Merge branch 'fix_download' of https://github.com/WuHaobo/PaddleClas into WuHaobo-fix_download

configs/pretrained.list

+ResNet18
+ResNet34
+ResNet50
+ResNet101
+ResNet152
+ResNet50_vc
+ResNet18_vd
+ResNet34_vd
+ResNet50_vd
+ResNet50_vd_v2
+ResNet101_vd
+ResNet152_vd
+ResNet200_vd
+ResNet50_vd_ssld
+MobileNetV3_large_x0_35
+MobileNetV3_large_x0_5
+MobileNetV3_large_x0_75
+MobileNetV3_large_x1_0
+MobileNetV3_large_x1_25
+MobileNetV3_small_x0_35
+MobileNetV3_small_x0_5
+MobileNetV3_small_x0_75
+MobileNetV3_small_x1_0
+MobileNetV3_small_x1_25
+MobileNetV3_large_x1_0_ssld
+MobileNetV3_large_x1_0_ssld_int8
+MobileNetV3_small_x1_0_ssld
+MobileNetV2_x0_25
+MobileNetV2_x0_5
+MobileNetV2_x0_75
+MobileNetV2
+MobileNetV2_x1_5
+MobileNetV2_x2_0
+MobileNetV2_ssld
+MobileNetV1_x0_25
+MobileNetV1_x0_5
+MobileNetV1_x0_75
+MobileNetV1
+MobileNetV1_ssld
+ShuffleNetV2_x0_25
+ShuffleNetV2_x0_33
+ShuffleNetV2_x0_5
+ShuffleNetV2
+ShuffleNetV2_x1_5
+ShuffleNetV2_x2_0
+ShuffleNetV2_swish
+ResNeXt50_32x4d
+ResNeXt50_64x4d
+ResNeXt101_32x4d
+ResNeXt101_64x4d
+ResNeXt152_32x4d
+ResNeXt152_64x4d
+ResNeXt50_vd_32x4d
+ResNeXt50_vd_64x4d
+ResNeXt101_vd_32x4d
+ResNeXt101_vd_64x4d
+ResNeXt152_vd_32x4d
+ResNeXt152_vd_64x4d
+SE_ResNet18_vd
+SE_ResNet34_vd
+SE_ResNet50_vd
+SE_ResNeXt50_32x4d
+SE_ResNeXt101_32x4d
+SE_ResNeXt50_vd_32x4d
+SENet154_vd
+Res2Net50_26w_4s
+Res2Net50_vd_26w_4s
+Res2Net50_14w_8s
+Res2Net101_vd_26w_4s
+Res2Net200_vd_26w_4s
+GoogLeNet
+InceptionV4
+Xception41
+Xception41_deeplab
+Xception65
+Xception65_deeplab
+Xception71
+HRNet_W18_C
+HRNet_W30_C
+HRNet_W32_C
+HRNet_W40_C
+HRNet_W44_C
+HRNet_W48_C
+HRNet_W64_C
+DPN68
+DPN92
+DPN98
+DPN107
+DPN131
+DenseNet121
+DenseNet161
+DenseNet169
+DenseNet201
+DenseNet264
+EfficientNetB0_small
+EfficientNetB0
+EfficientNetB1
+EfficientNetB2
+EfficientNetB3
+EfficientNetB4
+EfficientNetB5
+EfficientNetB6
+EfficientNetB7
+ResNeXt101_32x8d_wsl
+ResNeXt101_32x16d_wsl
+ResNeXt101_32x32d_wsl
+ResNeXt101_32x48d_wsl
+Fix_ResNeXt101_32x48d_wsl
+AlexNet
+SqueezeNet1_0
+SqueezeNet1_1
+VGG11
+VGG13
+VGG16
+VGG19
+DarkNet53
+ResNet50_ACNet_deploy

docs/zh_CN/extension/paddle_quantization.md

 # 模型量化
 
-模型量化是 [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim) 的特色功能之一，支持动态和静态两种量化训练方式，对权重全局量化和 Channel-Wise 量化，同时以兼容 Paddle Mobile 的格式保存模型。
+模型量化是 [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim) 的特色功能之一，支持动态和静态两种量化训练方式，对权重全局量化和 Channel-Wise 量化，同时以兼容 Paddle-Lite 的格式保存模型。
 [PaddleClas](https://github.com/PaddlePaddle/PaddleClas) 使用该量化工具，量化了78.9%的mobilenet_v3_large_x1_0的蒸馏模型, 量化后SD855上预测速度从19.308ms加速到14.395ms，存储大小从21M减小到10M， top1识别准确率75.9%。
 具体的训练方法可以参见 [PaddleSlim 量化训练](https://paddlepaddle.github.io/PaddleSlim/quick_start/quant_aware_tutorial.html)。

ppcls/modeling/__init__.py

@@ -17,4 +17,4 @@ from . import loss
 
 from .architectures import *
 from .loss import *
-from .utils import similar_architectures
+from .utils import *

ppcls/modeling/utils.py

@@ -12,8 +12,8 @@
 #See the License for the specific language governing permissions and
 #limitations under the License.
 
-import types
 import architectures
+import types
 from difflib import SequenceMatcher
 
 
@@ -28,12 +28,11 @@ def get_architectures():
     return names
 
 
-def similar_architectures(name='', thresh=0.1, topk=10):
+def similar_architectures(name='', names=[], thresh=0.1, topk=10):
     """
     inferred similar architectures
     """
     scores = []
-    names = get_architectures()
     for idx, n in enumerate(names):
         if n[:2] == '__': continue
         score = SequenceMatcher(None, n.lower(), name.lower()).quick_ratio()

ppcls/utils/check.py

@@ -20,6 +20,7 @@ import sys
 
 import paddle.fluid as fluid
 
+from ppcls.modeling import get_architectures
 from ppcls.modeling import similar_architectures
 from ppcls.utils import logger
 
@@ -60,7 +61,7 @@ def check_architecture(architecture):
     """
     assert isinstance(architecture, str), \
             ("the type of architecture({}) should be str". format(architecture))
-    similar_names = similar_architectures(architecture)
+    similar_names = similar_architectures(architecture, get_architectures())
     model_list = ', '.join(similar_names)
     err = "{} is not exist! Maybe you want: [{}]" \
           "".format(architecture, model_list)

ppcls/utils/model_zoo.py

@@ -17,12 +17,13 @@ from __future__ import division
 from __future__ import print_function
 
 import os
-import shutil
 import requests
-import tqdm
+import shutil
 import tarfile
+import tqdm
 import zipfile
 
+from ppcls.modeling import similar_architectures
 from ppcls.utils.check import check_architecture
 from ppcls.utils import logger
 
@@ -44,11 +45,7 @@ class ModelNameError(Exception):
     """ ModelNameError
     """
 
-    def __init__(self, message='', architecture=''):
-        similar_names = similar_architectures(architecture)
-        model_list = ', '.join(similar_names)
-        message += '\n{} is not exist. \nMaybe you want: [{}]'.format(
-            architecture, model_list)
+    def __init__(self, message=''):
         super(ModelNameError, self).__init__(message)
 
 
@@ -171,8 +168,24 @@ def _decompress(fname):
     os.remove(fname)
 
 
+def _check_pretrained_name(architecture):
+    assert isinstance(architecture, str), \
+            ("the type of architecture({}) should be str". format(architecture))
+    with open('./configs/pretrained.list') as flist:
+        pretrained = [line.strip() for line in flist]
+    similar_names = similar_architectures(architecture, pretrained)
+    model_list = ', '.join(similar_names)
+    err = "{} is not exist! Maybe you want: [{}]" \
+          "".format(architecture, model_list)
+    if architecture not in similar_names:
+        raise ModelNameError(err)
+
+
 def get(architecture, path, decompress=True):
-    check_architecture(architecture)
+    """
+    Get the pretrained model.
+    """
+    _check_pretrained_name(architecture)
     url = _get_url(architecture)
     fname = _download(url, path)
     if decompress: _decompress(fname)

tools/download.py

@@ -15,7 +15,6 @@
 import sys
 import argparse
 
-sys.path.append("../")
 from ppcls import model_zoo