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# copyright (c) 2022 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
#
# 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.
"""
Code is refer from:
https://github.com/RuijieJ/pren/blob/main/Nets/EfficientNet.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import re
import collections
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
__all__ = ['EfficientNetb3']
GlobalParams = collections.namedtuple('GlobalParams', [
'batch_norm_momentum', 'batch_norm_epsilon', 'dropout_rate', 'num_classes',
'width_coefficient', 'depth_coefficient', 'depth_divisor', 'min_depth',
'drop_connect_rate', 'image_size'
])
BlockArgs = collections.namedtuple('BlockArgs', [
'kernel_size', 'num_repeat', 'input_filters', 'output_filters',
'expand_ratio', 'id_skip', 'stride', 'se_ratio'
])
class BlockDecoder:
@staticmethod
def _decode_block_string(block_string):
assert isinstance(block_string, str)
ops = block_string.split('_')
options = {}
for op in ops:
splits = re.split(r'(\d.*)', op)
if len(splits) >= 2:
key, value = splits[:2]
options[key] = value
assert (('s' in options and len(options['s']) == 1) or
(len(options['s']) == 2 and options['s'][0] == options['s'][1]))
return BlockArgs(
kernel_size=int(options['k']),
num_repeat=int(options['r']),
input_filters=int(options['i']),
output_filters=int(options['o']),
expand_ratio=int(options['e']),
id_skip=('noskip' not in block_string),
se_ratio=float(options['se']) if 'se' in options else None,
stride=[int(options['s'][0])])
@staticmethod
def decode(string_list):
assert isinstance(string_list, list)
blocks_args = []
for block_string in string_list:
blocks_args.append(BlockDecoder._decode_block_string(block_string))
return blocks_args
def efficientnet(width_coefficient=None,
depth_coefficient=None,
dropout_rate=0.2,
drop_connect_rate=0.2,
image_size=None,
num_classes=1000):
blocks_args = [
'r1_k3_s11_e1_i32_o16_se0.25',
'r2_k3_s22_e6_i16_o24_se0.25',
'r2_k5_s22_e6_i24_o40_se0.25',
'r3_k3_s22_e6_i40_o80_se0.25',
'r3_k5_s11_e6_i80_o112_se0.25',
'r4_k5_s22_e6_i112_o192_se0.25',
'r1_k3_s11_e6_i192_o320_se0.25',
]
blocks_args = BlockDecoder.decode(blocks_args)
global_params = GlobalParams(
batch_norm_momentum=0.99,
batch_norm_epsilon=1e-3,
dropout_rate=dropout_rate,
drop_connect_rate=drop_connect_rate,
num_classes=num_classes,
width_coefficient=width_coefficient,
depth_coefficient=depth_coefficient,
depth_divisor=8,
min_depth=None,
image_size=image_size, )
return blocks_args, global_params
class EffUtils:
@staticmethod
def round_filters(filters, global_params):
""" Calculate and round number of filters based on depth multiplier. """
multiplier = global_params.width_coefficient
if not multiplier:
return filters
divisor = global_params.depth_divisor
min_depth = global_params.min_depth
filters *= multiplier
min_depth = min_depth or divisor
new_filters = max(min_depth,
int(filters + divisor / 2) // divisor * divisor)
if new_filters < 0.9 * filters:
new_filters += divisor
return int(new_filters)
@staticmethod
def round_repeats(repeats, global_params):
""" Round number of filters based on depth multiplier. """
multiplier = global_params.depth_coefficient
if not multiplier:
return repeats
return int(math.ceil(multiplier * repeats))
class MbConvBlock(nn.Layer):
def __init__(self, block_args):
super(MbConvBlock, self).__init__()
self._block_args = block_args
self.has_se = (self._block_args.se_ratio is not None) and \
(0 < self._block_args.se_ratio <= 1)
self.id_skip = block_args.id_skip
# expansion phase
self.inp = self._block_args.input_filters
oup = self._block_args.input_filters * self._block_args.expand_ratio
if self._block_args.expand_ratio != 1:
self._expand_conv = nn.Conv2D(self.inp, oup, 1, bias_attr=False)
self._bn0 = nn.BatchNorm(oup)
# depthwise conv phase
k = self._block_args.kernel_size
s = self._block_args.stride
if isinstance(s, list):
s = s[0]
self._depthwise_conv = nn.Conv2D(
oup,
oup,
groups=oup,
kernel_size=k,
stride=s,
padding='same',
bias_attr=False)
self._bn1 = nn.BatchNorm(oup)
# squeeze and excitation layer, if desired
if self.has_se:
num_squeezed_channels = max(1,
int(self._block_args.input_filters *
self._block_args.se_ratio))
self._se_reduce = nn.Conv2D(oup, num_squeezed_channels, 1)
self._se_expand = nn.Conv2D(num_squeezed_channels, oup, 1)
# output phase and some util class
self.final_oup = self._block_args.output_filters
self._project_conv = nn.Conv2D(oup, self.final_oup, 1, bias_attr=False)
self._bn2 = nn.BatchNorm(self.final_oup)
self._swish = nn.Swish()
def _drop_connect(self, inputs, p, training):
if not training:
return inputs
batch_size = inputs.shape[0]
keep_prob = 1 - p
random_tensor = keep_prob
random_tensor += paddle.rand([batch_size, 1, 1, 1], dtype=inputs.dtype)
random_tensor = paddle.to_tensor(random_tensor, place=inputs.place)
binary_tensor = paddle.floor(random_tensor)
output = inputs / keep_prob * binary_tensor
return output
def forward(self, inputs, drop_connect_rate=None):
# expansion and depthwise conv
x = inputs
if self._block_args.expand_ratio != 1:
x = self._swish(self._bn0(self._expand_conv(inputs)))
x = self._swish(self._bn1(self._depthwise_conv(x)))
# squeeze and excitation
if self.has_se:
x_squeezed = F.adaptive_avg_pool2d(x, 1)
x_squeezed = self._se_expand(
self._swish(self._se_reduce(x_squeezed)))
x = F.sigmoid(x_squeezed) * x
x = self._bn2(self._project_conv(x))
# skip conntection and drop connect
if self.id_skip and self._block_args.stride == 1 and \
self.inp == self.final_oup:
if drop_connect_rate:
x = self._drop_connect(
x, p=drop_connect_rate, training=self.training)
x = x + inputs
return x
class EfficientNetb3_PREN(nn.Layer):
def __init__(self, in_channels):
super(EfficientNetb3_PREN, self).__init__()
"""
the fllowing are efficientnetb3's superparams,
they means efficientnetb3 network's width, depth, resolution and
dropout respectively, to fit for text recognition task, the resolution
here is changed from 300 to 64.
"""
w, d, s, p = 1.2, 1.4, 64, 0.3
self._blocks_args, self._global_params = efficientnet(
width_coefficient=w,
depth_coefficient=d,
dropout_rate=p,
image_size=s)
self.out_channels = []
# stem
out_channels = EffUtils.round_filters(32, self._global_params)
self._conv_stem = nn.Conv2D(
in_channels, out_channels, 3, 2, padding='same', bias_attr=False)
self._bn0 = nn.BatchNorm(out_channels)
# build blocks
self._blocks = []
# to extract three feature maps for fpn based on efficientnetb3 backbone
self._concerned_block_idxes = [7, 17, 25]
_concerned_idx = 0
for i, block_args in enumerate(self._blocks_args):
block_args = block_args._replace(
input_filters=EffUtils.round_filters(block_args.input_filters,
self._global_params),
output_filters=EffUtils.round_filters(block_args.output_filters,
self._global_params),
num_repeat=EffUtils.round_repeats(block_args.num_repeat,
self._global_params))
self._blocks.append(
self.add_sublayer(f"{i}-0", MbConvBlock(block_args)))
_concerned_idx += 1
if _concerned_idx in self._concerned_block_idxes:
self.out_channels.append(block_args.output_filters)
if block_args.num_repeat > 1:
block_args = block_args._replace(
input_filters=block_args.output_filters, stride=1)
for j in range(block_args.num_repeat - 1):
self._blocks.append(
self.add_sublayer(f'{i}-{j+1}', MbConvBlock(block_args)))
_concerned_idx += 1
if _concerned_idx in self._concerned_block_idxes:
self.out_channels.append(block_args.output_filters)
self._swish = nn.Swish()
def forward(self, inputs):
outs = []
x = self._swish(self._bn0(self._conv_stem(inputs)))
for idx, block in enumerate(self._blocks):
drop_connect_rate = self._global_params.drop_connect_rate
if drop_connect_rate:
drop_connect_rate *= float(idx) / len(self._blocks)
x = block(x, drop_connect_rate=drop_connect_rate)
if idx in self._concerned_block_idxes:
outs.append(x)
return outs
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