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# 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.
import math
import numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddleseg import utils
from paddleseg.cvlibs import manager
MODEL_URLS = {
"MobileNetV2_x0_25":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_25_pretrained.pdparams",
"MobileNetV2_x0_5":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_5_pretrained.pdparams",
"MobileNetV2_x0_75":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_75_pretrained.pdparams",
"MobileNetV2":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_pretrained.pdparams",
"MobileNetV2_x1_5":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x1_5_pretrained.pdparams",
"MobileNetV2_x2_0":
"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x2_0_pretrained.pdparams"
}
__all__ = ["MobileNetV2"]
class ConvBNLayer(nn.Layer):
def __init__(self,
num_channels,
filter_size,
num_filters,
stride,
padding,
channels=None,
num_groups=1,
name=None,
use_cudnn=True):
super(ConvBNLayer, self).__init__()
self._conv = Conv2D(
in_channels=num_channels,
out_channels=num_filters,
kernel_size=filter_size,
stride=stride,
padding=padding,
groups=num_groups,
weight_attr=ParamAttr(name=name + "_weights"),
bias_attr=False)
self._batch_norm = BatchNorm(
num_filters,
param_attr=ParamAttr(name=name + "_bn_scale"),
bias_attr=ParamAttr(name=name + "_bn_offset"),
moving_mean_name=name + "_bn_mean",
moving_variance_name=name + "_bn_variance")
def forward(self, inputs, if_act=True):
y = self._conv(inputs)
y = self._batch_norm(y)
if if_act:
y = F.relu6(y)
return y
class InvertedResidualUnit(nn.Layer):
def __init__(self, num_channels, num_in_filter, num_filters, stride,
filter_size, padding, expansion_factor, name):
super(InvertedResidualUnit, self).__init__()
num_expfilter = int(round(num_in_filter * expansion_factor))
self._expand_conv = ConvBNLayer(
num_channels=num_channels,
num_filters=num_expfilter,
filter_size=1,
stride=1,
padding=0,
num_groups=1,
name=name + "_expand")
self._bottleneck_conv = ConvBNLayer(
num_channels=num_expfilter,
num_filters=num_expfilter,
filter_size=filter_size,
stride=stride,
padding=padding,
num_groups=num_expfilter,
use_cudnn=False,
name=name + "_dwise")
self._linear_conv = ConvBNLayer(
num_channels=num_expfilter,
num_filters=num_filters,
filter_size=1,
stride=1,
padding=0,
num_groups=1,
name=name + "_linear")
def forward(self, inputs, ifshortcut):
y = self._expand_conv(inputs, if_act=True)
y = self._bottleneck_conv(y, if_act=True)
y = self._linear_conv(y, if_act=False)
if ifshortcut:
y = paddle.add(inputs, y)
return y
class InvresiBlocks(nn.Layer):
def __init__(self, in_c, t, c, n, s, name):
super(InvresiBlocks, self).__init__()
self._first_block = InvertedResidualUnit(
num_channels=in_c,
num_in_filter=in_c,
num_filters=c,
stride=s,
filter_size=3,
padding=1,
expansion_factor=t,
name=name + "_1")
self._block_list = []
for i in range(1, n):
block = self.add_sublayer(
name + "_" + str(i + 1),
sublayer=InvertedResidualUnit(
num_channels=c,
num_in_filter=c,
num_filters=c,
stride=1,
filter_size=3,
padding=1,
expansion_factor=t,
name=name + "_" + str(i + 1)))
self._block_list.append(block)
def forward(self, inputs):
y = self._first_block(inputs, ifshortcut=False)
for block in self._block_list:
y = block(y, ifshortcut=True)
return y
class MobileNet(nn.Layer):
def __init__(self,
input_channels=3,
scale=1.0,
pretrained=None,
prefix_name=""):
super(MobileNet, self).__init__()
self.scale = scale
bottleneck_params_list = [
(1, 16, 1, 1),
(6, 24, 2, 2),
(6, 32, 3, 2),
(6, 64, 4, 2),
(6, 96, 3, 1),
(6, 160, 3, 2),
(6, 320, 1, 1),
]
self.conv1 = ConvBNLayer(
num_channels=input_channels,
num_filters=int(32 * scale),
filter_size=3,
stride=2,
padding=1,
name=prefix_name + "conv1_1")
self.block_list = []
i = 1
in_c = int(32 * scale)
for layer_setting in bottleneck_params_list:
t, c, n, s = layer_setting
i += 1
block = self.add_sublayer(
prefix_name + "conv" + str(i),
sublayer=InvresiBlocks(
in_c=in_c,
t=t,
c=int(c * scale),
n=n,
s=s,
name=prefix_name + "conv" + str(i)))
self.block_list.append(block)
in_c = int(c * scale)
self.out_c = int(1280 * scale) if scale > 1.0 else 1280
self.conv9 = ConvBNLayer(
num_channels=in_c,
num_filters=self.out_c,
filter_size=1,
stride=1,
padding=0,
name=prefix_name + "conv9")
self.feat_channels = [int(i * scale) for i in [16, 24, 32, 96, 1280]]
self.pretrained = pretrained
self.init_weight()
def forward(self, inputs):
feat_list = []
y = self.conv1(inputs, if_act=True)
block_index = 0
for block in self.block_list:
y = block(y)
if block_index in [0, 1, 2, 4]:
feat_list.append(y)
block_index += 1
y = self.conv9(y, if_act=True)
feat_list.append(y)
return feat_list
def init_weight(self):
utils.load_pretrained_model(self, self.pretrained)
@manager.BACKBONES.add_component
def MobileNetV2(**kwargs):
model = MobileNet(scale=1.0, **kwargs)
return model
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