs.push_back(&node); })
.def("clear_outputs", [](Node &self) { self.outputs.clear(); })
.def("outputs_remove",
[](Node &self, int node_id) {
for (auto it = self.outputs.begin(); it != self.outputs.end();
it++) {
if ((*it)->id() == node_id) {
self.outputs.erase(it);
}
auto pos = std::find_if(
self.outputs.begin(), self.outputs.end(),
[&node_id](const Node *n) { return n->id() == node_id; });
if (pos != self.outputs.end()) {
self.outputs.erase(pos);
}
})
.def("outputs_remove",
[](Node &self, Node &node) {
for (auto it = self.outputs.begin(); it != self.outputs.end();
it++) {
if (*it == &node) {
self.outputs.erase(it);
}
auto pos =
std::find(self.outputs.begin(), self.outputs.end(), &node);
if (pos != self.outputs.end()) {
self.outputs.erase(pos);
}
})
.def("outputs_append",
......
paddle/fluid/pybind/pybind.cc
浏览文件 @ c1455e60
......@@ -829,8 +829,7 @@ All parameter, weight, gradient are variables in Paddle.
m.def("disable_profiler", platform::DisableProfiler);
m.def("is_profiler_enabled", platform::IsProfileEnabled);
m.def("reset_profiler", platform::ResetProfiler);
m.def("get_pass", [](const py::bytes &binary_str) {
std::string pass_type(binary_str);
m.def("get_pass", [](const std::string &pass_type) {
auto pass = framework::ir::PassRegistry::Instance().Get(pass_type);
return std::shared_ptr<framework::ir::Pass>(std::move(pass));
});
......@@ -838,10 +837,9 @@ All parameter, weight, gradient are variables in Paddle.
py::class_<ir::Pass, std::shared_ptr<ir::Pass>> pass(m, "Pass");
pass.def(py::init())
.def("has", &ir::Pass::Has)
.def("set",
[](ir::Pass &self, const std::string &attr_name,
const ProgramDesc &attr) {
return self.Set(attr_name, new ProgramDesc(attr));
.def("set_not_owned",
[](ir::Pass &self, const std::string &attr_name, ProgramDesc &attr) {
self.SetNotOwned<ProgramDesc>(attr_name, &attr);
})
.def(
"set",
......@@ -850,7 +848,6 @@ All parameter, weight, gradient are variables in Paddle.
})
.def("set", [](ir::Pass &self, const std::string &name,
int val) { self.Set<const int>(name, new int(val)); })
.def("get_program", &ir::Pass::Get<ProgramDesc>)
.def("type", &ir::Pass::Type)
.def("apply", [](ir::Pass &self, std::shared_ptr<ir::Graph> graph) {
std::unique_ptr<ir::Graph> origin_graph(graph.get());
......
python/CMakeLists.txt
浏览文件 @ c1455e60
......@@ -64,6 +64,7 @@ if (WITH_TESTING)
add_subdirectory(paddle/dataset/tests)
add_subdirectory(paddle/fluid/tests)
add_subdirectory(paddle/fluid/contrib/tests)
add_subdirectory(paddle/fluid/contrib/slim/tests)
endif()
install(DIRECTORY ${PADDLE_PYTHON_PACKAGE_DIR}
DESTINATION opt/paddle/share/wheels
......
python/paddle/fluid/contrib/slim/quantization/quantization_pass.py
浏览文件 @ c1455e60
......@@ -13,14 +13,19 @@
# limitations under the License.
import collections
import numpy as np
import six
from ..... import compat as cpt
from .... import core
from ....framework import IrGraph
from ....framework import Program
from ....framework import Variable
from ....initializer import Constant
from .... import unique_name
__all__ = ['QuantizationTransformPass']
__all__ = [
'QuantizationTransformPass', 'QuantizationFreezePass', 'ConvertToInt8Pass',
'TransformForMobilePass'
]
class QuantizationTransformPass(object):
......@@ -35,7 +40,13 @@ class QuantizationTransformPass(object):
"""
Convert and rewrite the IrGraph according to weight and
activation quantization type.
Args:
scope(fluid.Scope): When activation use 'range_abs_max' as the quantize
type, this pass will create some new parameters. The scope is used to
initialize these new parameters.
program_exe(fluid.Executor): program_exe is used to initialize new
parameters described above.
weight_bits (int): quantization bit number for weights,
the bias is not quantized.
activation_bits (int): quantization bit number for activation.
......@@ -49,6 +60,7 @@ class QuantizationTransformPass(object):
support 'abs_max'. The 'range_abs_max' usually is not used for
weight, since weights are fixed once the model is well trained.
window_size (int): the window size for 'range_abs_max' quantization.
Examples:
.. code-block:: python
# The original graph will be rewrite.
......@@ -88,31 +100,35 @@ class QuantizationTransformPass(object):
self._quantizable_grad_ops = [
'%s_grad' % (op) for op in self._quantizable_ops
]
self._fake_quant_op_types = [
'fake_quantize_abs_max', 'fake_quantize_range_abs_max'
]
self._fake_dequant_op_types = ['fake_dequantize_max_abs']
self._is_test = None
self._global_step = None
def apply(self, graph):
"""
Quantize the graph for training process. According to weight and
activation quantization type, the graph will be added some fake
quantize operators and fake dequantize operators.
Args:
graph(IrGraph): the applied graph.
"""
assert isinstance(graph,
IrGraph), 'graph must be the instance of IrGraph.'
self._need_initialized.clear()
self._is_test = graph.is_test()
# marked the variable which has been dequantized.
dequantized_vars = collections.OrderedDict()
params = [p.name() for p in graph.all_parameters()]
persistable_vars = [p.name() for p in graph.all_persistable_vars()]
def _transform_forward(graph, op):
for var_node in op.inputs:
if var_node.name() in dequantized_vars:
dequant_var_node = dequantized_vars[var_node.name()]
else:
quant_bits = self._weight_bits if var_node.name() in params \
quant_bits = self._weight_bits if var_node.name() in persistable_vars \
else self._activation_bits
quant_type = self._weight_quantize_type if var_node.name() \
in params else self._activation_quantize_type
in persistable_vars else self._activation_quantize_type
quant_var_node, scale_var_node = self._insert_quant_op(
graph, var_node, quant_bits, quant_type)
dequant_var_node = self._insert_dequant_op(
......@@ -150,9 +166,14 @@ class QuantizationTransformPass(object):
assert self._program_exe is not None, \
'The program_exe cannot be set None when activation_quantize_type equals to range_abs_max.'
init_program = Program()
for var_desc, initializer in self._need_initialized.iteritems():
var = Variable(init_program.global_block())
var._set_desc(var_desc)
for var_desc, initializer in six.iteritems(self._need_initialized):
var = init_program.global_block().create_var(
name=var_desc.name(),
shape=var_desc.shape(),
dtype=var_desc.dtype(),
type=var_desc.type(),
lod_level=var_desc.lod_level(),
persistable=var_desc.persistable())
initializer(var, init_program.global_block())
self._program_exe.run(program=init_program, scope=self._scope)
......@@ -161,7 +182,7 @@ class QuantizationTransformPass(object):
def _create_global_step(self, graph):
if self._weight_quantize_type == 'range_abs_max' or \
self._activation_quantize_type == 'range_abs_max':
counter_name = '@STEP_COUNTER@'
counter_name = cpt.to_text('@STEP_COUNTER@')
for node in graph.all_vars():
if node.name() == counter_name:
self._global_step = node
......@@ -175,9 +196,14 @@ class QuantizationTransformPass(object):
Constant(value=0, force_cpu=True)
global_step_out = graph.create_var_node_from_desc(
global_step_in.var())
# The attribute of `op_role` is needed by ParallelExecutor.
increment_op = graph.create_op_node(
op_type='increment',
attrs={'step': 1.0},
attrs={
'step': 1.0,
'op_role':
core.op_proto_and_checker_maker.OpRole.Forward
},
inputs={'X': global_step_in},
outputs={'Out': global_step_out})
graph.link_to(global_step_in, increment_op)
......@@ -212,7 +238,10 @@ class QuantizationTransformPass(object):
var_dtype=var_node.var().dtype())
quant_op_node = graph.create_op_node(
op_type='fake_quantize_abs_max',
attrs={'bit_length': quant_bits},
attrs={
'bit_length': quant_bits,
'op_role': core.op_proto_and_checker_maker.OpRole.Forward
},
inputs={'X': var_node},
outputs={'Out': quant_var_node,
'OutScale': scale_var_node})
......@@ -257,7 +286,8 @@ class QuantizationTransformPass(object):
attrs = {
'window_size': self._window_size,
'bit_length': quant_bits,
'is_test': self._is_test
'is_test': self._is_test,
'op_role': core.op_proto_and_checker_maker.OpRole.Forward
}
quant_op_node = graph.create_op_node(
op_type='fake_quantize_range_abs_max',
......@@ -290,7 +320,10 @@ class QuantizationTransformPass(object):
max_range = (1 << (quant_bits - 1)) - 1
dequant_op_node = graph.create_op_node(
op_type='fake_dequantize_max_abs',
attrs={'max_range': float(max_range)},
attrs={
'max_range': float(max_range),
'op_role': core.op_proto_and_checker_maker.OpRole.Forward
},
inputs={'X': var_node,
'Scale': scale_var_node},
outputs={'Out': dequant_var_node})
......@@ -316,3 +349,330 @@ class QuantizationTransformPass(object):
Return the scale name of quantized variable for the input `var_name`.
"""
return "%s.scale" % (var_name)
class QuantizationFreezePass(object):
"""
The freeze pass is used to adjust the quantize operator order, for example:
1) `activation -> quant -> dequant -> conv2d` will be freezed into
`activation -> quant -> conv2d -> dequant`
2) `weight -> quant -> dequant -> conv2d` will be freezed into `weight -> conv2d`,
and weight will be sacled offline.
Args:
scope(fluid.Scope): scope is used to get the weight tensor values.
place(fluid.CPUPlace|fluid.CUDAPlace): place is used to restore the weight tensors.
weight_bits (int): quantization bit number for weights.
activation_bits (int): quantization bit number for activation.
weight_quantize_type (str): quantization type for weights, support 'abs_max'.
The 'range_abs_max' usually is not used for weight, since weights are fixed once the
model is well trained.
"""
def __init__(self,
scope,
place,
weight_bits=8,
activation_bits=8,
weight_quantize_type='abs_max'):
assert scope is not None, \
'The scope cannot be set None.'
assert place is not None, \
'The place cannot be set None.'
self._scope = scope
self._place = place
self._weight_bits = weight_bits
self._activation_bits = activation_bits
self._weight_quantize_type = weight_quantize_type
self._quantizable_ops = ['conv2d', 'depthwise_conv2d', 'mul']
self._fake_quant_op_names = [
'fake_quantize_abs_max', 'fake_quantize_range_abs_max'
]
self._fake_dequant_op_names = ['fake_dequantize_max_abs']
self._op_input_rename_map = collections.OrderedDict()
self._op_output_rename_map = collections.OrderedDict()
self._var_scale_map = collections.OrderedDict()
def apply(self, graph):
"""
Adjust quantize/dequantize operators order for the inference process.
Args:
graph(IrGraph): the applied graph.
"""
persistable_vars = [p.name() for p in graph.all_persistable_vars()]
ops = graph.all_ops()
for op_node in ops:
op_name = op_node.name()
if op_name in self._fake_quant_op_names:
input_arg_name = op_node.op().input('X')[0]
if input_arg_name in persistable_vars:
if self._weight_quantize_type == 'abs_max':
param = self._load_var(input_arg_name)
scale_v = np.max(np.abs(param))
else:
scale_v = self._load_var(op_node.op().output('OutScale')
[0])[0]
self._var_scale_map[input_arg_name] = scale_v
else:
scale_v = graph.var_node(op_node.op().output('OutScale')[0])
self._var_scale_map[input_arg_name] = scale_v
if input_arg_name in persistable_vars:
self._remove_fake_quant_and_dequant_op(graph, op_node)
# quantize weight and restore
param_v = self._load_var(input_arg_name)
quantized_param_v = self._quant(param_v, scale_v,
self._weight_bits)
self._restore_var(input_arg_name, quantized_param_v)
ops = graph.all_ops()
for op_node in ops:
op_name = op_node.name()
if op_name in self._fake_dequant_op_names:
self._remove_fake_quant_and_dequant_op(graph, op_node)
ops = graph.all_ops()
for op_node in ops:
op_name = op_node.name()
if op_name in self._quantizable_ops:
self._insert_post_dequant_op(graph, op_node)
for op_node in ops:
# insert dequant_op after fc/conv, need to rename inputs of the followed ops
for var_node in op_node.inputs:
name = var_node.name()
if name in self._op_output_rename_map:
old_in = graph.var_node(name)
new_in = self._op_output_rename_map[name]
graph.update_input_link(old_in, new_in, op_node)
# remove the unused var node in the graph
self._remove_unused_var_nodes(graph)
return graph
def _remove_fake_quant_and_dequant_op(self, graph, op_node):
k = op_node.op().output('Out')[0]
v = op_node.op().input('X')[0]
if v not in self._op_input_rename_map:
self._op_input_rename_map[k] = v
else:
self._op_input_rename_map[k] = self._op_input_rename_map[v]
graph.safe_remove_nodes(op_node)
def _insert_post_dequant_op(self, graph, op_node):
max_range = None
scale_var_node = None
persistable_vars = [p.name() for p in graph.all_persistable_vars()]
for var_node in op_node.inputs:
name = var_node.name()
if name in self._op_input_rename_map:
old_in = graph.var_node(name)
new_in = graph.var_node(self._op_input_rename_map[name])
new_in.clear_outputs()
graph.update_input_link(old_in, new_in, op_node)
original_var_name = self._original_var_name(name)
scale_v = self._var_scale_map[original_var_name]
if original_var_name in persistable_vars:
param_range = (1 << (self._weight_bits - 1)) - 1
act_range = (1 << (self._activation_bits - 1)) - 1
assert self._is_float(
scale_v), 'The scale of parameter %s is not a float.' % (
original_var_name)
max_range = param_range * act_range / scale_v
else:
assert isinstance(scale_v, core.Node)
scale_var_node = self._var_scale_map[original_var_name]
if len(op_node.outputs) != 1:
raise ValueError("Only support one output, but op %s has"
" more than one output." % (op_node.name()))
output_var_node = op_node.outputs[0]
dequant_var_node = graph.create_var_node(
name=self._dequantized_var_name(output_var_node.name()),
var_type=output_var_node.var().type(),
shape=output_var_node.var().shape(),
var_dtype=output_var_node.var().dtype())
dequant_op_node = graph.create_op_node(
op_type='fake_dequantize_max_abs',
attrs={
'max_range': float(max_range),
'op_role': core.op_proto_and_checker_maker.OpRole.Forward
},
inputs={'X': output_var_node,
'Scale': scale_var_node},
outputs={'Out': dequant_var_node})
graph.link_to(output_var_node, dequant_op_node)
graph.link_to(scale_var_node, dequant_op_node)
graph.link_to(dequant_op_node, dequant_var_node)
self._op_output_rename_map[output_var_node.name()] = dequant_var_node
return dequant_var_node
def _load_var(self, name):
return np.array(self._scope.find_var(name).get_tensor())
def _restore_var(self, name, array):
tensor = self._scope.find_var(name).get_tensor()
tensor.set(array, self._place)
def _remove_unused_var_nodes(self, graph):
all_used_vars = set()
ops = graph.all_ops()
for op_node in ops:
for input_node in op_node.inputs:
all_used_vars.add(input_node)
for output_node in op_node.outputs:
all_used_vars.add(output_node)
all_unused_vars = graph.all_vars() - all_used_vars
graph.safe_remove_nodes(all_unused_vars)
def _original_var_name(self, var_name):
"""
Return the original variable name.
"""
if var_name.endswith('.quantized.dequantized'):
return var_name[:-len('.quantized.dequantized')]
if var_name.endswith('.quantized'):
return var_name[:-len('.quantized')]
if var_name.endswith('.dequantized'):
return var_name[:-len('.dequantized')]
if var_name.endswith('.scale'):
return var_name[:-len('.scale')]
else:
return var_name
def _dequantized_var_name(self, var_name):
"""
Return dequantized variable name for the input `var_name`.
"""
return "%s.dequantized" % (var_name)
def _is_float(self, v):
return isinstance(v, float) or isinstance(v, np.float32) \
or isinstance(v, np.float64)
def _quant(self, x, scale, num_bits):
return np.round(x / scale * ((1 << (num_bits - 1)) - 1))
class ConvertToInt8Pass(object):
"""
Convert the weights into int8_t type.
Args:
scope(fluid.Scope): scope is used to get the weight tensor values.
place(fluid.CPUPlace|fluid.CUDAPlace): place is used to restore the
8bits weight tensors.
"""
def __init__(self, scope, place):
assert scope is not None, \
'The scope cannot be set None.'
assert place is not None, \
'The place cannot be set None.'
self._scope = scope
self._place = place
self._quantizable_ops = ['conv2d', 'depthwise_conv2d', 'mul']
def apply(self, graph):
"""
Convert weights' tpye of the graph. After that, the data type of the
graph weigths is int8_t.
Args:
graph(IrGraph): the applied graph.
"""
persistable_vars = [p.name() for p in graph.all_persistable_vars()]
ops = graph.all_ops()
input_map = {}
for op_node in ops:
op_name = op_node.name()
if op_name in self._quantizable_ops:
for var_node in op_node.inputs:
name = var_node.name()
if name in persistable_vars:
if name not in input_map:
int8_var_node = self._convert_to_int8(graph,
var_node)
input_map[name] = int8_var_node
graph.update_input_link(var_node, input_map[name],
op_node)
# remove the unused var node in the graph
self._remove_unused_var_nodes(graph)
return graph
def _convert_to_int8(self, graph, var_node):
int8_var_node_name = var_node.name() + ".int8"
int8_var_node = graph.create_param_node(
name=cpt.to_text(int8_var_node_name),
var_type=var_node.var().type(),
shape=var_node.var().shape(),
var_dtype=core.VarDesc.VarType.INT8)
array = self._load_var(var_node.name())
self._scope.var(int8_var_node_name)
self._store_var(int8_var_node_name, array, np.int8)
return int8_var_node
def _load_var(self, name):
return np.array(self._scope.find_var(name).get_tensor())
def _store_var(self, name, array, dtype):
tensor = self._scope.find_var(name).get_tensor()
tensor.set(array.astype(dtype), self._place)
def _remove_unused_var_nodes(self, graph):
all_used_vars = set()
ops = graph.all_ops()
for op_node in ops:
for input_node in op_node.inputs:
all_used_vars.add(input_node)
for output_node in op_node.outputs:
all_used_vars.add(output_node)
all_unused_vars = graph.all_vars() - all_used_vars
graph.safe_remove_nodes(all_unused_vars)
class TransformForMobilePass(object):
"""
This pass is used to convert the freezed graph for paddle-mobile execution.
"""
def __init__(self):
self._fake_quant_op_names = [
'fake_quantize_abs_max', 'fake_quantize_range_abs_max'
]
self._fake_dequant_op_names = ['fake_dequantize_max_abs']
def apply(self, graph):
"""
Because paddle-mobile use `quantize` an `dequantize` as the names of
quantize operator and dequantize operator, the `apply` function just
realize this logic.
Args:
graph(IrGraph): the graph will be transformed.
"""
ops = graph.all_ops()
for op_node in ops:
name = op_node.name()
if name in self._fake_quant_op_names:
op_node.op().set_type('quantize')
quant_node = graph.create_op_node_from_desc(op_node.op())
for input_node in op_node.inputs:
graph.link_to(input_node, quant_node)
for output_node in op_node.outputs:
graph.link_to(quant_node, output_node)
graph.safe_remove_nodes(op_node)
if name in self._fake_dequant_op_names:
op_node.op().set_type('dequantize')
dequant_node = graph.create_op_node_from_desc(op_node.op())
for input_node in op_node.inputs:
graph.link_to(input_node, dequant_node)
for output_node in op_node.outputs:
graph.link_to(dequant_node, output_node)
graph.safe_remove_nodes(op_node)
return graph
python/paddle/fluid/contrib/slim/tests/CMakeLists.txt 0 → 100644
浏览文件 @ c1455e60
file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
foreach(src ${TEST_OPS})
py_test(${src} SRCS ${src}.py)
endforeach()
python/paddle/fluid/contrib/slim/unitest/configs/config.yaml → python/paddle/fluid/contrib/slim/tests/configs/config.yaml
浏览文件 @ c1455e60
version: 1.0
include: ["./unitest/configs/pruners.yaml", "./unitest/configs/pruners_0.yaml"]
include: ["./configs/pruners.yaml", "./configs/pruners_0.yaml"]
pruners:
pruner_1:
class: 'RatioPruner'
......
python/paddle/fluid/contrib/slim/unitest/test_factory.py → python/paddle/fluid/contrib/slim/tests/test_factory.py
浏览文件 @ c1455e60
......@@ -18,7 +18,7 @@ import unittest
class TestFactory(unittest.TestCase):
def test_parse(self):
factory = ConfigFactory('./unitest/configs/config.yaml')
factory = ConfigFactory('./configs/config.yaml')
pruner = factory.instance('pruner_1')
self.assertEquals(pruner.ratios['conv1_1.w'], 0.3)
......
python/paddle/fluid/contrib/slim/tests/test_graph.py 0 → 100644
浏览文件 @ c1455e60
# 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.
from __future__ import print_function
import unittest
import paddle.fluid as fluid
import six
from paddle.fluid.framework import IrGraph
from paddle.fluid import core
def residual_block(num):
def conv_bn_layer(input,
ch_out,
filter_size,
stride,
padding,
act='relu',
bias_attr=False):
tmp = fluid.layers.conv2d(
input=input,
filter_size=filter_size,
num_filters=ch_out,
stride=stride,
padding=padding,
act=None,
bias_attr=bias_attr)
return fluid.layers.batch_norm(input=tmp, act=act)
data = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
hidden = data
for _ in six.moves.xrange(num):
conv = conv_bn_layer(hidden, 16, 3, 1, 1, act=None, bias_attr=True)
short = conv_bn_layer(hidden, 16, 1, 1, 0, act=None)
hidden = fluid.layers.elementwise_add(x=conv, y=short, act='relu')
fc = fluid.layers.fc(input=hidden, size=10)
loss = fluid.layers.cross_entropy(input=fc, label=label)
loss = fluid.layers.mean(loss)
return loss
class TestGraph(unittest.TestCase):
def test_graph_functions(self):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
loss = residual_block(2)
opt = fluid.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
graph = IrGraph(core.Graph(main.desc), for_test=False)
marked_nodes = set()
for op in graph.all_ops():
if op.name().find('conv2d') > -1:
marked_nodes.add(op)
graph.draw('.', 'residual', marked_nodes)
self.assertFalse(graph.has_circle())
self.assertEqual(graph.graph_num(), 1)
nodes = graph.topology_sort()
self.assertEqual(len(nodes), len(graph.all_ops()))
nodes_map = graph.build_adjacency_list()
self.assertEqual(len(nodes_map), len(graph.all_ops()))
nodes_num = len(graph.all_nodes())
graph.safe_remove_nodes(marked_nodes)
self.assertEqual(len(graph.all_nodes()), nodes_num - len(marked_nodes))
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/contrib/slim/unitest/test_quantization_pass.py → python/paddle/fluid/contrib/slim/tests/test_quantization_pass.py
浏览文件 @ c1455e60
......@@ -17,9 +17,12 @@ import random
import numpy as np
import paddle.fluid as fluid
import six
from paddle.fluid.framework import Program
import paddle
from paddle.fluid.framework import IrGraph
from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass
from paddle.fluid.contrib.slim.quantization import ConvertToInt8Pass
from paddle.fluid.contrib.slim.quantization import TransformForMobilePass
from paddle.fluid import core
......@@ -65,6 +68,28 @@ def residual_block(num):
return loss
def conv_net(img, label):
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=img,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu")
conv_pool_1 = fluid.layers.batch_norm(conv_pool_1)
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
pool_size=2,
pool_stride=2,
act="relu")
prediction = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
avg_loss = fluid.layers.mean(loss)
return avg_loss
class TestQuantizationTransformPass(unittest.TestCase):
def setUp(self):
self.quantizable_op_and_inputs = {
......@@ -171,5 +196,177 @@ class TestQuantizationTransformPass(unittest.TestCase):
self.residual_block_quant('range_abs_max')
class TestQuantizationFreezePass(unittest.TestCase):
def freeze_graph(self, use_cuda, seed, quant_type):
def build_program(main, startup, is_test):
main.random_seed = seed
startup.random_seed = seed
with fluid.unique_name.guard():
with fluid.program_guard(main, startup):
img = fluid.layers.data(
name='image', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(
name='label', shape=[1], dtype='int64')
loss = conv_net(img, label)
if not is_test:
opt = fluid.optimizer.Adam(learning_rate=0.001)
opt.minimize(loss)
return [img, label], loss
random.seed(0)
np.random.seed(0)
main = fluid.Program()
startup = fluid.Program()
test_program = fluid.Program()
feeds, loss = build_program(main, startup, False)
build_program(test_program, startup, True)
test_program = test_program.clone(for_test=True)
main_graph = IrGraph(core.Graph(main.desc), for_test=False)
test_graph = IrGraph(core.Graph(test_program.desc), for_test=True)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
scope = fluid.Scope()
with fluid.scope_guard(scope):
exe.run(startup)
transform_pass = QuantizationTransformPass(
scope=scope, program_exe=exe, activation_quantize_type=quant_type)
transform_pass.apply(main_graph)
transform_pass.apply(test_graph)
dev_name = '_gpu_' if use_cuda else '_cpu_'
marked_nodes = set()
for op in main_graph.all_ops():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
main_graph.draw('.', 'main' + dev_name + quant_type, marked_nodes)
marked_nodes = set()
for op in test_graph.all_ops():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test' + dev_name + quant_type, marked_nodes)
quantized_main_program = main_graph.to_program()
quantized_test_program = test_graph.to_program()
iters = 5
batch_size = 8
#train_exe = fluid.ParallelExecutor(
# main_program=quantized_main_program,
# use_cuda=bool(use_cuda),
# loss_name=loss.name,
# scope=scope)
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=batch_size)
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=batch_size)
feeder = fluid.DataFeeder(feed_list=feeds, place=place)
with fluid.scope_guard(scope):
for _ in range(iters):
data = next(train_reader())
loss_v = exe.run(program=quantized_main_program,
feed=feeder.feed(data),
fetch_list=[loss])
#loss_v = train_exe.run(feed=feeder.feed(data),
# fetch_list=[loss.name])
#print('{}: {}'.format('loss' + dev_name + quant_type, loss_v))
test_data = next(test_reader())
with fluid.program_guard(quantized_test_program):
w_var = fluid.framework._get_var('conv2d_1.w_0.quantized',
quantized_test_program)
# Testing
with fluid.scope_guard(scope):
test_loss1, w_quant = exe.run(program=quantized_test_program,
feed=feeder.feed(test_data),
fetch_list=[loss, w_var])
# Freeze graph for inference, but the weight of fc/conv is still float type.
freeze_pass = QuantizationFreezePass(scope=scope, place=place)
freeze_pass.apply(test_graph)
marked_nodes = set()
for op in test_graph.all_ops():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_freeze' + dev_name + quant_type,
marked_nodes)
server_program = test_graph.to_program()
with fluid.scope_guard(scope):
test_loss2, = exe.run(program=server_program,
feed=feeder.feed(test_data),
fetch_list=[loss])
self.assertAlmostEqual(test_loss1, test_loss2, delta=5e-3)
#print('{}: {}'.format('test_loss1' + dev_name + quant_type, test_loss1))
#print('{}: {}'.format('test_loss2' + dev_name + quant_type, test_loss2))
w_freeze = np.array(scope.find_var('conv2d_1.w_0').get_tensor())
# Maybe failed, this is due to the calculation precision
# self.assertAlmostEqual(np.sum(w_freeze), np.sum(w_quant))
#print('{}: {}'.format('w_freeze' + dev_name + quant_type,
# np.sum(w_freeze)))
#print('{}: {}'.format('w_quant' + dev_name + quant_type,
# np.sum(w_quant)))
# Convert parameter to 8-bit.
convert_int8_pass = ConvertToInt8Pass(scope=scope, place=place)
convert_int8_pass.apply(test_graph)
marked_nodes = set()
for op in test_graph.all_ops():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_int8' + dev_name + quant_type, marked_nodes)
server_program_int8 = test_graph.to_program()
# Save the 8-bit parameter and model file.
with fluid.scope_guard(scope):
fluid.io.save_inference_model('server_int8' + dev_name + quant_type,
['image', 'label'], [loss], exe,
server_program_int8)
# Test whether the 8-bit parameter and model file can be loaded successfully.
[infer, feed, fetch] = fluid.io.load_inference_model(
'server_int8' + dev_name + quant_type, exe)
# Check the loaded 8-bit weight.
w_8bit = np.array(scope.find_var('conv2d_1.w_0.int8').get_tensor())
self.assertEqual(w_8bit.dtype, np.int8)
self.assertEqual(np.sum(w_8bit), np.sum(w_freeze))
#print('{}: {}'.format('w_8bit' + dev_name + quant_type, np.sum(w_8bit)))
#print('{}: {}'.format('w_freeze' + dev_name + quant_type,
# np.sum(w_freeze)))
mobile_pass = TransformForMobilePass()
mobile_pass.apply(test_graph)
marked_nodes = set()
for op in test_graph.all_ops():
if op.name().find('quantize') > -1:
marked_nodes.add(op)
test_graph.draw('.', 'test_mobile' + dev_name + quant_type,
marked_nodes)
mobile_program = test_graph.to_program()
with fluid.scope_guard(scope):
fluid.io.save_inference_model('mobile_int8' + dev_name + quant_type,
['image', 'label'], [loss], exe,
mobile_program)
def test_freeze_graph_cuda_dynamic(self):
if fluid.core.is_compiled_with_cuda():
with fluid.unique_name.guard():
self.freeze_graph(True, seed=1, quant_type='abs_max')
def test_freeze_graph_cpu_dynamic(self):
with fluid.unique_name.guard():
self.freeze_graph(False, seed=2, quant_type='abs_max')
def test_freeze_graph_cuda_static(self):
if fluid.core.is_compiled_with_cuda():
with fluid.unique_name.guard():
self.freeze_graph(True, seed=1, quant_type='range_abs_max')
def test_freeze_graph_cpu_static(self):
with fluid.unique_name.guard():
self.freeze_graph(False, seed=2, quant_type='range_abs_max')
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/contrib/tests/test_quantize_transpiler.py
浏览文件 @ c1455e60
......@@ -204,9 +204,11 @@ class TestQuantizeTranspiler(unittest.TestCase):
build_program(test_program, startup, True)
test_program = test_program.clone(for_test=True)
quant_transpiler = QuantizeTranspiler()
quant_transpiler.training_transpile(main)
quant_transpiler.training_transpile(test_program)
quant_type = 'range_abs_max' # 'range_abs_max' or 'abs_max'
quant_transpiler = QuantizeTranspiler(
activation_quantize_type=quant_type)
quant_transpiler.training_transpile(main, startup)
quant_transpiler.training_transpile(test_program, startup)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
......
python/paddle/fluid/framework.py
浏览文件 @ c1455e60
......@@ -16,6 +16,8 @@ from __future__ import print_function
import collections
from collections import defaultdict
from collections import Iterable
import contextlib
from .wrapped_decorator import signature_safe_contextmanager
import os
import re
......@@ -1529,12 +1531,16 @@ class Block(object):
class IrGraph(object):
"""
IrGraph uses core.Graph as the delegation to accomplish the manipulation.
Python IrGraph. Beneath it is a core.Graph, which is used for
create a c++ Ir Pass Graph. An IrGraph is just a graph view of
a Program. In an IrGraph, both Variables and Operators are graph
nodes.
"""
def __init__(self, graph, for_test=False):
"""
Construct the IrGraph using core.Graph.
Construct an IrGraph using core.Graph.
Args:
graph(core.Graph): C++ Graph.
for_test(bool): True for the test graph and false for the train graph.
......@@ -1545,23 +1551,81 @@ class IrGraph(object):
self._for_test = for_test
def is_test(self):
"""
If the graph is used for testing, the function returns true. Otherwise, returns false.
"""
return self._for_test
def all_parameters(self):
param_nodes = set()
for node in self.graph.nodes():
if node.is_var() and node.var() is not None and node.var(
).persistable():
param_nodes.add(node)
return param_nodes
def all_nodes(self):
"""
Return all nodes included in the graph as a set.
"""
return {node for node in self.graph.nodes()}
def all_vars(self):
"""
Return all variable nodes included in the graph as a set.
"""
return {node for node in self.graph.nodes() if node.is_var()}
def all_persistable_vars(self):
"""
Return all persistable variable nodes included in the graph as a set.
"""
persistable_nodes = set()
for node in self.graph.nodes():
if node.is_var() and node.var() is not None and node.var(
).persistable():
persistable_nodes.add(node)
return persistable_nodes
def all_ops(self):
"""
Return all operator nodes included in the graph as a set.
"""
return {node for node in self.graph.nodes() if node.is_op()}
def var_node(self, name):
"""
Get a variable node by name from the graph.
Args:
name(str): the name of the variable node.
Raises:
ValueError: The If input's type is not str, or this graph
doesn't have a variable with the giving name.
Returns:
core.Node: the variable node with the giving name.
"""
if not isinstance(name, six.string_types):
raise TypeError(
"var require string as parameter, but get %s instead." %
(type(name)))
target_var_node = None
var_nodes = self.all_vars()
for var_node in var_nodes:
if var_node.name() == name:
target_var_node = var_node
if target_var_node is None:
raise ValueError("var_node %s not in this graph" % name)
return target_var_node
def create_param_node(self, name, var_type, shape, var_dtype):
"""
Create a persistable variable node in the graph. In IrGraph,
it can not distinguish between persistable variables and parameters.
Args:
name(str): the name of the persistable variable node.
vart_type(core.VarDesc.VarType): the type of the persistable variable node.
shape(list): the shape of the persistable variable node.
var_dtype(core.VarDesc.VarType): the data type of the persistable variable node.
Returns:
core.Node: the created persistable variable node.
"""
var_desc = core.VarDesc(name)
var_desc.set_type(var_type)
var_desc.set_shape(shape)
......@@ -1570,6 +1634,20 @@ class IrGraph(object):
return self.graph.create_var_node(var_desc)
def create_var_node(self, name, var_type, shape, var_dtype):
"""
Create a variable node in the graph. The created variable node is
not persistable.
Args:
name(str): the name of the variable node.
vart_type(core.VarDesc.VarType): the type of the variable node.
shape(list): the shape of the variable node.
var_dtype(core.VarDesc.VarType): the data type of the variable node.
Returns:
core.Node: the created variable node.
"""
var_desc = core.VarDesc(name)
var_desc.set_type(var_type)
var_desc.set_shape(shape)
......@@ -1577,19 +1655,41 @@ class IrGraph(object):
return self.graph.create_var_node(var_desc)
def create_var_node_from_desc(self, var_desc):
"""
Create a variable node by using an existing VarDesc in the graph.
Depend on the giving VarDesc, the created variable node may be persistable.
Args:
var_desc(core.VarDesc): the giving variable description.
Returns:
core.Node: the created variable node.
"""
return self.graph.create_var_node(var_desc)
def create_op_node(self, op_type, attrs, inputs, outputs):
"""
Create a operator node in the graph.
Args:
op_type(str): the type of the operator node.
attrs(dict): the attributes of the operator node.
inputs(dict): the inputs of the operator node.
outputs(dict): the outpus of the operator node.
Returns:
core.Node: the created operator node.
"""
op_desc = core.OpDesc()
op_desc.set_type(op_type)
for attr, value in attrs.iteritems():
for attr, value in six.iteritems(attrs):
self._update_desc_attr(op_desc, attr, value)
for input_name, var_nodes in inputs.iteritems():
for input_name, var_nodes in six.iteritems(inputs):
if not isinstance(var_nodes, list):
var_nodes = [var_nodes]
op_desc.set_input(input_name,
[var_node.name() for var_node in var_nodes])
for output_name, var_nodes in outputs.iteritems():
for output_name, var_nodes in six.iteritems(outputs):
if not isinstance(var_nodes, list):
var_nodes = [var_nodes]
op_desc.set_output(output_name,
......@@ -1597,11 +1697,29 @@ class IrGraph(object):
return self.graph.create_op_node(op_desc)
def create_op_node_from_desc(self, op_desc):
"""
Create a operator node by using an existing OpDesc in the graph.
Args:
op_desc(core.VarDesc): the giving operator description.
Returns:
core.Node: the created operator node.
"""
return self.graph.create_op_node(op_desc)
def update_input_link(self, old_input_node, new_input_node, op_node):
assert old_input_node in self.graph.nodes() and new_input_node in self.graph.nodes() and \
op_node in self.graph.nodes(), 'Th three arguments must be in the graph nodes.'
"""
Update the input's link of a operator node.
Args:
old_input_node(core.Node): the old input node of the giving op_node.
new_input_node(core.Node): the new input node of the giving op_node.
op_node(core.Node): the operator node that is needed to update input's link.
"""
assert old_input_node in self.graph.nodes() and new_input_node in \
self.graph.nodes() and op_node in self.graph.nodes(), \
'The three arguments(old_input_node&new_input_node&op_node) must be in the graph nodes.'
old_input_node.outputs_remove(op_node)
op_node.inputs_remove(old_input_node)
new_input_node.outputs_append(op_node)
......@@ -1609,17 +1727,85 @@ class IrGraph(object):
op_node.op()._rename_input(old_input_node.name(), new_input_node.name())
def link_to(self, node_in, node_out):
"""
Connect two nodes.
Args:
node_in(core.Node): the input node.
node_out(core.Node): the output node.
"""
assert node_in in self.graph.nodes() and node_out in self.graph.nodes(), \
'Th two arguments must be in the graph nodes.'
'The two arguments(node_in&node_out) must be in the graph nodes.'
node_in.outputs_append(node_out)
node_out.inputs_append(node_in)
def safe_remove_nodes(self, remove_nodes):
"""
Remove nodes safely since links connected to these removed nodes are
also removed.
Args:
remove_nodes(set): the nodes prepared to be removed.
"""
if not isinstance(remove_nodes, set):
if isinstance(remove_nodes, Iterable):
remove_nodes = set(remove_nodes)
else:
remove_nodes = {remove_nodes}
core.graph_safe_remove_nodes(self.graph, remove_nodes)
def draw(self, save_path, name, marked_nodes=None):
def has_circle(self):
"""
Check if the graph has a circle.
Returns:
bool: True if the graph has a circle else False.
"""
return core.has_circle(self.graph)
def graph_num(self):
"""
Count the number of unconnected graphs in this graph.
Returns:
int: the number of unconnected graphs.
"""
return core.graph_num(self.graph)
def topology_sort(self):
"""
Perform the topology sort operation on the graph.
Notes: the `graph` cannot contain a circle.
Returns:
set(core.Node): nodes in topology order.
"""
return core.topology_sort(self.graph)
def build_adjacency_list(self):
"""
Build an adjacency list of operations for the `graph`.
Returns:
dict{core.Node: set(core.Node)}: the adjacency list.
"""
return core.build_adjacency_list(self.graph)
def draw(self, save_path, name, marked_nodes=None, remove_ctr_var=True):
"""
Draw the graph. If `dot` command is installed, the drawn graph
will be saved as pdf file type, otherwise dot file type is used.
Args:
save_path(str): the save path of drawn graph.
name(str): the name of drawn graph.
marked_nodes(set(core.Node)): nodes that are needed to be marked.
Default value is None.
remove_ctr_var(bool): If it is set True, all control variable nodes
in the graph will be removed. Default value is True.
"""
def _convert_to_pdf(dot_file_path):
pdf_save_path = os.path.splitext(dot_file_path)[0] + '.pdf'
exited_code = subprocess.call('dot -Tpdf ' + dot_file_path \
......@@ -1629,15 +1815,17 @@ class IrGraph(object):
print('The {} is saved as the dot filetype.'.format(
dot_file_path))
if remove_ctr_var:
remove_ctr_vars = set()
ops_num = 0
for node in self.graph.nodes():
if node.is_ctrl_var():
remove_ctr_vars.add(node)
elif node.is_op():
self.safe_remove_nodes(remove_ctr_vars)
ops_num = 0
for node in self.graph.nodes():
if node.is_op():
ops_num += 1
print('Total ops num = {}.'.format(ops_num))
self.safe_remove_nodes(remove_ctr_vars)
if marked_nodes is not None:
if not isinstance(marked_nodes, set):
marked_nodes = set(marked_nodes)
......@@ -1652,10 +1840,20 @@ class IrGraph(object):
_convert_to_pdf(viz_dot_path)
def to_program(self):
"""
Convert the graph into a Program.
Notes: When the graph includes backward operator nodes, the
conversion process may be failed. Usually, this function is
only used to convert a test graph.
Returns:
Program: a program converted from the graph.
"""
convert_pass = core.get_pass('graph_to_program_pass')
convert_pass.set('program', Program().desc)
desc = core.ProgramDesc()
convert_pass.set_not_owned('program', desc)
convert_pass.apply(self.graph)
desc = convert_pass.get_program('program')
program = Program._construct_from_desc(desc)
return program
......
python/paddle/fluid/imperative/layers.py
浏览文件 @ c1455e60
......@@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import collections
import contextlib
import sys
import numpy as np
......@@ -30,31 +31,45 @@ class Layer(core.Layer):
def __init__(self, dtype=core.VarDesc.VarType.FP32, name=None):
self._built = False
self._dtype = dtype
self._parameters = collections.OrderedDict()
self._sub_layers = collections.OrderedDict()
def parameters(self, include_sublayers=True):
"""Returns a list of Parameters from current and sub-layers.
Args:
include_sublayers: If true, also include the parameters from
sublayers.
Returns a list of Parameters.
"""
ret = [p for p in self._parameters.values()]
if include_sublayers:
for l in self._sub_layers.values():
for p in l.parameters(include_sublayers):
ret.append(p)
return ret
def parameters(self):
params = []
for key in self.__dict__.keys():
value = self.__dict__[key]
if isinstance(value, framework.Parameter):
params.append(value)
elif isinstance(value, core.Layer):
params.extend(value.parameters())
elif isinstance(value, collections.Container):
if len(value) == 0:
continue
if isinstance(value[0], framework.Parameter):
params.extend(value)
elif isinstance(value[0], core.Layer):
for v in value:
params.extend(v.parameters())
return params
def sublayers(self, include_sublayers=True):
"""Returns a list of sub layers.
Args:
include_sublayers: If true, also include the layers from sublayers.
Returns a list of sub layers.
"""
ret = [l for l in self._sub_layers.values()]
if include_sublayers:
for l in self._sub_layers.values():
for sub_l in l.sublayers(include_sublayers):
ret.append(sub_l)
return ret
def clear_gradients(self):
for p in self.parameters():
p._clear_gradient()
def _build_once(self, inputs):
def _build_once(self, *args):
pass
def __call__(self, *inputs):
......@@ -71,6 +86,66 @@ class Layer(core.Layer):
def backward(self, *inputs):
raise ValueError("Layer shouldn't implement backward")
def add_sublayer(self, name, sublayer):
"""Adds a sub Layer instance.
Added sublayer can be access like self.name.
Args:
name: name of this sublayer.
sublayer: an instance of Layer.
Returns:
the sublayer passed in.
"""
assert isinstance(sublayer, core.Layer)
self._sub_layers[name] = sublayer
return sublayer
def add_parameter(self, name, parameter):
"""Adds a Parameter instance.
Added parameter can be access like self.name.
Args:
name: name of this sublayer.
parameter: an instance of Parameter.
Returns:
the parameter passed in.
"""
assert isinstance(parameter, framework.Parameter)
self._parameters[name] = parameter
return parameter
def __getattr__(self, name):
if name in self._parameters:
return self._parameters[name]
elif name in self._sub_layers:
return self._sub_layers[name]
def __setattr__(self, name, value):
if isinstance(value, framework.Parameter):
params = self.__dict__.get('_parameters', None)
if params is None:
raise ValueError(
"super(YourLayer, self).__init__() should be called first")
params[name] = value
elif isinstance(value, core.Layer):
layers = self.__dict__.get('_sub_layers', None)
if layers is None:
raise ValueError(
"super(YourLayer, self).__init__() should be called first")
layers[name] = value
else:
object.__setattr__(self, name, value)
def __delattr__(self, name):
if name in self._parameters:
del self._parameters[name]
elif name in self._sub_layers:
del self._sub_layers[name]
else:
object.__delattr__(self, name)
class PyLayer(core.PyLayer):
"""Layers composed of user-defined python codes."""
......
python/paddle/fluid/imperative/nn.py
浏览文件 @ c1455e60
......@@ -225,9 +225,6 @@ class FC(layers.Layer):
act=act,
name=name)
def parameters(self):
return [self._w, self._b]
def _build_once(self, input):
input_shape = input.shape
param_shape = [
......@@ -478,9 +475,6 @@ class Embedding(layers.Layer):
dtype=self._dtype,
is_bias=False)
def parameters(self):
return [self._w]
def forward(self, input):
out = self._helper.create_variable_for_type_inference(self._dtype)
self._helper.append_op(
......
python/paddle/fluid/layers/nn.py
浏览文件 @ c1455e60
......@@ -2930,6 +2930,7 @@ def batch_norm(input,
"momentum": momentum,
"epsilon": epsilon,
"is_test": is_test,
"data_layout": data_layout,
"use_mkldnn": False,
"fuse_with_relu": fuse_with_relu,
"use_global_stats": use_global_stats
......@@ -3235,7 +3236,7 @@ def group_norm(input,
# create output
mean_out = helper.create_variable(dtype=dtype, stop_gradient=True)
variance_out = helper.create_variable(dtype=dtype, stop_gradient=True)
group_norm_out = helper.create_variable(dtype)
group_norm_out = helper.create_variable(dtype=dtype)
helper.append_op(
type="group_norm",
......
python/paddle/fluid/tests/demo/file_reader/convert_data_to_recordio.py
浏览文件 @ c1455e60
......@@ -16,7 +16,6 @@ from __future__ import print_function
import sys
import paddle.fluid as fluid
import paddle.v2 as paddle
def load_vocab(filename):
......
python/paddle/fluid/tests/demo/pyreader.py
浏览文件 @ c1455e60
......@@ -20,7 +20,6 @@ import six
import paddle
import paddle.dataset.mnist as mnist
import paddle.fluid as fluid
import paddle.v2
def network(is_train):
......@@ -72,7 +71,7 @@ def main():
use_cuda=use_cuda, share_vars_from=trainer, main_program=test_prog)
train_reader.decorate_paddle_reader(
paddle.v2.reader.shuffle(
paddle.reader.shuffle(
paddle.batch(mnist.train(), 512), buf_size=8192))
test_reader.decorate_paddle_reader(paddle.batch(mnist.test(), 512))
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ c1455e60
......@@ -109,11 +109,12 @@ set_tests_properties(test_parallel_executor_fetch_feed PROPERTIES TIMEOUT 450)
py_test_modules(test_parallel_executor_transformer MODULES test_parallel_executor_transformer SERIAL)
if(NOT APPLE)
py_test_modules(test_image_classification_resnet MODULES test_image_classification_resnet SERIAL)
if(CMAKE_BUILD_TYPE STREQUAL "Debug")
# change the timeout from 600 to 1200, because in debug mode, this test need more time.
set_tests_properties(test_image_classification_resnet PROPERTIES TIMEOUT 1200)
endif()
endif()
if(CMAKE_BUILD_TYPE STREQUAL "Debug")
# change the timeout from 600 to 900, because in debug mode, this test need more time.
set_tests_properties(test_image_classification_resnet PROPERTIES TIMEOUT 900)
endif()
if (WITH_NGRAPH)
add_subdirectory(ngraph)
......
python/paddle/fluid/tests/unittests/ngraph/test_activation_ngraph_op.py
浏览文件 @ c1455e60
......@@ -18,17 +18,7 @@ import unittest
import numpy as np
import paddle.fluid.core as core
from paddle.fluid.tests.unittests.op_test import OpTest
from paddle.fluid.tests.unittests.test_activation_op import TestRelu, TestTanh
class TestNGRAPHReluDim2(TestRelu):
def setUp(self):
super(TestNGRAPHReluDim2, self).setUp()
class TestNGRAPHTanhDim2(TestTanh):
def setUp(self):
super(TestNGRAPHTanhDim2, self).setUp()
from paddle.fluid.tests.unittests.test_activation_op import TestSigmoid, TestRelu, TestTanh
class TestNGRAPHReluDim4(TestRelu):
......
python/paddle/utils/dump_config.py → python/paddle/fluid/tests/unittests/ngraph/test_batch_norm_ngraph_op.py
浏览文件 @ c1455e60
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
# 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.
......@@ -12,34 +12,26 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.trainer.config_parser import parse_config
from paddle.proto import TrainerConfig_pb2
import sys
from __future__ import print_function
import unittest
from paddle.fluid.tests.unittests.test_batch_norm_op import TestBatchNormOpTraining, TestBatchNormOpInference
class TestNGRAPHBatchNormOpTraining(TestBatchNormOpTraining):
def init_kernel_type(self):
super(TestNGRAPHBatchNormOpTraining, self).init_kernel_type()
class TestNGRAPHBatchNormOpInference(TestBatchNormOpInference):
def init_kernel_type(self):
super(TestNGRAPHBatchNormOpInference, self).init_kernel_type()
class TestNGRAPHBatchNormOpWithReluInference(TestBatchNormOpInference):
def init_kernel_type(self):
super(TestNGRAPHBatchNormOpWithReluInference, self).init_kernel_type()
__all__ = []
if __name__ == '__main__':
whole_conf = False
binary = False
if len(sys.argv) == 2:
conf = parse_config(sys.argv[1], '')
elif len(sys.argv) == 3:
conf = parse_config(sys.argv[1], sys.argv[2])
elif len(sys.argv) == 4:
conf = parse_config(sys.argv[1], sys.argv[2])
if sys.argv[3] == '--whole':
whole_conf = True
elif sys.argv[3] == '--binary':
binary = True
else:
raise RuntimeError()
assert isinstance(conf, TrainerConfig_pb2.TrainerConfig)
if whole_conf:
print(conf)
else:
if binary:
sys.stdout.write(conf.model_config.SerializeToString())
else:
print(conf.model_config)
unittest.main()
benchmark/paddle/rnn/imdb.py → python/paddle/fluid/tests/unittests/ngraph/test_sum_ngraph_op.py 100755 → 100644
浏览文件 @ c1455e60
......@@ -11,50 +11,9 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import six.moves.cPickle as pickle
import gzip
import os
import numpy
def get_dataset_file(dataset, default_dataset, origin):
data_dir, data_file = os.path.split(dataset)
if (not os.path.isfile(dataset)) and data_file == default_dataset:
from six.moves import urllib
print('Downloading data from %s' % origin)
urllib.request.urlretrieve(origin, dataset)
return dataset
def create_data(path="imdb.pkl"):
if (not os.path.isfile('imdb.train.pkl')):
path = get_dataset_file(
path, "imdb.pkl",
"http://www.iro.umontreal.ca/~lisa/deep/data/imdb.pkl")
if path.endswith(".gz"):
f = gzip.open(path, 'rb')
else:
f = open(path, 'rb')
train_set = pickle.load(f)
test_set = pickle.load(f)
f.close()
pickle.dump(train_set, open('imdb.train.pkl', 'wb'))
pickle.dump(test_set, open('imdb.test.pkl', 'wb'))
if (not os.path.isfile('train.list')):
file('train.list', 'w').write('imdb.train.pkl\n')
def main():
create_data('imdb.pkl')
import unittest
from paddle.fluid.tests.unittests.test_sum_op import TestSumOp, TestSelectedRowsSumOp, TestLoDTensorAndSelectedRowsOp
if __name__ == "__main__":
main()
unittest.main()
python/paddle/fluid/tests/unittests/test_base_layer.py 0 → 100644
浏览文件 @ c1455e60
# 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 unittest
import numpy as np
import paddle.fluid as fluid
from paddle.fluid.layer_helper import LayerHelper
class L1(fluid.imperative.Layer):
def __init__(self):
super(L1, self).__init__()
self._helper = LayerHelper(
'MyLayer',
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.1)))
self.w1 = self._helper.create_parameter(
attr=self._helper.param_attr,
shape=[2, 2],
dtype='float32',
is_bias=False)
self.w2 = self._helper.create_parameter(
attr=self._helper.param_attr,
shape=[2, 2],
dtype='float32',
is_bias=False)
def forward(self):
return self.w1 + self.w2
class L2(fluid.imperative.Layer):
def __init__(self):
super(L2, self).__init__()
self.layer1 = L1()
self.layer2 = L1()
def forward(self):
return self.layer1() + self.layer2()
class L3(fluid.imperative.Layer):
def __init__(self):
super(L3, self).__init__()
self.layer1 = L2()
self.layer2 = L2()
def forward(self):
return self.layer1() + self.layer2()
class TestBaseLayer(unittest.TestCase):
def test_one_level(self):
with fluid.imperative.guard():
l = L1()
ret = l()
self.assertEqual(l.w1.name, "MyLayer_0.w_0")
self.assertEqual(l.w2.name, "MyLayer_0.w_1")
self.assertTrue(np.allclose(ret._numpy(), 0.2 * np.ones([2, 2])))
def test_three_level(self):
with fluid.imperative.guard():
l = L3()
ret = l()
self.assertTrue(np.allclose(ret._numpy(), 0.8 * np.ones([2, 2])))
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_expand_op.py
浏览文件 @ c1455e60
......@@ -109,5 +109,32 @@ class TestExpandOpRank4(OpTest):
self.check_grad(['X'], 'Out')
class TestExpandOpInteger(OpTest):
def setUp(self):
self.op_type = "expand"
self.inputs = {
'X': np.random.randint(
10, size=(2, 4, 5)).astype("int32")
}
self.attrs = {'expand_times': [2, 1, 4]}
output = np.tile(self.inputs['X'], (2, 1, 4))
self.outputs = {'Out': output}
def test_check_output(self):
self.check_output()
class TestExpandOpBoolean(OpTest):
def setUp(self):
self.op_type = "expand"
self.inputs = {'X': np.random.randint(2, size=(2, 4, 5)).astype("bool")}
self.attrs = {'expand_times': [2, 1, 4]}
output = np.tile(self.inputs['X'], (2, 1, 4))
self.outputs = {'Out': output}
def test_check_output(self):
self.check_output()
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_imperative.py
浏览文件 @ c1455e60
......@@ -333,6 +333,18 @@ class TestImperative(unittest.TestCase):
self.assertTrue(np.allclose(dy_out, static_out))
self.assertTrue(np.allclose(dy_grad, static_grad))
params = mlp.parameters(True)
self.assertEqual("FC_0.w_0", params[0].name)
self.assertEqual("FC_0.b_0", params[1].name)
self.assertEqual("FC_1.w_0", params[2].name)
self.assertEqual("FC_1.b_0", params[3].name)
self.assertEqual(len(params), 4)
sublayers = mlp.sublayers(True)
self.assertEqual(mlp._fc1, sublayers[0])
self.assertEqual(mlp._fc2, sublayers[1])
self.assertEqual(len(sublayers), 2)
def test_rnn(self):
np_inp = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0],
[10.0, 11.0, 12.0]])
......
python/paddle/fluid/tests/unittests/test_imperative_gan.py
浏览文件 @ c1455e60
......@@ -33,9 +33,6 @@ class Discriminator(fluid.imperative.Layer):
self._fc1 = FC(size=32, act='elu', name="d_fc1")
self._fc2 = FC(size=1, name="d_fc2")
def parameters(self):
return self._fc1.parameters() + self._fc2.parameters()
def forward(self, inputs):
x = self._fc1(inputs)
return self._fc2(x)
......@@ -48,10 +45,6 @@ class Generator(fluid.imperative.Layer):
self._fc2 = FC(size=64, act='elu', name="g_fc2")
self._fc3 = FC(size=1, name="g_fc3")
def parameters(self):
return self._fc1.parameters() + self._fc2.parameters(
) + self._fc3.parameters()
def forward(self, inputs):
x = self._fc1(inputs)
x = self._fc2(x)
......
python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py
浏览文件 @ c1455e60
......@@ -75,16 +75,6 @@ class SimpleLSTMRNN(fluid.imperative.Layer):
self.hidden_array.append(pre_hidden)
self.cell_array.append(pre_cell)
def parameters(self):
parameters = list()
for param in self.weight_1_arr:
parameters.append(param)
for param in self.weight_2_arr:
parameters.append(param)
for bias in self.bias_arr:
parameters.append(bias)
return parameters
def forward(self, input_embedding, init_hidden=None, init_cell=None):
res = []
for index in range(self._num_steps):
......@@ -177,12 +167,6 @@ class PtbModel(fluid.imperative.Layer):
def _build_once(self, input, label, init_hidden, init_cell):
pass
def parameters(self):
parameters = self.simple_lstm_rnn.parameters() + [
self.softmax_weight, self.softmax_bias
] + self.embedding.parameters()
return parameters
def forward(self, input, label, init_hidden, init_cell):
init_h = fluid.layers.reshape(
......
python/paddle/fluid/tests/unittests/test_imperative_resnet.py
浏览文件 @ c1455e60
......@@ -21,7 +21,6 @@ import paddle
import paddle.fluid as fluid
from paddle.fluid import core
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.optimizer import SGDOptimizer
from paddle.fluid.imperative.nn import Conv2D, Pool2D, BatchNorm, FC
from paddle.fluid.imperative.base import to_variable
from test_imperative_base import new_program_scope
......@@ -173,11 +172,13 @@ class ResNet(fluid.imperative.Layer):
for block in range(len(depth)):
shortcut = False
for i in range(depth[block]):
bottleneck_block = BottleneckBlock(
bottleneck_block = self.add_sublayer(
'bb_%d_%d' % (block, i),
BottleneckBlock(
num_channels=num_channels,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut)
shortcut=shortcut))
num_channels = bottleneck_block._num_channels_out
self.bottleneck_block_list.append(bottleneck_block)
shortcut = True
......@@ -223,8 +224,7 @@ class TestImperativeResnet(unittest.TestCase):
batch_size=batch_size)
dy_param_init_value = {}
for param in fluid.default_main_program().global_block(
).all_parameters():
for param in resnet.parameters():
dy_param_init_value[param.name] = param._numpy()
for batch_id, data in enumerate(train_reader()):
......@@ -247,16 +247,14 @@ class TestImperativeResnet(unittest.TestCase):
dy_out = avg_loss._numpy()
if batch_id == 0:
for param in fluid.default_main_program().global_block(
).all_parameters():
for param in resnet.parameters():
if param.name not in dy_param_init_value:
dy_param_init_value[param.name] = param._numpy()
avg_loss._backward()
dy_grad_value = {}
for param in fluid.default_main_program().global_block(
).all_parameters():
for param in resnet.parameters():
if not param.stop_gradient:
np_array = np.array(param._ivar._grad_ivar().value()
.get_tensor())
......@@ -267,8 +265,7 @@ class TestImperativeResnet(unittest.TestCase):
resnet.clear_gradients()
dy_param_value = {}
for param in fluid.default_main_program().global_block(
).all_parameters():
for param in resnet.parameters():
dy_param_value[param.name] = param._numpy()
with new_program_scope():
......@@ -349,6 +346,7 @@ class TestImperativeResnet(unittest.TestCase):
self.assertTrue(np.allclose(static_out, dy_out))
self.assertEqual(len(dy_param_init_value), len(static_param_init_value))
for key, value in six.iteritems(static_param_init_value):
self.assertTrue(np.allclose(value, dy_param_init_value[key]))
self.assertTrue(np.isfinite(value.all()))
......
python/paddle/fluid/transpiler/memory_optimization_transpiler.py
浏览文件 @ c1455e60
......@@ -355,6 +355,10 @@ class ControlFlowGraph(object):
is_forward).dtype()
cache_dtype = self._find_var(block_desc, cache_var,
is_forward).dtype()
if x_dtype != cache_dtype:
if PRINT_LOG:
print("x_dtype and cache_dtype are different")
continue
if not compare_shape(x_shape, cache_shape, level):
continue
......
python/paddle/utils/dump_v2_config.py 已删除 100644 → 0
浏览文件 @ d376cf71
# Copyright (c) 2016 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 collections
from paddle.trainer_config_helpers.layers import LayerOutput
from paddle.v2.layer import parse_network
from paddle.proto import TrainerConfig_pb2
__all__ = ["dump_v2_config"]
def dump_v2_config(topology, save_path, binary=False):
""" Dump the network topology to a specified file.
This function is only used to dump network defined by using PaddlePaddle V2
APIs. This function will NOT dump configurations related to PaddlePaddle
optimizer.
:param topology: The output layers (can be more than one layers given in a
Python List or Tuple) of the entire network. Using the
specified layers (if more than one layer is given) as root,
traversing back to the data layer(s), all the layers
connected to the specified output layers will be dumped.
Layers not connceted to the specified will not be dumped.
:type topology: LayerOutput|List|Tuple
:param save_path: The path to save the dumped network topology.
:type save_path: str
:param binary: Whether to dump the serialized network topology or not.
The default value is false. NOTE that, if you call this
function to generate network topology for PaddlePaddle C-API,
a serialized version of network topology is required. When
using PaddlePaddle C-API, this flag MUST be set to True.
:type binary: bool
"""
if isinstance(topology, LayerOutput):
topology = [topology]
elif isinstance(topology, collections.Sequence):
for out_layer in topology:
assert isinstance(out_layer, LayerOutput), (
"The type of each element in the parameter topology "
"should be LayerOutput.")
else:
raise RuntimeError("Error input type for parameter topology.")
model_str = parse_network(topology)
with open(save_path, "w") as fout:
if binary:
fout.write(model_str.SerializeToString())
else:
fout.write(str(model_str))
python/paddle/utils/image_multiproc.py 已删除 100644 → 0
浏览文件 @ d376cf71
# 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, sys
import numpy as np
from PIL import Image
import six
from six.moves import cStringIO as StringIO
import multiprocessing
import functools
import itertools
from paddle.utils.image_util import *
from paddle.trainer.config_parser import logger
try:
import cv2
except ImportError:
logger.warning("OpenCV2 is not installed, using PIL to process")
cv2 = None
__all__ = ["CvTransformer", "PILTransformer", "MultiProcessImageTransformer"]
class CvTransformer(ImageTransformer):
"""
CvTransformer used python-opencv to process image.
"""
def __init__(
self,
min_size=None,
crop_size=None,
transpose=(2, 0, 1), # transpose to C * H * W
channel_swap=None,
mean=None,
is_train=True,
is_color=True):
ImageTransformer.__init__(self, transpose, channel_swap, mean, is_color)
self.min_size = min_size
self.crop_size = crop_size
self.is_train = is_train
def resize(self, im, min_size):
row, col = im.shape[:2]
new_row, new_col = min_size, min_size
if row > col:
new_row = min_size * row / col
else:
new_col = min_size * col / row
im = cv2.resize(im, (new_row, new_col), interpolation=cv2.INTER_CUBIC)
return im
def crop_and_flip(self, im):
"""
Return cropped image.
The size of the cropped image is inner_size * inner_size.
im: (H x W x K) ndarrays
"""
row, col = im.shape[:2]
start_h, start_w = 0, 0
if self.is_train:
start_h = np.random.randint(0, row - self.crop_size + 1)
start_w = np.random.randint(0, col - self.crop_size + 1)
else:
start_h = (row - self.crop_size) / 2
start_w = (col - self.crop_size) / 2
end_h, end_w = start_h + self.crop_size, start_w + self.crop_size
if self.is_color:
im = im[start_h:end_h, start_w:end_w, :]
else:
im = im[start_h:end_h, start_w:end_w]
if (self.is_train) and (np.random.randint(2) == 0):
if self.is_color:
im = im[:, ::-1, :]
else:
im = im[:, ::-1]
return im
def transform(self, im):
im = self.resize(im, self.min_size)
im = self.crop_and_flip(im)
# transpose, swap channel, sub mean
im = im.astype('float32')
ImageTransformer.transformer(self, im)
return im
def load_image_from_string(self, data):
flag = cv2.CV_LOAD_IMAGE_COLOR if self.is_color else cv2.CV_LOAD_IMAGE_GRAYSCALE
im = cv2.imdecode(np.fromstring(data, np.uint8), flag)
return im
def transform_from_string(self, data):
im = self.load_image_from_string(data)
return self.transform(im)
def load_image_from_file(self, file):
flag = cv2.CV_LOAD_IMAGE_COLOR if self.is_color else cv2.CV_LOAD_IMAGE_GRAYSCALE
im = cv2.imread(file, flag)
return im
def transform_from_file(self, file):
im = self.load_image_from_file(file)
return self.transform(im)
class PILTransformer(ImageTransformer):
"""
PILTransformer used PIL to process image.
"""
def __init__(
self,
min_size=None,
crop_size=None,
transpose=(2, 0, 1), # transpose to C * H * W
channel_swap=None,
mean=None,
is_train=True,
is_color=True):
ImageTransformer.__init__(self, transpose, channel_swap, mean, is_color)
self.min_size = min_size
self.crop_size = crop_size
self.is_train = is_train
def resize(self, im, min_size):
row, col = im.size[:2]
new_row, new_col = min_size, min_size
if row > col:
new_row = min_size * row / col
else:
new_col = min_size * col / row
im = im.resize((new_row, new_col), Image.ANTIALIAS)
return im
def crop_and_flip(self, im):
"""
Return cropped image.
The size of the cropped image is inner_size * inner_size.
"""
row, col = im.size[:2]
start_h, start_w = 0, 0
if self.is_train:
start_h = np.random.randint(0, row - self.crop_size + 1)
start_w = np.random.randint(0, col - self.crop_size + 1)
else:
start_h = (row - self.crop_size) / 2
start_w = (col - self.crop_size) / 2
end_h, end_w = start_h + self.crop_size, start_w + self.crop_size
im = im.crop((start_h, start_w, end_h, end_w))
if (self.is_train) and (np.random.randint(2) == 0):
im = im.transpose(Image.FLIP_LEFT_RIGHT)
return im
def transform(self, im):
im = self.resize(im, self.min_size)
im = self.crop_and_flip(im)
im = np.array(im, dtype=np.float32) # convert to numpy.array
# transpose, swap channel, sub mean
ImageTransformer.transformer(self, im)
return im
def load_image_from_string(self, data):
im = Image.open(StringIO(data))
return im
def transform_from_string(self, data):
im = self.load_image_from_string(data)
return self.transform(im)
def load_image_from_file(self, file):
im = Image.open(file)
return im
def transform_from_file(self, file):
im = self.load_image_from_file(file)
return self.transform(im)
def job(is_img_string, transformer, data_label_pack):
(data, label) = data_label_pack
if is_img_string:
return transformer.transform_from_string(data), label
else:
return transformer.transform_from_file(data), label
class MultiProcessImageTransformer(object):
def __init__(self,
procnum=10,
resize_size=None,
crop_size=None,
transpose=(2, 0, 1),
channel_swap=None,
mean=None,
is_train=True,
is_color=True,
is_img_string=True):
"""
Processing image with multi-process. If it is used in PyDataProvider,
the simple usage for CNN is as follows:
.. code-block:: python
def hool(settings, is_train, **kwargs):
settings.is_train = is_train
settings.mean_value = np.array([103.939,116.779,123.68], dtype=np.float32)
settings.input_types = [
dense_vector(3 * 224 * 224),
integer_value(1)]
settings.transformer = MultiProcessImageTransformer(
procnum=10,
resize_size=256,
crop_size=224,
transpose=(2, 0, 1),
mean=settings.mean_values,
is_train=settings.is_train)
@provider(init_hook=hook, pool_size=20480)
def process(settings, file_list):
with open(file_list, 'r') as fdata:
for line in fdata:
data_dic = np.load(line.strip()) # load the data batch pickled by Pickle.
data = data_dic['data']
labels = data_dic['label']
labels = np.array(labels, dtype=np.float32)
for im, lab in settings.dp.run(data, labels):
yield [im.astype('float32'), int(lab)]
:param procnum: processor number.
:type procnum: int
:param resize_size: the shorter edge size of image after resizing.
:type resize_size: int
:param crop_size: the croping size.
:type crop_size: int
:param transpose: the transpose order, Paddle only allow C * H * W order.
:type transpose: tuple or list
:param channel_swap: the channel swap order, RGB or BRG.
:type channel_swap: tuple or list
:param mean: the mean values of image, per-channel mean or element-wise mean.
:type mean: array, The dimension is 1 for per-channel mean.
The dimension is 3 for element-wise mean.
:param is_train: training peroid or testing peroid.
:type is_train: bool.
:param is_color: the image is color or gray.
:type is_color: bool.
:param is_img_string: The input can be the file name of image or image string.
:type is_img_string: bool.
"""
self.procnum = procnum
self.pool = multiprocessing.Pool(procnum)
self.is_img_string = is_img_string
if cv2 is not None:
self.transformer = CvTransformer(resize_size, crop_size, transpose,
channel_swap, mean, is_train,
is_color)
else:
self.transformer = PILTransformer(resize_size, crop_size, transpose,
channel_swap, mean, is_train,
is_color)
def run(self, data, label):
fun = functools.partial(job, self.is_img_string, self.transformer)
return self.pool.imap_unordered(
fun, six.moves.zip(data, label), chunksize=100 * self.procnum)
python/paddle/utils/make_model_diagram.py 已删除 100644 → 0
浏览文件 @ d376cf71
# Copyright (c) 2016 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.
# Generate dot diagram file for the given paddle model config
# The generated file can be viewed using Graphviz (http://graphviz.org)
from __future__ import print_function
import six
import sys
import traceback
from paddle.trainer.config_parser import parse_config
def make_layer_label(layer_config):
label = '%s type=%s' % (layer_config.name, layer_config.type)
if layer_config.reversed:
label += ' <=='
label2 = ''
if layer_config.active_type:
label2 += 'act=%s ' % layer_config.active_type
if layer_config.bias_parameter_name:
label2 += 'bias=%s ' % layer_config.bias_parameter_name
if label2:
label += '\l' + label2
return label
def make_diagram(config_file, dot_file, config_arg_str):
config = parse_config(config_file, config_arg_str)
make_diagram_from_proto(config.model_config, dot_file)
def make_diagram_from_proto(model_config, dot_file):
# print >> sys.stderr, config
name2id = {}
f = open(dot_file, 'w')
submodel_layers = set()
def make_link(link):
return 'l%s -> l%s;' % (name2id[link.layer_name],
name2id[link.link_name])
def make_mem(mem):
s = ''
if mem.boot_layer_name:
s += 'l%s -> l%s;\n' % (name2id[mem.boot_layer_name],
name2id[mem.layer_name])
s += 'l%s -> l%s [style=dashed];' % (name2id[mem.layer_name],
name2id[mem.link_name])
return s
print('digraph graphname {', file=f)
print('node [width=0.375,height=0.25];', file=f)
for i in six.moves.xrange(len(model_config.layers)):
l = model_config.layers[i]
name2id[l.name] = i
i = 0
for sub_model in model_config.sub_models:
if sub_model.name == 'root':
continue
print('subgraph cluster_%s {' % i, file=f)
print('style=dashed;', file=f)
label = '%s ' % sub_model.name
if sub_model.reversed:
label += '<=='
print('label = "%s";' % label, file=f)
i += 1
submodel_layers.add(sub_model.name)
for layer_name in sub_model.layer_names:
submodel_layers.add(layer_name)
lid = name2id[layer_name]
layer_config = model_config.layers[lid]
label = make_layer_label(layer_config)
print('l%s [label="%s", shape=box];' % (lid, label), file=f)
print('}', file=f)
for i in six.moves.xrange(len(model_config.layers)):
l = model_config.layers[i]
if l.name not in submodel_layers:
label = make_layer_label(l)
print('l%s [label="%s", shape=box];' % (i, label), file=f)
for sub_model in model_config.sub_models:
if sub_model.name == 'root':
continue
for link in sub_model.in_links:
print(make_link(link), file=f)
for link in sub_model.out_links:
print(make_link(link), file=f)
for mem in sub_model.memories:
print(make_mem(mem), file=f)
for i in six.moves.xrange(len(model_config.layers)):
for l in model_config.layers[i].inputs:
print(
'l%s -> l%s [label="%s"];' % (name2id[l.input_layer_name], i,
l.input_parameter_name),
file=f)
print('}', file=f)
f.close()
def usage():
print(
("Usage: python show_model_diagram.py" +
" CONFIG_FILE DOT_FILE [config_str]"),
file=sys.stderr)
exit(1)
if __name__ == '__main__':
if len(sys.argv) < 3 or len(sys.argv) > 4:
usage()
config_file = sys.argv[1]
dot_file = sys.argv[2]
config_arg_str = sys.argv[3] if len(sys.argv) == 4 else ''
try:
make_diagram(config_file, dot_file, config_arg_str)
except:
traceback.print_exc()
raise
python/paddle/utils/merge_model.py 已删除 100644 → 0
浏览文件 @ d376cf71
# Copyright (c) 2016 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 gzip
import struct
import os
from paddle.trainer_config_helpers.layers import LayerOutput
from paddle.v2.parameters import Parameters
from paddle.proto import ModelConfig_pb2
from paddle.v2.topology import Topology
def merge_v2_model(net, param_file, output_file):
'''Merge the model config and parameters into one file.
The model configuration file describes the model structure which
ends with .py. The parameters file stores the parameters of the model
which ends with .tar.gz.
@param net The output layer of the network for inference.
@param param_file Path of the parameters (.tar.gz) which is stored by
v2 api.
@param output_file Path of the merged file which will be generated.
Usage:
from paddle.utils.merge_model import merge_v2_model
# import your network configuration
from example_net import net_conf
net = net_conf(is_predict=True)
param_file = './param_pass_00000.tar.gz'
output_file = './output.paddle'
merge_v2_model(net, param_file, output_file)
'''
assert isinstance(net, LayerOutput), \
"The net should be the output of the network for inference"
assert os.path.exists(param_file), \
"The model parameters file %s does not exists " % (param_file)
model_proto = Topology(net).proto()
assert isinstance(model_proto, ModelConfig_pb2.ModelConfig)
with gzip.open(param_file) as f:
params = Parameters.from_tar(f)
if os.path.exists(output_file):
os.remove(output_file)
with open(output_file, 'w') as f:
param_names = [param.name for param in model_proto.parameters]
conf_str = model_proto.SerializeToString()
f.write(struct.pack('q', len(conf_str)))
f.write(conf_str)
for pname in param_names:
params.serialize(pname, f)
print('Generate %s success!' % (output_file))
python/paddle/utils/predefined_net.py 已删除 100644 → 0
浏览文件 @ d376cf71
# Copyright (c) 2016 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 numpy as np
import six
import os
from paddle.trainer.config_parser import *
from paddle.utils.preprocess_img import \
ImageClassificationDatasetCreater
from paddle.trainer_config_helpers import *
def image_data(data_dir,
processed_image_size,
overwrite=False,
color=True,
train_list="batches/train.list",
test_list="batches/test.list",
meta_file="batches/batches.meta",
use_jpeg=1):
"""
Predefined image data provider for image classification.
train_list: a text file containing a list of training batches.
test_list: a text file containing a list of test batches.
processed_image_size: all the input images will be resized into this size.
If the image is not square. Then the shorter edge will be resized into
this size, and the aspect ratio is kept the same.
color: whether the images are color or gray.
meta_path: the path of the meta file that stores the mean image file and
other dataset information, such as the size of images,
the size of the mean image, the number of classes.
async_load_data: whether to load image data asynchronuously.
"""
data_creator = ImageClassificationDatasetCreater(
data_dir, processed_image_size, color)
batch_data_dir = data_dir
train_list = os.path.join(batch_data_dir, train_list)
test_list = os.path.join(batch_data_dir, test_list)
meta_path = os.path.join(batch_data_dir, meta_file)
image_size = processed_image_size
conf = np.load(meta_path)
mean_image_size = conf["mean_image_size"]
is_color = conf["color"]
num_classes = conf["num_classes"]
color_string = "color" if is_color else "gray"
args = {
'meta': meta_path,
'mean_img_size': mean_image_size,
'img_size': image_size,
'num_classes': num_classes,
'use_jpeg': use_jpeg != 0,
'color': color_string
}
define_py_data_sources2(
train_list,
test_list,
module='image_provider',
obj='processData',
args=args)
return {
"image_size": image_size,
"num_classes": num_classes,
"is_color": is_color
}
def get_extra_layer_attr(drop_rate):
if drop_rate == 0:
return None
else:
return ExtraLayerAttribute(drop_rate=drop_rate)
def image_data_layers(image_size, num_classes, is_color=False,
is_predict=False):
"""
Data layers for image classification.
image_size: image size.
num_classes: num of classes.
is_color: whether the input images are color.
is_predict: whether the network is used for prediction.
"""
num_image_channels = 3 if is_color else 1
data_input = data_layer("input",
image_size * image_size * num_image_channels)
if is_predict:
return data_input, None, num_image_channels
else:
label_input = data_layer("label", 1)
return data_input, label_input, num_image_channels
def simple_conv_net(data_conf, is_color=False):
"""
A Wrapper for a simple network for MNIST digit recognition.
It contains two convolutional layers, one fully conencted layer, and
one softmax layer.
data_conf is a dictionary with the following keys:
image_size: image size.
num_classes: num of classes.
is_color: whether the input images are color.
"""
for k, v in six.iteritems(data_conf):
globals()[k] = v
data_input, label_input, num_image_channels = \
image_data_layers(image_size, num_classes, is_color, is_predict)
filter_sizes = [5, 5]
num_channels = [32, 64]
strides = [1, 1]
fc_dims = [500]
conv_bn_pool1 = img_conv_bn_pool(
name="g1",
input=data_input,
filter_size=filter_sizes[0],
num_channel=num_image_channels,
num_filters=num_channels[0],
conv_stride=1,
conv_padding=0,
pool_size=3,
pool_stride=2,
act=ReluActivation())
conv_bn_pool2 = img_conv_bn_pool(
name="g2",
input=conv_bn_pool1,
filter_size=filter_sizes[1],
num_channel=num_channels[0],
num_filters=num_channels[1],
conv_stride=1,
conv_padding=0,
pool_size=3,
pool_stride=2,
act=ReluActivation())
fc3 = fc_layer(
name="fc3", input=conv_bn_pool2, dim=fc_dims[0], act=ReluActivation())
fc3_dropped = dropout_layer(name="fc3_dropped", input=fc3, dropout_rate=0.5)
output = fc_layer(
name="output",
input=fc3_dropped,
dim=fc_dims[0],
act=SoftmaxActivation())
if is_predict:
end_of_network(output)
else:
cost = classify(name="cost", input=output, label=label_input)
end_of_network(cost)
def conv_layer_group(prefix_num,
num_layers,
input,
input_channels,
output_channels,
drop_rates=[],
strides=[],
with_bn=[]):
"""
A set of convolution layers, and batch normalization layers,
followed by one pooling layer.
It is utilized in VGG network for image classifcation.
prefix_num: the prefix number of the layer names.
For example, if prefix_num = 1, the first convolutioal layer's
name will be conv_1_1.
num_layers: number of the convolutional layers.
input: the name of the input layer.
input_channels: the number of channels of the input feature map.
output_channels: the number of channels of the output feature map.
drop_rates: the drop rates of the BN layers. It will be all zero by default.
strides: the stride of the convolution for the layers.
It will be all 1 by default.
with_bn: whether to use Batch Normalization for Conv layers.
By default, it is all false.
"""
if len(drop_rates) == 0: drop_rates = [0] * num_layers
if len(strides) == 0: strides = [1] * num_layers
if len(with_bn) == 0: with_bn = [False] * num_layers
assert (len(drop_rates) == num_layers)
assert (len(strides) == num_layers)
for i in range(1, num_layers + 1):
if i == 1:
i_conv_in = input
else:
i_conv_in = group_output
i_channels_conv = input_channels if i == 1 else output_channels
conv_act = LinearActivation() if with_bn[i - 1] else ReluActivation()
conv_output = img_conv_layer(
name="conv%d_%d" % (prefix_num, i),
input=i_conv_in,
filter_size=3,
num_channels=i_channels_conv,
num_filters=output_channels,
stride=strides[i - 1],
padding=1,
act=conv_act)
if with_bn[i - 1]:
bn = batch_norm_layer(
name="conv%d_%d_bn" % (prefix_num, i),
input=conv_output,
num_channels=output_channels,
act=ReluActivation(),
layer_attr=get_extra_layer_attr(drop_rate=drop_rates[i - 1]))
group_output = bn
else:
group_output = conv_output
pool = img_pool_layer(
name="pool%d" % prefix_num,
input=group_output,
pool_size=2,
num_channels=output_channels,
stride=2)
return pool
def vgg_conv_net(image_size,
num_classes,
num_layers,
channels,
strides,
with_bn,
fc_dims,
drop_rates,
drop_rates_fc=[],
is_color=True,
is_predict=False):
"""
A Wrapper for a VGG network for image classification.
It is a set of convolutional groups followed by several fully
connected layers, and a cross-entropy classifiation loss.
The detailed architecture of the paper can be found here:
Very Deep Convolutional Networks for Large-Scale Visual Recognition
http://www.robots.ox.ac.uk/~vgg/research/very_deep/
image_size: image size.
num_classes: num of classes.
num_layers: the number of layers for all the convolution groups.
channels: the number of output filters for all the convolution groups.
with_bn: whether each layer of a convolution group is followed by a
batch normalization.
drop_rates: the dropout rates for all the convolutional layers.
fc_dims: the dimension for all the fully connected layers.
is_color: whether the input images are color.
"""
data_input, label_input, num_image_channels = \
image_data_layers(image_size, num_classes, is_color, is_predict)
assert (len(num_layers) == len(channels))
assert (len(num_layers) == len(strides))
assert (len(num_layers) == len(with_bn))
num_fc_layers = len(fc_dims)
assert (num_fc_layers + 1 == len(drop_rates_fc))
for i in range(len(num_layers)):
input_layer = data_input if i == 0 else group_output
input_channels = 3 if i == 0 else channels[i - 1]
group_output = conv_layer_group(
prefix_num=i + 1,
num_layers=num_layers[i],
input=input_layer,
input_channels=input_channels,
output_channels=channels[i],
drop_rates=drop_rates[i],
strides=strides[i],
with_bn=with_bn[i])
conv_output_name = group_output
if drop_rates_fc[0] != 0.0:
dropped_pool_name = "pool_dropped"
conv_output_name = dropout_layer(
name=dropped_pool_name,
input=conv_output_name,
dropout_rate=drop_rates_fc[0])
for i in range(len(fc_dims)):
input_layer_name = conv_output_name if i == 0 else fc_output
active_type = LinearActivation() if i == len(
fc_dims) - 1 else ReluActivation()
drop_rate = 0.0 if i == len(fc_dims) - 1 else drop_rates_fc[i + 1]
fc_output = fc_layer(
name="fc%d" % (i + 1),
input=input_layer_name,
size=fc_dims[i],
act=active_type,
layer_attr=get_extra_layer_attr(drop_rate))
bn = batch_norm_layer(
name="fc_bn",
input=fc_output,
num_channels=fc_dims[len(fc_dims) - 1],
act=ReluActivation(),
layer_attr=get_extra_layer_attr(drop_rate=drop_rates_fc[-1]))
output = fc_layer(
name="output", input=bn, size=num_classes, act=SoftmaxActivation())
if is_predict:
outputs(output)
else:
cost = classification_cost(name="cost", input=output, label=label_input)
outputs(cost)
def vgg16_conv_net(image_size, num_classes, is_color=True, is_predict=False):
"""
A Wrapper for a 16 layers VGG network for image classification.
The detailed architecture of the paper can be found here:
Very Deep Convolutional Networks for Large-Scale Visual Recognition
http://www.robots.ox.ac.uk/~vgg/research/very_deep/
image_size: image size.
num_classes: num of classes.
is_color: whether the input images are color.
"""
vgg_conv_net(image_size, num_classes,
num_layers=[2, 2, 3, 3, 3],
channels=[64, 128, 256, 512, 512],
strides=[[], [], [], [], []],
with_bn=[[False, True], [False, True], [False, False, True], \
[False, False, True], [False, False, True]],
drop_rates=[[]] * 5,
drop_rates_fc=[0.0, 0.5, 0.5],
fc_dims=[4096, 4096],
is_predict=is_predict)
def small_vgg(data_conf, is_predict=False):
"""
A Wrapper for a small VGG network for CIFAR-10 image classification.
The detailed architecture of the paper can be found here:
92.45% on CIFAR-10 in Torch
http://torch.ch/blog/2015/07/30/cifar.html
Due to the constraints of CuDNN, it only has four convolutional groups
rather than five.
Thus, it only achieves 91.2% test accuracy and 98.1% training accuracy.
data_conf is a dictionary with the following keys:
image_size: image size.
num_classes: num of classes.
is_color: whether the input images are color.
"""
for k, v in six.iteritems(data_conf):
globals()[k] = v
vgg_conv_net(image_size, num_classes,
num_layers=[2, 2, 3, 3],
channels=[64, 128, 256, 512],
strides=[[], [], [], []],
with_bn=[[True, True], [True, True], [True, True, True], \
[True, True, True]],
drop_rates=[[0.3, 0.0], [0.4, 0.0],
[0.4, 0.4, 0.0], [0.4, 0.4, 0.0]],
drop_rates_fc=[0.5, 0.5],
fc_dims=[512],
is_predict=is_predict)
def training_settings(learning_rate=0.1,
batch_size=128,
algorithm="sgd",
momentum=0.9,
decay_rate=0.001):
"""
Training settings.
learning_rate: learning rate of the training.
batch_size: the size of each training batch.
algorithm: training algorithm, can be
- sgd
- adagrad
- adadelta
- rmsprop
momentum: momentum of the training algorithm.
decay_rate: weight decay rate.
"""
Settings(
algorithm=algorithm,
batch_size=batch_size,
learning_rate=learning_rate / float(batch_size))
default_momentum(momentum)
default_decay_rate(decay_rate * batch_size)
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