# copyright (c) 2021 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.
"""
This code is refer from:
https://github.com/liyunsheng13/micronet/blob/main/backbone/micronet.py
https://github.com/liyunsheng13/micronet/blob/main/backbone/activation.py
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.nn as nn
from ppocr.modeling.backbones.det_mobilenet_v3 import make_divisible
M0_cfgs = [
# s, n, c, ks, c1, c2, g1, g2, c3, g3, g4, y1, y2, y3, r
[2, 1, 8, 3, 2, 2, 0, 4, 8, 2, 2, 2, 0, 1, 1],
[2, 1, 12, 3, 2, 2, 0, 8, 12, 4, 4, 2, 2, 1, 1],
[2, 1, 16, 5, 2, 2, 0, 12, 16, 4, 4, 2, 2, 1, 1],
[1, 1, 32, 5, 1, 4, 4, 4, 32, 4, 4, 2, 2, 1, 1],
[2, 1, 64, 5, 1, 4, 8, 8, 64, 8, 8, 2, 2, 1, 1],
[1, 1, 96, 3, 1, 4, 8, 8, 96, 8, 8, 2, 2, 1, 2],
[1, 1, 384, 3, 1, 4, 12, 12, 0, 0, 0, 2, 2, 1, 2],
]
M1_cfgs = [
# s, n, c, ks, c1, c2, g1, g2, c3, g3, g4
[2, 1, 8, 3, 2, 2, 0, 6, 8, 2, 2, 2, 0, 1, 1],
[2, 1, 16, 3, 2, 2, 0, 8, 16, 4, 4, 2, 2, 1, 1],
[2, 1, 16, 5, 2, 2, 0, 16, 16, 4, 4, 2, 2, 1, 1],
[1, 1, 32, 5, 1, 6, 4, 4, 32, 4, 4, 2, 2, 1, 1],
[2, 1, 64, 5, 1, 6, 8, 8, 64, 8, 8, 2, 2, 1, 1],
[1, 1, 96, 3, 1, 6, 8, 8, 96, 8, 8, 2, 2, 1, 2],
[1, 1, 576, 3, 1, 6, 12, 12, 0, 0, 0, 2, 2, 1, 2],
]
M2_cfgs = [
# s, n, c, ks, c1, c2, g1, g2, c3, g3, g4
[2, 1, 12, 3, 2, 2, 0, 8, 12, 4, 4, 2, 0, 1, 1],
[2, 1, 16, 3, 2, 2, 0, 12, 16, 4, 4, 2, 2, 1, 1],
[1, 1, 24, 3, 2, 2, 0, 16, 24, 4, 4, 2, 2, 1, 1],
[2, 1, 32, 5, 1, 6, 6, 6, 32, 4, 4, 2, 2, 1, 1],
[1, 1, 32, 5, 1, 6, 8, 8, 32, 4, 4, 2, 2, 1, 2],
[1, 1, 64, 5, 1, 6, 8, 8, 64, 8, 8, 2, 2, 1, 2],
[2, 1, 96, 5, 1, 6, 8, 8, 96, 8, 8, 2, 2, 1, 2],
[1, 1, 128, 3, 1, 6, 12, 12, 128, 8, 8, 2, 2, 1, 2],
[1, 1, 768, 3, 1, 6, 16, 16, 0, 0, 0, 2, 2, 1, 2],
]
M3_cfgs = [
# s, n, c, ks, c1, c2, g1, g2, c3, g3, g4
[2, 1, 16, 3, 2, 2, 0, 12, 16, 4, 4, 0, 2, 0, 1],
[2, 1, 24, 3, 2, 2, 0, 16, 24, 4, 4, 0, 2, 0, 1],
[1, 1, 24, 3, 2, 2, 0, 24, 24, 4, 4, 0, 2, 0, 1],
[2, 1, 32, 5, 1, 6, 6, 6, 32, 4, 4, 0, 2, 0, 1],
[1, 1, 32, 5, 1, 6, 8, 8, 32, 4, 4, 0, 2, 0, 2],
[1, 1, 64, 5, 1, 6, 8, 8, 48, 8, 8, 0, 2, 0, 2],
[1, 1, 80, 5, 1, 6, 8, 8, 80, 8, 8, 0, 2, 0, 2],
[1, 1, 80, 5, 1, 6, 10, 10, 80, 8, 8, 0, 2, 0, 2],
[1, 1, 120, 5, 1, 6, 10, 10, 120, 10, 10, 0, 2, 0, 2],
[1, 1, 120, 5, 1, 6, 12, 12, 120, 10, 10, 0, 2, 0, 2],
[1, 1, 144, 3, 1, 6, 12, 12, 144, 12, 12, 0, 2, 0, 2],
[1, 1, 432, 3, 1, 3, 12, 12, 0, 0, 0, 0, 2, 0, 2],
]
def get_micronet_config(mode):
return eval(mode + '_cfgs')
class MaxGroupPooling(nn.Layer):
def __init__(self, channel_per_group=2):
super(MaxGroupPooling, self).__init__()
self.channel_per_group = channel_per_group
def forward(self, x):
if self.channel_per_group == 1:
return x
# max op
b, c, h, w = x.shape
# reshape
y = paddle.reshape(x, [b, c // self.channel_per_group, -1, h, w])
out = paddle.max(y, axis=2)
return out
class SpatialSepConvSF(nn.Layer):
def __init__(self, inp, oups, kernel_size, stride):
super(SpatialSepConvSF, self).__init__()
oup1, oup2 = oups
self.conv = nn.Sequential(
nn.Conv2D(
inp,
oup1, (kernel_size, 1), (stride, 1), (kernel_size // 2, 0),
bias_attr=False,
groups=1),
nn.BatchNorm2D(oup1),
nn.Conv2D(
oup1,
oup1 * oup2, (1, kernel_size), (1, stride),
(0, kernel_size // 2),
bias_attr=False,
groups=oup1),
nn.BatchNorm2D(oup1 * oup2),
ChannelShuffle(oup1), )
def forward(self, x):
out = self.conv(x)
return out
class ChannelShuffle(nn.Layer):
def __init__(self, groups):
super(ChannelShuffle, self).__init__()
self.groups = groups
def forward(self, x):
b, c, h, w = x.shape
channels_per_group = c // self.groups
# reshape
x = paddle.reshape(x, [b, self.groups, channels_per_group, h, w])
x = paddle.transpose(x, (0, 2, 1, 3, 4))
out = paddle.reshape(x, [b, -1, h, w])
return out
class StemLayer(nn.Layer):
def __init__(self, inp, oup, stride, groups=(4, 4)):
super(StemLayer, self).__init__()
g1, g2 = groups
self.stem = nn.Sequential(
SpatialSepConvSF(inp, groups, 3, stride),
MaxGroupPooling(2) if g1 * g2 == 2 * oup else nn.ReLU6())
def forward(self, x):
out = self.stem(x)
return out
class DepthSpatialSepConv(nn.Layer):
def __init__(self, inp, expand, kernel_size, stride):
super(DepthSpatialSepConv, self).__init__()
exp1, exp2 = expand
hidden_dim = inp * exp1
oup = inp * exp1 * exp2
self.conv = nn.Sequential(
nn.Conv2D(
inp,
inp * exp1, (kernel_size, 1), (stride, 1),
(kernel_size // 2, 0),
bias_attr=False,
groups=inp),
nn.BatchNorm2D(inp * exp1),
nn.Conv2D(
hidden_dim,
oup, (1, kernel_size),
1, (0, kernel_size // 2),
bias_attr=False,
groups=hidden_dim),
nn.BatchNorm2D(oup))
def forward(self, x):
x = self.conv(x)
return x
class GroupConv(nn.Layer):
def __init__(self, inp, oup, groups=2):
super(GroupConv, self).__init__()
self.inp = inp
self.oup = oup
self.groups = groups
self.conv = nn.Sequential(
nn.Conv2D(
inp, oup, 1, 1, 0, bias_attr=False, groups=self.groups[0]),
nn.BatchNorm2D(oup))
def forward(self, x):
x = self.conv(x)
return x
class DepthConv(nn.Layer):
def __init__(self, inp, oup, kernel_size, stride):
super(DepthConv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2D(
inp,
oup,
kernel_size,
stride,
kernel_size // 2,
bias_attr=False,
groups=inp),
nn.BatchNorm2D(oup))
def forward(self, x):
out = self.conv(x)
return out
class DYShiftMax(nn.Layer):
def __init__(self,
inp,
oup,
reduction=4,
act_max=1.0,
act_relu=True,
init_a=[0.0, 0.0],
init_b=[0.0, 0.0],
relu_before_pool=False,
g=None,
expansion=False):
super(DYShiftMax, self).__init__()
self.oup = oup
self.act_max = act_max * 2
self.act_relu = act_relu
self.avg_pool = nn.Sequential(nn.ReLU() if relu_before_pool == True else
nn.Sequential(), nn.AdaptiveAvgPool2D(1))
self.exp = 4 if act_relu else 2
self.init_a = init_a
self.init_b = init_b
# determine squeeze
squeeze = make_divisible(inp // reduction, 4)
if squeeze < 4:
squeeze = 4
self.fc = nn.Sequential(
nn.Linear(inp, squeeze),
nn.ReLU(), nn.Linear(squeeze, oup * self.exp), nn.Hardsigmoid())
if g is None:
g = 1
self.g = g[1]
if self.g != 1 and expansion:
self.g = inp // self.g
self.gc = inp // self.g
index = paddle.to_tensor([range(inp)])
index = paddle.reshape(index, [1, inp, 1, 1])
index = paddle.reshape(index, [1, self.g, self.gc, 1, 1])
indexgs = paddle.split(index, [1, self.g - 1], axis=1)
indexgs = paddle.concat((indexgs[1], indexgs[0]), axis=1)
indexs = paddle.split(indexgs, [1, self.gc - 1], axis=2)
indexs = paddle.concat((indexs[1], indexs[0]), axis=2)
self.index = paddle.reshape(indexs, [inp])
self.expansion = expansion
def forward(self, x):
x_in = x
x_out = x
b, c, _, _ = x_in.shape
y = self.avg_pool(x_in)
y = paddle.reshape(y, [b, c])
y = self.fc(y)
y = paddle.reshape(y, [b, self.oup * self.exp, 1, 1])
y = (y - 0.5) * self.act_max
n2, c2, h2, w2 = x_out.shape
x2 = paddle.to_tensor(x_out.numpy()[:, self.index.numpy(), :, :])
if self.exp == 4:
temp = y.shape
a1, b1, a2, b2 = paddle.split(y, temp[1] // self.oup, axis=1)
a1 = a1 + self.init_a[0]
a2 = a2 + self.init_a[1]
b1 = b1 + self.init_b[0]
b2 = b2 + self.init_b[1]
z1 = x_out * a1 + x2 * b1
z2 = x_out * a2 + x2 * b2
out = paddle.maximum(z1, z2)
elif self.exp == 2:
temp = y.shape
a1, b1 = paddle.split(y, temp[1] // self.oup, axis=1)
a1 = a1 + self.init_a[0]
b1 = b1 + self.init_b[0]
out = x_out * a1 + x2 * b1
return out
class DYMicroBlock(nn.Layer):
def __init__(self,
inp,
oup,
kernel_size=3,
stride=1,
ch_exp=(2, 2),
ch_per_group=4,
groups_1x1=(1, 1),
depthsep=True,
shuffle=False,
activation_cfg=None):
super(DYMicroBlock, self).__init__()
self.identity = stride == 1 and inp == oup
y1, y2, y3 = activation_cfg['dy']
act_reduction = 8 * activation_cfg['ratio']
init_a = activation_cfg['init_a']
init_b = activation_cfg['init_b']
t1 = ch_exp
gs1 = ch_per_group
hidden_fft, g1, g2 = groups_1x1
hidden_dim2 = inp * t1[0] * t1[1]
if gs1[0] == 0:
self.layers = nn.Sequential(
DepthSpatialSepConv(inp, t1, kernel_size, stride),
DYShiftMax(
hidden_dim2,
hidden_dim2,
act_max=2.0,
act_relu=True if y2 == 2 else False,
init_a=init_a,
reduction=act_reduction,
init_b=init_b,
g=gs1,
expansion=False) if y2 > 0 else nn.ReLU6(),
ChannelShuffle(gs1[1]) if shuffle else nn.Sequential(),
ChannelShuffle(hidden_dim2 // 2)
if shuffle and y2 != 0 else nn.Sequential(),
GroupConv(hidden_dim2, oup, (g1, g2)),
DYShiftMax(
oup,
oup,
act_max=2.0,
act_relu=False,
init_a=[1.0, 0.0],
reduction=act_reduction // 2,
init_b=[0.0, 0.0],
g=(g1, g2),
expansion=False) if y3 > 0 else nn.Sequential(),
ChannelShuffle(g2) if shuffle else nn.Sequential(),
ChannelShuffle(oup // 2)
if shuffle and oup % 2 == 0 and y3 != 0 else nn.Sequential(), )
elif g2 == 0:
self.layers = nn.Sequential(
GroupConv(inp, hidden_dim2, gs1),
DYShiftMax(
hidden_dim2,
hidden_dim2,
act_max=2.0,
act_relu=False,
init_a=[1.0, 0.0],
reduction=act_reduction,
init_b=[0.0, 0.0],
g=gs1,
expansion=False) if y3 > 0 else nn.Sequential(), )
else:
self.layers = nn.Sequential(
GroupConv(inp, hidden_dim2, gs1),
DYShiftMax(
hidden_dim2,
hidden_dim2,
act_max=2.0,
act_relu=True if y1 == 2 else False,
init_a=init_a,
reduction=act_reduction,
init_b=init_b,
g=gs1,
expansion=False) if y1 > 0 else nn.ReLU6(),
ChannelShuffle(gs1[1]) if shuffle else nn.Sequential(),
DepthSpatialSepConv(hidden_dim2, (1, 1), kernel_size, stride)
if depthsep else
DepthConv(hidden_dim2, hidden_dim2, kernel_size, stride),
nn.Sequential(),
DYShiftMax(
hidden_dim2,
hidden_dim2,
act_max=2.0,
act_relu=True if y2 == 2 else False,
init_a=init_a,
reduction=act_reduction,
init_b=init_b,
g=gs1,
expansion=True) if y2 > 0 else nn.ReLU6(),
ChannelShuffle(hidden_dim2 // 4)
if shuffle and y1 != 0 and y2 != 0 else nn.Sequential()
if y1 == 0 and y2 == 0 else ChannelShuffle(hidden_dim2 // 2),
GroupConv(hidden_dim2, oup, (g1, g2)),
DYShiftMax(
oup,
oup,
act_max=2.0,
act_relu=False,
init_a=[1.0, 0.0],
reduction=act_reduction // 2
if oup < hidden_dim2 else act_reduction,
init_b=[0.0, 0.0],
g=(g1, g2),
expansion=False) if y3 > 0 else nn.Sequential(),
ChannelShuffle(g2) if shuffle else nn.Sequential(),
ChannelShuffle(oup // 2)
if shuffle and y3 != 0 else nn.Sequential(), )
def forward(self, x):
identity = x
out = self.layers(x)
if self.identity:
out = out + identity
return out
class MicroNet(nn.Layer):
"""
the MicroNet backbone network for recognition module.
Args:
mode(str): {'M0', 'M1', 'M2', 'M3'}
Four models are proposed based on four different computational costs (4M, 6M, 12M, 21M MAdds)
Default: 'M3'.
"""
def __init__(self, mode='M3', **kwargs):
super(MicroNet, self).__init__()
self.cfgs = get_micronet_config(mode)
activation_cfg = {}
if mode == 'M0':
input_channel = 4
stem_groups = 2, 2
out_ch = 384
activation_cfg['init_a'] = 1.0, 1.0
activation_cfg['init_b'] = 0.0, 0.0
elif mode == 'M1':
input_channel = 6
stem_groups = 3, 2
out_ch = 576
activation_cfg['init_a'] = 1.0, 1.0
activation_cfg['init_b'] = 0.0, 0.0
elif mode == 'M2':
input_channel = 8
stem_groups = 4, 2
out_ch = 768
activation_cfg['init_a'] = 1.0, 1.0
activation_cfg['init_b'] = 0.0, 0.0
elif mode == 'M3':
input_channel = 12
stem_groups = 4, 3
out_ch = 432
activation_cfg['init_a'] = 1.0, 0.5
activation_cfg['init_b'] = 0.0, 0.5
else:
raise NotImplementedError("mode[" + mode +
"_model] is not implemented!")
layers = [StemLayer(3, input_channel, stride=2, groups=stem_groups)]
for idx, val in enumerate(self.cfgs):
s, n, c, ks, c1, c2, g1, g2, c3, g3, g4, y1, y2, y3, r = val
t1 = (c1, c2)
gs1 = (g1, g2)
gs2 = (c3, g3, g4)
activation_cfg['dy'] = [y1, y2, y3]
activation_cfg['ratio'] = r
output_channel = c
layers.append(
DYMicroBlock(
input_channel,
output_channel,
kernel_size=ks,
stride=s,
ch_exp=t1,
ch_per_group=gs1,
groups_1x1=gs2,
depthsep=True,
shuffle=True,
activation_cfg=activation_cfg, ))
input_channel = output_channel
for i in range(1, n):
layers.append(
DYMicroBlock(
input_channel,
output_channel,
kernel_size=ks,
stride=1,
ch_exp=t1,
ch_per_group=gs1,
groups_1x1=gs2,
depthsep=True,
shuffle=True,
activation_cfg=activation_cfg, ))
input_channel = output_channel
self.features = nn.Sequential(*layers)
self.pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
self.out_channels = make_divisible(out_ch)
def forward(self, x):
x = self.features(x)
x = self.pool(x)
return x