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Merge pull request #95 from littletomatodonkey/add_effnet_small 

add effnet_small_eval
pull/96/head
dyning 2020-05-04 09:21:17 +08:00 committed by GitHub
parent f4430af835 cd221a3476
commit eef1e879c8
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4 changed files with 91 additions and 35 deletions
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29
docs/zh_CN/faq.md
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@ -18,3 +18,32 @@
>>
* 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。
>>
* Q: 在评测`EfficientNetB0_small`模型时，为什么最终的精度始终比官网的低0.3%左右？
* A: `EfficientNet`系列的网络在进行resize的时候，是使用`cubic插值方式`(resize参数的interpolation值设置为2)，而其他模型默认情况下为None，因此在训练和评估的时候需要显式地指定resiz的interpolation值。具体地，可以参考以下配置中预处理过程中ResizeImage的参数。
```
VALID:
batch_size: 16
num_workers: 4
file_list: "./dataset/ILSVRC2012/val_list.txt"
data_dir: "./dataset/ILSVRC2012/"
shuffle_seed: 0
transforms:
- DecodeImage:
to_rgb: True
to_np: False
channel_first: False
- ResizeImage:
resize_short: 256
interpolation: 2
- CropImage:
size: 224
- NormalizeImage:
scale: 1.0/255.0
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]
order: ''
- ToCHWImage:
```

20
ppcls/data/imaug/operators.py
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@ -22,7 +22,6 @@ from __future__ import unicode_literals
import six
import math
import random
import functools
import cv2
import numpy as np
@ -38,8 +37,8 @@ class DecodeImage(object):
def __init__(self, to_rgb=True, to_np=False, channel_first=False):
self.to_rgb = to_rgb
self.to_np = to_np #to numpy
self.channel_first = channel_first #only enabled when to_np is True
self.to_np = to_np # to numpy
self.channel_first = channel_first # only enabled when to_np is True
def __call__(self, img):
if six.PY2:
@ -64,7 +63,8 @@ class DecodeImage(object):
class ResizeImage(object):
""" resize image """
def __init__(self, size=None, resize_short=None):
def __init__(self, size=None, resize_short=None, interpolation=-1):
self.interpolation = interpolation if interpolation >= 0 else None
if resize_short is not None and resize_short > 0:
self.resize_short = resize_short
self.w = None
@ -86,8 +86,10 @@ class ResizeImage(object):
else:
w = self.w
h = self.h
return cv2.resize(img, (w, h))
if self.interpolation is None:
return cv2.resize(img, (w, h))
else:
return cv2.resize(img, (w, h), interpolation=self.interpolation)
class CropImage(object):
@ -138,8 +140,7 @@ class RandCropImage(object):
scale_max = min(scale[1], bound)
scale_min = min(scale[0], bound)
target_area = img_w * img_h * random.uniform(\
scale_min, scale_max)
target_area = img_w * img_h * random.uniform(scale_min, scale_max)
target_size = math.sqrt(target_area)
w = int(target_size * w)
h = int(target_size * h)
@ -176,7 +177,8 @@ class NormalizeImage(object):
"""
def __init__(self, scale=None, mean=None, std=None, order='chw'):
if isinstance(scale, str): scale = eval(scale)
if isinstance(scale, str):
scale = eval(scale)
self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
mean = mean if mean is not None else [0.485, 0.456, 0.406]
std = std if std is not None else [0.229, 0.224, 0.225]

2
ppcls/modeling/architectures/__init__.py
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@ -36,7 +36,7 @@ from .densenet import DenseNet121, DenseNet161, DenseNet169, DenseNet201, DenseN
from .squeezenet import SqueezeNet1_0, SqueezeNet1_1
from .darknet import DarkNet53
from .resnext101_wsl import ResNeXt101_32x8d_wsl, ResNeXt101_32x16d_wsl, ResNeXt101_32x32d_wsl, ResNeXt101_32x48d_wsl, Fix_ResNeXt101_32x48d_wsl
from .efficientnet import EfficientNet, EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6, EfficientNetB7
from .efficientnet import EfficientNet, EfficientNetB0, EfficientNetB0_small, EfficientNetB1, EfficientNetB2, EfficientNetB3, EfficientNetB4, EfficientNetB5, EfficientNetB6, EfficientNetB7
from .res2net import Res2Net50_48w_2s, Res2Net50_26w_4s, Res2Net50_14w_8s, Res2Net50_26w_6s, Res2Net50_26w_8s, Res2Net101_26w_4s, Res2Net152_26w_4s
from .res2net_vd import Res2Net50_vd_48w_2s, Res2Net50_vd_26w_4s, Res2Net50_vd_14w_8s, Res2Net50_vd_26w_6s, Res2Net50_vd_26w_8s, Res2Net101_vd_26w_4s, Res2Net152_vd_26w_4s, Res2Net200_vd_26w_4s
from .hrnet import HRNet_W18_C, HRNet_W30_C, HRNet_W32_C, HRNet_W40_C, HRNet_W44_C, HRNet_W48_C, HRNet_W60_C, HRNet_W64_C, SE_HRNet_W18_C, SE_HRNet_W30_C, SE_HRNet_W32_C, SE_HRNet_W40_C, SE_HRNet_W44_C, SE_HRNet_W48_C, SE_HRNet_W60_C, SE_HRNet_W64_C

75
ppcls/modeling/architectures/efficientnet.py
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@ -1,16 +1,16 @@
#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
@ -192,15 +192,17 @@ class EfficientNet():
if is_test:
return inputs
keep_prob = 1.0 - prob
random_tensor = keep_prob + fluid.layers.uniform_random_batch_size_like(
inputs, [-1, 1, 1, 1], min=0., max=1.)
random_tensor = keep_prob + \
fluid.layers.uniform_random_batch_size_like(
inputs, [-1, 1, 1, 1], min=0., max=1.)
binary_tensor = fluid.layers.floor(random_tensor)
output = inputs / keep_prob * binary_tensor
return output
def _expand_conv_norm(self, inputs, block_args, is_test, name=None):
# Expansion phase
oup = block_args.input_filters * block_args.expand_ratio # number of output channels
oup = block_args.input_filters * \
block_args.expand_ratio # number of output channels
if block_args.expand_ratio != 1:
conv = self.conv_bn_layer(
@ -222,7 +224,8 @@ class EfficientNet():
s = block_args.stride
if isinstance(s, list) or isinstance(s, tuple):
s = s[0]
oup = block_args.input_filters * block_args.expand_ratio # number of output channels
oup = block_args.input_filters * \
block_args.expand_ratio # number of output channels
conv = self.conv_bn_layer(
inputs,
@ -285,7 +288,7 @@ class EfficientNet():
name=conv_name,
use_bias=use_bias)
if use_bn == False:
if use_bn is False:
return conv
else:
bn_name = name + bn_name
@ -325,7 +328,8 @@ class EfficientNet():
drop_connect_rate=None,
name=None):
# Expansion and Depthwise Convolution
oup = block_args.input_filters * block_args.expand_ratio # number of output channels
oup = block_args.input_filters * \
block_args.expand_ratio # number of output channels
has_se = self.use_se and (block_args.se_ratio is not None) and (
0 < block_args.se_ratio <= 1)
id_skip = block_args.id_skip # skip connection and drop connect
@ -346,8 +350,11 @@ class EfficientNet():
conv = self._project_conv_norm(conv, block_args, is_test, name)
# Skip connection and drop connect
input_filters, output_filters = block_args.input_filters, block_args.output_filters
if id_skip and block_args.stride == 1 and input_filters == output_filters:
input_filters = block_args.input_filters
output_filters = block_args.output_filters
if id_skip and \
block_args.stride == 1 and \
input_filters == output_filters:
if drop_connect_rate:
conv = self._drop_connect(conv, drop_connect_rate,
self.is_test)
@ -412,7 +419,8 @@ class EfficientNet():
num_repeat=round_repeats(block_args.num_repeat,
self._global_params))
# The first block needs to take care of stride and filter size increase.
# The first block needs to take care of stride,
# and filter size increase.
drop_connect_rate = self._global_params.drop_connect_rate
if drop_connect_rate:
drop_connect_rate *= float(idx) / block_size
@ -440,7 +448,9 @@ class EfficientNet():
class BlockDecoder(object):
""" Block Decoder for readability, straight from the official TensorFlow repository """
"""
Block Decoder, straight from the official TensorFlow repository.
"""
@staticmethod
def _decode_block_string(block_string):
@ -456,9 +466,10 @@ class BlockDecoder(object):
options[key] = value
# Check stride
assert (
('s' in options and len(options['s']) == 1) or
(len(options['s']) == 2 and options['s'][0] == options['s'][1]))
cond_1 = ('s' in options and len(options['s']) == 1)
cond_2 = ((len(options['s']) == 2)
and (options['s'][0] == options['s'][1]))
assert (cond_1 or cond_2)
return BlockArgs(
kernel_size=int(options['k']),
@ -487,10 +498,11 @@ class BlockDecoder(object):
@staticmethod
def decode(string_list):
"""
Decodes a list of string notations to specify blocks inside the network.
Decode a list of string notations to specify blocks in the network.
:param string_list: a list of strings, each string is a notation of block
:return: a list of BlockArgs namedtuples of block args
string_list: list of strings, each string is a notation of block
return
list of BlockArgs namedtuples of block args
"""
assert isinstance(string_list, list)
blocks_args = []
@ -525,6 +537,19 @@ def EfficientNetB0(is_test=False,
return model
def EfficientNetB0_small(is_test=False,
padding_type='DYNAMIC',
override_params=None,
use_se=False):
model = EfficientNet(
name='b0',
is_test=is_test,
padding_type=padding_type,
override_params=override_params,
use_se=use_se)
return model
def EfficientNetB1(is_test=False,
padding_type='SAME',
override_params=None,