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PaddleClas
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Merge branch 'develop' into android_demo_doc

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MANIFEST.in
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......@@ -2,7 +2,7 @@ include LICENSE.txt
include README.md
include docs/en/whl_en.md
recursive-include deploy/python *.py
recursive-include deploy/utils *.py
recursive-include ppcls/arch *.py
recursive-include ppcls/utils *.py *.txt
recursive-include deploy/configs *.yaml
recursive-include deploy/utils get_image_list.py config.py logger.py predictor.py
recursive-include ppcls/ *.py *.txt
\ No newline at end of file
__init__.py
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......@@ -14,4 +14,4 @@
__all__ = ['PaddleClas']
from .paddleclas import PaddleClas
from ppcls.arch.backbone import *
from .ppcls.arch.backbone import *
deploy/python/build_gallery.py
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......@@ -12,10 +12,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))
import cv2
import faiss
......@@ -23,10 +19,9 @@ import numpy as np
from tqdm import tqdm
import pickle
from python.predict_rec import RecPredictor
from utils import logger
from utils import config
from paddleclas.deploy.utils import logger, config
from paddleclas.deploy.python.predict_rec import RecPredictor
from paddleclas.deploy.python.predict_rec import RecPredictor
def split_datafile(data_file, image_root, delimiter="\t"):
......
deploy/python/predict_cls.py
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......@@ -11,21 +11,17 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))
import os
import cv2
import numpy as np
from utils import logger
from utils import config
from utils.predictor import Predictor
from utils.get_image_list import get_image_list
from python.preprocess import create_operators
from python.postprocess import build_postprocess
from paddleclas.deploy.utils import logger, config
from paddleclas.deploy.utils.predictor import Predictor
from paddleclas.deploy.utils.get_image_list import get_image_list
from paddleclas.deploy.python.preprocess import create_operators
from paddleclas.deploy.python.postprocess import build_postprocess
class ClsPredictor(Predictor):
......
deploy/python/predict_det.py
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......@@ -11,29 +11,23 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))
from utils import logger
from utils import config
from utils.predictor import Predictor
from utils.get_image_list import get_image_list
from det_preprocess import det_preprocess
from preprocess import create_operators
import os
import argparse
import time
from functools import reduce
import yaml
import ast
from functools import reduce
import cv2
import numpy as np
import cv2
import paddle
from paddleclas.deploy.utils import logger, config
from paddleclas.deploy.utils.predictor import Predictor
from paddleclas.deploy.utils.get_image_list import get_image_list
from paddleclas.deploy.python.preprocess import create_operators
from paddleclas.deploy.python.det_preprocess import det_preprocess
class DetPredictor(Predictor):
def __init__(self, config):
......
deploy/python/predict_rec.py
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......@@ -12,20 +12,15 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))
import cv2
import numpy as np
from utils import logger
from utils import config
from utils.predictor import Predictor
from utils.get_image_list import get_image_list
from preprocess import create_operators
from postprocess import build_postprocess
from paddleclas.deploy.utils import logger, config
from paddleclas.deploy.utils.predictor import Predictor
from paddleclas.deploy.utils.get_image_list import get_image_list
from paddleclas.deploy.python.preprocess import create_operators
from paddleclas.deploy.python.postprocess import build_postprocess
class RecPredictor(Predictor):
......
deploy/python/predict_system.py
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......@@ -12,24 +12,18 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))
import copy
import cv2
import numpy as np
import cv2
import faiss
import pickle
from python.predict_rec import RecPredictor
from python.predict_det import DetPredictor
from utils import logger
from utils import config
from utils.get_image_list import get_image_list
from utils.draw_bbox import draw_bbox_results
from paddleclas.deploy.utils import logger, config
from paddleclas.deploy.utils.get_image_list import get_image_list
from paddleclas.deploy.utils.draw_bbox import draw_bbox_results
from paddleclas.deploy.python.predict_rec import RecPredictor
from paddleclas.deploy.python.predict_det import DetPredictor
class SystemPredictor(object):
......
deploy/python/preprocess.py
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......@@ -29,7 +29,7 @@ import importlib
from PIL import Image
from paddle.vision.transforms import ToTensor, Normalize
from python.det_preprocess import DetNormalizeImage, DetPadStride, DetPermute, DetResize
from paddleclas.deploy.python.det_preprocess import DetNormalizeImage, DetPadStride, DetPermute, DetResize
def create_operators(params):
......
deploy/utils/config.py
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......@@ -17,7 +17,7 @@ import copy
import argparse
import yaml
from utils import logger
from . import logger
__all__ = ['get_config']
......
docs/en/algorithm_introduction/ImageNet_models_en.md
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......@@ -27,7 +27,7 @@
- [20. DLA series](#20)
- [21. RedNet series](#21)
- [22. TNT series](#22)
- [23. CSwinTransformer series](#23)
- [23. CSWinTransformer series](#23)
- [24. PVTV2 series](#24)
- [25. MobileViT series](#25)
- [26. Other models](#26)
......@@ -400,14 +400,14 @@ The accuracy and speed indicators of SwinTransformer series models are shown in
| Model | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| SwinTransformer_tiny_patch4_window7_224 | 0.8069 | 0.9534 | 6.59 | 9.68 | 16.32 | 4.35 | 28.26 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_tiny_patch4_window7_224_infer.tar) |
| SwinTransformer_small_patch4_window7_224 | 0.8275 | 0.9613 | 12.54 | 17.07 | 28.08 | 8.51 | 49.56 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_small_patch4_window7_224_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_small_patch4_window7_224_infer.tar) |
| SwinTransformer_base_patch4_window7_224 | 0.8300 | 0.9626 | 13.37 | 23.53 | 39.11 | 15.13 | 87.70 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window7_224_infer.tar) |
| SwinTransformer_base_patch4_window12_384 | 0.8439 | 0.9693 | 19.52 | 64.56 | 123.30 | 44.45 | 87.70 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window12_384_infer.tar) |
| SwinTransformer_base_patch4_window7_224<sup>[1]</sup> | 0.8487 | 0.9746 | 13.53 | 23.46 | 39.13 | 15.13 | 87.70 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_22kto1k_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window7_224_infer.tar) |
| SwinTransformer_base_patch4_window12_384<sup>[1]</sup> | 0.8642 | 0.9807 | 19.65 | 64.72 | 123.42 | 44.45 | 87.70 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_22kto1k_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window12_384_infer.tar) |
| SwinTransformer_large_patch4_window7_224<sup>[1]</sup> | 0.8596 | 0.9783 | 15.74 | 38.57 | 71.49 | 34.02 | 196.43 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window7_224_22kto1k_infer.tar) |
| SwinTransformer_large_patch4_window12_384<sup>[1]</sup> | 0.8719 | 0.9823 | 32.61 | 116.59 | 223.23 | 99.97 | 196.43 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window12_384_22kto1k_infer.tar) |
| SwinTransformer_tiny_patch4_window7_224 | 0.8069 | 0.9534 | 6.59 | 9.68 | 16.32 | 4.35 | 28.26 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_tiny_patch4_window7_224_infer.tar) |
| SwinTransformer_small_patch4_window7_224 | 0.8275 | 0.9613 | 12.54 | 17.07 | 28.08 | 8.51 | 49.56 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_small_patch4_window7_224_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_small_patch4_window7_224_infer.tar) |
| SwinTransformer_base_patch4_window7_224 | 0.8300 | 0.9626 | 13.37 | 23.53 | 39.11 | 15.13 | 87.70 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_base_patch4_window7_224_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window7_224_infer.tar) |
| SwinTransformer_base_patch4_window12_384 | 0.8439 | 0.9693 | 19.52 | 64.56 | 123.30 | 44.45 | 87.70 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_base_patch4_window12_384_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window12_384_infer.tar) |
| SwinTransformer_base_patch4_window7_224<sup>[1]</sup> | 0.8487 | 0.9746 | 13.53 | 23.46 | 39.13 | 15.13 | 87.70 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_base_patch4_window7_224_22kto1k_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window7_224_infer.tar) |
| SwinTransformer_base_patch4_window12_384<sup>[1]</sup> | 0.8642 | 0.9807 | 19.65 | 64.72 | 123.42 | 44.45 | 87.70 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_base_patch4_window12_384_22kto1k_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window12_384_infer.tar) |
| SwinTransformer_large_patch4_window7_224<sup>[1]</sup> | 0.8596 | 0.9783 | 15.74 | 38.57 | 71.49 | 34.02 | 196.43 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window7_224_22kto1k_infer.tar) |
| SwinTransformer_large_patch4_window12_384<sup>[1]</sup> | 0.8719 | 0.9823 | 32.61 | 116.59 | 223.23 | 99.97 | 196.43 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window12_384_22kto1k_infer.tar) |
[1]：It is pre-trained based on the ImageNet22k dataset, and then transferred and learned from the ImageNet1k dataset.
......
docs/en/installation/install_paddleclas_en.md
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......@@ -23,7 +23,13 @@ git clone https://gitee.com/paddlepaddle/PaddleClas.git -b develop
<a name='2'></a>
## 2. Install requirements
## 2. Install PaddleClas and requirements
It is recommanded that installing from PyPI:
```shell
pip install paddleclas
```
PaddleClas dependencies are listed in file `requirements.txt`, you can use the following command to install the dependencies.
......
docs/zh_CN/algorithm_introduction/ImageNet_models.md
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......@@ -31,7 +31,7 @@
- [DLA 系列](#DLA)
- [RedNet 系列](#RedNet)
- [TNT 系列](#TNT)
- [CSwinTransformer 系列](#CSwinTransformer)
- [CSWinTransformer 系列](#CSWinTransformer)
- [PVTV2 系列](#PVTV2)
- [MobileViT 系列](#MobileViT)
- [其他模型](#Others)
......@@ -426,14 +426,14 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| SwinTransformer_tiny_patch4_window7_224 | 0.8069 | 0.9534 | 6.59 | 9.68 | 16.32 | 4.35 | 28.26 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_tiny_patch4_window7_224_infer.tar) |
| SwinTransformer_small_patch4_window7_224 | 0.8275 | 0.9613 | 12.54 | 17.07 | 28.08 | 8.51 | 49.56 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_small_patch4_window7_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_small_patch4_window7_224_infer.tar) |
| SwinTransformer_base_patch4_window7_224 | 0.8300 | 0.9626 | 13.37 | 23.53 | 39.11 | 15.13 | 87.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window7_224_infer.tar) |
| SwinTransformer_base_patch4_window12_384 | 0.8439 | 0.9693 | 19.52 | 64.56 | 123.30 | 44.45 | 87.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window12_384_infer.tar) |
| SwinTransformer_base_patch4_window7_224<sup>[1]</sup> | 0.8487 | 0.9746 | 13.53 | 23.46 | 39.13 | 15.13 | 87.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window7_224_infer.tar) |
| SwinTransformer_base_patch4_window12_384<sup>[1]</sup> | 0.8642 | 0.9807 | 19.65 | 64.72 | 123.42 | 44.45 | 87.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window12_384_infer.tar) |
| SwinTransformer_large_patch4_window7_224<sup>[1]</sup> | 0.8596 | 0.9783 | 15.74 | 38.57 | 71.49 | 34.02 | 196.43 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window7_224_22kto1k_infer.tar) |
| SwinTransformer_large_patch4_window12_384<sup>[1]</sup> | 0.8719 | 0.9823 | 32.61 | 116.59 | 223.23 | 99.97 | 196.43 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window12_384_22kto1k_infer.tar) |
| SwinTransformer_tiny_patch4_window7_224 | 0.8069 | 0.9534 | 6.59 | 9.68 | 16.32 | 4.35 | 28.26 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_tiny_patch4_window7_224_infer.tar) |
| SwinTransformer_small_patch4_window7_224 | 0.8275 | 0.9613 | 12.54 | 17.07 | 28.08 | 8.51 | 49.56 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_small_patch4_window7_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_small_patch4_window7_224_infer.tar) |
| SwinTransformer_base_patch4_window7_224 | 0.8300 | 0.9626 | 13.37 | 23.53 | 39.11 | 15.13 | 87.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_base_patch4_window7_224_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window7_224_infer.tar) |
| SwinTransformer_base_patch4_window12_384 | 0.8439 | 0.9693 | 19.52 | 64.56 | 123.30 | 44.45 | 87.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_base_patch4_window12_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window12_384_infer.tar) |
| SwinTransformer_base_patch4_window7_224<sup>[1]</sup> | 0.8487 | 0.9746 | 13.53 | 23.46 | 39.13 | 15.13 | 87.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_base_patch4_window7_224_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window7_224_infer.tar) |
| SwinTransformer_base_patch4_window12_384<sup>[1]</sup> | 0.8642 | 0.9807 | 19.65 | 64.72 | 123.42 | 44.45 | 87.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_base_patch4_window12_384_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_base_patch4_window12_384_infer.tar) |
| SwinTransformer_large_patch4_window7_224<sup>[1]</sup> | 0.8596 | 0.9783 | 15.74 | 38.57 | 71.49 | 34.02 | 196.43 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window7_224_22kto1k_infer.tar) |
| SwinTransformer_large_patch4_window12_384<sup>[1]</sup> | 0.8719 | 0.9823 | 32.61 | 116.59 | 223.23 | 99.97 | 196.43 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SwinTransformer_large_patch4_window12_384_22kto1k_infer.tar) |
[1]：基于 ImageNet22k 数据集预训练，然后在 ImageNet1k 数据集迁移学习得到。
......
docs/zh_CN/inference_deployment/whl_deploy.md
浏览文件 @ 7db32e7a
......@@ -92,7 +92,6 @@ Predict complete!
* 命令行中
```bash
from paddleclas import PaddleClas, get_default_confg
paddleclas --model_name=ViT_base_patch16_384 --infer_imgs='docs/images/inference_deployment/whl_demo.jpg' --resize_short=384 --crop_size=384
```
......
docs/zh_CN/installation/install_paddleclas.md
浏览文件 @ 7db32e7a
......@@ -96,7 +96,13 @@ git clone https://gitee.com/paddlepaddle/PaddleClas.git -b release/2.4
```
<a name='1.3'></a>
### 1.3 安装 Python 依赖库
### 1.3 安装 PaddleClas 及其 Python 依赖库
建议直接从 PyPI 安装 PaddleClas：
```shell
pip install paddleclas
```
PaddleClas 的 Python 依赖库在 `requirements.txt` 中给出，可通过如下命令安装：
......@@ -114,5 +120,3 @@ pip install --upgrade -r requirements.txt -i https://mirror.baidu.com/pypi/simpl
如果您对自动化制作docker镜像感兴趣，或有自定义需求，请访问[PaddlePaddle/PaddleCloud](https://github.com/PaddlePaddle/PaddleCloud/tree/main/tekton)做进一步了解。
**备注**：当前的镜像中的 PaddleClas 代码默认使用最新的 release/2.4 分支。
paddleclas.py
浏览文件 @ 7db32e7a
......@@ -13,11 +13,6 @@
# limitations under the License.
import os
import sys
__dir__ = os.path.dirname(__file__)
sys.path.append(os.path.join(__dir__, ""))
sys.path.append(os.path.join(__dir__, "deploy"))
from typing import Union, Generator
import argparse
import shutil
......@@ -33,12 +28,16 @@ from tqdm import tqdm
from prettytable import PrettyTable
import paddle
from deploy.python.predict_cls import ClsPredictor
from deploy.utils.get_image_list import get_image_list
from deploy.utils import config
from .ppcls.arch import backbone
from .ppcls.utils import logger
from .deploy.python.predict_cls import ClsPredictor
from .deploy.utils.get_image_list import get_image_list
from .deploy.utils import config
import ppcls.arch.backbone as backbone
from ppcls.utils import logger
# for the PaddleClas Project
from . import deploy
from . import ppcls
# for building model with loading pretrained weights from backbone
logger.init_logger()
......@@ -51,6 +50,11 @@ BASE_IMAGES_DIR = os.path.join(BASE_DIR, "images")
IMN_MODEL_BASE_DOWNLOAD_URL = "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/{}_infer.tar"
IMN_MODEL_SERIES = {
"AlexNet": ["AlexNet"],
"CSWinTransformer": [
"CSWinTransformer_tiny_224", "CSWinTransformer_small_224",
"CSWinTransformer_base_224", "CSWinTransformer_base_384",
"CSWinTransformer_large_224", "CSWinTransformer_large_384"
],
"DarkNet": ["DarkNet53"],
"DeiT": [
"DeiT_base_distilled_patch16_224", "DeiT_base_distilled_patch16_384",
......@@ -82,6 +86,8 @@ IMN_MODEL_SERIES = {
"HRNet_W48_C_ssld"
],
"Inception": ["GoogLeNet", "InceptionV3", "InceptionV4"],
"LeViT":
["LeViT_128S", "LeViT_128", "LeViT_192", "LeViT_256", "LeViT_384"],
"MixNet": ["MixNet_S", "MixNet_M", "MixNet_L"],
"MobileNetV1": [
"MobileNetV1_x0_25", "MobileNetV1_x0_5", "MobileNetV1_x0_75",
......@@ -100,6 +106,7 @@ IMN_MODEL_SERIES = {
"MobileNetV3_large_x1_0", "MobileNetV3_large_x1_25",
"MobileNetV3_small_x1_0_ssld", "MobileNetV3_large_x1_0_ssld"
],
"MobileViT": ["MobileViT_XXS", "MobileViT_XS", "MobileViT_S"],
"PPHGNet": [
"PPHGNet_tiny",
"PPHGNet_small",
......@@ -111,6 +118,10 @@ IMN_MODEL_SERIES = {
"PPLCNet_x1_0", "PPLCNet_x1_5", "PPLCNet_x2_0", "PPLCNet_x2_5"
],
"PPLCNetV2": ["PPLCNetV2_base"],
"PVTV2": [
"PVT_V2_B0", "PVT_V2_B1", "PVT_V2_B2", "PVT_V2_B2_Linear", "PVT_V2_B3",
"PVT_V2_B4", "PVT_V2_B5"
],
"RedNet": ["RedNet26", "RedNet38", "RedNet50", "RedNet101", "RedNet152"],
"RegNet": ["RegNetX_4GF"],
"Res2Net": [
......@@ -163,6 +174,7 @@ IMN_MODEL_SERIES = {
"pcpvt_small", "pcpvt_base", "pcpvt_large", "alt_gvt_small",
"alt_gvt_base", "alt_gvt_large"
],
"TNT": ["TNT_small"],
"VGG": ["VGG11", "VGG13", "VGG16", "VGG19"],
"VisionTransformer": [
"ViT_base_patch16_224", "ViT_base_patch16_384", "ViT_base_patch32_384",
......@@ -202,6 +214,7 @@ class InputModelError(Exception):
def init_config(model_type, model_name, inference_model_dir, **kwargs):
cfg_path = f"deploy/configs/PULC/{model_name}/inference_{model_name}.yaml" if model_type == "pulc" else "deploy/configs/inference_cls.yaml"
__dir__ = os.path.dirname(__file__)
cfg_path = os.path.join(__dir__, cfg_path)
cfg = config.get_config(cfg_path, show=False)
......@@ -453,10 +466,6 @@ class PaddleClas(object):
"""PaddleClas.
"""
if not os.environ.get('ppcls', False):
os.environ.setdefault('ppcls', 'True')
print_info()
def __init__(self,
model_name: str=None,
inference_model_dir: str=None,
......@@ -471,6 +480,7 @@ class PaddleClas(object):
topk (int, optional): Return the top k prediction results with the highest score. Defaults to 5.
"""
super().__init__()
self.model_type, inference_model_dir = self._check_input_model(
model_name, inference_model_dir)
self._config = init_config(self.model_type, model_name,
......@@ -595,6 +605,7 @@ class PaddleClas(object):
def main():
"""Function API used for commad line.
"""
print_info()
cfg = args_cfg()
clas_engine = PaddleClas(**cfg)
res = clas_engine.predict(cfg["infer_imgs"], print_pred=True)
......
ppcls/arch/__init__.py
浏览文件 @ 7db32e7a
......@@ -23,11 +23,11 @@ from . import backbone, gears
from .backbone import *
from .gears import build_gear
from .utils import *
from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
from ppcls.utils import logger
from ppcls.utils.save_load import load_dygraph_pretrain
from ppcls.arch.slim import prune_model, quantize_model
from ppcls.arch.distill.afd_attention import LinearTransformStudent, LinearTransformTeacher
from .backbone.base.theseus_layer import TheseusLayer
from ..utils import logger
from ..utils.save_load import load_dygraph_pretrain
from .slim import prune_model, quantize_model
from .distill.afd_attention import LinearTransformStudent, LinearTransformTeacher
__all__ = ["build_model", "RecModel", "DistillationModel", "AttentionModel"]
......
ppcls/arch/backbone/__init__.py
浏览文件 @ 7db32e7a
......@@ -15,65 +15,65 @@
import sys
import inspect
from ppcls.arch.backbone.legendary_models.mobilenet_v1 import MobileNetV1_x0_25, MobileNetV1_x0_5, MobileNetV1_x0_75, MobileNetV1
from ppcls.arch.backbone.legendary_models.mobilenet_v3 import MobileNetV3_small_x0_35, MobileNetV3_small_x0_5, MobileNetV3_small_x0_75, MobileNetV3_small_x1_0, MobileNetV3_small_x1_25, MobileNetV3_large_x0_35, MobileNetV3_large_x0_5, MobileNetV3_large_x0_75, MobileNetV3_large_x1_0, MobileNetV3_large_x1_25
from ppcls.arch.backbone.legendary_models.resnet import ResNet18, ResNet18_vd, ResNet34, ResNet34_vd, ResNet50, ResNet50_vd, ResNet101, ResNet101_vd, ResNet152, ResNet152_vd, ResNet200_vd
from ppcls.arch.backbone.legendary_models.vgg import VGG11, VGG13, VGG16, VGG19
from ppcls.arch.backbone.legendary_models.inception_v3 import InceptionV3
from ppcls.arch.backbone.legendary_models.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_W64_C
from ppcls.arch.backbone.legendary_models.pp_lcnet import PPLCNet_x0_25, PPLCNet_x0_35, PPLCNet_x0_5, PPLCNet_x0_75, PPLCNet_x1_0, PPLCNet_x1_5, PPLCNet_x2_0, PPLCNet_x2_5
from ppcls.arch.backbone.legendary_models.pp_lcnet_v2 import PPLCNetV2_base
from ppcls.arch.backbone.legendary_models.esnet import ESNet_x0_25, ESNet_x0_5, ESNet_x0_75, ESNet_x1_0
from ppcls.arch.backbone.legendary_models.pp_hgnet import PPHGNet_tiny, PPHGNet_small, PPHGNet_base
from .legendary_models.mobilenet_v1 import MobileNetV1_x0_25, MobileNetV1_x0_5, MobileNetV1_x0_75, MobileNetV1
from .legendary_models.mobilenet_v3 import MobileNetV3_small_x0_35, MobileNetV3_small_x0_5, MobileNetV3_small_x0_75, MobileNetV3_small_x1_0, MobileNetV3_small_x1_25, MobileNetV3_large_x0_35, MobileNetV3_large_x0_5, MobileNetV3_large_x0_75, MobileNetV3_large_x1_0, MobileNetV3_large_x1_25
from .legendary_models.resnet import ResNet18, ResNet18_vd, ResNet34, ResNet34_vd, ResNet50, ResNet50_vd, ResNet101, ResNet101_vd, ResNet152, ResNet152_vd, ResNet200_vd
from .legendary_models.vgg import VGG11, VGG13, VGG16, VGG19
from .legendary_models.inception_v3 import InceptionV3
from .legendary_models.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_W64_C
from .legendary_models.pp_lcnet import PPLCNet_x0_25, PPLCNet_x0_35, PPLCNet_x0_5, PPLCNet_x0_75, PPLCNet_x1_0, PPLCNet_x1_5, PPLCNet_x2_0, PPLCNet_x2_5
from .legendary_models.pp_lcnet_v2 import PPLCNetV2_base
from .legendary_models.esnet import ESNet_x0_25, ESNet_x0_5, ESNet_x0_75, ESNet_x1_0
from .legendary_models.pp_hgnet import PPHGNet_tiny, PPHGNet_small, PPHGNet_base
from ppcls.arch.backbone.model_zoo.resnet_vc import ResNet50_vc
from ppcls.arch.backbone.model_zoo.resnext import ResNeXt50_32x4d, ResNeXt50_64x4d, ResNeXt101_32x4d, ResNeXt101_64x4d, ResNeXt152_32x4d, ResNeXt152_64x4d
from ppcls.arch.backbone.model_zoo.resnext_vd import ResNeXt50_vd_32x4d, ResNeXt50_vd_64x4d, ResNeXt101_vd_32x4d, ResNeXt101_vd_64x4d, ResNeXt152_vd_32x4d, ResNeXt152_vd_64x4d
from ppcls.arch.backbone.model_zoo.res2net import Res2Net50_26w_4s, Res2Net50_14w_8s
from ppcls.arch.backbone.model_zoo.res2net_vd import Res2Net50_vd_26w_4s, Res2Net101_vd_26w_4s, Res2Net200_vd_26w_4s
from ppcls.arch.backbone.model_zoo.se_resnet_vd import SE_ResNet18_vd, SE_ResNet34_vd, SE_ResNet50_vd
from ppcls.arch.backbone.model_zoo.se_resnext_vd import SE_ResNeXt50_vd_32x4d, SE_ResNeXt50_vd_32x4d, SENet154_vd
from ppcls.arch.backbone.model_zoo.se_resnext import SE_ResNeXt50_32x4d, SE_ResNeXt101_32x4d, SE_ResNeXt152_64x4d
from ppcls.arch.backbone.model_zoo.dpn import DPN68, DPN92, DPN98, DPN107, DPN131
from ppcls.arch.backbone.model_zoo.densenet import DenseNet121, DenseNet161, DenseNet169, DenseNet201, DenseNet264
from ppcls.arch.backbone.model_zoo.efficientnet import EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6, EfficientNetB7, EfficientNetB0_small
from ppcls.arch.backbone.model_zoo.resnest import ResNeSt50_fast_1s1x64d, ResNeSt50, ResNeSt101
from ppcls.arch.backbone.model_zoo.googlenet import GoogLeNet
from ppcls.arch.backbone.model_zoo.mobilenet_v2 import MobileNetV2_x0_25, MobileNetV2_x0_5, MobileNetV2_x0_75, MobileNetV2, MobileNetV2_x1_5, MobileNetV2_x2_0
from ppcls.arch.backbone.model_zoo.shufflenet_v2 import ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_x0_5, ShuffleNetV2_x1_0, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0, ShuffleNetV2_swish
from ppcls.arch.backbone.model_zoo.ghostnet import GhostNet_x0_5, GhostNet_x1_0, GhostNet_x1_3
from ppcls.arch.backbone.model_zoo.alexnet import AlexNet
from ppcls.arch.backbone.model_zoo.inception_v4 import InceptionV4
from ppcls.arch.backbone.model_zoo.xception import Xception41, Xception65, Xception71
from ppcls.arch.backbone.model_zoo.xception_deeplab import Xception41_deeplab, Xception65_deeplab
from ppcls.arch.backbone.model_zoo.resnext101_wsl import ResNeXt101_32x8d_wsl, ResNeXt101_32x16d_wsl, ResNeXt101_32x32d_wsl, ResNeXt101_32x48d_wsl
from ppcls.arch.backbone.model_zoo.squeezenet import SqueezeNet1_0, SqueezeNet1_1
from ppcls.arch.backbone.model_zoo.darknet import DarkNet53
from ppcls.arch.backbone.model_zoo.regnet import RegNetX_200MF, RegNetX_4GF, RegNetX_32GF, RegNetY_200MF, RegNetY_4GF, RegNetY_32GF
from ppcls.arch.backbone.model_zoo.vision_transformer import ViT_small_patch16_224, ViT_base_patch16_224, ViT_base_patch16_384, ViT_base_patch32_384, ViT_large_patch16_224, ViT_large_patch16_384, ViT_large_patch32_384
from ppcls.arch.backbone.model_zoo.distilled_vision_transformer import DeiT_tiny_patch16_224, DeiT_small_patch16_224, DeiT_base_patch16_224, DeiT_tiny_distilled_patch16_224, DeiT_small_distilled_patch16_224, DeiT_base_distilled_patch16_224, DeiT_base_patch16_384, DeiT_base_distilled_patch16_384
from ppcls.arch.backbone.legendary_models.swin_transformer import SwinTransformer_tiny_patch4_window7_224, SwinTransformer_small_patch4_window7_224, SwinTransformer_base_patch4_window7_224, SwinTransformer_base_patch4_window12_384, SwinTransformer_large_patch4_window7_224, SwinTransformer_large_patch4_window12_384
from ppcls.arch.backbone.model_zoo.cswin_transformer import CSWinTransformer_tiny_224, CSWinTransformer_small_224, CSWinTransformer_base_224, CSWinTransformer_large_224, CSWinTransformer_base_384, CSWinTransformer_large_384
from ppcls.arch.backbone.model_zoo.mixnet import MixNet_S, MixNet_M, MixNet_L
from ppcls.arch.backbone.model_zoo.rexnet import ReXNet_1_0, ReXNet_1_3, ReXNet_1_5, ReXNet_2_0, ReXNet_3_0
from ppcls.arch.backbone.model_zoo.gvt import pcpvt_small, pcpvt_base, pcpvt_large, alt_gvt_small, alt_gvt_base, alt_gvt_large
from ppcls.arch.backbone.model_zoo.levit import LeViT_128S, LeViT_128, LeViT_192, LeViT_256, LeViT_384
from ppcls.arch.backbone.model_zoo.dla import DLA34, DLA46_c, DLA46x_c, DLA60, DLA60x, DLA60x_c, DLA102, DLA102x, DLA102x2, DLA169
from ppcls.arch.backbone.model_zoo.rednet import RedNet26, RedNet38, RedNet50, RedNet101, RedNet152
from ppcls.arch.backbone.model_zoo.tnt import TNT_small
from ppcls.arch.backbone.model_zoo.hardnet import HarDNet68, HarDNet85, HarDNet39_ds, HarDNet68_ds
from ppcls.arch.backbone.model_zoo.cspnet import CSPDarkNet53
from ppcls.arch.backbone.model_zoo.pvt_v2 import PVT_V2_B0, PVT_V2_B1, PVT_V2_B2_Linear, PVT_V2_B2, PVT_V2_B3, PVT_V2_B4, PVT_V2_B5
from ppcls.arch.backbone.model_zoo.mobilevit import MobileViT_XXS, MobileViT_XS, MobileViT_S
from ppcls.arch.backbone.model_zoo.repvgg import RepVGG_A0, RepVGG_A1, RepVGG_A2, RepVGG_B0, RepVGG_B1, RepVGG_B2, RepVGG_B1g2, RepVGG_B1g4, RepVGG_B2g4, RepVGG_B3g4
from ppcls.arch.backbone.model_zoo.van import VAN_tiny
from ppcls.arch.backbone.model_zoo.peleenet import PeleeNet
from ppcls.arch.backbone.model_zoo.convnext import ConvNeXt_tiny
from .model_zoo.resnet_vc import ResNet50_vc
from .model_zoo.resnext import ResNeXt50_32x4d, ResNeXt50_64x4d, ResNeXt101_32x4d, ResNeXt101_64x4d, ResNeXt152_32x4d, ResNeXt152_64x4d
from .model_zoo.resnext_vd import ResNeXt50_vd_32x4d, ResNeXt50_vd_64x4d, ResNeXt101_vd_32x4d, ResNeXt101_vd_64x4d, ResNeXt152_vd_32x4d, ResNeXt152_vd_64x4d
from .model_zoo.res2net import Res2Net50_26w_4s, Res2Net50_14w_8s
from .model_zoo.res2net_vd import Res2Net50_vd_26w_4s, Res2Net101_vd_26w_4s, Res2Net200_vd_26w_4s
from .model_zoo.se_resnet_vd import SE_ResNet18_vd, SE_ResNet34_vd, SE_ResNet50_vd
from .model_zoo.se_resnext_vd import SE_ResNeXt50_vd_32x4d, SE_ResNeXt50_vd_32x4d, SENet154_vd
from .model_zoo.se_resnext import SE_ResNeXt50_32x4d, SE_ResNeXt101_32x4d, SE_ResNeXt152_64x4d
from .model_zoo.dpn import DPN68, DPN92, DPN98, DPN107, DPN131
from .model_zoo.densenet import DenseNet121, DenseNet161, DenseNet169, DenseNet201, DenseNet264
from .model_zoo.efficientnet import EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6, EfficientNetB7, EfficientNetB0_small
from .model_zoo.resnest import ResNeSt50_fast_1s1x64d, ResNeSt50, ResNeSt101
from .model_zoo.googlenet import GoogLeNet
from .model_zoo.mobilenet_v2 import MobileNetV2_x0_25, MobileNetV2_x0_5, MobileNetV2_x0_75, MobileNetV2, MobileNetV2_x1_5, MobileNetV2_x2_0
from .model_zoo.shufflenet_v2 import ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_x0_5, ShuffleNetV2_x1_0, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0, ShuffleNetV2_swish
from .model_zoo.ghostnet import GhostNet_x0_5, GhostNet_x1_0, GhostNet_x1_3
from .model_zoo.alexnet import AlexNet
from .model_zoo.inception_v4 import InceptionV4
from .model_zoo.xception import Xception41, Xception65, Xception71
from .model_zoo.xception_deeplab import Xception41_deeplab, Xception65_deeplab
from .model_zoo.resnext101_wsl import ResNeXt101_32x8d_wsl, ResNeXt101_32x16d_wsl, ResNeXt101_32x32d_wsl, ResNeXt101_32x48d_wsl
from .model_zoo.squeezenet import SqueezeNet1_0, SqueezeNet1_1
from .model_zoo.darknet import DarkNet53
from .model_zoo.regnet import RegNetX_200MF, RegNetX_4GF, RegNetX_32GF, RegNetY_200MF, RegNetY_4GF, RegNetY_32GF
from .model_zoo.vision_transformer import ViT_small_patch16_224, ViT_base_patch16_224, ViT_base_patch16_384, ViT_base_patch32_384, ViT_large_patch16_224, ViT_large_patch16_384, ViT_large_patch32_384
from .model_zoo.distilled_vision_transformer import DeiT_tiny_patch16_224, DeiT_small_patch16_224, DeiT_base_patch16_224, DeiT_tiny_distilled_patch16_224, DeiT_small_distilled_patch16_224, DeiT_base_distilled_patch16_224, DeiT_base_patch16_384, DeiT_base_distilled_patch16_384
from .legendary_models.swin_transformer import SwinTransformer_tiny_patch4_window7_224, SwinTransformer_small_patch4_window7_224, SwinTransformer_base_patch4_window7_224, SwinTransformer_base_patch4_window12_384, SwinTransformer_large_patch4_window7_224, SwinTransformer_large_patch4_window12_384
from .model_zoo.cswin_transformer import CSWinTransformer_tiny_224, CSWinTransformer_small_224, CSWinTransformer_base_224, CSWinTransformer_large_224, CSWinTransformer_base_384, CSWinTransformer_large_384
from .model_zoo.mixnet import MixNet_S, MixNet_M, MixNet_L
from .model_zoo.rexnet import ReXNet_1_0, ReXNet_1_3, ReXNet_1_5, ReXNet_2_0, ReXNet_3_0
from .model_zoo.gvt import pcpvt_small, pcpvt_base, pcpvt_large, alt_gvt_small, alt_gvt_base, alt_gvt_large
from .model_zoo.levit import LeViT_128S, LeViT_128, LeViT_192, LeViT_256, LeViT_384
from .model_zoo.dla import DLA34, DLA46_c, DLA46x_c, DLA60, DLA60x, DLA60x_c, DLA102, DLA102x, DLA102x2, DLA169
from .model_zoo.rednet import RedNet26, RedNet38, RedNet50, RedNet101, RedNet152
from .model_zoo.tnt import TNT_small
from .model_zoo.hardnet import HarDNet68, HarDNet85, HarDNet39_ds, HarDNet68_ds
from .model_zoo.cspnet import CSPDarkNet53
from .model_zoo.pvt_v2 import PVT_V2_B0, PVT_V2_B1, PVT_V2_B2_Linear, PVT_V2_B2, PVT_V2_B3, PVT_V2_B4, PVT_V2_B5
from .model_zoo.mobilevit import MobileViT_XXS, MobileViT_XS, MobileViT_S
from .model_zoo.repvgg import RepVGG_A0, RepVGG_A1, RepVGG_A2, RepVGG_B0, RepVGG_B1, RepVGG_B2, RepVGG_B1g2, RepVGG_B1g4, RepVGG_B2g4, RepVGG_B3g4
from .model_zoo.van import VAN_tiny
from .model_zoo.peleenet import PeleeNet
from .model_zoo.convnext import ConvNeXt_tiny
from ppcls.arch.backbone.variant_models.resnet_variant import ResNet50_last_stage_stride1
from ppcls.arch.backbone.variant_models.vgg_variant import VGG19Sigmoid
from ppcls.arch.backbone.variant_models.pp_lcnet_variant import PPLCNet_x2_5_Tanh
from ppcls.arch.backbone.model_zoo.adaface_ir_net import AdaFace_IR_18, AdaFace_IR_34, AdaFace_IR_50, AdaFace_IR_101, AdaFace_IR_152, AdaFace_IR_SE_50, AdaFace_IR_SE_101, AdaFace_IR_SE_152, AdaFace_IR_SE_200
from .variant_models.resnet_variant import ResNet50_last_stage_stride1
from .variant_models.vgg_variant import VGG19Sigmoid
from .variant_models.pp_lcnet_variant import PPLCNet_x2_5_Tanh
from .model_zoo.adaface_ir_net import AdaFace_IR_18, AdaFace_IR_34, AdaFace_IR_50, AdaFace_IR_101, AdaFace_IR_152, AdaFace_IR_SE_50, AdaFace_IR_SE_101, AdaFace_IR_SE_152, AdaFace_IR_SE_200
# help whl get all the models' api (class type) and components' api (func type)
......
ppcls/arch/backbone/base/theseus_layer.py
浏览文件 @ 7db32e7a
......@@ -15,7 +15,7 @@
from typing import Tuple, List, Dict, Union, Callable, Any
from paddle import nn
from ppcls.utils import logger
from ....utils import logger
class Identity(nn.Layer):
......
ppcls/arch/backbone/legendary_models/esnet.py
浏览文件 @ 7db32e7a
......@@ -22,8 +22,8 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D
from paddle.nn.initializer import KaimingNormal
from paddle.regularizer import L2Decay
from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ..base.theseus_layer import TheseusLayer
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"ESNet_x0_25":
......
ppcls/arch/backbone/legendary_models/hrnet.py
浏览文件 @ 7db32e7a
......@@ -25,8 +25,8 @@ from paddle import ParamAttr
from paddle.nn.functional import upsample
from paddle.nn.initializer import Uniform
from ppcls.arch.backbone.base.theseus_layer import TheseusLayer, Identity
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ..base.theseus_layer import TheseusLayer, Identity
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"HRNet_W18_C":
......
ppcls/arch/backbone/legendary_models/inception_v3.py
浏览文件 @ 7db32e7a
......@@ -23,8 +23,8 @@ from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ..base.theseus_layer import TheseusLayer
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"InceptionV3":
......
ppcls/arch/backbone/legendary_models/mobilenet_v1.py
浏览文件 @ 7db32e7a
......@@ -22,8 +22,8 @@ from paddle.nn import Conv2D, BatchNorm, Linear, ReLU, Flatten
from paddle.nn import AdaptiveAvgPool2D
from paddle.nn.initializer import KaimingNormal
from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ..base.theseus_layer import TheseusLayer
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"MobileNetV1_x0_25":
......
ppcls/arch/backbone/legendary_models/mobilenet_v3.py
浏览文件 @ 7db32e7a
......@@ -21,8 +21,9 @@ import paddle.nn as nn
from paddle import ParamAttr
from paddle.nn import AdaptiveAvgPool2D, BatchNorm, Conv2D, Dropout, Linear
from paddle.regularizer import L2Decay
from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ..base.theseus_layer import TheseusLayer
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"MobileNetV3_small_x0_35":
......
ppcls/arch/backbone/legendary_models/pp_hgnet.py
浏览文件 @ 7db32e7a
......@@ -20,8 +20,8 @@ from paddle.nn import Conv2D, BatchNorm2D, ReLU, AdaptiveAvgPool2D, MaxPool2D
from paddle.regularizer import L2Decay
from paddle import ParamAttr
from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ..base.theseus_layer import TheseusLayer
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"PPHGNet_tiny":
......@@ -199,6 +199,7 @@ class PPHGNet(TheseusLayer):
Returns:
model: nn.Layer. Specific PPHGNet model depends on args.
"""
def __init__(self,
stem_channels,
stage_config,
......
ppcls/arch/backbone/legendary_models/pp_lcnet.py
浏览文件 @ 7db32e7a
......@@ -20,8 +20,9 @@ from paddle import ParamAttr
from paddle.nn import AdaptiveAvgPool2D, BatchNorm2D, Conv2D, Dropout, Linear
from paddle.regularizer import L2Decay
from paddle.nn.initializer import KaimingNormal
from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ..base.theseus_layer import TheseusLayer
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"PPLCNet_x0_25":
......@@ -229,64 +230,59 @@ class PPLCNet(TheseusLayer):
stride=stride_list[0],
lr_mult=self.lr_mult_list[0])
self.blocks2 = nn.Sequential(*[
self.blocks2 = nn.Sequential(* [
DepthwiseSeparable(
num_channels=make_divisible(in_c * scale),
num_filters=make_divisible(out_c * scale),
dw_size=k,
stride=s,
use_se=se,
lr_mult=self.lr_mult_list[1])
for i, (k, in_c, out_c, s, se
) in enumerate(self.net_config["blocks2"])
lr_mult=self.lr_mult_list[1]) for i, (k, in_c, out_c, s, se) in
enumerate(self.net_config["blocks2"])
])
self.blocks3 = nn.Sequential(*[
self.blocks3 = nn.Sequential(* [
DepthwiseSeparable(
num_channels=make_divisible(in_c * scale),
num_filters=make_divisible(out_c * scale),
dw_size=k,
stride=s,
use_se=se,
lr_mult=self.lr_mult_list[2])
for i, (k, in_c, out_c, s, se
) in enumerate(self.net_config["blocks3"])
lr_mult=self.lr_mult_list[2]) for i, (k, in_c, out_c, s, se) in
enumerate(self.net_config["blocks3"])
])
self.blocks4 = nn.Sequential(*[
self.blocks4 = nn.Sequential(* [
DepthwiseSeparable(
num_channels=make_divisible(in_c * scale),
num_filters=make_divisible(out_c * scale),
dw_size=k,
stride=s,
use_se=se,
lr_mult=self.lr_mult_list[3])
for i, (k, in_c, out_c, s, se
) in enumerate(self.net_config["blocks4"])
lr_mult=self.lr_mult_list[3]) for i, (k, in_c, out_c, s, se) in
enumerate(self.net_config["blocks4"])
])
self.blocks5 = nn.Sequential(*[
self.blocks5 = nn.Sequential(* [
DepthwiseSeparable(
num_channels=make_divisible(in_c * scale),
num_filters=make_divisible(out_c * scale),
dw_size=k,
stride=s,
use_se=se,
lr_mult=self.lr_mult_list[4])
for i, (k, in_c, out_c, s, se
) in enumerate(self.net_config["blocks5"])
lr_mult=self.lr_mult_list[4]) for i, (k, in_c, out_c, s, se) in
enumerate(self.net_config["blocks5"])
])
self.blocks6 = nn.Sequential(*[
self.blocks6 = nn.Sequential(* [
DepthwiseSeparable(
num_channels=make_divisible(in_c * scale),
num_filters=make_divisible(out_c * scale),
dw_size=k,
stride=s,
use_se=se,
lr_mult=self.lr_mult_list[5])
for i, (k, in_c, out_c, s, se
) in enumerate(self.net_config["blocks6"])
lr_mult=self.lr_mult_list[5]) for i, (k, in_c, out_c, s, se) in
enumerate(self.net_config["blocks6"])
])
self.avg_pool = AdaptiveAvgPool2D(1)
......
ppcls/arch/backbone/legendary_models/pp_lcnet_v2.py
浏览文件 @ 7db32e7a
......@@ -21,8 +21,9 @@ from paddle import ParamAttr
from paddle.nn import AdaptiveAvgPool2D, BatchNorm2D, Conv2D, Dropout, Linear
from paddle.regularizer import L2Decay
from paddle.nn.initializer import KaimingNormal
from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ..base.theseus_layer import TheseusLayer
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"PPLCNetV2_base":
......
ppcls/arch/backbone/legendary_models/resnet.py
浏览文件 @ 7db32e7a
......@@ -26,9 +26,9 @@ from paddle.nn.initializer import Uniform
from paddle.regularizer import L2Decay
import math
from ppcls.utils import logger
from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils import logger
from ..base.theseus_layer import TheseusLayer
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"ResNet18":
......@@ -328,7 +328,7 @@ class ResNet(TheseusLayer):
[32, 32, 3, 1], [32, 64, 3, 1]]
}
self.stem = nn.Sequential(*[
self.stem = nn.Sequential(* [
ConvBNLayer(
num_channels=in_c,
num_filters=out_c,
......
ppcls/arch/backbone/legendary_models/swin_transformer.py
浏览文件 @ 7db32e7a
......@@ -21,23 +21,23 @@ import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn.initializer import TruncatedNormal, Constant
from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
from ppcls.arch.backbone.model_zoo.vision_transformer import trunc_normal_, zeros_, ones_, to_2tuple, DropPath, Identity
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ..model_zoo.vision_transformer import trunc_normal_, zeros_, ones_, to_2tuple, DropPath, Identity
from ..base.theseus_layer import TheseusLayer
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"SwinTransformer_tiny_patch4_window7_224":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams",
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams",
"SwinTransformer_small_patch4_window7_224":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_small_patch4_window7_224_pretrained.pdparams",
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_small_patch4_window7_224_pretrained.pdparams",
"SwinTransformer_base_patch4_window7_224":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_pretrained.pdparams",
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_base_patch4_window7_224_pretrained.pdparams",
"SwinTransformer_base_patch4_window12_384":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_pretrained.pdparams",
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_base_patch4_window12_384_pretrained.pdparams",
"SwinTransformer_large_patch4_window7_224":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams",
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams",
"SwinTransformer_large_patch4_window12_384":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams",
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams",
}
__all__ = list(MODEL_URLS.keys())
......
ppcls/arch/backbone/legendary_models/vgg.py
浏览文件 @ 7db32e7a
......@@ -20,8 +20,8 @@ import paddle.nn as nn
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import MaxPool2D
from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ..base.theseus_layer import TheseusLayer
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"VGG11":
......
ppcls/arch/backbone/model_zoo/alexnet.py
浏览文件 @ 7db32e7a
......@@ -23,7 +23,7 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"AlexNet":
......
ppcls/arch/backbone/model_zoo/convnext.py
浏览文件 @ 7db32e7a
......@@ -18,7 +18,7 @@ import paddle
import paddle.nn as nn
from paddle.nn.initializer import TruncatedNormal, Constant
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"ConvNeXt_tiny": "", # TODO
......@@ -176,7 +176,7 @@ class ConvNeXt(nn.Layer):
]
cur = 0
for i in range(4):
stage = nn.Sequential(*[
stage = nn.Sequential(* [
Block(
dim=dims[i],
drop_path=dp_rates[cur + j],
......
ppcls/arch/backbone/model_zoo/cspnet.py
浏览文件 @ 7db32e7a
......@@ -20,7 +20,7 @@ import paddle.nn as nn
import paddle.nn.functional as F
from paddle import ParamAttr
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"CSPDarkNet53":
......
ppcls/arch/backbone/model_zoo/cswin_transformer.py
浏览文件 @ 7db32e7a
......@@ -21,7 +21,7 @@ import paddle
import paddle.nn as nn
from .vision_transformer import trunc_normal_, zeros_, ones_, to_2tuple, DropPath, Identity
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"CSWinTransformer_tiny_224":
......
ppcls/arch/backbone/model_zoo/darknet.py
浏览文件 @ 7db32e7a
......@@ -23,7 +23,7 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"DarkNet53":
......
ppcls/arch/backbone/model_zoo/densenet.py
浏览文件 @ 7db32e7a
......@@ -28,7 +28,7 @@ from paddle.nn.initializer import Uniform
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"DenseNet121":
......
ppcls/arch/backbone/model_zoo/distilled_vision_transformer.py
浏览文件 @ 7db32e7a
......@@ -19,7 +19,7 @@ import paddle
import paddle.nn as nn
from .vision_transformer import VisionTransformer, Identity, trunc_normal_, zeros_
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"DeiT_tiny_patch16_224":
......
ppcls/arch/backbone/model_zoo/dla.py
浏览文件 @ 7db32e7a
......@@ -23,8 +23,8 @@ import paddle.nn.functional as F
from paddle.nn.initializer import Normal, Constant
from ppcls.arch.backbone.base.theseus_layer import Identity
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ..base.theseus_layer import Identity
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"DLA34":
......
ppcls/arch/backbone/model_zoo/dpn.py
浏览文件 @ 7db32e7a
......@@ -29,7 +29,7 @@ from paddle.nn.initializer import Uniform
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"DPN68":
......
ppcls/arch/backbone/model_zoo/efficientnet.py
浏览文件 @ 7db32e7a
......@@ -26,7 +26,7 @@ import collections
import re
import copy
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"EfficientNetB0_small":
......
ppcls/arch/backbone/model_zoo/ghostnet.py
浏览文件 @ 7db32e7a
......@@ -24,7 +24,7 @@ from paddle.nn import Conv2D, BatchNorm, AdaptiveAvgPool2D, Linear
from paddle.regularizer import L2Decay
from paddle.nn.initializer import Uniform, KaimingNormal
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"GhostNet_x0_5":
......
ppcls/arch/backbone/model_zoo/googlenet.py
浏览文件 @ 7db32e7a
......@@ -24,7 +24,7 @@ from paddle.nn.initializer import Uniform
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"GoogLeNet":
......
ppcls/arch/backbone/model_zoo/gvt.py
浏览文件 @ 7db32e7a
......@@ -25,7 +25,7 @@ from paddle.regularizer import L2Decay
from .vision_transformer import trunc_normal_, normal_, zeros_, ones_, to_2tuple, DropPath, Identity, Mlp
from .vision_transformer import Block as ViTBlock
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"pcpvt_small":
......
ppcls/arch/backbone/model_zoo/hardnet.py
浏览文件 @ 7db32e7a
......@@ -18,7 +18,7 @@
import paddle
import paddle.nn as nn
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
'HarDNet39_ds':
......
ppcls/arch/backbone/model_zoo/inception_v4.py
浏览文件 @ 7db32e7a
......@@ -23,7 +23,7 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"InceptionV4":
......
ppcls/arch/backbone/model_zoo/levit.py
浏览文件 @ 7db32e7a
......@@ -27,7 +27,7 @@ from paddle.regularizer import L2Decay
from .vision_transformer import trunc_normal_, zeros_, ones_, Identity
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"LeViT_128S":
......
ppcls/arch/backbone/model_zoo/mixnet.py
浏览文件 @ 7db32e7a
......@@ -20,7 +20,7 @@ from functools import reduce
import paddle
import paddle.nn as nn
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"MixNet_S":
......
ppcls/arch/backbone/model_zoo/mobilenet_v2.py
浏览文件 @ 7db32e7a
......@@ -28,7 +28,7 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"MobileNetV2_x0_25":
......
ppcls/arch/backbone/model_zoo/mobilevit.py
浏览文件 @ 7db32e7a
......@@ -23,7 +23,7 @@ import paddle.nn.functional as F
from paddle.nn.initializer import KaimingUniform, TruncatedNormal, Constant
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"MobileViT_XXS":
......
ppcls/arch/backbone/model_zoo/peleenet.py
浏览文件 @ 7db32e7a
......@@ -22,7 +22,7 @@ import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn.initializer import Normal, Constant
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"PeleeNet": "" # TODO
......@@ -37,7 +37,8 @@ ones_ = Constant(value=1.)
class _DenseLayer(nn.Layer):
def __init__(self, num_input_features, growth_rate, bottleneck_width, drop_rate):
def __init__(self, num_input_features, growth_rate, bottleneck_width,
drop_rate):
super(_DenseLayer, self).__init__()
growth_rate = int(growth_rate / 2)
......@@ -71,11 +72,12 @@ class _DenseLayer(nn.Layer):
class _DenseBlock(nn.Sequential):
def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):
def __init__(self, num_layers, num_input_features, bn_size, growth_rate,
drop_rate):
super(_DenseBlock, self).__init__()
for i in range(num_layers):
layer = _DenseLayer(num_input_features + i *
growth_rate, growth_rate, bn_size, drop_rate)
layer = _DenseLayer(num_input_features + i * growth_rate,
growth_rate, bn_size, drop_rate)
setattr(self, 'denselayer%d' % (i + 1), layer)
......@@ -83,16 +85,32 @@ class _StemBlock(nn.Layer):
def __init__(self, num_input_channels, num_init_features):
super(_StemBlock, self).__init__()
num_stem_features = int(num_init_features/2)
num_stem_features = int(num_init_features / 2)
self.stem1 = BasicConv2D(
num_input_channels, num_init_features, kernel_size=3, stride=2, padding=1)
num_input_channels,
num_init_features,
kernel_size=3,
stride=2,
padding=1)
self.stem2a = BasicConv2D(
num_init_features, num_stem_features, kernel_size=1, stride=1, padding=0)
num_init_features,
num_stem_features,
kernel_size=1,
stride=1,
padding=0)
self.stem2b = BasicConv2D(
num_stem_features, num_init_features, kernel_size=3, stride=2, padding=1)
num_stem_features,
num_init_features,
kernel_size=3,
stride=2,
padding=1)
self.stem3 = BasicConv2D(
2*num_init_features, num_init_features, kernel_size=1, stride=1, padding=0)
2 * num_init_features,
num_init_features,
kernel_size=1,
stride=1,
padding=0)
self.pool = nn.MaxPool2D(kernel_size=2, stride=2)
def forward(self, x):
......@@ -109,11 +127,10 @@ class _StemBlock(nn.Layer):
class BasicConv2D(nn.Layer):
def __init__(self, in_channels, out_channels, activation=True, **kwargs):
super(BasicConv2D, self).__init__()
self.conv = nn.Conv2D(in_channels, out_channels,
bias_attr=False, **kwargs)
self.conv = nn.Conv2D(
in_channels, out_channels, bias_attr=False, **kwargs)
self.norm = nn.BatchNorm2D(out_channels)
self.activation = activation
......@@ -141,15 +158,18 @@ class PeleeNetDY(nn.Layer):
class_num (int) - number of classification classes
"""
def __init__(self, growth_rate=32, block_config=[3, 4, 8, 6],
num_init_features=32, bottleneck_width=[1, 2, 4, 4],
drop_rate=0.05, class_num=1000):
def __init__(self,
growth_rate=32,
block_config=[3, 4, 8, 6],
num_init_features=32,
bottleneck_width=[1, 2, 4, 4],
drop_rate=0.05,
class_num=1000):
super(PeleeNetDY, self).__init__()
self.features = nn.Sequential(*[
('stemblock', _StemBlock(3, num_init_features)),
])
self.features = nn.Sequential(* [('stemblock', _StemBlock(
3, num_init_features)), ])
if type(growth_rate) is list:
growth_rates = growth_rate
......@@ -168,7 +188,8 @@ class PeleeNetDY(nn.Layer):
# Each denseblock
num_features = num_init_features
for i, num_layers in enumerate(block_config):
block = _DenseBlock(num_layers=num_layers,
block = _DenseBlock(
num_layers=num_layers,
num_input_features=num_features,
bn_size=bottleneck_widths[i],
growth_rate=growth_rates[i],
......@@ -176,12 +197,22 @@ class PeleeNetDY(nn.Layer):
setattr(self.features, 'denseblock%d' % (i + 1), block)
num_features = num_features + num_layers * growth_rates[i]
setattr(self.features, 'transition%d' % (i + 1), BasicConv2D(
num_features, num_features, kernel_size=1, stride=1, padding=0))
setattr(
self.features,
'transition%d' % (i + 1),
BasicConv2D(
num_features,
num_features,
kernel_size=1,
stride=1,
padding=0))
if i != len(block_config) - 1:
setattr(self.features, 'transition%d_pool' %
(i + 1), nn.AvgPool2D(kernel_size=2, stride=2))
setattr(
self.features,
'transition%d_pool' % (i + 1),
nn.AvgPool2D(
kernel_size=2, stride=2))
num_features = num_features
# Linear layer
......@@ -192,7 +223,8 @@ class PeleeNetDY(nn.Layer):
def forward(self, x):
features = self.features(x)
out = F.avg_pool2d(features, kernel_size=features.shape[2:4]).flatten(1)
out = F.avg_pool2d(
features, kernel_size=features.shape[2:4]).flatten(1)
if self.drop_rate > 0:
out = F.dropout(out, p=self.drop_rate, training=self.training)
out = self.classifier(out)
......
ppcls/arch/backbone/model_zoo/pvt_v2.py
浏览文件 @ 7db32e7a
......@@ -24,7 +24,7 @@ from paddle.nn.initializer import TruncatedNormal, Constant
from .vision_transformer import trunc_normal_, zeros_, ones_, to_2tuple, DropPath, Identity, drop_path
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"PVT_V2_B0":
......
ppcls/arch/backbone/model_zoo/rednet.py
浏览文件 @ 7db32e7a
......@@ -20,7 +20,7 @@ import paddle.nn as nn
from paddle.vision.models import resnet
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"RedNet26":
......
ppcls/arch/backbone/model_zoo/regnet.py
浏览文件 @ 7db32e7a
......@@ -29,7 +29,7 @@ from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"RegNetX_200MF":
......
ppcls/arch/backbone/model_zoo/repvgg.py
浏览文件 @ 7db32e7a
......@@ -19,7 +19,7 @@ import paddle.nn as nn
import paddle
import numpy as np
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"RepVGG_A0":
......
ppcls/arch/backbone/model_zoo/res2net.py
浏览文件 @ 7db32e7a
......@@ -29,7 +29,7 @@ from paddle.nn.initializer import Uniform
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"Res2Net50_26w_4s":
......
ppcls/arch/backbone/model_zoo/res2net_vd.py
浏览文件 @ 7db32e7a
......@@ -29,7 +29,7 @@ from paddle.nn.initializer import Uniform
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"Res2Net50_vd_26w_4s":
......
ppcls/arch/backbone/model_zoo/resnest.py
浏览文件 @ 7db32e7a
......@@ -30,7 +30,7 @@ from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.regularizer import L2Decay
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"ResNeSt50_fast_1s1x64d":
......
ppcls/arch/backbone/model_zoo/resnet_vc.py
浏览文件 @ 7db32e7a
......@@ -29,7 +29,7 @@ from paddle.nn.initializer import Uniform
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"ResNet50_vc":
......
ppcls/arch/backbone/model_zoo/resnext.py
浏览文件 @ 7db32e7a
......@@ -29,7 +29,7 @@ from paddle.nn.initializer import Uniform
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"ResNeXt50_32x4d":
......
ppcls/arch/backbone/model_zoo/resnext101_wsl.py
浏览文件 @ 7db32e7a
......@@ -22,7 +22,7 @@ from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"ResNeXt101_32x8d_wsl":
......
ppcls/arch/backbone/model_zoo/resnext_vd.py
浏览文件 @ 7db32e7a
......@@ -29,7 +29,7 @@ from paddle.nn.initializer import Uniform
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"ResNeXt50_vd_32x4d":
......
ppcls/arch/backbone/model_zoo/rexnet.py
浏览文件 @ 7db32e7a
......@@ -24,7 +24,7 @@ from paddle import ParamAttr
import paddle.nn as nn
from math import ceil
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"ReXNet_1_0":
......
ppcls/arch/backbone/model_zoo/se_resnet_vd.py
浏览文件 @ 7db32e7a
......@@ -28,7 +28,7 @@ from paddle.nn.initializer import Uniform
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"SE_ResNet18_vd":
......
ppcls/arch/backbone/model_zoo/se_resnext.py
浏览文件 @ 7db32e7a
......@@ -29,7 +29,7 @@ from paddle.nn.initializer import Uniform
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"SE_ResNeXt50_32x4d":
......
ppcls/arch/backbone/model_zoo/se_resnext_vd.py
浏览文件 @ 7db32e7a
......@@ -29,7 +29,7 @@ from paddle.nn.initializer import Uniform
import math
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"SE_ResNeXt50_vd_32x4d":
......
ppcls/arch/backbone/model_zoo/shufflenet_v2.py
浏览文件 @ 7db32e7a
......@@ -24,7 +24,7 @@ from paddle.nn import Layer, Conv2D, MaxPool2D, AdaptiveAvgPool2D, BatchNorm, Li
from paddle.nn.initializer import KaimingNormal
from paddle.nn.functional import swish
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"ShuffleNetV2_x0_25":
......
ppcls/arch/backbone/model_zoo/squeezenet.py
浏览文件 @ 7db32e7a
......@@ -21,7 +21,7 @@ import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"SqueezeNet1_0":
......
ppcls/arch/backbone/model_zoo/tnt.py
浏览文件 @ 7db32e7a
......@@ -23,8 +23,8 @@ import paddle.nn as nn
from paddle.nn.initializer import TruncatedNormal, Constant
from ppcls.arch.backbone.base.theseus_layer import Identity
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ..base.theseus_layer import Identity
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"TNT_small":
......
ppcls/arch/backbone/model_zoo/van.py
浏览文件 @ 7db32e7a
......@@ -21,7 +21,7 @@ import paddle
import paddle.nn as nn
from paddle.nn.initializer import TruncatedNormal, Constant
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"VAN_tiny": "", # TODO
......
ppcls/arch/backbone/model_zoo/vision_transformer.py
浏览文件 @ 7db32e7a
......@@ -22,7 +22,7 @@ import paddle
import paddle.nn as nn
from paddle.nn.initializer import TruncatedNormal, Constant, Normal
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"ViT_small_patch16_224":
......
ppcls/arch/backbone/model_zoo/xception.py
浏览文件 @ 7db32e7a
......@@ -24,7 +24,7 @@ from paddle.nn.initializer import Uniform
import math
import sys
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"Xception41":
......
ppcls/arch/backbone/model_zoo/xception_deeplab.py
浏览文件 @ 7db32e7a
......@@ -21,7 +21,7 @@ import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
MODEL_URLS = {
"Xception41_deeplab":
......
ppcls/arch/backbone/variant_models/pp_lcnet_variant.py
浏览文件 @ 7db32e7a
import paddle
from paddle.nn import Sigmoid
from paddle.nn import Tanh
from ppcls.arch.backbone.legendary_models.pp_lcnet import PPLCNet_x2_5
from ..legendary_models.pp_lcnet import PPLCNet_x2_5
__all__ = ["PPLCNet_x2_5_Tanh"]
......
ppcls/arch/backbone/variant_models/resnet_variant.py
浏览文件 @ 7db32e7a
from paddle.nn import Conv2D
from ppcls.arch.backbone.legendary_models.resnet import ResNet50, MODEL_URLS, _load_pretrained
from ..legendary_models.resnet import ResNet50, MODEL_URLS, _load_pretrained
__all__ = ["ResNet50_last_stage_stride1"]
......
ppcls/arch/backbone/variant_models/vgg_variant.py
浏览文件 @ 7db32e7a
import paddle
from paddle.nn import Sigmoid
from ppcls.arch.backbone.legendary_models.vgg import VGG19
from ..legendary_models.vgg import VGG19
__all__ = ["VGG19Sigmoid"]
......
ppcls/arch/gears/bnneck.py
浏览文件 @ 7db32e7a
......@@ -17,7 +17,7 @@ from __future__ import absolute_import, division, print_function
import paddle
import paddle.nn as nn
from ppcls.arch.utils import get_param_attr_dict
from ..utils import get_param_attr_dict
class BNNeck(nn.Layer):
......
ppcls/arch/gears/fc.py
浏览文件 @ 7db32e7a
......@@ -19,7 +19,7 @@ from __future__ import print_function
import paddle
import paddle.nn as nn
from ppcls.arch.utils import get_param_attr_dict
from ..utils import get_param_attr_dict
class FC(nn.Layer):
......
ppcls/arch/slim/__init__.py
浏览文件 @ 7db32e7a
......@@ -12,5 +12,5 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from ppcls.arch.slim.prune import prune_model
from ppcls.arch.slim.quant import quantize_model
from .prune import prune_model
from .quant import quantize_model
ppcls/arch/slim/prune.py
浏览文件 @ 7db32e7a
......@@ -14,7 +14,7 @@
from __future__ import absolute_import, division, print_function
import paddle
from ppcls.utils import logger
from ...utils import logger
def prune_model(config, model):
......@@ -37,7 +37,6 @@ def prune_model(config, model):
model.pruner = None
def _prune_model(config, model):
from paddleslim.analysis import dygraph_flops as flops
logger.info("FLOPs before pruning: {}GFLOPs".format(
......
ppcls/arch/slim/quant.py
浏览文件 @ 7db32e7a
......@@ -14,7 +14,7 @@
from __future__ import absolute_import, division, print_function
import paddle
from ppcls.utils import logger
from ...utils import logger
QUANT_CONFIG = {
# weight preprocess type, default is None and no preprocessing is performed.
......
ppcls/configs/ImageNet/Distillation/mv3_large_x1_0_distill_mv3_small_x1_0.yaml
浏览文件 @ 7db32e7a
......@@ -140,8 +140,7 @@ Infer:
order: ''
- ToCHWImage:
PostProcess:
name: DistillationPostProcess
func: Topk
name: Topk
topk: 5
class_id_map_file: "ppcls/utils/imagenet1k_label_list.txt"
......
ppcls/configs/ImageNet/Distillation/res2net200_vd_distill_pphgnet_base.yaml
浏览文件 @ 7db32e7a
......@@ -153,8 +153,7 @@ Infer:
order: ''
- ToCHWImage:
PostProcess:
name: DistillationPostProcess
func: Topk
name: Topk
topk: 5
class_id_map_file: "ppcls/utils/imagenet1k_label_list.txt"
......
ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_afd.yaml
浏览文件 @ 7db32e7a
......@@ -184,8 +184,7 @@ Infer:
order: ''
- ToCHWImage:
PostProcess:
name: DistillationPostProcess
func: Topk
name: Topk
topk: 5
class_id_map_file: "ppcls/utils/imagenet1k_label_list.txt"
......
ppcls/configs/ImageNet/Distillation/resnet34_distill_resnet18_dkd.yaml
浏览文件 @ 7db32e7a
......@@ -139,8 +139,7 @@ Infer:
order: ''
- ToCHWImage:
PostProcess:
name: DistillationPostProcess
func: Topk
name: Topk
topk: 5
class_id_map_file: "ppcls/utils/imagenet1k_label_list.txt"
......
ppcls/data/dataloader/DistributedRandomIdentitySampler.py
浏览文件 @ 7db32e7a
......@@ -14,24 +14,27 @@
from __future__ import absolute_import
from __future__ import division
from collections import defaultdict
import numpy as np
import copy
import random
from collections import defaultdict
import numpy as np
from paddle.io import DistributedBatchSampler, Sampler
class DistributedRandomIdentitySampler(DistributedBatchSampler):
"""
Randomly sample N identities, then for each identity,
randomly sample K instances, therefore batch size is N*K.
"""Randomly sample N identities, then for each identity,
randomly sample K instances, therefore batch size equals to N * K.
Args:
- data_source (list): list of (img_path, pid, camid).
- num_instances (int): number of instances per identity in a batch.
- batch_size (int): number of examples in a batch.
dataset(Dataset): Dataset which contains list of (img_path, pid, camid))
batch_size (int): batch size
num_instances (int): number of instance(s) within an class
drop_last (bool): whether to discard the data at the end
"""
def __init__(self, dataset, batch_size, num_instances, drop_last, **args):
assert batch_size % num_instances == 0, \
f"batch_size({batch_size}) must be divisible by num_instances({num_instances}) when using DistributedRandomIdentitySampler"
self.dataset = dataset
self.batch_size = batch_size
self.num_instances = num_instances
......
ppcls/data/dataloader/pk_sampler.py
浏览文件 @ 7db32e7a
......@@ -14,27 +14,27 @@
from __future__ import absolute_import
from __future__ import division
from collections import defaultdict
import numpy as np
import random
from paddle.io import DistributedBatchSampler
from ppcls.utils import logger
class PKSampler(DistributedBatchSampler):
"""
First, randomly sample P identities.
"""First, randomly sample P identities.
Then for each identity randomly sample K instances.
Therefore batch size is P*K, and the sampler called PKSampler.
Therefore batch size equals to P * K, and the sampler called PKSampler.
Args:
dataset (paddle.io.Dataset): list of (img_path, pid, cam_id).
sample_per_id(int): number of instances per identity in a batch.
batch_size (int): number of examples in a batch.
shuffle(bool): whether to shuffle indices order before generating
batch indices. Default False.
dataset (Dataset): Dataset which contains list of (img_path, pid, camid))
batch_size (int): batch size
sample_per_id (int): number of instance(s) within an class
shuffle (bool, optional): _description_. Defaults to True.
drop_last (bool, optional): whether to discard the data at the end. Defaults to True.
sample_method (str, optional): sample method when generating prob_list. Defaults to "sample_avg_prob".
"""
def __init__(self,
dataset,
batch_size,
......@@ -42,10 +42,9 @@ class PKSampler(DistributedBatchSampler):
shuffle=True,
drop_last=True,
sample_method="sample_avg_prob"):
super().__init__(
dataset, batch_size, shuffle=shuffle, drop_last=drop_last)
super().__init__(dataset, batch_size, shuffle=shuffle, drop_last=drop_last)
assert batch_size % sample_per_id == 0, \
"PKSampler configs error, Sample_per_id must be a divisor of batch_size."
f"PKSampler configs error, sample_per_id({sample_per_id}) must be a divisor of batch_size({batch_size})."
assert hasattr(self.dataset,
"labels"), "Dataset must have labels attribute."
self.sample_per_label = sample_per_id
......
ppcls/utils/check.py
浏览文件 @ 7db32e7a
......@@ -22,10 +22,8 @@ import sys
import paddle
from paddle import is_compiled_with_cuda
from ppcls.arch import get_architectures
from ppcls.arch import similar_architectures
from ppcls.arch import get_blacklist_model_in_static_mode
from ppcls.utils import logger
from ..arch.utils import get_architectures, similar_architectures, get_blacklist_model_in_static_mode
from . import logger
def check_version():
......
ppcls/utils/config.py
浏览文件 @ 7db32e7a
......@@ -16,8 +16,9 @@ import os
import copy
import argparse
import yaml
from ppcls.utils import logger
from ppcls.utils import check
from . import logger
from . import check
__all__ = ['get_config']
......
ppcls/utils/download.py
浏览文件 @ 7db32e7a
......@@ -28,7 +28,7 @@ import time
from collections import OrderedDict
from tqdm import tqdm
from ppcls.utils import logger
from . import logger
__all__ = ['get_weights_path_from_url']
......
ppcls/utils/gallery2fc.py
浏览文件 @ 7db32e7a
......@@ -20,12 +20,12 @@ import sys
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.abspath(os.path.join(__dir__, '../../')))
from ppcls.arch import build_model
from ppcls.utils.config import parse_config, parse_args
from ppcls.utils.save_load import load_dygraph_pretrain
from ppcls.utils.logger import init_logger
from ppcls.data import create_operators
from ppcls.arch.slim import quantize_model
from ..arch import build_model
from .config import parse_config, parse_args
from .save_load import load_dygraph_pretrain
from .logger import init_logger
from ..data import create_operators
from ..arch.slim import quantize_model
class GalleryLayer(paddle.nn.Layer):
......
ppcls/utils/model_zoo.py
浏览文件 @ 7db32e7a
......@@ -23,8 +23,8 @@ import tarfile
import tqdm
import zipfile
from ppcls.arch import similar_architectures
from ppcls.utils import logger
from ..arch.utils import similar_architectures
from . import logger
__all__ = ['get']
......
ppcls/utils/save_load.py
浏览文件 @ 7db32e7a
......@@ -20,7 +20,7 @@ import errno
import os
import paddle
from ppcls.utils import logger
from . import logger
from .download import get_weights_path_from_url
__all__ = ['init_model', 'save_model', 'load_dygraph_pretrain']
......
setup.py
浏览文件 @ 7db32e7a
......@@ -33,7 +33,7 @@ setup(
package_dir={'paddleclas': ''},
include_package_data=True,
entry_points={
"console_scripts": ["paddleclas= paddleclas.paddleclas:main"]
"console_scripts": ["paddleclas=paddleclas.paddleclas:main"]
},
version='0.0.0',
install_requires=requirements,
......
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