From 7ef474169d63a08163f49c72905e3b2696c992e2 Mon Sep 17 00:00:00 2001
From: littletomatodonkey <2120160898@bit.edu.cn>
Date: Fri, 30 Oct 2020 00:20:48 +0800
Subject: [PATCH] polish api to Paddle2.0-rc (#346)
polish codes and docs to adapt Paddle2.0-rc
---
README.md | 2 +-
README_cn.md | 2 +-
configs/RegNet/RegNetX_4GF.yaml | 75 ++++
configs/ResNeSt/ResNeSt50.yaml | 78 ++++
configs/ResNeSt/ResNeSt50_fast_1s1x64d.yaml | 78 ++++
.../ResNeXt101_wsl/ResNeXt101_32x8d_wsl.yaml | 36 ++
docs/en/models/ResNeSt_RegNet_en.md | 2 +-
docs/en/tutorials/getting_started_en.md | 14 +-
docs/zh_CN/models/ResNeSt_RegNet.md | 2 +-
docs/zh_CN/tutorials/getting_started.md | 13 +-
ppcls/modeling/architectures/__init__.py | 2 +-
ppcls/modeling/architectures/alexnet.py | 12 +-
ppcls/modeling/architectures/darknet.py | 10 +-
ppcls/modeling/architectures/densenet.py | 14 +-
ppcls/modeling/architectures/dpn.py | 14 +-
ppcls/modeling/architectures/efficientnet.py | 12 +-
ppcls/modeling/architectures/ghostnet.py | 24 +-
ppcls/modeling/architectures/googlenet.py | 18 +-
ppcls/modeling/architectures/hrnet.py | 24 +-
ppcls/modeling/architectures/inception_v4.py | 20 +-
ppcls/modeling/architectures/mobilenet_v1.py | 14 +-
ppcls/modeling/architectures/mobilenet_v2.py | 10 +-
ppcls/modeling/architectures/mobilenet_v3.py | 35 +-
ppcls/modeling/architectures/regnet.py | 76 ++--
ppcls/modeling/architectures/res2net.py | 15 +-
ppcls/modeling/architectures/res2net_vd.py | 17 +-
ppcls/modeling/architectures/resnest.py | 54 +--
ppcls/modeling/architectures/resnet.py | 17 +-
ppcls/modeling/architectures/resnet_acnet.py | 332 ------------------
ppcls/modeling/architectures/resnet_vc.py | 17 +-
ppcls/modeling/architectures/resnet_vd.py | 21 +-
ppcls/modeling/architectures/resnext.py | 14 +-
.../modeling/architectures/resnext101_wsl.py | 14 +-
ppcls/modeling/architectures/resnext_vd.py | 16 +-
ppcls/modeling/architectures/se_resnet_vd.py | 20 +-
ppcls/modeling/architectures/se_resnext.py | 45 ++-
ppcls/modeling/architectures/se_resnext_vd.py | 48 ++-
ppcls/modeling/architectures/shufflenet_v2.py | 12 +-
ppcls/modeling/architectures/squeezenet.py | 18 +-
ppcls/modeling/architectures/vgg.py | 14 +-
ppcls/modeling/architectures/xception.py | 22 +-
.../architectures/xception_deeplab.py | 14 +-
ppcls/modeling/loss.py | 5 +-
ppcls/optimizer/learning_rate.py | 18 +-
ppcls/optimizer/optimizer.py | 2 +-
ppcls/utils/check.py | 3 +-
ppcls/utils/save_load.py | 11 +-
tools/benchmark/benchmark_acc.py | 21 +-
tools/benchmark/benchmark_list.txt | 2 +-
tools/eval.py | 8 +-
tools/export_model.py | 4 +-
tools/infer/infer.py | 15 +-
tools/program.py | 4 +-
tools/train.py | 9 +-
54 files changed, 687 insertions(+), 712 deletions(-)
create mode 100644 configs/RegNet/RegNetX_4GF.yaml
create mode 100644 configs/ResNeSt/ResNeSt50.yaml
create mode 100644 configs/ResNeSt/ResNeSt50_fast_1s1x64d.yaml
create mode 100644 configs/ResNeXt101_wsl/ResNeXt101_32x8d_wsl.yaml
delete mode 100644 ppcls/modeling/architectures/resnet_acnet.py
diff --git a/README.md b/README.md
index 355f15c99..d15599fcf 100644
--- a/README.md
+++ b/README.md
@@ -292,7 +292,7 @@ Accuracy and inference time metrics of ResNeSt and RegNet series models are show
| Model | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | Download Address |
|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|
| ResNeSt50_<br>fast_1s1x64d | 0.8035 | 0.9528 | 3.45405 | 8.72680 | 8.68 | 26.3 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeSt50_fast_1s1x64d_pretrained.pdparams) |
-| ResNeSt50 | 0.8102 | 0.9542 | 6.69042 | 8.01664 | 10.78 | 27.5 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeSt50_pretrained.pdparams) |
+| ResNeSt50 | 0.8083 | 0.9542 | 6.69042 | 8.01664 | 10.78 | 27.5 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeSt50_pretrained.pdparams) |
| RegNetX_4GF | 0.785 | 0.9416 | 6.46478 | 11.19862 | 8 | 22.1 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/RegNetX_4GF_pretrained.pdparams) |
diff --git a/README_cn.md b/README_cn.md
index 4f981f3f7..4c5ac02cf 100644
--- a/README_cn.md
+++ b/README_cn.md
@@ -295,7 +295,7 @@ ResNeSt与RegNet系列模型的精度、速度指标如下表所示，更多关
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | Flops(G) | Params(M) | 下载地址 |
|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|
| ResNeSt50_<br>fast_1s1x64d | 0.8035 | 0.9528 | 3.45405 | 8.72680 | 8.68 | 26.3 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeSt50_fast_1s1x64d_pretrained.pdparams) |
-| ResNeSt50 | 0.8102 | 0.9542 | 6.69042 | 8.01664 | 10.78 | 27.5 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeSt50_pretrained.pdparams) |
+| ResNeSt50 | 0.8083 | 0.9542 | 6.69042 | 8.01664 | 10.78 | 27.5 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeSt50_pretrained.pdparams) |
| RegNetX_4GF | 0.785 | 0.9416 | 6.46478 | 11.19862 | 8 | 22.1 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/RegNetX_4GF_pretrained.pdparams) |
diff --git a/configs/RegNet/RegNetX_4GF.yaml b/configs/RegNet/RegNetX_4GF.yaml
new file mode 100644
index 000000000..21001b534
--- /dev/null
+++ b/configs/RegNet/RegNetX_4GF.yaml
@@ -0,0 +1,75 @@
+mode: 'train'
+ARCHITECTURE:
+ name: 'RegNetX_4GF'
+
+pretrained_model: ""
+model_save_dir: "./output/"
+classes_num: 1000
+total_images: 1281167
+save_interval: 1
+validate: True
+valid_interval: 1
+epochs: 100
+topk: 5
+image_shape: [3, 224, 224]
+
+use_mix: False
+ls_epsilon: -1
+
+LEARNING_RATE:
+ function: 'CosineWarmup'
+ params:
+ lr: 0.4
+ warmup_epoch: 5
+
+OPTIMIZER:
+ function: 'Momentum'
+ params:
+ momentum: 0.9
+ regularizer:
+ function: 'L2'
+ factor: 0.000050
+
+TRAIN:
+ batch_size: 512
+ num_workers: 4
+ file_list: "./dataset/ILSVRC2012/train_list.txt"
+ data_dir: "./dataset/ILSVRC2012/"
+ shuffle_seed: 0
+ transforms:
+ - DecodeImage:
+ to_rgb: True
+ to_np: False
+ channel_first: False
+ - RandCropImage:
+ size: 224
+ - RandFlipImage:
+ flip_code: 1
+ - NormalizeImage:
+ scale: 1./255.
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ - ToCHWImage:
+
+VALID:
+ batch_size: 256
+ num_workers: 4
+ file_list: "./dataset/ILSVRC2012/val_list.txt"
+ data_dir: "./dataset/ILSVRC2012/"
+ shuffle_seed: 0
+ transforms:
+ - DecodeImage:
+ to_rgb: True
+ to_np: False
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - CropImage:
+ size: 224
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ - ToCHWImage:
diff --git a/configs/ResNeSt/ResNeSt50.yaml b/configs/ResNeSt/ResNeSt50.yaml
new file mode 100644
index 000000000..01e056da0
--- /dev/null
+++ b/configs/ResNeSt/ResNeSt50.yaml
@@ -0,0 +1,78 @@
+mode: 'train'
+ARCHITECTURE:
+ name: 'ResNeSt50'
+
+pretrained_model: ""
+model_save_dir: "./output/"
+classes_num: 1000
+total_images: 1281167
+save_interval: 1
+validate: True
+valid_interval: 1
+epochs: 300
+topk: 5
+image_shape: [3, 224, 224]
+
+use_mix: True
+ls_epsilon: 0.1
+
+LEARNING_RATE:
+ function: 'CosineWarmup'
+ params:
+ lr: 0.1
+
+OPTIMIZER:
+ function: 'Momentum'
+ params:
+ momentum: 0.9
+ regularizer:
+ function: 'L2'
+ factor: 0.000070
+
+TRAIN:
+ batch_size: 256
+ num_workers: 4
+ file_list: "./dataset/ILSVRC2012/train_list.txt"
+ data_dir: "./dataset/ILSVRC2012/"
+ shuffle_seed: 0
+ transforms:
+ - DecodeImage:
+ to_rgb: True
+ to_np: False
+ channel_first: False
+ - RandCropImage:
+ size: 224
+ - RandFlipImage:
+ flip_code: 1
+ - AutoAugment:
+ - NormalizeImage:
+ scale: 1./255.
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ - ToCHWImage:
+ mix:
+ - CutmixOperator:
+ alpha: 0.2
+
+VALID:
+ batch_size: 64
+ num_workers: 4
+ file_list: "./dataset/ILSVRC2012/val_list.txt"
+ data_dir: "./dataset/ILSVRC2012/"
+ shuffle_seed: 0
+ transforms:
+ - DecodeImage:
+ to_rgb: True
+ to_np: False
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - CropImage:
+ size: 224
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ - ToCHWImage:
diff --git a/configs/ResNeSt/ResNeSt50_fast_1s1x64d.yaml b/configs/ResNeSt/ResNeSt50_fast_1s1x64d.yaml
new file mode 100644
index 000000000..99cb1137e
--- /dev/null
+++ b/configs/ResNeSt/ResNeSt50_fast_1s1x64d.yaml
@@ -0,0 +1,78 @@
+mode: 'train'
+ARCHITECTURE:
+ name: 'ResNeSt50_fast_1s1x64d'
+
+pretrained_model: ""
+model_save_dir: "./output/"
+classes_num: 1000
+total_images: 1281167
+save_interval: 1
+validate: True
+valid_interval: 1
+epochs: 300
+topk: 5
+image_shape: [3, 224, 224]
+
+use_mix: True
+ls_epsilon: 0.1
+
+LEARNING_RATE:
+ function: 'CosineWarmup'
+ params:
+ lr: 0.1
+
+OPTIMIZER:
+ function: 'Momentum'
+ params:
+ momentum: 0.9
+ regularizer:
+ function: 'L2'
+ factor: 0.000070
+
+TRAIN:
+ batch_size: 256
+ num_workers: 4
+ file_list: "./dataset/ILSVRC2012/train_list.txt"
+ data_dir: "./dataset/ILSVRC2012/"
+ shuffle_seed: 0
+ transforms:
+ - DecodeImage:
+ to_rgb: True
+ to_np: False
+ channel_first: False
+ - RandCropImage:
+ size: 224
+ - RandFlipImage:
+ flip_code: 1
+ - AutoAugment:
+ - NormalizeImage:
+ scale: 1./255.
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ - ToCHWImage:
+ mix:
+ - CutmixOperator:
+ alpha: 0.2
+
+VALID:
+ batch_size: 64
+ num_workers: 4
+ file_list: "./dataset/ILSVRC2012/val_list.txt"
+ data_dir: "./dataset/ILSVRC2012/"
+ shuffle_seed: 0
+ transforms:
+ - DecodeImage:
+ to_rgb: True
+ to_np: False
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - CropImage:
+ size: 224
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ - ToCHWImage:
diff --git a/configs/ResNeXt101_wsl/ResNeXt101_32x8d_wsl.yaml b/configs/ResNeXt101_wsl/ResNeXt101_32x8d_wsl.yaml
new file mode 100644
index 000000000..dd35f19b9
--- /dev/null
+++ b/configs/ResNeXt101_wsl/ResNeXt101_32x8d_wsl.yaml
@@ -0,0 +1,36 @@
+mode: 'valid'
+ARCHITECTURE:
+ name: 'ResNeXt101_32x8d_wsl'
+
+pretrained_model: ""
+model_save_dir: "./output/"
+classes_num: 1000
+total_images: 1281167
+save_interval: 1
+validate: True
+valid_interval: 1
+epochs: 120
+topk: 5
+image_shape: [3, 224, 224]
+
+VALID:
+ batch_size: 64
+ num_workers: 4
+ file_list: "./dataset/ILSVRC2012/val_list.txt"
+ data_dir: "./dataset/ILSVRC2012/"
+ shuffle_seed: 0
+ transforms:
+ - DecodeImage:
+ to_rgb: True
+ to_np: False
+ channel_first: False
+ - ResizeImage:
+ resize_short: 224
+ - CropImage:
+ size: 224
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ - ToCHWImage:
diff --git a/docs/en/models/ResNeSt_RegNet_en.md b/docs/en/models/ResNeSt_RegNet_en.md
index 959597a82..a2203ad98 100644
--- a/docs/en/models/ResNeSt_RegNet_en.md
+++ b/docs/en/models/ResNeSt_RegNet_en.md
@@ -9,7 +9,7 @@ RegNet was proposed in 2020 by Facebook to deepen the concept of design space. B
| Models | Top1 | Top5 | Reference<br>top1 | Reference<br>top5 | FLOPS<br>(G) | Parameters<br>(M) |
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| ResNeSt50_fast_1s1x64d | 0.8035 | 0.9528| 0.8035 | -| 8.68 | 26.3 |
-| ResNeSt50 | 0.8102 | 0.9542| 0.8113 | -| 10.78 | 27.5 |
+| ResNeSt50 | 0.8083 | 0.9542| 0.8113 | -| 10.78 | 27.5 |
| RegNetX_4GF | 0.7850 | 0.9416| 0.7860 | -| 8.0 | 22.1 |
diff --git a/docs/en/tutorials/getting_started_en.md b/docs/en/tutorials/getting_started_en.md
index 00341cb71..7731707d8 100644
--- a/docs/en/tutorials/getting_started_en.md
+++ b/docs/en/tutorials/getting_started_en.md
@@ -221,12 +221,14 @@ Firstly, you should export inference model using `tools/export_model.py`.
python tools/export_model.py \
--model=MobileNetV3_large_x1_0 \
--pretrained_model=./output/MobileNetV3_large_x1_0/best_model/ppcls \
- --output_path=./exported_model
+ --output_path=./inference/cls_infer
```
-Among them, the `--model` parameter is used to specify the model name, `--pretrained_model` parameter is used to specify the model file path, the path does not need to include the model file suffix name, and `--output_path` is used to specify the storage path of the converted model .
+Among them, the `--model` parameter is used to specify the model name, `--pretrained_model` parameter is used to specify the model file path, the path does not need to include the model file suffix name, and `--output_path` is used to specify the storage path of the converted model.
-**Note**: In the file `export_model.py:line53`, the `shape` parameter is the shape of the model input image, the default is `224*224`. Please modify it according to the actual situation, as shown below:
+**Note**:
+1. File prefix must be assigned in `--output_path`. If `--output_path=./inference/cls_infer`, then three files will be generated in the folder `inference`, they are `cls_infer.pdiparams`, `cls_infer.pdmodel` and `cls_infer.pdiparams.info`.
+2. In the file `export_model.py:line53`, the `shape` parameter is the shape of the model input image, the default is `224*224`. Please modify it according to the actual situation, as shown below:
```python
50 # Please modify the 'shape' according to actual needs
@@ -236,7 +238,7 @@ Among them, the `--model` parameter is used to specify the model name, `--pretra
54 ])
```
-The above command will generate the model structure file (`__model__`) and the model weight file (`__variables__`), and then the inference engine can be used for inference:
+The above command will generate the model structure file (`cls_infer.pdmodel`) and the model weight file (`cls_infer.pdiparams`), and then the inference engine can be used for inference:
```bash
python tools/infer/predict.py \
@@ -248,8 +250,8 @@ python tools/infer/predict.py \
```
Among them:
+ `image_file`(i): The path of the image file to be predicted, such as `./test.jpeg`;
-+ `model_file`(m): Model file path, such as `./MobileNetV3_large_x1_0/__model__`;
-+ `params_file`(p): Weight file path, such as `./MobileNetV3_large_x1_0/__variables__`;
++ `model_file`(m): Model file path, such as `./MobileNetV3_large_x1_0/cls_infer.pdmodel`;
++ `params_file`(p): Weight file path, such as `./MobileNetV3_large_x1_0/cls_infer.pdiparams`;
+ `use_tensorrt`: Whether to use the TesorRT, default by `True`;
+ `use_gpu`: Whether to use the GPU, default by `True`.
diff --git a/docs/zh_CN/models/ResNeSt_RegNet.md b/docs/zh_CN/models/ResNeSt_RegNet.md
index a995347b0..3e7453549 100644
--- a/docs/zh_CN/models/ResNeSt_RegNet.md
+++ b/docs/zh_CN/models/ResNeSt_RegNet.md
@@ -12,7 +12,7 @@ RegNet是由facebook于2020年提出，旨在深化设计空间理念的概念
| Models | Top1 | Top5 | Reference<br>top1 | Reference<br>top5 | FLOPS<br>(G) | Parameters<br>(M) |
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| ResNeSt50_fast_1s1x64d | 0.8035 | 0.9528| 0.8035 | -| 8.68 | 26.3 |
-| ResNeSt50 | 0.8102 | 0.9542| 0.8113 | -| 10.78 | 27.5 |
+| ResNeSt50 | 0.8083 | 0.9542| 0.8113 | -| 10.78 | 27.5 |
| RegNetX_4GF | 0.7850 | 0.9416| 0.7860 | -| 8.0 | 22.1 |
diff --git a/docs/zh_CN/tutorials/getting_started.md b/docs/zh_CN/tutorials/getting_started.md
index 13bb0eccb..1991bccf3 100644
--- a/docs/zh_CN/tutorials/getting_started.md
+++ b/docs/zh_CN/tutorials/getting_started.md
@@ -235,12 +235,14 @@ python tools/infer/infer.py \
python tools/export_model.py \
--model=MobileNetV3_large_x1_0 \
--pretrained_model=./output/MobileNetV3_large_x1_0/best_model/ppcls \
- --output_path=./exported_model
+ --output_path=./inference/cls_infer
```
其中，参数`--model`用于指定模型名称，`--pretrained_model`用于指定模型文件路径，该路径仍无需包含模型文件后缀名（如[1.3 模型恢复训练](#1.3)），`--output_path`用于指定转换后模型的存储路径。
-**注意**：文件`export_model.py:line53`中，`shape`参数为模型输入图像的`shape`，默认为`224*224`，请根据实际情况修改，如下所示：
+**注意**：
+1. `--output_path`中必须指定文件名的前缀，若`--output_path=./inference/cls_infer`，则会在`inference`文件夹下生成`cls_infer.pdiparams`、`cls_infer.pdmodel`和`cls_infer.pdiparams.info`文件。
+2. 文件`export_model.py:line53`中，`shape`参数为模型输入图像的`shape`，默认为`224*224`，请根据实际情况修改，如下所示：
```python
50 # Please modify the 'shape' according to actual needs
51 @to_static(input_spec=[
@@ -248,8 +250,9 @@ python tools/export_model.py \
53 shape=[None, 3, 224, 224], dtype='float32')
54 ])
```
+2.
-上述命令将生成模型结构文件（`__model__`）和模型权重文件（`__variables__`），然后可以使用预测引擎进行推理：
+上述命令将生成模型结构文件（`cls_infer.pdmodel`）和模型权重文件（`cls_infer.pdiparams`），然后可以使用预测引擎进行推理：
```bash
python tools/infer/predict.py \
@@ -261,8 +264,8 @@ python tools/infer/predict.py \
```
其中：
+ `image_file`(简写 i)：待预测的图片文件路径，如 `./test.jpeg`
-+ `model_file`(简写 m)：模型文件路径，如 `./MobileNetV3_large_x1_0/__model__`
-+ `params_file`(简写 p)：权重文件路径，如 `./MobileNetV3_large_x1_0/__variables__`
++ `model_file`(简写 m)：模型文件路径，如 `./MobileNetV3_large_x1_0/cls_infer.pdmodel`
++ `params_file`(简写 p)：权重文件路径，如 `./MobileNetV3_large_x1_0/cls_infer.pdiparams`
+ `use_tensorrt`：是否使用 TesorRT 预测引擎，默认值：`True`
+ `use_gpu`：是否使用 GPU 预测，默认值：`True`。
diff --git a/ppcls/modeling/architectures/__init__.py b/ppcls/modeling/architectures/__init__.py
index 6fcc50e87..afd18de19 100644
--- a/ppcls/modeling/architectures/__init__.py
+++ b/ppcls/modeling/architectures/__init__.py
@@ -22,7 +22,7 @@ from .res2net_vd import Res2Net50_vd_48w_2s, Res2Net50_vd_26w_4s, Res2Net50_vd_1
from .se_resnet_vd import SE_ResNet18_vd, SE_ResNet34_vd, SE_ResNet50_vd, SE_ResNet101_vd, SE_ResNet152_vd, SE_ResNet200_vd
from .se_resnext_vd import SE_ResNeXt50_vd_32x4d, SE_ResNeXt50_vd_32x4d, SENet154_vd
from .se_resnext import SE_ResNeXt50_32x4d, SE_ResNeXt101_32x4d, SE_ResNeXt152_64x4d
-from .dpn import DPN68
+from .dpn import DPN68, DPN92, DPN98, DPN107, DPN131
from .densenet import DenseNet121
from .hrnet import HRNet_W18_C, HRNet_W30_C, HRNet_W32_C, HRNet_W40_C, HRNet_W44_C, HRNet_W48_C, HRNet_W60_C, HRNet_W64_C, SE_HRNet_W18_C, SE_HRNet_W30_C, SE_HRNet_W32_C, SE_HRNet_W40_C, SE_HRNet_W44_C, SE_HRNet_W48_C, SE_HRNet_W60_C, SE_HRNet_W64_C
from .efficientnet import EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6, EfficientNetB7
diff --git a/ppcls/modeling/architectures/alexnet.py b/ppcls/modeling/architectures/alexnet.py
index 337a71137..a9155ca4a 100644
--- a/ppcls/modeling/architectures/alexnet.py
+++ b/ppcls/modeling/architectures/alexnet.py
@@ -2,8 +2,8 @@ import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout, ReLU
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout, ReLU
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
@@ -25,7 +25,7 @@ class ConvPoolLayer(nn.Layer):
self.relu = ReLU() if act == "relu" else None
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=input_channels,
out_channels=output_channels,
kernel_size=filter_size,
@@ -36,7 +36,7 @@ class ConvPoolLayer(nn.Layer):
name=name + "_weights", initializer=Uniform(-stdv, stdv)),
bias_attr=ParamAttr(
name=name + "_offset", initializer=Uniform(-stdv, stdv)))
- self._pool = MaxPool2d(kernel_size=3, stride=2, padding=0)
+ self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
def forward(self, inputs):
x = self._conv(inputs)
@@ -57,7 +57,7 @@ class AlexNetDY(nn.Layer):
self._conv2 = ConvPoolLayer(
64, 192, 5, 1, 2, stdv, act="relu", name="conv2")
stdv = 1.0 / math.sqrt(192 * 3 * 3)
- self._conv3 = Conv2d(
+ self._conv3 = Conv2D(
192,
384,
3,
@@ -68,7 +68,7 @@ class AlexNetDY(nn.Layer):
bias_attr=ParamAttr(
name="conv3_offset", initializer=Uniform(-stdv, stdv)))
stdv = 1.0 / math.sqrt(384 * 3 * 3)
- self._conv4 = Conv2d(
+ self._conv4 = Conv2D(
384,
256,
3,
diff --git a/ppcls/modeling/architectures/darknet.py b/ppcls/modeling/architectures/darknet.py
index e75e407e2..5aef16d7c 100644
--- a/ppcls/modeling/architectures/darknet.py
+++ b/ppcls/modeling/architectures/darknet.py
@@ -2,8 +2,8 @@ import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
@@ -20,7 +20,7 @@ class ConvBNLayer(nn.Layer):
name=None):
super(ConvBNLayer, self).__init__()
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=input_channels,
out_channels=output_channels,
kernel_size=filter_size,
@@ -56,7 +56,7 @@ class BasicBlock(nn.Layer):
def forward(self, inputs):
x = self._conv1(inputs)
x = self._conv2(x)
- return paddle.elementwise_add(x=inputs, y=x)
+ return paddle.add(x=inputs, y=x)
class DarkNet(nn.Layer):
@@ -104,7 +104,7 @@ class DarkNet(nn.Layer):
self._basic_block_43 = BasicBlock(1024, 512, name="stage.4.2")
self._basic_block_44 = BasicBlock(1024, 512, name="stage.4.3")
- self._pool = AdaptiveAvgPool2d(1)
+ self._pool = AdaptiveAvgPool2D(1)
stdv = 1.0 / math.sqrt(1024.0)
self._out = Linear(
diff --git a/ppcls/modeling/architectures/densenet.py b/ppcls/modeling/architectures/densenet.py
index 7179073c5..cc7391554 100644
--- a/ppcls/modeling/architectures/densenet.py
+++ b/ppcls/modeling/architectures/densenet.py
@@ -20,8 +20,8 @@ import numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
@@ -51,7 +51,7 @@ class BNACConvLayer(nn.Layer):
moving_mean_name=name + '_bn_mean',
moving_variance_name=name + '_bn_variance')
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
@@ -145,7 +145,7 @@ class TransitionLayer(nn.Layer):
stride=1,
name=name)
- self.pool2d_avg = AvgPool2d(kernel_size=2, stride=2, padding=0)
+ self.pool2d_avg = AvgPool2D(kernel_size=2, stride=2, padding=0)
def forward(self, input):
y = self.conv_ac_func(input)
@@ -165,7 +165,7 @@ class ConvBNLayer(nn.Layer):
name=None):
super(ConvBNLayer, self).__init__()
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
@@ -214,7 +214,7 @@ class DenseNet(nn.Layer):
act='relu',
name="conv1")
- self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self.pool2d_max = MaxPool2D(kernel_size=3, stride=2, padding=1)
self.block_config = block_config
@@ -256,7 +256,7 @@ class DenseNet(nn.Layer):
moving_mean_name='conv5_blk_bn_mean',
moving_variance_name='conv5_blk_bn_variance')
- self.pool2d_avg = AdaptiveAvgPool2d(1)
+ self.pool2d_avg = AdaptiveAvgPool2D(1)
stdv = 1.0 / math.sqrt(num_features * 1.0)
diff --git a/ppcls/modeling/architectures/dpn.py b/ppcls/modeling/architectures/dpn.py
index af23789d3..cff0caffc 100644
--- a/ppcls/modeling/architectures/dpn.py
+++ b/ppcls/modeling/architectures/dpn.py
@@ -21,8 +21,8 @@ import sys
import paddle
from paddle import ParamAttr
import paddle.nn as nn
-from paddle.nn import Conv2d, BatchNorm, Linear
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+from paddle.nn import Conv2D, BatchNorm, Linear
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
@@ -49,7 +49,7 @@ class ConvBNLayer(nn.Layer):
name=None):
super(ConvBNLayer, self).__init__()
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
@@ -93,7 +93,7 @@ class BNACConvLayer(nn.Layer):
moving_mean_name=name + '_bn_mean',
moving_variance_name=name + '_bn_variance')
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
@@ -202,7 +202,7 @@ class DualPathFactory(nn.Layer):
c1x1_c, num_or_sections=[self.num_1x1_c, self.inc], axis=1)
# OUTPUTS
- summ = paddle.elementwise_add(x=data_o1, y=c1x1_c1)
+ summ = paddle.add(x=data_o1, y=c1x1_c1)
dense = paddle.concat([data_o2, c1x1_c2], axis=1)
# tensor, channels
return [summ, dense]
@@ -236,7 +236,7 @@ class DPN(nn.Layer):
act='relu',
name="conv1")
- self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self.pool2d_max = MaxPool2D(kernel_size=3, stride=2, padding=1)
num_channel_dpn = init_num_filter
@@ -301,7 +301,7 @@ class DPN(nn.Layer):
moving_mean_name='final_concat_bn_mean',
moving_variance_name='final_concat_bn_variance')
- self.pool2d_avg = AdaptiveAvgPool2d(1)
+ self.pool2d_avg = AdaptiveAvgPool2D(1)
stdv = 0.01
diff --git a/ppcls/modeling/architectures/efficientnet.py b/ppcls/modeling/architectures/efficientnet.py
index 8b3c63df7..b0a299113 100644
--- a/ppcls/modeling/architectures/efficientnet.py
+++ b/ppcls/modeling/architectures/efficientnet.py
@@ -2,8 +2,8 @@ import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
import math
import collections
import re
@@ -300,7 +300,7 @@ class Conv2ds(nn.Layer):
padding = padding_type
groups = 1 if groups is None else groups
- self._conv = Conv2d(
+ self._conv = Conv2D(
input_channels,
output_channels,
filter_size,
@@ -484,7 +484,7 @@ class SEBlock(nn.Layer):
cur_stage=None):
super(SEBlock, self).__init__()
- self._pool = AdaptiveAvgPool2d(1)
+ self._pool = AdaptiveAvgPool2D(1)
self._conv1 = Conv2ds(
input_channels,
num_squeezed_channels,
@@ -582,7 +582,7 @@ class MbConvBlock(nn.Layer):
self.block_args.input_filters == self.block_args.output_filters:
if self.drop_connect_rate:
x = _drop_connect(x, self.drop_connect_rate, not self.training)
- x = paddle.elementwise_add(x, inputs)
+ x = paddle.add(x, inputs)
return x
@@ -755,7 +755,7 @@ class EfficientNet(nn.Layer):
bn_name="_bn1",
model_name=self.name,
cur_stage=7)
- self._pool = AdaptiveAvgPool2d(1)
+ self._pool = AdaptiveAvgPool2D(1)
if self._global_params.dropout_rate:
self._drop = Dropout(
diff --git a/ppcls/modeling/architectures/ghostnet.py b/ppcls/modeling/architectures/ghostnet.py
index b37f2230f..090f0c445 100644
--- a/ppcls/modeling/architectures/ghostnet.py
+++ b/ppcls/modeling/architectures/ghostnet.py
@@ -17,9 +17,9 @@ import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
-from paddle.nn import Conv2d, BatchNorm, AdaptiveAvgPool2d, Linear
-from paddle.fluid.regularizer import L2DecayRegularizer
-from paddle.nn.initializer import Uniform
+from paddle.nn import Conv2D, BatchNorm, AdaptiveAvgPool2D, Linear
+from paddle.regularizer import L2Decay
+from paddle.nn.initializer import Uniform, KaimingNormal
class ConvBNLayer(nn.Layer):
@@ -32,7 +32,7 @@ class ConvBNLayer(nn.Layer):
act="relu",
name=None):
super(ConvBNLayer, self).__init__()
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
@@ -40,7 +40,7 @@ class ConvBNLayer(nn.Layer):
padding=(kernel_size - 1) // 2,
groups=groups,
weight_attr=ParamAttr(
- initializer=nn.initializer.MSRA(), name=name + "_weights"),
+ initializer=KaimingNormal(), name=name + "_weights"),
bias_attr=False)
bn_name = name + "_bn"
@@ -49,11 +49,9 @@ class ConvBNLayer(nn.Layer):
num_channels=out_channels,
act=act,
param_attr=ParamAttr(
- name=bn_name + "_scale",
- regularizer=L2DecayRegularizer(regularization_coeff=0.0)),
+ name=bn_name + "_scale", regularizer=L2Decay(0.0)),
bias_attr=ParamAttr(
- name=bn_name + "_offset",
- regularizer=L2DecayRegularizer(regularization_coeff=0.0)),
+ name=bn_name + "_offset", regularizer=L2Decay(0.0)),
moving_mean_name=bn_name + "_mean",
moving_variance_name=name +
"_variance" # wrong due to an old typo, will be fixed later.
@@ -68,7 +66,7 @@ class ConvBNLayer(nn.Layer):
class SEBlock(nn.Layer):
def __init__(self, num_channels, reduction_ratio=4, name=None):
super(SEBlock, self).__init__()
- self.pool2d_gap = AdaptiveAvgPool2d(1)
+ self.pool2d_gap = AdaptiveAvgPool2D(1)
self._num_channels = num_channels
stdv = 1.0 / math.sqrt(num_channels * 1.0)
med_ch = num_channels // reduction_ratio
@@ -92,7 +90,7 @@ class SEBlock(nn.Layer):
squeeze = self.squeeze(pool)
squeeze = F.relu(squeeze)
excitation = self.excitation(squeeze)
- excitation = paddle.fluid.layers.clip(x=excitation, min=0, max=1)
+ excitation = paddle.clip(x=excitation, min=0, max=1)
excitation = paddle.reshape(
excitation, shape=[-1, self._num_channels, 1, 1])
out = inputs * excitation
@@ -208,7 +206,7 @@ class GhostBottleneck(nn.Layer):
else:
shortcut = self.shortcut_depthwise(inputs)
shortcut = self.shortcut_conv(shortcut)
- return paddle.elementwise_add(x=x, y=shortcut, axis=-1)
+ return paddle.add(x=x, y=shortcut)
class GhostNet(nn.Layer):
@@ -273,7 +271,7 @@ class GhostNet(nn.Layer):
groups=1,
act="relu",
name="conv_last")
- self.pool2d_gap = AdaptiveAvgPool2d(1)
+ self.pool2d_gap = AdaptiveAvgPool2D(1)
in_channels = output_channels
self._fc0_output_channels = 1280
self.fc_0 = ConvBNLayer(
diff --git a/ppcls/modeling/architectures/googlenet.py b/ppcls/modeling/architectures/googlenet.py
index 2e990a7f0..e23f6cd52 100644
--- a/ppcls/modeling/architectures/googlenet.py
+++ b/ppcls/modeling/architectures/googlenet.py
@@ -2,8 +2,8 @@ import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
@@ -29,7 +29,7 @@ class ConvLayer(nn.Layer):
name=None):
super(ConvLayer, self).__init__()
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
@@ -73,7 +73,7 @@ class Inception(nn.Layer):
name="inception_" + name + "_5x5_reduce")
self._conv5 = ConvLayer(
filter5R, filter5, 5, name="inception_" + name + "_5x5")
- self._pool = MaxPool2d(kernel_size=3, stride=1, padding=1)
+ self._pool = MaxPool2D(kernel_size=3, stride=1, padding=1)
self._convprj = ConvLayer(
input_channels, proj, 1, name="inception_" + name + "_3x3_proj")
@@ -99,7 +99,7 @@ class GoogleNetDY(nn.Layer):
def __init__(self, class_dim=1000):
super(GoogleNetDY, self).__init__()
self._conv = ConvLayer(3, 64, 7, 2, name="conv1")
- self._pool = MaxPool2d(kernel_size=3, stride=2)
+ self._pool = MaxPool2D(kernel_size=3, stride=2)
self._conv_1 = ConvLayer(64, 64, 1, name="conv2_1x1")
self._conv_2 = ConvLayer(64, 192, 3, name="conv2_3x3")
@@ -124,7 +124,7 @@ class GoogleNetDY(nn.Layer):
self._ince5b = Inception(
832, 832, 384, 192, 384, 48, 128, 128, name="ince5b")
- self._pool_5 = AvgPool2d(kernel_size=7, stride=7)
+ self._pool_5 = AvgPool2D(kernel_size=7, stride=7)
self._drop = Dropout(p=0.4, mode="downscale_in_infer")
self._fc_out = Linear(
@@ -132,7 +132,7 @@ class GoogleNetDY(nn.Layer):
class_dim,
weight_attr=xavier(1024, 1, "out"),
bias_attr=ParamAttr(name="out_offset"))
- self._pool_o1 = AvgPool2d(kernel_size=5, stride=3)
+ self._pool_o1 = AvgPool2D(kernel_size=5, stride=3)
self._conv_o1 = ConvLayer(512, 128, 1, name="conv_o1")
self._fc_o1 = Linear(
1152,
@@ -145,7 +145,7 @@ class GoogleNetDY(nn.Layer):
class_dim,
weight_attr=xavier(1024, 1, "out1"),
bias_attr=ParamAttr(name="out1_offset"))
- self._pool_o2 = AvgPool2d(kernel_size=5, stride=3)
+ self._pool_o2 = AvgPool2D(kernel_size=5, stride=3)
self._conv_o2 = ConvLayer(528, 128, 1, name="conv_o2")
self._fc_o2 = Linear(
1152,
@@ -184,7 +184,6 @@ class GoogleNetDY(nn.Layer):
x = self._drop(x)
x = paddle.squeeze(x, axis=[2, 3])
out = self._fc_out(x)
- out = F.softmax(out)
x = self._pool_o1(ince4a)
x = self._conv_o1(x)
@@ -193,7 +192,6 @@ class GoogleNetDY(nn.Layer):
x = F.relu(x)
x = self._drop_o1(x)
out1 = self._out1(x)
- out1 = F.softmax(out1)
x = self._pool_o2(ince4d)
x = self._conv_o2(x)
diff --git a/ppcls/modeling/architectures/hrnet.py b/ppcls/modeling/architectures/hrnet.py
index 1bd976572..697441f91 100644
--- a/ppcls/modeling/architectures/hrnet.py
+++ b/ppcls/modeling/architectures/hrnet.py
@@ -21,8 +21,8 @@ import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
-from paddle.nn import Conv2d, BatchNorm, Linear
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+from paddle.nn import Conv2D, BatchNorm, Linear
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
@@ -58,7 +58,7 @@ class ConvBNLayer(nn.Layer):
name=None):
super(ConvBNLayer, self).__init__()
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
@@ -245,7 +245,8 @@ class BottleneckBlock(nn.Layer):
if self.has_se:
conv3 = self.se(conv3)
- y = paddle.elementwise_add(x=conv3, y=residual, act="relu")
+ y = paddle.add(x=conv3, y=residual)
+ y = F.relu(y)
return y
@@ -303,7 +304,8 @@ class BasicBlock(nn.Layer):
if self.has_se:
conv2 = self.se(conv2)
- y = paddle.elementwise_add(x=conv2, y=residual, act="relu")
+ y = paddle.add(x=conv2, y=residual)
+ y = F.relu(y)
return y
@@ -311,7 +313,7 @@ class SELayer(nn.Layer):
def __init__(self, num_channels, num_filters, reduction_ratio, name=None):
super(SELayer, self).__init__()
- self.pool2d_gap = AdaptiveAvgPool2d(1)
+ self.pool2d_gap = AdaptiveAvgPool2D(1)
self._num_channels = num_channels
@@ -482,17 +484,15 @@ class FuseLayers(nn.Layer):
y = self.residual_func_list[residual_func_idx](input[j])
residual_func_idx += 1
- y = F.resize_nearest(input=y, scale=2**(j - i))
- residual = paddle.elementwise_add(
- x=residual, y=y, act=None)
+ y = F.upsample(y, scale_factor=2**(j - i), mode="nearest")
+ residual = paddle.add(x=residual, y=y)
elif j < i:
y = input[j]
for k in range(i - j):
y = self.residual_func_list[residual_func_idx](y)
residual_func_idx += 1
- residual = paddle.elementwise_add(
- x=residual, y=y, act=None)
+ residual = paddle.add(x=residual, y=y)
residual = F.relu(residual)
outs.append(residual)
@@ -623,7 +623,7 @@ class HRNet(nn.Layer):
stride=1,
name="cls_head_last_conv")
- self.pool2d_avg = AdaptiveAvgPool2d(1)
+ self.pool2d_avg = AdaptiveAvgPool2D(1)
stdv = 1.0 / math.sqrt(2048 * 1.0)
diff --git a/ppcls/modeling/architectures/inception_v4.py b/ppcls/modeling/architectures/inception_v4.py
index 5d23d3650..b8ba09849 100644
--- a/ppcls/modeling/architectures/inception_v4.py
+++ b/ppcls/modeling/architectures/inception_v4.py
@@ -16,8 +16,8 @@ import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
@@ -36,7 +36,7 @@ class ConvBNLayer(nn.Layer):
name=None):
super(ConvBNLayer, self).__init__()
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
@@ -68,7 +68,7 @@ class InceptionStem(nn.Layer):
self._conv_2 = ConvBNLayer(32, 32, 3, act="relu", name="conv2_3x3_s1")
self._conv_3 = ConvBNLayer(
32, 64, 3, padding=1, act="relu", name="conv3_3x3_s1")
- self._pool = MaxPool2d(kernel_size=3, stride=2, padding=0)
+ self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
self._conv2 = ConvBNLayer(
64, 96, 3, stride=2, act="relu", name="inception_stem1_3x3_s2")
self._conv1_1 = ConvBNLayer(
@@ -123,7 +123,7 @@ class InceptionStem(nn.Layer):
class InceptionA(nn.Layer):
def __init__(self, name):
super(InceptionA, self).__init__()
- self._pool = AvgPool2d(kernel_size=3, stride=1, padding=1)
+ self._pool = AvgPool2D(kernel_size=3, stride=1, padding=1)
self._conv1 = ConvBNLayer(
384, 96, 1, act="relu", name="inception_a" + name + "_1x1")
self._conv2 = ConvBNLayer(
@@ -178,7 +178,7 @@ class InceptionA(nn.Layer):
class ReductionA(nn.Layer):
def __init__(self):
super(ReductionA, self).__init__()
- self._pool = MaxPool2d(kernel_size=3, stride=2, padding=0)
+ self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
self._conv2 = ConvBNLayer(
384, 384, 3, stride=2, act="relu", name="reduction_a_3x3")
self._conv3_1 = ConvBNLayer(
@@ -201,7 +201,7 @@ class ReductionA(nn.Layer):
class InceptionB(nn.Layer):
def __init__(self, name=None):
super(InceptionB, self).__init__()
- self._pool = AvgPool2d(kernel_size=3, stride=1, padding=1)
+ self._pool = AvgPool2D(kernel_size=3, stride=1, padding=1)
self._conv1 = ConvBNLayer(
1024, 128, 1, act="relu", name="inception_b" + name + "_1x1")
self._conv2 = ConvBNLayer(
@@ -278,7 +278,7 @@ class InceptionB(nn.Layer):
class ReductionB(nn.Layer):
def __init__(self):
super(ReductionB, self).__init__()
- self._pool = MaxPool2d(kernel_size=3, stride=2, padding=0)
+ self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
self._conv2_1 = ConvBNLayer(
1024, 192, 1, act="relu", name="reduction_b_3x3_reduce")
self._conv2_2 = ConvBNLayer(
@@ -319,7 +319,7 @@ class ReductionB(nn.Layer):
class InceptionC(nn.Layer):
def __init__(self, name=None):
super(InceptionC, self).__init__()
- self._pool = AvgPool2d(kernel_size=3, stride=1, padding=1)
+ self._pool = AvgPool2D(kernel_size=3, stride=1, padding=1)
self._conv1 = ConvBNLayer(
1536, 256, 1, act="relu", name="inception_c" + name + "_1x1")
self._conv2 = ConvBNLayer(
@@ -411,7 +411,7 @@ class InceptionV4DY(nn.Layer):
self._inceptionC_2 = InceptionC(name="2")
self._inceptionC_3 = InceptionC(name="3")
- self.avg_pool = AdaptiveAvgPool2d(1)
+ self.avg_pool = AdaptiveAvgPool2D(1)
self._drop = Dropout(p=0.2, mode="downscale_in_infer")
stdv = 1.0 / math.sqrt(1536 * 1.0)
self.out = Linear(
diff --git a/ppcls/modeling/architectures/mobilenet_v1.py b/ppcls/modeling/architectures/mobilenet_v1.py
index ee66f2967..5b4bc5f82 100644
--- a/ppcls/modeling/architectures/mobilenet_v1.py
+++ b/ppcls/modeling/architectures/mobilenet_v1.py
@@ -21,9 +21,9 @@ import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
-from paddle.nn.initializer import MSRA
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
+from paddle.nn.initializer import KaimingNormal
import math
__all__ = [
@@ -44,7 +44,7 @@ class ConvBNLayer(nn.Layer):
name=None):
super(ConvBNLayer, self).__init__()
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
@@ -52,7 +52,7 @@ class ConvBNLayer(nn.Layer):
padding=padding,
groups=num_groups,
weight_attr=ParamAttr(
- initializer=MSRA(), name=name + "_weights"),
+ initializer=KaimingNormal(), name=name + "_weights"),
bias_attr=False)
self._batch_norm = BatchNorm(
@@ -227,13 +227,13 @@ class MobileNet(nn.Layer):
name="conv6"))
self.block_list.append(conv6)
- self.pool2d_avg = AdaptiveAvgPool2d(1)
+ self.pool2d_avg = AdaptiveAvgPool2D(1)
self.out = Linear(
int(1024 * scale),
class_dim,
weight_attr=ParamAttr(
- initializer=MSRA(), name="fc7_weights"),
+ initializer=KaimingNormal(), name="fc7_weights"),
bias_attr=ParamAttr(name="fc7_offset"))
def forward(self, inputs):
diff --git a/ppcls/modeling/architectures/mobilenet_v2.py b/ppcls/modeling/architectures/mobilenet_v2.py
index 2aa8a7576..7f041f5cf 100644
--- a/ppcls/modeling/architectures/mobilenet_v2.py
+++ b/ppcls/modeling/architectures/mobilenet_v2.py
@@ -21,8 +21,8 @@ import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
import math
@@ -45,7 +45,7 @@ class ConvBNLayer(nn.Layer):
use_cudnn=True):
super(ConvBNLayer, self).__init__()
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
@@ -108,7 +108,7 @@ class InvertedResidualUnit(nn.Layer):
y = self._bottleneck_conv(y, if_act=True)
y = self._linear_conv(y, if_act=False)
if ifshortcut:
- y = paddle.elementwise_add(inputs, y)
+ y = paddle.add(inputs, y)
return y
@@ -199,7 +199,7 @@ class MobileNet(nn.Layer):
padding=0,
name="conv9")
- self.pool2d_avg = AdaptiveAvgPool2d(1)
+ self.pool2d_avg = AdaptiveAvgPool2D(1)
self.out = Linear(
self.out_c,
diff --git a/ppcls/modeling/architectures/mobilenet_v3.py b/ppcls/modeling/architectures/mobilenet_v3.py
index 9666c9b82..f25782d14 100644
--- a/ppcls/modeling/architectures/mobilenet_v3.py
+++ b/ppcls/modeling/architectures/mobilenet_v3.py
@@ -21,9 +21,10 @@ import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
-from paddle.fluid.regularizer import L2Decay
+from paddle.nn.functional.activation import hard_sigmoid, hard_swish
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
+from paddle.regularizer import L2Decay
import math
@@ -111,7 +112,8 @@ class MobileNetV3(nn.Layer):
i = 0
inplanes = make_divisible(inplanes * scale)
for (k, exp, c, se, nl, s) in self.cfg:
- self.block_list.append(
+ block = self.add_sublayer(
+ "conv" + str(i + 2),
ResidualUnit(
in_c=inplanes,
mid_c=make_divisible(scale * exp),
@@ -121,8 +123,7 @@ class MobileNetV3(nn.Layer):
use_se=se,
act=nl,
name="conv" + str(i + 2)))
- self.add_sublayer(
- sublayer=self.block_list[-1], name="conv" + str(i + 2))
+ self.block_list.append(block)
inplanes = make_divisible(scale * c)
i += 1
@@ -137,9 +138,9 @@ class MobileNetV3(nn.Layer):
act="hard_swish",
name="conv_last")
- self.pool = AdaptiveAvgPool2d(1)
+ self.pool = AdaptiveAvgPool2D(1)
- self.last_conv = Conv2d(
+ self.last_conv = Conv2D(
in_channels=make_divisible(scale * self.cls_ch_squeeze),
out_channels=self.cls_ch_expand,
kernel_size=1,
@@ -158,6 +159,7 @@ class MobileNetV3(nn.Layer):
def forward(self, inputs, label=None):
x = self.conv1(inputs)
+
for block in self.block_list:
x = block(x)
@@ -165,10 +167,11 @@ class MobileNetV3(nn.Layer):
x = self.pool(x)
x = self.last_conv(x)
- x = F.hard_swish(x)
+ x = hard_swish(x)
x = self.dropout(x)
x = paddle.reshape(x, shape=[x.shape[0], x.shape[1]])
x = self.out(x)
+
return x
@@ -187,7 +190,7 @@ class ConvBNLayer(nn.Layer):
super(ConvBNLayer, self).__init__()
self.if_act = if_act
self.act = act
- self.conv = Conv2d(
+ self.conv = Conv2D(
in_channels=in_c,
out_channels=out_c,
kernel_size=filter_size,
@@ -213,7 +216,7 @@ class ConvBNLayer(nn.Layer):
if self.act == "relu":
x = F.relu(x)
elif self.act == "hard_swish":
- x = F.hard_swish(x)
+ x = hard_swish(x)
else:
print("The activation function is selected incorrectly.")
exit()
@@ -272,15 +275,15 @@ class ResidualUnit(nn.Layer):
x = self.mid_se(x)
x = self.linear_conv(x)
if self.if_shortcut:
- x = paddle.elementwise_add(inputs, x)
+ x = paddle.add(inputs, x)
return x
class SEModule(nn.Layer):
def __init__(self, channel, reduction=4, name=""):
super(SEModule, self).__init__()
- self.avg_pool = AdaptiveAvgPool2d(1)
- self.conv1 = Conv2d(
+ self.avg_pool = AdaptiveAvgPool2D(1)
+ self.conv1 = Conv2D(
in_channels=channel,
out_channels=channel // reduction,
kernel_size=1,
@@ -288,7 +291,7 @@ class SEModule(nn.Layer):
padding=0,
weight_attr=ParamAttr(name=name + "_1_weights"),
bias_attr=ParamAttr(name=name + "_1_offset"))
- self.conv2 = Conv2d(
+ self.conv2 = Conv2D(
in_channels=channel // reduction,
out_channels=channel,
kernel_size=1,
@@ -302,7 +305,7 @@ class SEModule(nn.Layer):
outputs = self.conv1(outputs)
outputs = F.relu(outputs)
outputs = self.conv2(outputs)
- outputs = F.hard_sigmoid(outputs)
+ outputs = hard_sigmoid(outputs)
return paddle.multiply(x=inputs, y=outputs, axis=0)
diff --git a/ppcls/modeling/architectures/regnet.py b/ppcls/modeling/architectures/regnet.py
index 464fd62b2..8c2ec57e8 100644
--- a/ppcls/modeling/architectures/regnet.py
+++ b/ppcls/modeling/architectures/regnet.py
@@ -20,8 +20,9 @@ import numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+import paddle.nn.functional as F
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
@@ -30,6 +31,7 @@ __all__ = [
"RegNetY_4GF", "RegNetY_32GF"
]
+
def quantize_float(f, q):
"""Converts a float to closest non-zero int divisible by q."""
return int(round(f / q) * q)
@@ -39,9 +41,7 @@ def adjust_ws_gs_comp(ws, bms, gs):
"""Adjusts the compatibility of widths and groups."""
ws_bot = [int(w * b) for w, b in zip(ws, bms)]
gs = [min(g, w_bot) for g, w_bot in zip(gs, ws_bot)]
- ws_bot = [
- quantize_float(w_bot, g) for w_bot, g in zip(ws_bot, gs)
- ]
+ ws_bot = [quantize_float(w_bot, g) for w_bot, g in zip(ws_bot, gs)]
ws = [int(w_bot / b) for w_bot, b in zip(ws_bot, bms)]
return ws, gs
@@ -81,7 +81,7 @@ class ConvBNLayer(nn.Layer):
name=None):
super(ConvBNLayer, self).__init__()
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
@@ -98,21 +98,22 @@ class ConvBNLayer(nn.Layer):
bias_attr=ParamAttr(bn_name + ".output.1.b_0"),
moving_mean_name=bn_name + "_mean",
moving_variance_name=bn_name + "_variance")
+
def forward(self, inputs):
y = self._conv(inputs)
y = self._batch_norm(y)
return y
-
-
+
+
class BottleneckBlock(nn.Layer):
def __init__(self,
num_channels,
num_filters,
stride,
- bm,
- gw,
+ bm,
+ gw,
se_on,
- se_r,
+ se_r,
shortcut=True,
name=None):
super(BottleneckBlock, self).__init__()
@@ -121,7 +122,7 @@ class BottleneckBlock(nn.Layer):
w_b = int(round(num_filters * bm))
# Compute the number of groups
num_gs = w_b // gw
- self.se_on = se_on
+ self.se_on = se_on
self.conv0 = ConvBNLayer(
num_channels=num_channels,
num_filters=w_b,
@@ -174,15 +175,16 @@ class BottleneckBlock(nn.Layer):
else:
short = self.short(inputs)
- y = paddle.elementwise_add(x=short, y=conv2, act="relu")
+ y = paddle.add(x=short, y=conv2)
+ y = F.relu(y)
return y
-
+
class SELayer(nn.Layer):
def __init__(self, num_channels, num_filters, reduction_ratio, name=None):
super(SELayer, self).__init__()
- self.pool2d_gap = AdaptiveAvgPool2d(1)
+ self.pool2d_gap = AdaptiveAvgPool2D(1)
self._num_channels = num_channels
@@ -214,15 +216,23 @@ class SELayer(nn.Layer):
excitation, shape=[-1, self._num_channels, 1, 1])
out = input * excitation
return out
-
+
class RegNet(nn.Layer):
- def __init__(self, w_a, w_0, w_m, d, group_w, bot_mul, q=8, se_on=False, class_dim=1000):
+ def __init__(self,
+ w_a,
+ w_0,
+ w_m,
+ d,
+ group_w,
+ bot_mul,
+ q=8,
+ se_on=False,
+ class_dim=1000):
super(RegNet, self).__init__()
-
+
# Generate RegNet ws per block
- b_ws, num_s, max_s, ws_cont = generate_regnet(
- w_a, w_0, w_m, d, q)
+ b_ws, num_s, max_s, ws_cont = generate_regnet(w_a, w_0, w_m, d, q)
# Convert to per stage format
ws, ds = get_stages_from_blocks(b_ws, b_ws)
# Generate group widths and bot muls
@@ -258,7 +268,8 @@ class RegNet(nn.Layer):
num_channels = stem_w if block == i == 0 else in_channels
# Stride apply to the first block of the stage
b_stride = stride if i == 0 else 1
- conv_name = "s" + str(block + 1) + "_b" + str(i + 1) # chr(97 + i)
+ conv_name = "s" + str(block + 1) + "_b" + str(i +
+ 1) # chr(97 + i)
bottleneck_block = self.add_sublayer(
conv_name,
BottleneckBlock(
@@ -275,7 +286,7 @@ class RegNet(nn.Layer):
self.block_list.append(bottleneck_block)
shortcut = True
- self.pool2d_avg = AdaptiveAvgPool2d(1)
+ self.pool2d_avg = AdaptiveAvgPool2D(1)
self.pool2d_avg_channels = w_out
@@ -297,7 +308,7 @@ class RegNet(nn.Layer):
y = self.out(y)
return y
-
+
def RegNetX_200MF(**args):
model = RegNet(
w_a=36.44, w_0=24, w_m=2.49, d=13, group_w=8, bot_mul=1.0, q=8, **args)
@@ -306,13 +317,27 @@ def RegNetX_200MF(**args):
def RegNetX_4GF(**args):
model = RegNet(
- w_a=38.65, w_0=96, w_m=2.43, d=23, group_w=40, bot_mul=1.0, q=8, **args)
+ w_a=38.65,
+ w_0=96,
+ w_m=2.43,
+ d=23,
+ group_w=40,
+ bot_mul=1.0,
+ q=8,
+ **args)
return model
def RegNetX_32GF(**args):
model = RegNet(
- w_a=69.86, w_0=320, w_m=2.0, d=23, group_w=168, bot_mul=1.0, q=8, **args)
+ w_a=69.86,
+ w_0=320,
+ w_m=2.0,
+ d=23,
+ group_w=168,
+ bot_mul=1.0,
+ q=8,
+ **args)
return model
@@ -356,4 +381,3 @@ def RegNetY_32GF(**args):
se_on=True,
**args)
return model
-
diff --git a/ppcls/modeling/architectures/res2net.py b/ppcls/modeling/architectures/res2net.py
index 4253519e0..1949262af 100644
--- a/ppcls/modeling/architectures/res2net.py
+++ b/ppcls/modeling/architectures/res2net.py
@@ -21,8 +21,8 @@ import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
@@ -46,7 +46,7 @@ class ConvBNLayer(nn.Layer):
name=None, ):
super(ConvBNLayer, self).__init__()
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
@@ -104,7 +104,7 @@ class BottleneckBlock(nn.Layer):
act='relu',
name=name + '_branch2b_' + str(s + 1)))
self.conv1_list.append(conv1)
- self.pool2d_avg = AvgPool2d(kernel_size=3, stride=stride, padding=1)
+ self.pool2d_avg = AvgPool2D(kernel_size=3, stride=stride, padding=1)
self.conv2 = ConvBNLayer(
num_channels=num_filters,
@@ -143,7 +143,8 @@ class BottleneckBlock(nn.Layer):
short = inputs
else:
short = self.short(inputs)
- y = paddle.elementwise_add(x=short, y=conv2, act='relu')
+ y = paddle.add(x=short, y=conv2)
+ y = F.relu(y)
return y
@@ -179,7 +180,7 @@ class Res2Net(nn.Layer):
stride=2,
act='relu',
name="conv1")
- self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self.pool2d_max = MaxPool2D(kernel_size=3, stride=2, padding=1)
self.block_list = []
for block in range(len(depth)):
@@ -207,7 +208,7 @@ class Res2Net(nn.Layer):
self.block_list.append(bottleneck_block)
shortcut = True
- self.pool2d_avg = AdaptiveAvgPool2d(1)
+ self.pool2d_avg = AdaptiveAvgPool2D(1)
self.pool2d_avg_channels = num_channels[-1] * 2
diff --git a/ppcls/modeling/architectures/res2net_vd.py b/ppcls/modeling/architectures/res2net_vd.py
index 60232d692..e4ffb7735 100644
--- a/ppcls/modeling/architectures/res2net_vd.py
+++ b/ppcls/modeling/architectures/res2net_vd.py
@@ -21,8 +21,8 @@ import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
@@ -48,9 +48,9 @@ class ConvBNLayer(nn.Layer):
super(ConvBNLayer, self).__init__()
self.is_vd_mode = is_vd_mode
- self._pool2d_avg = AvgPool2d(
+ self._pool2d_avg = AvgPool2D(
kernel_size=2, stride=2, padding=0, ceil_mode=True)
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
@@ -110,7 +110,7 @@ class BottleneckBlock(nn.Layer):
act='relu',
name=name + '_branch2b_' + str(s + 1)))
self.conv1_list.append(conv1)
- self.pool2d_avg = AvgPool2d(kernel_size=3, stride=stride, padding=1)
+ self.pool2d_avg = AvgPool2D(kernel_size=3, stride=stride, padding=1)
self.conv2 = ConvBNLayer(
num_channels=num_filters,
@@ -150,7 +150,8 @@ class BottleneckBlock(nn.Layer):
short = inputs
else:
short = self.short(inputs)
- y = paddle.elementwise_add(x=short, y=conv2, act='relu')
+ y = paddle.add(x=short, y=conv2)
+ y = F.relu(y)
return y
@@ -200,7 +201,7 @@ class Res2Net_vd(nn.Layer):
stride=1,
act='relu',
name="conv1_3")
- self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self.pool2d_max = MaxPool2D(kernel_size=3, stride=2, padding=1)
self.block_list = []
for block in range(len(depth)):
@@ -228,7 +229,7 @@ class Res2Net_vd(nn.Layer):
self.block_list.append(bottleneck_block)
shortcut = True
- self.pool2d_avg = AdaptiveAvgPool2d(1)
+ self.pool2d_avg = AdaptiveAvgPool2D(1)
self.pool2d_avg_channels = num_channels[-1] * 2
diff --git a/ppcls/modeling/architectures/resnest.py b/ppcls/modeling/architectures/resnest.py
index 20ac4ddfd..88465d521 100644
--- a/ppcls/modeling/architectures/resnest.py
+++ b/ppcls/modeling/architectures/resnest.py
@@ -20,11 +20,12 @@ import numpy as np
import paddle
import math
import paddle.nn as nn
+import paddle.nn.functional as F
from paddle import ParamAttr
-from paddle.nn.initializer import MSRA
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
-from paddle.fluid.regularizer import L2Decay
+from paddle.nn.initializer import KaimingNormal
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
+from paddle.regularizer import L2Decay
__all__ = ["ResNeSt50_fast_1s1x64d", "ResNeSt50"]
@@ -43,7 +44,7 @@ class ConvBNLayer(nn.Layer):
bn_decay = 0.0
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
@@ -121,7 +122,7 @@ class SplatConv(nn.Layer):
act="relu",
name=name + "_splat1")
- self.avg_pool2d = AdaptiveAvgPool2d(1)
+ self.avg_pool2d = AdaptiveAvgPool2D(1)
inter_channels = int(max(in_channels * radix // reduction_factor, 32))
@@ -136,7 +137,7 @@ class SplatConv(nn.Layer):
name=name + "_splat2")
# to calc atten
- self.conv3 = Conv2d(
+ self.conv3 = Conv2D(
in_channels=inter_channels,
out_channels=channels * radix,
kernel_size=1,
@@ -144,7 +145,7 @@ class SplatConv(nn.Layer):
padding=0,
groups=groups,
weight_attr=ParamAttr(
- name=name + "_splat_weights", initializer=MSRA()),
+ name=name + "_splat_weights", initializer=KaimingNormal()),
bias_attr=False)
self.rsoftmax = rSoftmax(radix=radix, cardinality=groups)
@@ -154,7 +155,7 @@ class SplatConv(nn.Layer):
if self.radix > 1:
splited = paddle.split(x, num_or_sections=self.radix, axis=1)
- gap = paddle.sums(splited)
+ gap = paddle.add_n(splited)
else:
gap = x
@@ -167,10 +168,10 @@ class SplatConv(nn.Layer):
if self.radix > 1:
attens = paddle.split(atten, num_or_sections=self.radix, axis=1)
- y = paddle.sums(
- [att * split for (att, split) in zip(attens, splited)])
+ y = paddle.add_n(
+ [split * att for (att, split) in zip(attens, splited)])
else:
- y = atten * x
+ y = x * atten
return y
@@ -217,7 +218,7 @@ class BottleneckBlock(nn.Layer):
name=name + "_conv1")
if avd and avd_first and (stride > 1 or is_first):
- self.avg_pool2d_1 = AvgPool2d(
+ self.avg_pool2d_1 = AvgPool2D(
kernel_size=3, stride=stride, padding=1)
if radix >= 1:
@@ -245,7 +246,7 @@ class BottleneckBlock(nn.Layer):
name=name + "_conv2")
if avd and avd_first == False and (stride > 1 or is_first):
- self.avg_pool2d_2 = AvgPool2d(
+ self.avg_pool2d_2 = AvgPool2D(
kernel_size=3, stride=stride, padding=1)
self.conv3 = ConvBNLayer(
@@ -260,13 +261,13 @@ class BottleneckBlock(nn.Layer):
if stride != 1 or self.inplanes != self.planes * 4:
if avg_down:
if dilation == 1:
- self.avg_pool2d_3 = AvgPool2d(
+ self.avg_pool2d_3 = AvgPool2D(
kernel_size=stride, stride=stride, padding=0)
else:
- self.avg_pool2d_3 = AvgPool2d(
+ self.avg_pool2d_3 = AvgPool2D(
kernel_size=1, stride=1, padding=0, ceil_mode=True)
- self.conv4 = Conv2d(
+ self.conv4 = Conv2D(
in_channels=self.inplanes,
out_channels=planes * 4,
kernel_size=1,
@@ -274,10 +275,10 @@ class BottleneckBlock(nn.Layer):
padding=0,
groups=1,
weight_attr=ParamAttr(
- name=name + "_weights", initializer=MSRA()),
+ name=name + "_weights", initializer=KaimingNormal()),
bias_attr=False)
else:
- self.conv4 = Conv2d(
+ self.conv4 = Conv2D(
in_channels=self.inplanes,
out_channels=planes * 4,
kernel_size=1,
@@ -285,7 +286,8 @@ class BottleneckBlock(nn.Layer):
padding=0,
groups=1,
weight_attr=ParamAttr(
- name=name + "_shortcut_weights", initializer=MSRA()),
+ name=name + "_shortcut_weights",
+ initializer=KaimingNormal()),
bias_attr=False)
bn_decay = 0.0
@@ -294,10 +296,9 @@ class BottleneckBlock(nn.Layer):
act=None,
param_attr=ParamAttr(
name=name + "_shortcut_scale",
- regularizer=L2Decay(regularization_coeff=bn_decay)),
+ regularizer=L2Decay(bn_decay)),
bias_attr=ParamAttr(
- name + "_shortcut_offset",
- regularizer=L2Decay(regularization_coeff=bn_decay)),
+ name + "_shortcut_offset", regularizer=L2Decay(bn_decay)),
moving_mean_name=name + "_shortcut_mean",
moving_variance_name=name + "_shortcut_variance")
@@ -324,7 +325,8 @@ class BottleneckBlock(nn.Layer):
short = self._batch_norm(short)
- y = paddle.elementwise_add(x=short, y=x, act="relu")
+ y = paddle.add(x=short, y=x)
+ y = F.relu(y)
return y
@@ -495,7 +497,7 @@ class ResNeSt(nn.Layer):
act="relu",
name="conv1")
- self.max_pool2d = MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self.max_pool2d = MaxPool2D(kernel_size=3, stride=2, padding=1)
self.layer1 = ResNeStLayer(
inplanes=self.stem_width * 2
@@ -624,7 +626,7 @@ class ResNeSt(nn.Layer):
stride=2,
name="layer4")
- self.pool2d_avg = AdaptiveAvgPool2d(1)
+ self.pool2d_avg = AdaptiveAvgPool2D(1)
self.out_channels = 2048
diff --git a/ppcls/modeling/architectures/resnet.py b/ppcls/modeling/architectures/resnet.py
index f86597917..4ed449749 100644
--- a/ppcls/modeling/architectures/resnet.py
+++ b/ppcls/modeling/architectures/resnet.py
@@ -20,8 +20,9 @@ import numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+import paddle.nn.functional as F
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
@@ -40,7 +41,7 @@ class ConvBNLayer(nn.Layer):
name=None):
super(ConvBNLayer, self).__init__()
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
@@ -118,7 +119,8 @@ class BottleneckBlock(nn.Layer):
else:
short = self.short(inputs)
- y = paddle.elementwise_add(x=short, y=conv2, act="relu")
+ y = paddle.add(x=short, y=conv2)
+ y = F.relu(y)
return y
@@ -163,7 +165,8 @@ class BasicBlock(nn.Layer):
short = inputs
else:
short = self.short(inputs)
- y = paddle.elementwise_add(x=short, y=conv1, act="relu")
+ y = paddle.add(x=short, y=conv1)
+ y = F.relu(y)
return y
@@ -196,7 +199,7 @@ class ResNet(nn.Layer):
stride=2,
act="relu",
name="conv1")
- self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self.pool2d_max = MaxPool2D(kernel_size=3, stride=2, padding=1)
self.block_list = []
if layers >= 50:
@@ -238,7 +241,7 @@ class ResNet(nn.Layer):
self.block_list.append(basic_block)
shortcut = True
- self.pool2d_avg = AdaptiveAvgPool2d(1)
+ self.pool2d_avg = AdaptiveAvgPool2D(1)
self.pool2d_avg_channels = num_channels[-1] * 2
diff --git a/ppcls/modeling/architectures/resnet_acnet.py b/ppcls/modeling/architectures/resnet_acnet.py
deleted file mode 100644
index 45638a0c8..000000000
--- a/ppcls/modeling/architectures/resnet_acnet.py
+++ /dev/null
@@ -1,332 +0,0 @@
-# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import math
-
-import paddle.fluid as fluid
-from paddle.fluid.param_attr import ParamAttr
-
-__all__ = [
- "ResNet18_ACNet", "ResNet34_ACNet", "ResNet50_ACNet", "ResNet101_ACNet",
- "ResNet152_ACNet"
-]
-
-
-class ResNetACNet(object):
- """ ACNet """
-
- def __init__(self, layers=50, deploy=False):
- """init"""
- self.layers = layers
- self.deploy = deploy
-
- def net(self, input, class_dim=1000):
- """model"""
- layers = self.layers
- supported_layers = [18, 34, 50, 101, 152]
- assert layers in supported_layers, \
- "supported layers are {} but input layer is {}".format(
- supported_layers, layers)
-
- if layers == 18:
- depth = [2, 2, 2, 2]
- elif layers == 34 or layers == 50:
- depth = [3, 4, 6, 3]
- elif layers == 101:
- depth = [3, 4, 23, 3]
- elif layers == 152:
- depth = [3, 8, 36, 3]
- num_filters = [64, 128, 256, 512]
-
- conv = self.conv_bn_layer(
- input=input,
- num_filters=64,
- filter_size=7,
- stride=2,
- act='relu',
- name="conv1")
- conv = fluid.layers.pool2d(
- input=conv,
- pool_size=3,
- pool_stride=2,
- pool_padding=1,
- pool_type='max')
- if layers >= 50:
- for block in range(len(depth)):
- for i in range(depth[block]):
- if layers in [101, 152] and block == 2:
- if i == 0:
- conv_name = "res" + str(block + 2) + "a"
- else:
- conv_name = "res" + str(block + 2) + "b" + str(i)
- else:
- conv_name = "res" + str(block + 2) + chr(97 + i)
- conv = self.bottleneck_block(
- input=conv,
- num_filters=num_filters[block],
- stride=2 if i == 0 and block != 0 else 1,
- name=conv_name)
- else:
- for block in range(len(depth)):
- for i in range(depth[block]):
- conv_name = "res" + str(block + 2) + chr(97 + i)
- conv = self.basic_block(
- input=conv,
- num_filters=num_filters[block],
- stride=2 if i == 0 and block != 0 else 1,
- is_first=block == i == 0,
- name=conv_name)
-
- pool = fluid.layers.pool2d(
- input=conv, pool_size=7, pool_type='avg', global_pooling=True)
-
- stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
- out = fluid.layers.fc(
- input=pool,
- size=class_dim,
- param_attr=fluid.param_attr.ParamAttr(
- initializer=fluid.initializer.Uniform(-stdv, stdv)))
- return out
-
- def conv_bn_layer(self, **kwargs):
- """
- conv_bn_layer
- """
- if kwargs['filter_size'] == 1:
- return self.conv_bn_layer_ori(**kwargs)
- else:
- return self.conv_bn_layer_ac(**kwargs)
-
- # conv bn+relu
- def conv_bn_layer_ori(self,
- input,
- num_filters,
- filter_size,
- stride=1,
- groups=1,
- act=None,
- name=None):
- """
- standard convbn
- used for 1x1 convbn in acnet
- """
- conv = fluid.layers.conv2d(
- input=input,
- num_filters=num_filters,
- filter_size=filter_size,
- stride=stride,
- padding=(filter_size - 1) // 2,
- groups=groups,
- act=None,
- param_attr=ParamAttr(name=name + "_weights"),
- bias_attr=False,
- name=name + '.conv2d.output.1')
-
- if name == "conv1":
- bn_name = "bn_" + name
- else:
- bn_name = "bn" + name[3:]
- return fluid.layers.batch_norm(
- input=conv,
- act=act,
- name=bn_name + '.output.1',
- param_attr=ParamAttr(name=bn_name + '_scale'),
- bias_attr=ParamAttr(bn_name + '_offset'),
- moving_mean_name=bn_name + '_mean',
- moving_variance_name=bn_name + '_variance', )
-
- # conv bn+relu
- def conv_bn_layer_ac(self,
- input,
- num_filters,
- filter_size,
- stride=1,
- groups=1,
- act=None,
- name=None):
- """ ACNet conv bn """
- padding = (filter_size - 1) // 2
-
- square_conv = fluid.layers.conv2d(
- input=input,
- num_filters=num_filters,
- filter_size=filter_size,
- stride=stride,
- padding=padding,
- groups=groups,
- act=act if self.deploy else None,
- param_attr=ParamAttr(name=name + "_acsquare_weights"),
- bias_attr=ParamAttr(name=name + "_acsquare_bias")
- if self.deploy else False,
- name=name + '.acsquare.conv2d.output.1')
-
- if self.deploy:
- return square_conv
- else:
- ver_conv = fluid.layers.conv2d(
- input=input,
- num_filters=num_filters,
- filter_size=(filter_size, 1),
- stride=stride,
- padding=(padding, 0),
- groups=groups,
- act=None,
- param_attr=ParamAttr(name=name + "_acver_weights"),
- bias_attr=False,
- name=name + '.acver.conv2d.output.1')
-
- hor_conv = fluid.layers.conv2d(
- input=input,
- num_filters=num_filters,
- filter_size=(1, filter_size),
- stride=stride,
- padding=(0, padding),
- groups=groups,
- act=None,
- param_attr=ParamAttr(name=name + "_achor_weights"),
- bias_attr=False,
- name=name + '.achor.conv2d.output.1')
-
- if name == "conv1":
- bn_name = "bn_" + name
- else:
- bn_name = "bn" + name[3:]
-
- square_bn = fluid.layers.batch_norm(
- input=square_conv,
- act=None,
- name=bn_name + '.acsquare.output.1',
- param_attr=ParamAttr(name=bn_name + '_acsquare_scale'),
- bias_attr=ParamAttr(bn_name + '_acsquare_offset'),
- moving_mean_name=bn_name + '_acsquare_mean',
- moving_variance_name=bn_name + '_acsquare_variance', )
-
- ver_bn = fluid.layers.batch_norm(
- input=ver_conv,
- act=None,
- name=bn_name + '.acver.output.1',
- param_attr=ParamAttr(name=bn_name + '_acver_scale'),
- bias_attr=ParamAttr(bn_name + '_acver_offset'),
- moving_mean_name=bn_name + '_acver_mean',
- moving_variance_name=bn_name + '_acver_variance', )
-
- hor_bn = fluid.layers.batch_norm(
- input=hor_conv,
- act=None,
- name=bn_name + '.achor.output.1',
- param_attr=ParamAttr(name=bn_name + '_achor_scale'),
- bias_attr=ParamAttr(bn_name + '_achor_offset'),
- moving_mean_name=bn_name + '_achor_mean',
- moving_variance_name=bn_name + '_achor_variance', )
-
- return fluid.layers.elementwise_add(
- x=square_bn, y=ver_bn + hor_bn, act=act)
-
- def shortcut(self, input, ch_out, stride, is_first, name):
- """ shortcut """
- ch_in = input.shape[1]
- if ch_in != ch_out or stride != 1 or is_first is True:
- return self.conv_bn_layer(
- input=input,
- num_filters=ch_out,
- filter_size=1,
- stride=stride,
- name=name)
- else:
- return input
-
- def bottleneck_block(self, input, num_filters, stride, name):
- """" bottleneck_block """
- conv0 = self.conv_bn_layer(
- input=input,
- num_filters=num_filters,
- filter_size=1,
- act='relu',
- name=name + "_branch2a")
- conv1 = self.conv_bn_layer(
- input=conv0,
- num_filters=num_filters,
- filter_size=3,
- stride=stride,
- act='relu',
- name=name + "_branch2b")
- conv2 = self.conv_bn_layer(
- input=conv1,
- num_filters=num_filters * 4,
- filter_size=1,
- act=None,
- name=name + "_branch2c")
-
- short = self.shortcut(
- input,
- num_filters * 4,
- stride,
- is_first=False,
- name=name + "_branch1")
-
- return fluid.layers.elementwise_add(
- x=short, y=conv2, act='relu', name=name + ".add.output.5")
-
- def basic_block(self, input, num_filters, stride, is_first, name):
- """ basic_block """
- conv0 = self.conv_bn_layer(
- input=input,
- num_filters=num_filters,
- filter_size=3,
- act='relu',
- stride=stride,
- name=name + "_branch2a")
- conv1 = self.conv_bn_layer(
- input=conv0,
- num_filters=num_filters,
- filter_size=3,
- act=None,
- name=name + "_branch2b")
- short = self.shortcut(
- input, num_filters, stride, is_first, name=name + "_branch1")
- return fluid.layers.elementwise_add(x=short, y=conv1, act='relu')
-
-
-def ResNet18_ACNet(deploy=False):
- """ResNet18 + ACNet"""
- model = ResNetACNet(layers=18, deploy=deploy)
- return model
-
-
-def ResNet34_ACNet(deploy=False):
- """ResNet34 + ACNet"""
- model = ResNetACNet(layers=34, deploy=deploy)
- return model
-
-
-def ResNet50_ACNet(deploy=False):
- """ResNet50 + ACNet"""
- model = ResNetACNet(layers=50, deploy=deploy)
- return model
-
-
-def ResNet101_ACNet(deploy=False):
- """ResNet101 + ACNet"""
- model = ResNetACNet(layers=101, deploy=deploy)
- return model
-
-
-def ResNet152_ACNet(deploy=False):
- """ResNet152 + ACNet"""
- model = ResNetACNet(layers=152, deploy=deploy)
- return model
diff --git a/ppcls/modeling/architectures/resnet_vc.py b/ppcls/modeling/architectures/resnet_vc.py
index bbc4e0a83..5a9374c15 100644
--- a/ppcls/modeling/architectures/resnet_vc.py
+++ b/ppcls/modeling/architectures/resnet_vc.py
@@ -20,8 +20,9 @@ import numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+import paddle.nn.functional as F
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
@@ -42,7 +43,7 @@ class ConvBNLayer(nn.Layer):
name=None):
super(ConvBNLayer, self).__init__()
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
@@ -120,7 +121,8 @@ class BottleneckBlock(nn.Layer):
else:
short = self.short(inputs)
- y = paddle.elementwise_add(x=short, y=conv2, act='relu')
+ y = paddle.add(x=short, y=conv2)
+ y = F.relu(y)
return y
@@ -165,7 +167,8 @@ class BasicBlock(nn.Layer):
short = inputs
else:
short = self.short(inputs)
- y = paddle.elementwise_add(x=short, y=conv1, act='relu')
+ y = paddle.add(x=short, y=conv1)
+ y = F.relu(y)
return y
@@ -213,7 +216,7 @@ class ResNet_vc(nn.Layer):
act='relu',
name="conv1_3")
- self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self.pool2d_max = MaxPool2D(kernel_size=3, stride=2, padding=1)
self.block_list = []
if layers >= 50:
@@ -255,7 +258,7 @@ class ResNet_vc(nn.Layer):
self.block_list.append(basic_block)
shortcut = True
- self.pool2d_avg = AdaptiveAvgPool2d(1)
+ self.pool2d_avg = AdaptiveAvgPool2D(1)
self.pool2d_avg_channels = num_channels[-1] * 2
diff --git a/ppcls/modeling/architectures/resnet_vd.py b/ppcls/modeling/architectures/resnet_vd.py
index 00a6c0138..944a0b0cc 100644
--- a/ppcls/modeling/architectures/resnet_vd.py
+++ b/ppcls/modeling/architectures/resnet_vd.py
@@ -20,8 +20,9 @@ import numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+import paddle.nn.functional as F
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
@@ -44,9 +45,9 @@ class ConvBNLayer(nn.Layer):
name=None):
super(ConvBNLayer, self).__init__()
self.is_vd_mode = is_vd_mode
- self._pool2d_avg = AvgPool2d(
+ self._pool2d_avg = AvgPool2D(
kernel_size=2, stride=2, padding=0, ceil_mode=True)
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
@@ -130,7 +131,8 @@ class BottleneckBlock(nn.Layer):
short = inputs
else:
short = self.short(inputs)
- y = paddle.elementwise_add(x=short, y=conv2, act='relu')
+ y = paddle.add(x=short, y=conv2)
+ y = F.relu(y)
return y
@@ -178,7 +180,8 @@ class BasicBlock(nn.Layer):
short = inputs
else:
short = self.short(inputs)
- y = paddle.elementwise_add(x=short, y=conv1, act='relu')
+ y = paddle.add(x=short, y=conv1)
+ y = F.relu(y)
return y
@@ -243,7 +246,7 @@ class ResNet_vd(nn.Layer):
act='relu',
lr_mult=self.lr_mult_list[0],
name="conv1_3")
- self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self.pool2d_max = MaxPool2D(kernel_size=3, stride=2, padding=1)
self.block_list = []
if layers >= 50:
@@ -285,11 +288,11 @@ class ResNet_vd(nn.Layer):
shortcut=shortcut,
if_first=block == i == 0,
name=conv_name,
- lr_mult=lr_mult))
+ lr_mult=self.lr_mult_list[block + 1]))
self.block_list.append(basic_block)
shortcut = True
- self.pool2d_avg = AdaptiveAvgPool2d(1)
+ self.pool2d_avg = AdaptiveAvgPool2D(1)
self.pool2d_avg_channels = num_channels[-1] * 2
diff --git a/ppcls/modeling/architectures/resnext.py b/ppcls/modeling/architectures/resnext.py
index b47815152..f56d6ba92 100644
--- a/ppcls/modeling/architectures/resnext.py
+++ b/ppcls/modeling/architectures/resnext.py
@@ -20,8 +20,9 @@ import numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+import paddle.nn.functional as F
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
@@ -43,7 +44,7 @@ class ConvBNLayer(nn.Layer):
name=None):
super(ConvBNLayer, self).__init__()
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
@@ -122,7 +123,8 @@ class BottleneckBlock(nn.Layer):
else:
short = self.short(inputs)
- y = paddle.elementwise_add(x=short, y=conv2, act='relu')
+ y = paddle.add(x=short, y=conv2)
+ y = F.relu(y)
return y
@@ -157,7 +159,7 @@ class ResNeXt(nn.Layer):
stride=2,
act='relu',
name="res_conv1")
- self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self.pool2d_max = MaxPool2D(kernel_size=3, stride=2, padding=1)
self.block_list = []
for block in range(len(depth)):
@@ -183,7 +185,7 @@ class ResNeXt(nn.Layer):
self.block_list.append(bottleneck_block)
shortcut = True
- self.pool2d_avg = AdaptiveAvgPool2d(1)
+ self.pool2d_avg = AdaptiveAvgPool2D(1)
self.pool2d_avg_channels = num_channels[-1] * 2
diff --git a/ppcls/modeling/architectures/resnext101_wsl.py b/ppcls/modeling/architectures/resnext101_wsl.py
index 40d0bc5e2..7130812b1 100644
--- a/ppcls/modeling/architectures/resnext101_wsl.py
+++ b/ppcls/modeling/architectures/resnext101_wsl.py
@@ -2,8 +2,8 @@ import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
__all__ = [
@@ -26,7 +26,7 @@ class ConvBNLayer(nn.Layer):
conv_name = name + ".0"
else:
conv_name = name
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=input_channels,
out_channels=output_channels,
kernel_size=filter_size,
@@ -114,7 +114,9 @@ class BottleneckBlock(nn.Layer):
x = self._conv1(x)
x = self._conv2(x)
y = self._short(inputs)
- return paddle.elementwise_add(x, y, act="relu")
+ y = paddle.add(x, y)
+ y = F.relu(y)
+ return y
class ResNeXt101WSL(nn.Layer):
@@ -134,7 +136,7 @@ class ResNeXt101WSL(nn.Layer):
for i in [1, 2, 4, 8]] # [256, 512, 1024, 2048]
self._conv_stem = ConvBNLayer(
3, 64, 7, stride=2, act="relu", name="conv1")
- self._pool = MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self._pool = MaxPool2D(kernel_size=3, stride=2, padding=1)
self._conv1_0 = BottleneckBlock(
64,
@@ -371,7 +373,7 @@ class ResNeXt101WSL(nn.Layer):
width=self.width,
name="layer4.2")
- self._avg_pool = AdaptiveAvgPool2d(1)
+ self._avg_pool = AdaptiveAvgPool2D(1)
self._out = Linear(
num_filters[3] // (width // 8),
class_dim,
diff --git a/ppcls/modeling/architectures/resnext_vd.py b/ppcls/modeling/architectures/resnext_vd.py
index 6fe1f79a1..a05b64523 100644
--- a/ppcls/modeling/architectures/resnext_vd.py
+++ b/ppcls/modeling/architectures/resnext_vd.py
@@ -20,8 +20,9 @@ import numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+import paddle.nn.functional as F
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
@@ -46,9 +47,9 @@ class ConvBNLayer(nn.Layer):
super(ConvBNLayer, self).__init__()
self.is_vd_mode = is_vd_mode
- self._pool2d_avg = AvgPool2d(
+ self._pool2d_avg = AvgPool2D(
kernel_size=2, stride=2, padding=0, ceil_mode=True)
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
@@ -131,7 +132,8 @@ class BottleneckBlock(nn.Layer):
else:
short = self.short(inputs)
- y = paddle.elementwise_add(x=short, y=conv2, act='relu')
+ y = paddle.add(x=short, y=conv2)
+ y = F.relu(y)
return y
@@ -181,7 +183,7 @@ class ResNeXt(nn.Layer):
act='relu',
name="conv1_3")
- self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self.pool2d_max = MaxPool2D(kernel_size=3, stride=2, padding=1)
self.block_list = []
for block in range(len(depth)):
@@ -208,7 +210,7 @@ class ResNeXt(nn.Layer):
self.block_list.append(bottleneck_block)
shortcut = True
- self.pool2d_avg = AdaptiveAvgPool2d(1)
+ self.pool2d_avg = AdaptiveAvgPool2D(1)
self.pool2d_avg_channels = num_channels[-1] * 2
diff --git a/ppcls/modeling/architectures/se_resnet_vd.py b/ppcls/modeling/architectures/se_resnet_vd.py
index 219bc7536..a3ba43efa 100644
--- a/ppcls/modeling/architectures/se_resnet_vd.py
+++ b/ppcls/modeling/architectures/se_resnet_vd.py
@@ -20,8 +20,8 @@ import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
@@ -46,10 +46,10 @@ class ConvBNLayer(nn.Layer):
super(ConvBNLayer, self).__init__()
self.is_vd_mode = is_vd_mode
- self._pool2d_avg = AvgPool2d(
+ self._pool2d_avg = AvgPool2D(
kernel_size=2, stride=2, padding=0, ceil_mode=True)
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
@@ -135,7 +135,8 @@ class BottleneckBlock(nn.Layer):
short = inputs
else:
short = self.short(inputs)
- y = paddle.elementwise_add(x=short, y=scale, act='relu')
+ y = paddle.add(x=short, y=scale)
+ y = F.relu(y)
return y
@@ -190,7 +191,8 @@ class BasicBlock(nn.Layer):
short = inputs
else:
short = self.short(inputs)
- y = paddle.elementwise_add(x=short, y=scale, act='relu')
+ y = paddle.add(x=short, y=scale)
+ y = F.relu(y)
return y
@@ -198,7 +200,7 @@ class SELayer(nn.Layer):
def __init__(self, num_channels, num_filters, reduction_ratio, name=None):
super(SELayer, self).__init__()
- self.pool2d_gap = AdaptiveAvgPool2d(1)
+ self.pool2d_gap = AdaptiveAvgPool2D(1)
self._num_channels = num_channels
@@ -277,7 +279,7 @@ class SE_ResNet_vd(nn.Layer):
stride=1,
act='relu',
name="conv1_3")
- self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self.pool2d_max = MaxPool2D(kernel_size=3, stride=2, padding=1)
self.block_list = []
if layers >= 50:
@@ -321,7 +323,7 @@ class SE_ResNet_vd(nn.Layer):
self.block_list.append(basic_block)
shortcut = True
- self.pool2d_avg = AdaptiveAvgPool2d(1)
+ self.pool2d_avg = AdaptiveAvgPool2D(1)
self.pool2d_avg_channels = num_channels[-1] * 2
diff --git a/ppcls/modeling/architectures/se_resnext.py b/ppcls/modeling/architectures/se_resnext.py
index 68b8d98f6..d323e852f 100644
--- a/ppcls/modeling/architectures/se_resnext.py
+++ b/ppcls/modeling/architectures/se_resnext.py
@@ -20,8 +20,8 @@ import numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
@@ -30,18 +30,17 @@ __all__ = ["SE_ResNeXt50_32x4d", "SE_ResNeXt101_32x4d", "SE_ResNeXt152_64x4d"]
class ConvBNLayer(nn.Layer):
- def __init__(
- self,
- num_channels,
- num_filters,
- filter_size,
- stride=1,
- groups=1,
- act=None,
- name=None):
+ def __init__(self,
+ num_channels,
+ num_filters,
+ filter_size,
+ stride=1,
+ groups=1,
+ act=None,
+ name=None):
super(ConvBNLayer, self).__init__()
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
@@ -124,7 +123,8 @@ class BottleneckBlock(nn.Layer):
short = inputs
else:
short = self.short(inputs)
- y = paddle.elementwise_add(x=short, y=scale, act='relu')
+ y = paddle.add(x=short, y=scale)
+ y = F.relu(y)
return y
@@ -132,7 +132,7 @@ class SELayer(nn.Layer):
def __init__(self, num_channels, num_filters, reduction_ratio, name=None):
super(SELayer, self).__init__()
- self.pool2d_gap = AdaptiveAvgPool2d(1)
+ self.pool2d_gap = AdaptiveAvgPool2D(1)
self._num_channels = num_channels
@@ -142,8 +142,7 @@ class SELayer(nn.Layer):
num_channels,
med_ch,
weight_attr=ParamAttr(
- initializer=Uniform(-stdv, stdv),
- name=name + "_sqz_weights"),
+ initializer=Uniform(-stdv, stdv), name=name + "_sqz_weights"),
bias_attr=ParamAttr(name=name + '_sqz_offset'))
self.relu = nn.ReLU()
stdv = 1.0 / math.sqrt(med_ch * 1.0)
@@ -151,11 +150,10 @@ class SELayer(nn.Layer):
med_ch,
num_filters,
weight_attr=ParamAttr(
- initializer=Uniform(-stdv, stdv),
- name=name + "_exc_weights"),
+ initializer=Uniform(-stdv, stdv), name=name + "_exc_weights"),
bias_attr=ParamAttr(name=name + '_exc_offset'))
self.sigmoid = nn.Sigmoid()
-
+
def forward(self, input):
pool = self.pool2d_gap(input)
pool = paddle.reshape(pool, shape=[-1, self._num_channels])
@@ -224,7 +222,7 @@ class ResNeXt(nn.Layer):
act='relu',
name="conv3")
- self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self.pool2d_max = MaxPool2D(kernel_size=3, stride=2, padding=1)
self.block_list = []
n = 1 if layers == 50 or layers == 101 else 3
@@ -247,7 +245,7 @@ class ResNeXt(nn.Layer):
self.block_list.append(bottleneck_block)
shortcut = True
- self.pool2d_avg = AdaptiveAvgPool2d(1)
+ self.pool2d_avg = AdaptiveAvgPool2D(1)
self.pool2d_avg_channels = num_channels[-1] * 2
@@ -257,8 +255,7 @@ class ResNeXt(nn.Layer):
self.pool2d_avg_channels,
class_dim,
weight_attr=ParamAttr(
- initializer=Uniform(-stdv, stdv),
- name="fc6_weights"),
+ initializer=Uniform(-stdv, stdv), name="fc6_weights"),
bias_attr=ParamAttr(name="fc6_offset"))
def forward(self, inputs):
@@ -269,7 +266,7 @@ class ResNeXt(nn.Layer):
y = self.conv1_2(y)
y = self.conv1_3(y)
y = self.pool2d_max(y)
-
+
for block in self.block_list:
y = block(y)
y = self.pool2d_avg(y)
diff --git a/ppcls/modeling/architectures/se_resnext_vd.py b/ppcls/modeling/architectures/se_resnext_vd.py
index 4dbfefdb4..d3d556f9f 100644
--- a/ppcls/modeling/architectures/se_resnext_vd.py
+++ b/ppcls/modeling/architectures/se_resnext_vd.py
@@ -20,8 +20,9 @@ import numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+import paddle.nn.functional as F
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
@@ -30,22 +31,21 @@ __all__ = ["SE_ResNeXt50_vd_32x4d", "SE_ResNeXt50_vd_32x4d", "SENet154_vd"]
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,
+ name=None):
super(ConvBNLayer, self).__init__()
self.is_vd_mode = is_vd_mode
- self._pool2d_avg = AvgPool2d(
+ self._pool2d_avg = AvgPool2D(
kernel_size=2, stride=2, padding=0, ceil_mode=True)
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
@@ -131,7 +131,8 @@ class BottleneckBlock(nn.Layer):
short = inputs
else:
short = self.short(inputs)
- y = paddle.elementwise_add(x=short, y=scale, act='relu')
+ y = paddle.add(x=short, y=scale)
+ y = F.relu(y)
return y
@@ -139,7 +140,7 @@ class SELayer(nn.Layer):
def __init__(self, num_channels, num_filters, reduction_ratio, name=None):
super(SELayer, self).__init__()
- self.pool2d_gap = AdaptiveAvgPool2d(1)
+ self.pool2d_gap = AdaptiveAvgPool2D(1)
self._num_channels = num_channels
@@ -149,8 +150,7 @@ class SELayer(nn.Layer):
num_channels,
med_ch,
weight_attr=ParamAttr(
- initializer=Uniform(-stdv, stdv),
- name=name + "_sqz_weights"),
+ initializer=Uniform(-stdv, stdv), name=name + "_sqz_weights"),
bias_attr=ParamAttr(name=name + '_sqz_offset'))
self.relu = nn.ReLU()
stdv = 1.0 / math.sqrt(med_ch * 1.0)
@@ -158,11 +158,10 @@ class SELayer(nn.Layer):
med_ch,
num_filters,
weight_attr=ParamAttr(
- initializer=Uniform(-stdv, stdv),
- name=name + "_exc_weights"),
+ initializer=Uniform(-stdv, stdv), name=name + "_exc_weights"),
bias_attr=ParamAttr(name=name + '_exc_offset'))
self.sigmoid = nn.Sigmoid()
-
+
def forward(self, input):
pool = self.pool2d_gap(input)
pool = paddle.reshape(pool, shape=[-1, self._num_channels])
@@ -223,7 +222,7 @@ class ResNeXt(nn.Layer):
act='relu',
name="conv1_3")
- self.pool2d_max = MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self.pool2d_max = MaxPool2D(kernel_size=3, stride=2, padding=1)
self.block_list = []
n = 1 if layers == 50 or layers == 101 else 3
@@ -246,7 +245,7 @@ class ResNeXt(nn.Layer):
self.block_list.append(bottleneck_block)
shortcut = True
- self.pool2d_avg = AdaptiveAvgPool2d(1)
+ self.pool2d_avg = AdaptiveAvgPool2D(1)
self.pool2d_avg_channels = num_channels[-1] * 2
@@ -256,8 +255,7 @@ class ResNeXt(nn.Layer):
self.pool2d_avg_channels,
class_dim,
weight_attr=ParamAttr(
- initializer=Uniform(-stdv, stdv),
- name="fc6_weights"),
+ initializer=Uniform(-stdv, stdv), name="fc6_weights"),
bias_attr=ParamAttr(name="fc6_offset"))
def forward(self, inputs):
diff --git a/ppcls/modeling/architectures/shufflenet_v2.py b/ppcls/modeling/architectures/shufflenet_v2.py
index 9e06c955c..e344a142f 100644
--- a/ppcls/modeling/architectures/shufflenet_v2.py
+++ b/ppcls/modeling/architectures/shufflenet_v2.py
@@ -17,8 +17,8 @@ from __future__ import division
from __future__ import print_function
from paddle import ParamAttr, reshape, transpose, concat, split
-from paddle.nn import Layer, Conv2d, MaxPool2d, AdaptiveAvgPool2d, BatchNorm, Linear
-from paddle.nn.initializer import MSRA
+from paddle.nn import Layer, Conv2D, MaxPool2D, AdaptiveAvgPool2D, BatchNorm, Linear
+from paddle.nn.initializer import KaimingNormal
from paddle.nn.functional import swish
__all__ = [
@@ -56,7 +56,7 @@ class ConvBNLayer(Layer):
act=None,
name=None, ):
super(ConvBNLayer, self).__init__()
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
@@ -64,7 +64,7 @@ class ConvBNLayer(Layer):
padding=padding,
groups=groups,
weight_attr=ParamAttr(
- initializer=MSRA(), name=name + "_weights"),
+ initializer=KaimingNormal(), name=name + "_weights"),
bias_attr=False)
self._batch_norm = BatchNorm(
@@ -228,7 +228,7 @@ class ShuffleNet(Layer):
padding=1,
act=act,
name='stage1_conv')
- self._max_pool = MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self._max_pool = MaxPool2D(kernel_size=3, stride=2, padding=1)
# 2. bottleneck sequences
self._block_list = []
@@ -263,7 +263,7 @@ class ShuffleNet(Layer):
act=act,
name='conv5')
# 4. pool
- self._pool2d_avg = AdaptiveAvgPool2d(1)
+ self._pool2d_avg = AdaptiveAvgPool2D(1)
self._out_c = stage_out_channels[-1]
# 5. fc
self._fc = Linear(
diff --git a/ppcls/modeling/architectures/squeezenet.py b/ppcls/modeling/architectures/squeezenet.py
index 1e38d8413..eb380f252 100644
--- a/ppcls/modeling/architectures/squeezenet.py
+++ b/ppcls/modeling/architectures/squeezenet.py
@@ -2,8 +2,8 @@ import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
__all__ = ["SqueezeNet1_0", "SqueezeNet1_1"]
@@ -16,7 +16,7 @@ class MakeFireConv(nn.Layer):
padding=0,
name=None):
super(MakeFireConv, self).__init__()
- self._conv = Conv2d(
+ self._conv = Conv2D(
input_channels,
output_channels,
filter_size,
@@ -62,14 +62,14 @@ class SqueezeNet(nn.Layer):
self.version = version
if self.version == "1.0":
- self._conv = Conv2d(
+ self._conv = Conv2D(
3,
96,
7,
stride=2,
weight_attr=ParamAttr(name="conv1_weights"),
bias_attr=ParamAttr(name="conv1_offset"))
- self._pool = MaxPool2d(kernel_size=3, stride=2, padding=0)
+ self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
self._conv1 = MakeFire(96, 16, 64, 64, name="fire2")
self._conv2 = MakeFire(128, 16, 64, 64, name="fire3")
self._conv3 = MakeFire(128, 32, 128, 128, name="fire4")
@@ -81,7 +81,7 @@ class SqueezeNet(nn.Layer):
self._conv8 = MakeFire(512, 64, 256, 256, name="fire9")
else:
- self._conv = Conv2d(
+ self._conv = Conv2D(
3,
64,
3,
@@ -89,7 +89,7 @@ class SqueezeNet(nn.Layer):
padding=1,
weight_attr=ParamAttr(name="conv1_weights"),
bias_attr=ParamAttr(name="conv1_offset"))
- self._pool = MaxPool2d(kernel_size=3, stride=2, padding=0)
+ self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
self._conv1 = MakeFire(64, 16, 64, 64, name="fire2")
self._conv2 = MakeFire(128, 16, 64, 64, name="fire3")
@@ -102,13 +102,13 @@ class SqueezeNet(nn.Layer):
self._conv8 = MakeFire(512, 64, 256, 256, name="fire9")
self._drop = Dropout(p=0.5, mode="downscale_in_infer")
- self._conv9 = Conv2d(
+ self._conv9 = Conv2D(
512,
class_dim,
1,
weight_attr=ParamAttr(name="conv10_weights"),
bias_attr=ParamAttr(name="conv10_offset"))
- self._avg_pool = AdaptiveAvgPool2d(1)
+ self._avg_pool = AdaptiveAvgPool2D(1)
def forward(self, inputs):
x = self._conv(inputs)
diff --git a/ppcls/modeling/architectures/vgg.py b/ppcls/modeling/architectures/vgg.py
index dec7c3d76..27619c314 100644
--- a/ppcls/modeling/architectures/vgg.py
+++ b/ppcls/modeling/architectures/vgg.py
@@ -2,8 +2,8 @@ import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
__all__ = ["VGG11", "VGG13", "VGG16", "VGG19"]
@@ -13,7 +13,7 @@ class ConvBlock(nn.Layer):
super(ConvBlock, self).__init__()
self.groups = groups
- self._conv_1 = Conv2d(
+ self._conv_1 = Conv2D(
in_channels=input_channels,
out_channels=output_channels,
kernel_size=3,
@@ -22,7 +22,7 @@ class ConvBlock(nn.Layer):
weight_attr=ParamAttr(name=name + "1_weights"),
bias_attr=False)
if groups == 2 or groups == 3 or groups == 4:
- self._conv_2 = Conv2d(
+ self._conv_2 = Conv2D(
in_channels=output_channels,
out_channels=output_channels,
kernel_size=3,
@@ -31,7 +31,7 @@ class ConvBlock(nn.Layer):
weight_attr=ParamAttr(name=name + "2_weights"),
bias_attr=False)
if groups == 3 or groups == 4:
- self._conv_3 = Conv2d(
+ self._conv_3 = Conv2D(
in_channels=output_channels,
out_channels=output_channels,
kernel_size=3,
@@ -40,7 +40,7 @@ class ConvBlock(nn.Layer):
weight_attr=ParamAttr(name=name + "3_weights"),
bias_attr=False)
if groups == 4:
- self._conv_4 = Conv2d(
+ self._conv_4 = Conv2D(
in_channels=output_channels,
out_channels=output_channels,
kernel_size=3,
@@ -49,7 +49,7 @@ class ConvBlock(nn.Layer):
weight_attr=ParamAttr(name=name + "4_weights"),
bias_attr=False)
- self._pool = MaxPool2d(kernel_size=2, stride=2, padding=0)
+ self._pool = MaxPool2D(kernel_size=2, stride=2, padding=0)
def forward(self, inputs):
x = self._conv_1(inputs)
diff --git a/ppcls/modeling/architectures/xception.py b/ppcls/modeling/architectures/xception.py
index 8293c31b8..cf2ea35a6 100644
--- a/ppcls/modeling/architectures/xception.py
+++ b/ppcls/modeling/architectures/xception.py
@@ -2,8 +2,8 @@ import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
@@ -21,7 +21,7 @@ class ConvBNLayer(nn.Layer):
name=None):
super(ConvBNLayer, self).__init__()
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
@@ -75,7 +75,7 @@ class EntryFlowBottleneckBlock(nn.Layer):
super(EntryFlowBottleneckBlock, self).__init__()
self.relu_first = relu_first
- self._short = Conv2d(
+ self._short = Conv2D(
in_channels=input_channels,
out_channels=output_channels,
kernel_size=1,
@@ -93,7 +93,7 @@ class EntryFlowBottleneckBlock(nn.Layer):
output_channels,
stride=1,
name=name + "_branch2b_weights")
- self._pool = MaxPool2d(kernel_size=3, stride=stride, padding=1)
+ self._pool = MaxPool2D(kernel_size=3, stride=stride, padding=1)
def forward(self, inputs):
conv0 = inputs
@@ -104,7 +104,7 @@ class EntryFlowBottleneckBlock(nn.Layer):
conv2 = F.relu(conv1)
conv2 = self._conv2(conv2)
pool = self._pool(conv2)
- return paddle.elementwise_add(x=short, y=pool)
+ return paddle.add(x=short, y=pool)
class EntryFlow(nn.Layer):
@@ -181,7 +181,7 @@ class MiddleFlowBottleneckBlock(nn.Layer):
conv1 = self._conv_1(conv1)
conv2 = F.relu(conv1)
conv2 = self._conv_2(conv2)
- return paddle.elementwise_add(x=inputs, y=conv2)
+ return paddle.add(x=inputs, y=conv2)
class MiddleFlow(nn.Layer):
@@ -249,7 +249,7 @@ class ExitFlowBottleneckBlock(nn.Layer):
name):
super(ExitFlowBottleneckBlock, self).__init__()
- self._short = Conv2d(
+ self._short = Conv2D(
in_channels=input_channels,
out_channels=output_channels2,
kernel_size=1,
@@ -267,7 +267,7 @@ class ExitFlowBottleneckBlock(nn.Layer):
output_channels2,
stride=1,
name=name + "_branch2b_weights")
- self._pool = MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self._pool = MaxPool2D(kernel_size=3, stride=2, padding=1)
def forward(self, inputs):
short = self._short(inputs)
@@ -276,7 +276,7 @@ class ExitFlowBottleneckBlock(nn.Layer):
conv2 = F.relu(conv1)
conv2 = self._conv_2(conv2)
pool = self._pool(conv2)
- return paddle.elementwise_add(x=short, y=pool)
+ return paddle.add(x=short, y=pool)
class ExitFlow(nn.Layer):
@@ -289,7 +289,7 @@ class ExitFlow(nn.Layer):
728, 728, 1024, name=name + "_1")
self._conv_1 = SeparableConv(1024, 1536, stride=1, name=name + "_2")
self._conv_2 = SeparableConv(1536, 2048, stride=1, name=name + "_3")
- self._pool = AdaptiveAvgPool2d(1)
+ self._pool = AdaptiveAvgPool2D(1)
stdv = 1.0 / math.sqrt(2048 * 1.0)
self._out = Linear(
2048,
diff --git a/ppcls/modeling/architectures/xception_deeplab.py b/ppcls/modeling/architectures/xception_deeplab.py
index 2e77ed541..a90c27eae 100644
--- a/ppcls/modeling/architectures/xception_deeplab.py
+++ b/ppcls/modeling/architectures/xception_deeplab.py
@@ -2,8 +2,8 @@ import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
-from paddle.nn import Conv2d, BatchNorm, Linear, Dropout
-from paddle.nn import AdaptiveAvgPool2d, MaxPool2d, AvgPool2d
+from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
+from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
__all__ = ["Xception41_deeplab", "Xception65_deeplab", "Xception71_deeplab"]
@@ -69,7 +69,7 @@ class ConvBNLayer(nn.Layer):
name=None):
super(ConvBNLayer, self).__init__()
- self._conv = Conv2d(
+ self._conv = Conv2D(
in_channels=input_channels,
out_channels=output_channels,
kernel_size=filter_size,
@@ -102,7 +102,7 @@ class Seperate_Conv(nn.Layer):
name=None):
super(Seperate_Conv, self).__init__()
- self._conv1 = Conv2d(
+ self._conv1 = Conv2D(
in_channels=input_channels,
out_channels=input_channels,
kernel_size=filter,
@@ -121,7 +121,7 @@ class Seperate_Conv(nn.Layer):
bias_attr=ParamAttr(name=name + "/depthwise/BatchNorm/beta"),
moving_mean_name=name + "/depthwise/BatchNorm/moving_mean",
moving_variance_name=name + "/depthwise/BatchNorm/moving_variance")
- self._conv2 = Conv2d(
+ self._conv2 = Conv2D(
input_channels,
output_channels,
1,
@@ -244,7 +244,7 @@ class Xception_Block(nn.Layer):
skip = self._short(inputs)
else:
skip = inputs
- return paddle.elementwise_add(x, skip)
+ return paddle.add(x, skip)
class XceptionDeeplab(nn.Layer):
@@ -347,7 +347,7 @@ class XceptionDeeplab(nn.Layer):
self.stride = s
self._drop = Dropout(p=0.5, mode="downscale_in_infer")
- self._pool = AdaptiveAvgPool2d(1)
+ self._pool = AdaptiveAvgPool2D(1)
self._fc = Linear(
self.chns[1][-1],
class_dim,
diff --git a/ppcls/modeling/loss.py b/ppcls/modeling/loss.py
index c9c2c685f..29c45869c 100644
--- a/ppcls/modeling/loss.py
+++ b/ppcls/modeling/loss.py
@@ -38,8 +38,7 @@ class Loss(object):
one_hot_target = F.one_hot(target, self._class_dim)
else:
one_hot_target = target
- soft_target = F.label_smooth(
- one_hot_target, epsilon=self._epsilon, dtype="float32")
+ soft_target = F.label_smooth(one_hot_target, epsilon=self._epsilon)
soft_target = paddle.reshape(soft_target, shape=[-1, self._class_dim])
return soft_target
@@ -47,7 +46,7 @@ class Loss(object):
if self._label_smoothing:
target = self._labelsmoothing(target)
input = -F.log_softmax(input, axis=-1)
- cost = paddle.reduce_sum(target * input, dim=-1)
+ cost = paddle.sum(target * input, axis=-1)
else:
cost = F.cross_entropy(input=input, label=target)
avg_cost = paddle.mean(cost)
diff --git a/ppcls/optimizer/learning_rate.py b/ppcls/optimizer/learning_rate.py
index c74cb2ab8..156697e99 100644
--- a/ppcls/optimizer/learning_rate.py
+++ b/ppcls/optimizer/learning_rate.py
@@ -19,15 +19,15 @@ from __future__ import print_function
import sys
import math
-from paddle.optimizer.lr_scheduler import LinearLrWarmup
-from paddle.optimizer.lr_scheduler import PiecewiseLR
-from paddle.optimizer.lr_scheduler import CosineAnnealingLR
-from paddle.optimizer.lr_scheduler import ExponentialLR
+from paddle.optimizer.lr import LinearWarmup
+from paddle.optimizer.lr import PiecewiseDecay
+from paddle.optimizer.lr import CosineAnnealingDecay
+from paddle.optimizer.lr import ExponentialDecay
__all__ = ['LearningRateBuilder']
-class Cosine(CosineAnnealingLR):
+class Cosine(CosineAnnealingDecay):
"""
Cosine learning rate decay
lr = 0.05 * (math.cos(epoch * (math.pi / epochs)) + 1)
@@ -46,7 +46,7 @@ class Cosine(CosineAnnealingLR):
self.update_specified = False
-class Piecewise(PiecewiseLR):
+class Piecewise(PiecewiseDecay):
"""
Piecewise learning rate decay
@@ -66,7 +66,7 @@ class Piecewise(PiecewiseLR):
self.update_specified = False
-class CosineWarmup(LinearLrWarmup):
+class CosineWarmup(LinearWarmup):
"""
Cosine learning rate decay with warmup
[0, warmup_epoch): linear warmup
@@ -96,7 +96,7 @@ class CosineWarmup(LinearLrWarmup):
self.update_specified = False
-class ExponentialWarmup(LinearLrWarmup):
+class ExponentialWarmup(LinearWarmup):
"""
Exponential learning rate decay with warmup
[0, warmup_epoch): linear warmup
@@ -120,7 +120,7 @@ class ExponentialWarmup(LinearLrWarmup):
warmup_step = warmup_epoch * step_each_epoch
start_lr = 0.0
end_lr = lr
- lr_sch = ExponentialLR(lr, decay_rate)
+ lr_sch = ExponentialDecay(lr, decay_rate)
super(ExponentialWarmup, self).__init__(
learning_rate=lr_sch,
diff --git a/ppcls/optimizer/optimizer.py b/ppcls/optimizer/optimizer.py
index dec619705..35d4af511 100644
--- a/ppcls/optimizer/optimizer.py
+++ b/ppcls/optimizer/optimizer.py
@@ -19,7 +19,7 @@ from __future__ import print_function
import sys
import paddle
-import paddle.fluid.regularizer as regularizer
+import paddle.regularizer as regularizer
__all__ = ['OptimizerBuilder']
diff --git a/ppcls/utils/check.py b/ppcls/utils/check.py
index 655fb7d32..4790451bd 100644
--- a/ppcls/utils/check.py
+++ b/ppcls/utils/check.py
@@ -20,8 +20,7 @@ import os
import sys
import paddle
-# TODO: need to be fixed in the future.
-from paddle.fluid import is_compiled_with_cuda
+from paddle import is_compiled_with_cuda
from ppcls.modeling import get_architectures
from ppcls.modeling import similar_architectures
diff --git a/ppcls/utils/save_load.py b/ppcls/utils/save_load.py
index 11f9a0329..243d12de3 100644
--- a/ppcls/utils/save_load.py
+++ b/ppcls/utils/save_load.py
@@ -23,7 +23,7 @@ import shutil
import tempfile
import paddle
-from paddle.io import load_program_state
+from paddle.static import load_program_state
from ppcls.utils import logger
@@ -65,7 +65,7 @@ def load_dygraph_pretrain(model, path=None, load_static_weights=False):
model.set_dict(param_state_dict)
return
- param_state_dict, optim_state_dict = paddle.load(path)
+ param_state_dict = paddle.load(path + ".pdparams")
model.set_dict(param_state_dict)
return
@@ -106,7 +106,8 @@ def init_model(config, net, optimizer=None):
"Given dir {}.pdparams not exist.".format(checkpoints)
assert os.path.exists(checkpoints + ".pdopt"), \
"Given dir {}.pdopt not exist.".format(checkpoints)
- para_dict, opti_dict = paddle.load(checkpoints)
+ para_dict = paddle.load(checkpoints + ".pdparams")
+ opti_dict = paddle.load(checkpoints + ".pdopt")
net.set_dict(para_dict)
optimizer.set_state_dict(opti_dict)
logger.info(
@@ -142,8 +143,8 @@ def save_model(net, optimizer, model_path, epoch_id, prefix='ppcls'):
_mkdir_if_not_exist(model_path)
model_prefix = os.path.join(model_path, prefix)
- paddle.save(net.state_dict(), model_prefix)
- paddle.save(optimizer.state_dict(), model_prefix)
+ paddle.save(net.state_dict(), model_prefix + ".pdparams")
+ paddle.save(optimizer.state_dict(), model_prefix + ".pdopt")
logger.info(
logger.coloring("Already save model in {}".format(model_path),
"HEADER"))
diff --git a/tools/benchmark/benchmark_acc.py b/tools/benchmark/benchmark_acc.py
index 61f025e3e..aa471713a 100644
--- a/tools/benchmark/benchmark_acc.py
+++ b/tools/benchmark/benchmark_acc.py
@@ -20,7 +20,9 @@ sys.path.append(__dir__)
sys.path.append(os.path.abspath(os.path.join(__dir__, '..')))
sys.path.append(os.path.abspath(os.path.join(__dir__, '../..')))
-from multiprocessing import Process, Manager
+import paddle
+
+from multiprocessing import Manager
import tools.eval as eval
from ppcls.utils.model_zoo import _download, _decompress
from ppcls.utils import logger
@@ -80,18 +82,25 @@ def main(args):
args.config = model_info["config_path"]
args.override = [
"pretrained_model={}".format(pretrained_path),
- "VALID.batch_size=128",
+ "VALID.batch_size=256",
"VALID.num_workers=16",
"load_static_weights=True",
"print_interval=100",
]
+
manager = Manager()
return_dict = manager.dict()
- p = Process(target=eval.main, args=(args, return_dict))
- p.start()
- p.join()
+
+ # A hack method to avoid name conflict.
+ # Multi-process maybe a better method here.
+ # More details can be seen in branch 2.0-beta.
+ # TODO: fluid needs to be removed in the future.
+ with paddle.utils.unique_name.guard():
+ eval.main(args, return_dict)
+
top1_acc = return_dict.get("top1_acc", 0.0)
- except:
+ except Exception as e:
+ logger.error(e)
top1_acc = 0.0
diff = abs(top1_acc - model_info["top1_acc"])
if diff > 0.001:
diff --git a/tools/benchmark/benchmark_list.txt b/tools/benchmark/benchmark_list.txt
index f427509e6..66ad980c7 100644
--- a/tools/benchmark/benchmark_list.txt
+++ b/tools/benchmark/benchmark_list.txt
@@ -20,7 +20,7 @@
0.8077 InceptionV4 configs/Inception/InceptionV4.yaml https://paddle-imagenet-models-name.bj.bcebos.com/InceptionV4_pretrained.tar
0.8255 ResNeXt101_32x8d_wsl configs/ResNeXt101_wsl/ResNeXt101_32x8d_wsl.yaml https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x8d_wsl_pretrained.tar
0.8035 ResNeSt50_fast_1s1x64d configs/ResNeSt/ResNeSt50_fast_1s1x64d.yaml https://paddle-imagenet-models-name.bj.bcebos.com/ResNeSt50_fast_1s1x64d_pretrained.pdparams
-0.8102 ResNeSt50 configs/ResNeSt/ResNeSt50.yaml https://paddle-imagenet-models-name.bj.bcebos.com/ResNeSt50_pretrained.pdparams
+0.8083 ResNeSt50 configs/ResNeSt/ResNeSt50.yaml https://paddle-imagenet-models-name.bj.bcebos.com/ResNeSt50_pretrained.pdparams
0.785 RegNetX_4GF configs/RegNet/RegNetX_4GF.yaml https://paddle-imagenet-models-name.bj.bcebos.com/RegNetX_4GF_pretrained.pdparams
0.7402 GhostNet_x1_0 configs/GhostNet/GhostNet_x1_0.yaml https://paddle-imagenet-models-name.bj.bcebos.com/GhostNet_x1_0_pretrained.pdparams
0.567 AlexNet configs/AlexNet/AlexNet.yaml https://paddle-imagenet-models-name.bj.bcebos.com/AlexNet_pretrained.tar
diff --git a/tools/eval.py b/tools/eval.py
index f95dcafc9..d83a2874c 100644
--- a/tools/eval.py
+++ b/tools/eval.py
@@ -49,13 +49,11 @@ def parse_args():
def main(args, return_dict={}):
config = get_config(args.config, overrides=args.override, show=True)
+ config.mode = "valid"
# assign place
use_gpu = config.get("use_gpu", True)
- if use_gpu:
- gpu_id = ParallelEnv().dev_id
- place = paddle.CUDAPlace(gpu_id)
- else:
- place = paddle.CPUPlace()
+ place = 'gpu:{}'.format(ParallelEnv().dev_id) if use_gpu else 'cpu'
+ place = paddle.set_device(place)
paddle.disable_static(place)
diff --git a/tools/export_model.py b/tools/export_model.py
index 969c730d4..87d5ade37 100644
--- a/tools/export_model.py
+++ b/tools/export_model.py
@@ -33,7 +33,8 @@ def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--model", type=str)
parser.add_argument("-p", "--pretrained_model", type=str)
- parser.add_argument("-o", "--output_path", type=str)
+ parser.add_argument(
+ "-o", "--output_path", type=str, default="./inference/cls_infer")
parser.add_argument("--class_dim", type=int, default=1000)
parser.add_argument("--load_static_weights", type=str2bool, default=True)
# parser.add_argument("--img_size", type=int, default=224)
@@ -61,7 +62,6 @@ class Net(paddle.nn.Layer):
def main():
args = parse_args()
- paddle.disable_static()
net = architectures.__dict__[args.model]
model = Net(net, to_static, args.class_dim)
diff --git a/tools/infer/infer.py b/tools/infer/infer.py
index 12833ac5d..1e8b96422 100644
--- a/tools/infer/infer.py
+++ b/tools/infer/infer.py
@@ -38,6 +38,7 @@ def parse_args():
parser.add_argument("-i", "--image_file", type=str)
parser.add_argument("-m", "--model", type=str)
parser.add_argument("-p", "--pretrained_model", type=str)
+ parser.add_argument("--class_num", type=int, default=1000)
parser.add_argument("--use_gpu", type=str2bool, default=True)
parser.add_argument(
"--load_static_weights",
@@ -114,15 +115,10 @@ def main():
args = parse_args()
operators = create_operators()
# assign the place
- if args.use_gpu:
- gpu_id = ParallelEnv().dev_id
- place = paddle.CUDAPlace(gpu_id)
- else:
- place = paddle.CPUPlace()
+ place = 'gpu:{}'.format(ParallelEnv().dev_id) if args.use_gpu else 'cpu'
+ place = paddle.set_device(place)
- paddle.disable_static(place)
-
- net = architectures.__dict__[args.model]()
+ net = architectures.__dict__[args.model](class_dim=args.class_num)
load_dygraph_pretrain(net, args.pretrained_model, args.load_static_weights)
image_list = get_image_list(args.image_file)
for idx, filename in enumerate(image_list):
@@ -133,8 +129,7 @@ def main():
outputs = net(data)
if args.model == "GoogLeNet":
outputs = outputs[0]
- else:
- outputs = F.softmax(outputs)
+ outputs = F.softmax(outputs)
outputs = outputs.numpy()
probs = postprocess(outputs)
diff --git a/tools/program.py b/tools/program.py
index d6f666804..a55bacefd 100644
--- a/tools/program.py
+++ b/tools/program.py
@@ -141,12 +141,12 @@ def create_metric(out,
"""
if architecture["name"] == "GoogLeNet":
assert len(out) == 3, "GoogLeNet should have 3 outputs"
- softmax_out = out[0]
+ out = out[0]
else:
# just need student label to get metrics
if use_distillation:
out = out[1]
- softmax_out = F.softmax(out)
+ softmax_out = F.softmax(out)
fetchs = OrderedDict()
# set top1 to fetchs
diff --git a/tools/train.py b/tools/train.py
index 7baeaa2b8..343e47de0 100644
--- a/tools/train.py
+++ b/tools/train.py
@@ -55,17 +55,12 @@ def main(args):
config = get_config(args.config, overrides=args.override, show=True)
# assign the place
use_gpu = config.get("use_gpu", True)
- if use_gpu:
- gpu_id = ParallelEnv().dev_id
- place = paddle.CUDAPlace(gpu_id)
- else:
- place = paddle.CPUPlace()
+ place = 'gpu:{}'.format(ParallelEnv().dev_id) if use_gpu else 'cpu'
+ place = paddle.set_device(place)
use_data_parallel = int(os.getenv("PADDLE_TRAINERS_NUM", 1)) != 1
config["use_data_parallel"] = use_data_parallel
- paddle.disable_static(place)
-
net = program.create_model(config.ARCHITECTURE, config.classes_num)
optimizer, lr_scheduler = program.create_optimizer(