polish download pretrain

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/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

ppcls/modeling/utils.py

@@ -12,10 +12,12 @@
 #See the License for the specific language governing permissions and
 #limitations under the License.
 
-import types
 import architectures
+import types
 from difflib import SequenceMatcher
 
+from ppcls.utils.config import get_config
+
 
 def get_architectures():
     """
@@ -28,12 +30,18 @@ def get_architectures():
     return names
 
 
-def similar_architectures(name='', thresh=0.1, topk=10):
+def get_pretrained():
+    """
+    get all of model pretrained
+    """
+    return get_config('./pretrained.list')
+
+
+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

@@ -23,6 +23,8 @@ import tarfile
 import tqdm
 import zipfile
 
+from ppcls.modeling import get_pretrained
+from ppcls.modeling import similar_architectures
 from ppcls.utils.check import check_architecture
 from ppcls.utils import logger
 
@@ -40,6 +42,14 @@ class UrlError(Exception):
         super(UrlError, self).__init__(message)
 
 
+class ModelNameError(Exception):
+    """ ModelNameError
+    """
+
+    def __init__(self, message=''):
+        super(ModelNameError, self).__init__(message)
+
+
 class RetryError(Exception):
     """ RetryError
     """
@@ -159,19 +169,22 @@ def _decompress(fname):
     os.remove(fname)
 
 
+def _check_pretrained_name(architecture):
+    assert isinstance(architecture, str), \
+            ("the type of architecture({}) should be str". format(architecture))
+    similar_names = similar_architectures(architecture, get_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):
     """
     Get the pretrained model.
-
-    Args:
-        architecture: the name of which architecture to get. 
-            If the name is not exist, will raises UrlError with error code 404.
-        path: which dir to save the pretrained model.
-        decompress: decompress the download or not.
-
-    Raises:
-        RetryError or UrlError if download failed
     """
+    _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