forked from PulseFocusPlatform/PulseFocusPlatform
292 lines
8.9 KiB
Python
292 lines
8.9 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# 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.
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
|
|
import collections
|
|
import math
|
|
import re
|
|
|
|
from paddle import fluid
|
|
from paddle.fluid.regularizer import L2Decay
|
|
|
|
from ppdet.core.workspace import register
|
|
|
|
__all__ = ['EfficientNet']
|
|
|
|
GlobalParams = collections.namedtuple('GlobalParams', [
|
|
'batch_norm_momentum', 'batch_norm_epsilon', 'width_coefficient',
|
|
'depth_coefficient', 'depth_divisor'
|
|
])
|
|
|
|
BlockArgs = collections.namedtuple('BlockArgs', [
|
|
'kernel_size', 'num_repeat', 'input_filters', 'output_filters',
|
|
'expand_ratio', 'stride', 'se_ratio'
|
|
])
|
|
|
|
GlobalParams.__new__.__defaults__ = (None, ) * len(GlobalParams._fields)
|
|
BlockArgs.__new__.__defaults__ = (None, ) * len(BlockArgs._fields)
|
|
|
|
|
|
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']),
|
|
se_ratio=float(options['se']) if 'se' in options else None,
|
|
stride=int(options['s'][0]))
|
|
|
|
|
|
def get_model_params(scale):
|
|
block_strings = [
|
|
'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',
|
|
]
|
|
block_args = []
|
|
for block_string in block_strings:
|
|
block_args.append(_decode_block_string(block_string))
|
|
|
|
params_dict = {
|
|
# width, depth
|
|
'b0': (1.0, 1.0),
|
|
'b1': (1.0, 1.1),
|
|
'b2': (1.1, 1.2),
|
|
'b3': (1.2, 1.4),
|
|
'b4': (1.4, 1.8),
|
|
'b5': (1.6, 2.2),
|
|
'b6': (1.8, 2.6),
|
|
'b7': (2.0, 3.1),
|
|
}
|
|
|
|
w, d = params_dict[scale]
|
|
|
|
global_params = GlobalParams(
|
|
batch_norm_momentum=0.99,
|
|
batch_norm_epsilon=1e-3,
|
|
width_coefficient=w,
|
|
depth_coefficient=d,
|
|
depth_divisor=8)
|
|
|
|
return block_args, global_params
|
|
|
|
|
|
def round_filters(filters, global_params):
|
|
multiplier = global_params.width_coefficient
|
|
if not multiplier:
|
|
return filters
|
|
divisor = global_params.depth_divisor
|
|
filters *= multiplier
|
|
min_depth = divisor
|
|
new_filters = max(min_depth,
|
|
int(filters + divisor / 2) // divisor * divisor)
|
|
if new_filters < 0.9 * filters: # prevent rounding by more than 10%
|
|
new_filters += divisor
|
|
return int(new_filters)
|
|
|
|
|
|
def round_repeats(repeats, global_params):
|
|
multiplier = global_params.depth_coefficient
|
|
if not multiplier:
|
|
return repeats
|
|
return int(math.ceil(multiplier * repeats))
|
|
|
|
|
|
def conv2d(inputs,
|
|
num_filters,
|
|
filter_size,
|
|
stride=1,
|
|
padding='SAME',
|
|
groups=1,
|
|
use_bias=False,
|
|
name='conv2d'):
|
|
param_attr = fluid.ParamAttr(name=name + '_weights')
|
|
bias_attr = False
|
|
if use_bias:
|
|
bias_attr = fluid.ParamAttr(
|
|
name=name + '_offset', regularizer=L2Decay(0.))
|
|
feats = fluid.layers.conv2d(
|
|
inputs,
|
|
num_filters,
|
|
filter_size,
|
|
groups=groups,
|
|
name=name,
|
|
stride=stride,
|
|
padding=padding,
|
|
param_attr=param_attr,
|
|
bias_attr=bias_attr)
|
|
return feats
|
|
|
|
|
|
def batch_norm(inputs, momentum, eps, name=None):
|
|
param_attr = fluid.ParamAttr(name=name + '_scale', regularizer=L2Decay(0.))
|
|
bias_attr = fluid.ParamAttr(name=name + '_offset', regularizer=L2Decay(0.))
|
|
return fluid.layers.batch_norm(
|
|
input=inputs,
|
|
momentum=momentum,
|
|
epsilon=eps,
|
|
name=name,
|
|
moving_mean_name=name + '_mean',
|
|
moving_variance_name=name + '_variance',
|
|
param_attr=param_attr,
|
|
bias_attr=bias_attr)
|
|
|
|
|
|
def mb_conv_block(inputs,
|
|
input_filters,
|
|
output_filters,
|
|
expand_ratio,
|
|
kernel_size,
|
|
stride,
|
|
momentum,
|
|
eps,
|
|
se_ratio=None,
|
|
name=None):
|
|
feats = inputs
|
|
num_filters = input_filters * expand_ratio
|
|
|
|
if expand_ratio != 1:
|
|
feats = conv2d(feats, num_filters, 1, name=name + '_expand_conv')
|
|
feats = batch_norm(feats, momentum, eps, name=name + '_bn0')
|
|
feats = fluid.layers.swish(feats)
|
|
|
|
feats = conv2d(
|
|
feats,
|
|
num_filters,
|
|
kernel_size,
|
|
stride,
|
|
groups=num_filters,
|
|
name=name + '_depthwise_conv')
|
|
feats = batch_norm(feats, momentum, eps, name=name + '_bn1')
|
|
feats = fluid.layers.swish(feats)
|
|
|
|
if se_ratio is not None:
|
|
filter_squeezed = max(1, int(input_filters * se_ratio))
|
|
squeezed = fluid.layers.pool2d(
|
|
feats, pool_type='avg', global_pooling=True)
|
|
squeezed = conv2d(
|
|
squeezed,
|
|
filter_squeezed,
|
|
1,
|
|
use_bias=True,
|
|
name=name + '_se_reduce')
|
|
squeezed = fluid.layers.swish(squeezed)
|
|
squeezed = conv2d(
|
|
squeezed, num_filters, 1, use_bias=True, name=name + '_se_expand')
|
|
feats = feats * fluid.layers.sigmoid(squeezed)
|
|
|
|
feats = conv2d(feats, output_filters, 1, name=name + '_project_conv')
|
|
feats = batch_norm(feats, momentum, eps, name=name + '_bn2')
|
|
|
|
if stride == 1 and input_filters == output_filters:
|
|
feats = fluid.layers.elementwise_add(feats, inputs)
|
|
|
|
return feats
|
|
|
|
|
|
@register
|
|
class EfficientNet(object):
|
|
"""
|
|
EfficientNet, see https://arxiv.org/abs/1905.11946
|
|
|
|
Args:
|
|
scale (str): compounding scale factor, 'b0' - 'b7'.
|
|
use_se (bool): use squeeze and excite module.
|
|
norm_type (str): normalization type, 'bn' and 'sync_bn' are supported
|
|
"""
|
|
__shared__ = ['norm_type']
|
|
|
|
def __init__(self, scale='b0', use_se=True, norm_type='bn'):
|
|
assert scale in ['b' + str(i) for i in range(8)], \
|
|
"valid scales are b0 - b7"
|
|
assert norm_type in ['bn', 'sync_bn'], \
|
|
"only 'bn' and 'sync_bn' are supported"
|
|
|
|
super(EfficientNet, self).__init__()
|
|
self.norm_type = norm_type
|
|
self.scale = scale
|
|
self.use_se = use_se
|
|
|
|
def __call__(self, inputs):
|
|
blocks_args, global_params = get_model_params(self.scale)
|
|
momentum = global_params.batch_norm_momentum
|
|
eps = global_params.batch_norm_epsilon
|
|
|
|
num_filters = round_filters(32, global_params)
|
|
feats = conv2d(
|
|
inputs,
|
|
num_filters=num_filters,
|
|
filter_size=3,
|
|
stride=2,
|
|
name='_conv_stem')
|
|
feats = batch_norm(feats, momentum=momentum, eps=eps, name='_bn0')
|
|
feats = fluid.layers.swish(feats)
|
|
|
|
layer_count = 0
|
|
feature_maps = []
|
|
|
|
for b, block_arg in enumerate(blocks_args):
|
|
for r in range(block_arg.num_repeat):
|
|
input_filters = round_filters(block_arg.input_filters,
|
|
global_params)
|
|
output_filters = round_filters(block_arg.output_filters,
|
|
global_params)
|
|
kernel_size = block_arg.kernel_size
|
|
stride = block_arg.stride
|
|
se_ratio = None
|
|
if self.use_se:
|
|
se_ratio = block_arg.se_ratio
|
|
|
|
if r > 0:
|
|
input_filters = output_filters
|
|
stride = 1
|
|
|
|
feats = mb_conv_block(
|
|
feats,
|
|
input_filters,
|
|
output_filters,
|
|
block_arg.expand_ratio,
|
|
kernel_size,
|
|
stride,
|
|
momentum,
|
|
eps,
|
|
se_ratio=se_ratio,
|
|
name='_blocks.{}.'.format(layer_count))
|
|
|
|
layer_count += 1
|
|
|
|
feature_maps.append(feats)
|
|
|
|
return list(feature_maps[i] for i in [2, 4, 6])
|