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fix quick start code and doc

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littletomatodonkey 2020-10-21 09:21:22 +00:00
parent 7592654c6b
commit 7153ee1129
5 changed files with 69 additions and 52 deletions
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31
docs/en/tutorials/quick_start_en.md
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@ -40,33 +40,28 @@ cd ../../
## Environment
### Set PYTHONPATH
```bash
export PYTHONPATH=./:$PYTHONPATH
```
### Download pretrained model
You can use the following commands to downdload the pretrained models.
```bash
python tools/download.py -a ResNet50_vd -p ./pretrained -d True
python tools/download.py -a ResNet50_vd_ssld -p ./pretrained -d True
python tools/download.py -a MobileNetV3_large_x1_0 -p ./pretrained -d True
mkdir pretrained
cd pretrained
wget https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar
wget https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
wget https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_pretrained.tar
tar -xf ResNet50_vd_pretrained.tar
tar -xf ResNet50_vd_ssld_pretrained.tar
tar -xf MobileNetV3_large_x1_0_pretrained.tar
cd ../
```
Paramters：
+ `architecture`(shortname: a): model name.
+ `path`(shortname: p) download path.
+ `decompress`(shortname: d) whether to decompress.
* All experiments are running on the NVIDIA® Tesla® V100 sigle card.
**Note**: If you want to download the pretrained models on Windows environment, you can copy the links to the web page and download, you can use the the thirdparty tools such as `7Zip` to uncompress the tar files.
## Training
* All experiments are running on the NVIDIA® Tesla® V100 sigle card.
### Train from scratch
* Train ResNet50_vd

2
docs/zh_CN/tutorials/install.md
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@ -9,7 +9,7 @@
## 二、安装PaddlePaddle
运行PaddleClas需要PaddlePaddle Fluid v1.7或更高版本。请参照[安装文档](http://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
运行PaddleClas需要`PaddlePaddle 2.0-beta`或更高版本。请参照[安装文档](http://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
如果已经安装好了cuda、cudnn、nccl或者安装好了docker、nvidia-docker运行环境，可以pip安装最新GPU版本PaddlePaddle

30
docs/zh_CN/tutorials/quick_start.md
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@ -40,25 +40,23 @@ cd ../../
## 二、环境准备
### 2.1 设置PYTHONPATH环境变量
### 2.1 下载预训练模型
通过下面的命令下载所需要的预训练模型。
```bash
export PYTHONPATH=./:$PYTHONPATH
mkdir pretrained
cd pretrained
wget https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar
wget https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
wget https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_pretrained.tar
tar -xf ResNet50_vd_pretrained.tar
tar -xf ResNet50_vd_ssld_pretrained.tar
tar -xf MobileNetV3_large_x1_0_pretrained.tar
cd ../
```
### 下载预训练模型
通过tools/download.py下载所需要的预训练模型。
```bash
python tools/download.py -a ResNet50_vd -p ./pretrained -d True
python tools/download.py -a ResNet50_vd_ssld -p ./pretrained -d True
python tools/download.py -a MobileNetV3_large_x1_0 -p ./pretrained -d True
```
参数说明：
+ `architecture`（简写 a）：模型结构
+ `path`（简写 p）：下载路径
+ `decompress` （简写 d）：是否解压
**注意**：如果是在windows中下载预训练模型的话，需要把地址拷贝到网页中并下载，同时使用`7Zip`等工具进行解压。
### 2.2 环境说明
@ -161,7 +159,7 @@ python -m paddle.distributed.launch \
* 如果希望体验`3.6节`的知识蒸馏部分，可以首先保存训练得到的ResNet50_vd预训练模型到合适的位置，作为蒸馏时教师模型的预训练模型。脚本如下所示。
```shell
cp -r output/ResNet50_vd/19/ ./pretrained/flowers102_R50_vd_final/
cp -r output/ResNet50_vd/best_model/ ./pretrained/flowers102_R50_vd_final/
```
### 3.6 知识蒸馏小试牛刀

51
ppcls/modeling/architectures/resnet_vd.py
View File

@ -32,18 +32,17 @@ __all__ = [
class ConvBNLayer(nn.Layer):
def __init__(
self,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
is_vd_mode=False,
act=None,
name=None, ):
def __init__(self,
num_channels,
num_filters,
filter_size,
stride=1,
groups=1,
is_vd_mode=False,
act=None,
lr_mult=1.0,
name=None):
super(ConvBNLayer, self).__init__()
self.is_vd_mode = is_vd_mode
self._pool2d_avg = AvgPool2d(
kernel_size=2, stride=2, padding=0, ceil_mode=True)
@ -54,7 +53,8 @@ class ConvBNLayer(nn.Layer):
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
weight_attr=ParamAttr(name=name + "_weights"),
weight_attr=ParamAttr(
name=name + "_weights", learning_rate=lr_mult),
bias_attr=False)
if name == "conv1":
bn_name = "bn_" + name
@ -83,6 +83,7 @@ class BottleneckBlock(nn.Layer):
stride,
shortcut=True,
if_first=False,
lr_mult=1.0,
name=None):
super(BottleneckBlock, self).__init__()
@ -91,6 +92,7 @@ class BottleneckBlock(nn.Layer):
num_filters=num_filters,
filter_size=1,
act='relu',
lr_mult=lr_mult,
name=name + "_branch2a")
self.conv1 = ConvBNLayer(
num_channels=num_filters,
@ -98,12 +100,14 @@ class BottleneckBlock(nn.Layer):
filter_size=3,
stride=stride,
act='relu',
lr_mult=lr_mult,
name=name + "_branch2b")
self.conv2 = ConvBNLayer(
num_channels=num_filters,
num_filters=num_filters * 4,
filter_size=1,
act=None,
lr_mult=lr_mult,
name=name + "_branch2c")
if not shortcut:
@ -137,6 +141,7 @@ class BasicBlock(nn.Layer):
stride,
shortcut=True,
if_first=False,
lr_mult=1.0,
name=None):
super(BasicBlock, self).__init__()
self.stride = stride
@ -178,7 +183,10 @@ class BasicBlock(nn.Layer):
class ResNet_vd(nn.Layer):
def __init__(self, layers=50, class_dim=1000):
def __init__(self,
layers=50,
class_dim=1000,
lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0]):
super(ResNet_vd, self).__init__()
self.layers = layers
@ -187,6 +195,16 @@ class ResNet_vd(nn.Layer):
"supported layers are {} but input layer is {}".format(
supported_layers, layers)
self.lr_mult_list = lr_mult_list
assert isinstance(self.lr_mult_list, (
list, tuple
)), "lr_mult_list should be in (list, tuple) but got {}".format(
type(self.lr_mult_list))
assert len(
self.lr_mult_list
) == 5, "lr_mult_list length should should be 5 but got {}".format(
len(self.lr_mult_list))
if layers == 18:
depth = [2, 2, 2, 2]
elif layers == 34 or layers == 50:
@ -207,6 +225,7 @@ class ResNet_vd(nn.Layer):
filter_size=3,
stride=2,
act='relu',
lr_mult=self.lr_mult_list[0],
name="conv1_1")
self.conv1_2 = ConvBNLayer(
num_channels=32,
@ -214,6 +233,7 @@ class ResNet_vd(nn.Layer):
filter_size=3,
stride=1,
act='relu',
lr_mult=self.lr_mult_list[0],
name="conv1_2")
self.conv1_3 = ConvBNLayer(
num_channels=32,
@ -221,6 +241,7 @@ class ResNet_vd(nn.Layer):
filter_size=3,
stride=1,
act='relu',
lr_mult=self.lr_mult_list[0],
name="conv1_3")
self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
@ -245,6 +266,7 @@ class ResNet_vd(nn.Layer):
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut,
if_first=block == i == 0,
lr_mult=self.lr_mult_list[block + 1],
name=conv_name))
self.block_list.append(bottleneck_block)
shortcut = True
@ -262,7 +284,8 @@ class ResNet_vd(nn.Layer):
stride=2 if i == 0 and block != 0 else 1,
shortcut=shortcut,
if_first=block == i == 0,
name=conv_name))
name=conv_name,
lr_mult=lr_mult))
self.block_list.append(basic_block)
shortcut = True

7
ppcls/modeling/loss.py
View File

@ -53,9 +53,10 @@ class Loss(object):
avg_cost = paddle.mean(cost)
return avg_cost
def _kldiv(self, input, target):
cost = target * F.log(target / input) * self._class_dim
cost = paddle.sum(cost)
def _kldiv(self, input, target, name=None):
eps = 1.0e-10
cost = target * paddle.log(
(target + eps) / (input + eps)) * self._class_dim
return cost
def _jsdiv(self, input, target):