diff --git a/docs/zh_CN/faq.md b/docs/zh_CN/faq.md
index 500b61dbfa883ae56c8757e4ef02d981a51d7cd6..fe83d31ba03bf649aab1bff097d78874310fb4e6 100644
--- a/docs/zh_CN/faq.md
+++ b/docs/zh_CN/faq.md
@@ -12,4 +12,9 @@
>>
* Q: 在配置文件的`TRAIN`字段中配置了`mix`的参数，为什么`mixup`的数据增广预处理没有生效呢？
-* A: 使用mixup时，数据预处理部分与模型输入部分均需要修改，因此还需要在配置文件中显式地配置`use_mix: True`，才能使得`mixup`生效。
\ No newline at end of file
+* A: 使用mixup时，数据预处理部分与模型输入部分均需要修改，因此还需要在配置文件中显式地配置`use_mix: True`，才能使得`mixup`生效。
+
+
+>>
+* Q: 评估和预测时，已经指定了预训练模型所在文件夹的地址，但是仍然无法导入参数，这么为什么呢？
+* A: 加载预训练模型时，需要指定预训练模型的前缀，例如预训练模型参数所在的文件夹为`output/ResNet50_vd/19`，预训练模型参数的名称为`output/ResNet50_vd/19/ppcls.pdparams`，则`pretrained_model`参数需要指定为`output/ResNet50_vd/19/ppcls`，PaddleClas会自动补齐`.pdparams`的后缀。
diff --git a/docs/zh_CN/models/Mobile.md b/docs/zh_CN/models/Mobile.md
index 6ef51b5ef07605d8bc3b8412eb7b4ef38c7a7752..6cf4ed90241ca959e7c5b66f9a14a8c415e7dd87 100644
--- a/docs/zh_CN/models/Mobile.md
+++ b/docs/zh_CN/models/Mobile.md
@@ -32,7 +32,7 @@ MobileNetV3是Google于2019年提出的一种基于NAS的新的轻量级网络
| MobileNetV2_x2_0 | 0.752 | 0.926 | | | 2.320 | 11.130 |
| MobileNetV2_ssld | 0.7674 | 0.9339 | | | 0.600 | 3.440 |
| MobileNetV3_large_
x1_25 | 0.764 | 0.930 | 0.766 | | 0.714 | 7.440 |
-| MobileNetV3_large_
x1_0 | 0.753 | 0.753 | 0.752 | | 0.450 | 5.470 |
+| MobileNetV3_large_
x1_0 | 0.753 | 0.923 | 0.752 | | 0.450 | 5.470 |
| MobileNetV3_large_
x0_75 | 0.731 | 0.911 | 0.733 | | 0.296 | 3.910 |
| MobileNetV3_large_
x0_5 | 0.692 | 0.885 | 0.688 | | 0.138 | 2.670 |
| MobileNetV3_large_
x0_35 | 0.643 | 0.855 | 0.642 | | 0.077 | 2.100 |
diff --git a/docs/zh_CN/tutorials/getting_started.md b/docs/zh_CN/tutorials/getting_started.md
index aee6351e61197e0070b5bd6c18525224a5ae8cc7..11bea8411362ac0b4122d9e6da8d9f9c7d24b54e 100644
--- a/docs/zh_CN/tutorials/getting_started.md
+++ b/docs/zh_CN/tutorials/getting_started.md
@@ -68,6 +68,8 @@ python tools/eval.py \
```
可以更改configs/eval.yaml中的`ARCHITECTURE.name`字段和pretrained_model字段来配置评估模型，也可以通过-o参数更新配置。
+**注意：** 加载预训练模型时，需要指定预训练模型的前缀，例如预训练模型参数所在的文件夹为`output/ResNet50_vd/19`，预训练模型参数的名称为`output/ResNet50_vd/19/ppcls.pdparams`，则`pretrained_model`参数需要指定为`output/ResNet50_vd/19/ppcls`，PaddleClas会自动补齐`.pdparams`的后缀。
+
## 三、模型推理
PaddlePaddle提供三种方式进行预测推理，接下来介绍如何用预测引擎进行推理：
diff --git a/ppcls/modeling/architectures/mobilenet_v3.py b/ppcls/modeling/architectures/mobilenet_v3.py
index 2ec175db43e4cadbe0742ae9d55b1f5a218b70fe..d2efcedb105a9f95c30c9506cf683864203afbba 100644
--- a/ppcls/modeling/architectures/mobilenet_v3.py
+++ b/ppcls/modeling/architectures/mobilenet_v3.py
@@ -1,23 +1,22 @@
-#copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
+# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
-#Licensed under the Apache License, Version 2.0 (the "License");
-#you may not use this file except in compliance with the License.
-#You may obtain a copy of the License at
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
-#Unless required by applicable law or agreed to in writing, software
-#distributed under the License is distributed on an "AS IS" BASIS,
-#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#See the License for the specific language governing permissions and
-#limitations under the License.
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle.fluid as fluid
-from paddle.fluid.initializer import MSRA
from paddle.fluid.param_attr import ParamAttr
__all__ = [
@@ -30,9 +29,19 @@ __all__ = [
class MobileNetV3():
- def __init__(self, scale=1.0, model_name='small'):
+ def __init__(self,
+ scale=1.0,
+ model_name='small',
+ lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0]):
self.scale = scale
self.inplanes = 16
+
+ self.lr_mult_list = lr_mult_list
+ assert len(self.lr_mult_list) == 5, \
+ "lr_mult_list length in MobileNetV3 must be 5 but got {}!!".format(
+ len(self.lr_mult_list))
+ self.curr_stage = 0
+
if model_name == "large":
self.cfg = [
# k, exp, c, se, nl, s,
@@ -54,6 +63,7 @@ class MobileNetV3():
]
self.cls_ch_squeeze = 960
self.cls_ch_expand = 1280
+ self.lr_interval = 3
elif model_name == "small":
self.cfg = [
# k, exp, c, se, nl, s,
@@ -71,9 +81,10 @@ class MobileNetV3():
]
self.cls_ch_squeeze = 576
self.cls_ch_expand = 1280
+ self.lr_interval = 2
else:
- raise NotImplementedError("mode[" + model_name +
- "_model] is not implemented!")
+ raise NotImplementedError(
+ "mode[{}_model] is not implemented!".format(model_name))
def net(self, input, class_dim=1000):
scale = self.scale
@@ -81,7 +92,7 @@ class MobileNetV3():
cfg = self.cfg
cls_ch_squeeze = self.cls_ch_squeeze
cls_ch_expand = self.cls_ch_expand
- #conv1
+ # conv1
conv = self.conv_bn_layer(
input,
filter_size=3,
@@ -107,6 +118,7 @@ class MobileNetV3():
name='conv' + str(i + 2))
inplanes = self.make_divisible(scale * layer_cfg[2])
i += 1
+ self.curr_stage = i
conv = self.conv_bn_layer(
input=conv,
@@ -149,6 +161,10 @@ class MobileNetV3():
name=None,
use_cudnn=True,
res_last_bn_init=False):
+ lr_idx = self.curr_stage // self.lr_interval
+ lr_idx = min(lr_idx, len(self.lr_mult_list) - 1)
+ lr_mult = self.lr_mult_list[lr_idx]
+
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
@@ -158,7 +174,8 @@ class MobileNetV3():
groups=num_groups,
act=None,
use_cudnn=use_cudnn,
- param_attr=ParamAttr(name=name + '_weights'),
+ param_attr=ParamAttr(
+ name=name + '_weights', learning_rate=lr_mult),
bias_attr=False)
bn_name = name + '_bn'
bn = fluid.layers.batch_norm(
@@ -189,6 +206,10 @@ class MobileNetV3():
return new_v
def se_block(self, input, num_out_filter, ratio=4, name=None):
+ lr_idx = self.curr_stage // self.lr_interval
+ lr_idx = min(lr_idx, len(self.lr_mult_list) - 1)
+ lr_mult = self.lr_mult_list[lr_idx]
+
num_mid_filter = num_out_filter // ratio
pool = fluid.layers.pool2d(
input=input, pool_type='avg', global_pooling=True, use_cudnn=False)
@@ -197,15 +218,19 @@ class MobileNetV3():
filter_size=1,
num_filters=num_mid_filter,
act='relu',
- param_attr=ParamAttr(name=name + '_1_weights'),
- bias_attr=ParamAttr(name=name + '_1_offset'))
+ param_attr=ParamAttr(
+ name=name + '_1_weights', learning_rate=lr_mult),
+ bias_attr=ParamAttr(
+ name=name + '_1_offset', learning_rate=lr_mult))
conv2 = fluid.layers.conv2d(
input=conv1,
filter_size=1,
num_filters=num_out_filter,
act='hard_sigmoid',
- param_attr=ParamAttr(name=name + '_2_weights'),
- bias_attr=ParamAttr(name=name + '_2_offset'))
+ param_attr=ParamAttr(
+ name=name + '_2_weights', learning_rate=lr_mult),
+ bias_attr=ParamAttr(
+ name=name + '_2_offset', learning_rate=lr_mult))
scale = fluid.layers.elementwise_mul(x=input, y=conv2, axis=0)
return scale
@@ -275,8 +300,8 @@ def MobileNetV3_small_x0_75():
return model
-def MobileNetV3_small_x1_0():
- model = MobileNetV3(model_name='small', scale=1.0)
+def MobileNetV3_small_x1_0(**args):
+ model = MobileNetV3(model_name='small', scale=1.0, **args)
return model
@@ -300,8 +325,8 @@ def MobileNetV3_large_x0_75():
return model
-def MobileNetV3_large_x1_0():
- model = MobileNetV3(model_name='large', scale=1.0)
+def MobileNetV3_large_x1_0(**args):
+ model = MobileNetV3(model_name='large', scale=1.0, **args)
return model