mirror of https://github.com/alibaba/EasyCV.git
130 lines
5.1 KiB
Python
130 lines
5.1 KiB
Python
# Modified from https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/ppocr/modeling/backbones/rec_mobilenet_v3.py
|
|
import torch.nn as nn
|
|
|
|
from easycv.models.registry import BACKBONES
|
|
from .det_mobilenet_v3 import (Activation, ConvBNLayer, ResidualUnit,
|
|
make_divisible)
|
|
|
|
|
|
@BACKBONES.register_module()
|
|
class OCRRecMobileNetV3(nn.Module):
|
|
"""mobilenetv3 backbone for ocr recognition
|
|
"""
|
|
|
|
def __init__(self,
|
|
in_channels=3,
|
|
model_name='small',
|
|
scale=0.5,
|
|
large_stride=None,
|
|
small_stride=None,
|
|
**kwargs):
|
|
super(OCRRecMobileNetV3, self).__init__()
|
|
if small_stride is None:
|
|
small_stride = [2, 2, 2, 2]
|
|
if large_stride is None:
|
|
large_stride = [1, 2, 2, 2]
|
|
|
|
assert isinstance(large_stride, list), 'large_stride type must ' \
|
|
'be list but got {}'.format(type(large_stride))
|
|
assert isinstance(small_stride, list), 'small_stride type must ' \
|
|
'be list but got {}'.format(type(small_stride))
|
|
assert len(large_stride) == 4, 'large_stride length must be ' \
|
|
'4 but got {}'.format(len(large_stride))
|
|
assert len(small_stride) == 4, 'small_stride length must be ' \
|
|
'4 but got {}'.format(len(small_stride))
|
|
|
|
if model_name == 'large':
|
|
cfg = [
|
|
# k, exp, c, se, nl, s,
|
|
[3, 16, 16, False, 'relu', large_stride[0]],
|
|
[3, 64, 24, False, 'relu', (large_stride[1], 1)],
|
|
[3, 72, 24, False, 'relu', 1],
|
|
[5, 72, 40, True, 'relu', (large_stride[2], 1)],
|
|
[5, 120, 40, True, 'relu', 1],
|
|
[5, 120, 40, True, 'relu', 1],
|
|
[3, 240, 80, False, 'hard_swish', 1],
|
|
[3, 200, 80, False, 'hard_swish', 1],
|
|
[3, 184, 80, False, 'hard_swish', 1],
|
|
[3, 184, 80, False, 'hard_swish', 1],
|
|
[3, 480, 112, True, 'hard_swish', 1],
|
|
[3, 672, 112, True, 'hard_swish', 1],
|
|
[5, 672, 160, True, 'hard_swish', (large_stride[3], 1)],
|
|
[5, 960, 160, True, 'hard_swish', 1],
|
|
[5, 960, 160, True, 'hard_swish', 1],
|
|
]
|
|
cls_ch_squeeze = 960
|
|
elif model_name == 'small':
|
|
cfg = [
|
|
# k, exp, c, se, nl, s,
|
|
[3, 16, 16, True, 'relu', (small_stride[0], 1)],
|
|
[3, 72, 24, False, 'relu', (small_stride[1], 1)],
|
|
[3, 88, 24, False, 'relu', 1],
|
|
[5, 96, 40, True, 'hard_swish', (small_stride[2], 1)],
|
|
[5, 240, 40, True, 'hard_swish', 1],
|
|
[5, 240, 40, True, 'hard_swish', 1],
|
|
[5, 120, 48, True, 'hard_swish', 1],
|
|
[5, 144, 48, True, 'hard_swish', 1],
|
|
[5, 288, 96, True, 'hard_swish', (small_stride[3], 1)],
|
|
[5, 576, 96, True, 'hard_swish', 1],
|
|
[5, 576, 96, True, 'hard_swish', 1],
|
|
]
|
|
cls_ch_squeeze = 576
|
|
else:
|
|
raise NotImplementedError('mode[' + model_name +
|
|
'_model] is not implemented!')
|
|
|
|
supported_scale = [0.35, 0.5, 0.75, 1.0, 1.25]
|
|
assert scale in supported_scale, \
|
|
'supported scales are {} but input scale is {}'.format(supported_scale, scale)
|
|
|
|
inplanes = 16
|
|
# conv1
|
|
self.conv1 = ConvBNLayer(
|
|
in_channels=in_channels,
|
|
out_channels=make_divisible(inplanes * scale),
|
|
kernel_size=3,
|
|
stride=2,
|
|
padding=1,
|
|
groups=1,
|
|
if_act=True,
|
|
act='hard_swish',
|
|
name='conv1')
|
|
i = 0
|
|
block_list = []
|
|
inplanes = make_divisible(inplanes * scale)
|
|
for (k, exp, c, se, nl, s) in cfg:
|
|
block_list.append(
|
|
ResidualUnit(
|
|
in_channels=inplanes,
|
|
mid_channels=make_divisible(scale * exp),
|
|
out_channels=make_divisible(scale * c),
|
|
kernel_size=k,
|
|
stride=s,
|
|
use_se=se,
|
|
act=nl,
|
|
name='conv' + str(i + 2)))
|
|
inplanes = make_divisible(scale * c)
|
|
i += 1
|
|
self.blocks = nn.Sequential(*block_list)
|
|
|
|
self.conv2 = ConvBNLayer(
|
|
in_channels=inplanes,
|
|
out_channels=make_divisible(scale * cls_ch_squeeze),
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
groups=1,
|
|
if_act=True,
|
|
act='hard_swish',
|
|
name='conv_last')
|
|
|
|
self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
|
|
self.out_channels = make_divisible(scale * cls_ch_squeeze)
|
|
|
|
def forward(self, x):
|
|
x = self.conv1(x)
|
|
x = self.blocks(x)
|
|
x = self.conv2(x)
|
|
x = self.pool(x)
|
|
return x
|