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- # copyright (c) 2020 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 paddle
- import paddle.nn as nn
- from paddle import ParamAttr
- from paddle.regularizer import L2Decay
- from paddle.nn import AdaptiveAvgPool2D, BatchNorm, Conv2D, Dropout, Linear
-
- from paddleseg.cvlibs import manager
- from paddleseg.utils import utils, logger
- from paddleseg.models import layers
-
- __all__ = [
- "MobileNetV3_small_x0_35", "MobileNetV3_small_x0_5",
- "MobileNetV3_small_x0_75", "MobileNetV3_small_x1_0",
- "MobileNetV3_small_x1_25", "MobileNetV3_large_x0_35",
- "MobileNetV3_large_x0_5", "MobileNetV3_large_x0_75",
- "MobileNetV3_large_x1_0", "MobileNetV3_large_x1_25"
- ]
-
- MODEL_STAGES_PATTERN = {
- "MobileNetV3_small": ["blocks[0]", "blocks[2]", "blocks[7]", "blocks[10]"],
- "MobileNetV3_large":
- ["blocks[0]", "blocks[2]", "blocks[5]", "blocks[11]", "blocks[14]"]
- }
-
- # "large", "small" is just for MobinetV3_large, MobileNetV3_small respectively.
- # The type of "large" or "small" config is a list. Each element(list) represents a depthwise block, which is composed of k, exp, se, act, s.
- # k: kernel_size
- # exp: middle channel number in depthwise block
- # c: output channel number in depthwise block
- # se: whether to use SE block
- # act: which activation to use
- # s: stride in depthwise block
- # d: dilation rate in depthwise block
- NET_CONFIG = {
- "large": [
- # k, exp, c, se, act, s
- [3, 16, 16, False, "relu", 1],
- [3, 64, 24, False, "relu", 2],
- [3, 72, 24, False, "relu", 1], # x4
- [5, 72, 40, True, "relu", 2],
- [5, 120, 40, True, "relu", 1],
- [5, 120, 40, True, "relu", 1], # x8
- [3, 240, 80, False, "hardswish", 2],
- [3, 200, 80, False, "hardswish", 1],
- [3, 184, 80, False, "hardswish", 1],
- [3, 184, 80, False, "hardswish", 1],
- [3, 480, 112, True, "hardswish", 1],
- [3, 672, 112, True, "hardswish", 1], # x16
- [5, 672, 160, True, "hardswish", 2],
- [5, 960, 160, True, "hardswish", 1],
- [5, 960, 160, True, "hardswish", 1], # x32
- ],
- "small": [
- # k, exp, c, se, act, s
- [3, 16, 16, True, "relu", 2],
- [3, 72, 24, False, "relu", 2],
- [3, 88, 24, False, "relu", 1],
- [5, 96, 40, True, "hardswish", 2],
- [5, 240, 40, True, "hardswish", 1],
- [5, 240, 40, True, "hardswish", 1],
- [5, 120, 48, True, "hardswish", 1],
- [5, 144, 48, True, "hardswish", 1],
- [5, 288, 96, True, "hardswish", 2],
- [5, 576, 96, True, "hardswish", 1],
- [5, 576, 96, True, "hardswish", 1],
- ],
- "large_os8": [
- # k, exp, c, se, act, s, {d}
- [3, 16, 16, False, "relu", 1],
- [3, 64, 24, False, "relu", 2],
- [3, 72, 24, False, "relu", 1], # x4
- [5, 72, 40, True, "relu", 2],
- [5, 120, 40, True, "relu", 1],
- [5, 120, 40, True, "relu", 1], # x8
- [3, 240, 80, False, "hardswish", 1],
- [3, 200, 80, False, "hardswish", 1, 2],
- [3, 184, 80, False, "hardswish", 1, 2],
- [3, 184, 80, False, "hardswish", 1, 2],
- [3, 480, 112, True, "hardswish", 1, 2],
- [3, 672, 112, True, "hardswish", 1, 2],
- [5, 672, 160, True, "hardswish", 1, 2],
- [5, 960, 160, True, "hardswish", 1, 4],
- [5, 960, 160, True, "hardswish", 1, 4],
- ],
- "small_os8": [
- # k, exp, c, se, act, s, {d}
- [3, 16, 16, True, "relu", 2],
- [3, 72, 24, False, "relu", 2],
- [3, 88, 24, False, "relu", 1],
- [5, 96, 40, True, "hardswish", 1],
- [5, 240, 40, True, "hardswish", 1, 2],
- [5, 240, 40, True, "hardswish", 1, 2],
- [5, 120, 48, True, "hardswish", 1, 2],
- [5, 144, 48, True, "hardswish", 1, 2],
- [5, 288, 96, True, "hardswish", 1, 2],
- [5, 576, 96, True, "hardswish", 1, 4],
- [5, 576, 96, True, "hardswish", 1, 4],
- ]
- }
-
- OUT_INDEX = {"large": [2, 5, 11, 14], "small": [0, 2, 7, 10]}
-
-
- def _make_divisible(v, divisor=8, min_value=None):
- if min_value is None:
- min_value = divisor
- new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
- if new_v < 0.9 * v:
- new_v += divisor
- return new_v
-
-
- def _create_act(act):
- if act == "hardswish":
- return nn.Hardswish()
- elif act == "relu":
- return nn.ReLU()
- elif act is None:
- return None
- else:
- raise RuntimeError(
- "The activation function is not supported: {}".format(act))
-
-
- class MobileNetV3(nn.Layer):
- """
- MobileNetV3
- Args:
- config: list. MobileNetV3 depthwise blocks config.
- in_channels (int, optional): The channels of input image. Default: 3.
- scale: float=1.0. The coefficient that controls the size of network parameters.
- Returns:
- model: nn.Layer. Specific MobileNetV3 model depends on args.
- """
-
- def __init__(self,
- config,
- stages_pattern,
- out_index,
- in_channels=3,
- scale=1.0,
- pretrained=None):
- super().__init__()
-
- self.cfg = config
- self.out_index = out_index
- self.scale = scale
- self.pretrained = pretrained
- inplanes = 16
-
- self.conv = ConvBNLayer(
- in_c=in_channels,
- out_c=_make_divisible(inplanes * self.scale),
- filter_size=3,
- stride=2,
- padding=1,
- num_groups=1,
- if_act=True,
- act="hardswish")
- self.blocks = nn.Sequential(*[
- ResidualUnit(
- in_c=_make_divisible(inplanes * self.scale if i == 0 else
- self.cfg[i - 1][2] * self.scale),
- mid_c=_make_divisible(self.scale * exp),
- out_c=_make_divisible(self.scale * c),
- filter_size=k,
- stride=s,
- use_se=se,
- act=act,
- dilation=td[0] if td else 1)
- for i, (k, exp, c, se, act, s, *td) in enumerate(self.cfg)
- ])
-
- out_channels = [config[idx][2] for idx in self.out_index]
- self.feat_channels = [
- _make_divisible(self.scale * c) for c in out_channels
- ]
-
- self.init_res(stages_pattern)
- self.init_weight()
-
- def init_weight(self):
- if self.pretrained is not None:
- utils.load_entire_model(self, self.pretrained)
-
- def init_res(self, stages_pattern, return_patterns=None,
- return_stages=None):
- if return_patterns and return_stages:
- msg = f"The 'return_patterns' would be ignored when 'return_stages' is set."
- logger.warning(msg)
- return_stages = None
-
- if return_stages is True:
- return_patterns = stages_pattern
- # return_stages is int or bool
- if type(return_stages) is int:
- return_stages = [return_stages]
- if isinstance(return_stages, list):
- if max(return_stages) > len(stages_pattern) or min(
- return_stages) < 0:
- msg = f"The 'return_stages' set error. Illegal value(s) have been ignored. The stages' pattern list is {stages_pattern}."
- logger.warning(msg)
- return_stages = [
- val for val in return_stages
- if val >= 0 and val < len(stages_pattern)
- ]
- return_patterns = [stages_pattern[i] for i in return_stages]
-
- def forward(self, x):
- x = self.conv(x)
-
- feat_list = []
- for idx, block in enumerate(self.blocks):
- x = block(x)
- if idx in self.out_index:
- feat_list.append(x)
-
- return feat_list
-
-
- class ConvBNLayer(nn.Layer):
- def __init__(self,
- in_c,
- out_c,
- filter_size,
- stride,
- padding,
- num_groups=1,
- if_act=True,
- act=None,
- dilation=1):
- super().__init__()
-
- self.conv = Conv2D(
- in_channels=in_c,
- out_channels=out_c,
- kernel_size=filter_size,
- stride=stride,
- padding=padding,
- groups=num_groups,
- bias_attr=False,
- dilation=dilation)
- self.bn = BatchNorm(
- num_channels=out_c,
- act=None,
- param_attr=ParamAttr(regularizer=L2Decay(0.0)),
- bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
- self.if_act = if_act
- self.act = _create_act(act)
-
- def forward(self, x):
- x = self.conv(x)
- x = self.bn(x)
- if self.if_act:
- x = self.act(x)
- return x
-
-
- class ResidualUnit(nn.Layer):
- def __init__(self,
- in_c,
- mid_c,
- out_c,
- filter_size,
- stride,
- use_se,
- act=None,
- dilation=1):
- super().__init__()
- self.if_shortcut = stride == 1 and in_c == out_c
- self.if_se = use_se
-
- self.expand_conv = ConvBNLayer(
- in_c=in_c,
- out_c=mid_c,
- filter_size=1,
- stride=1,
- padding=0,
- if_act=True,
- act=act)
- self.bottleneck_conv = ConvBNLayer(
- in_c=mid_c,
- out_c=mid_c,
- filter_size=filter_size,
- stride=stride,
- padding=int((filter_size - 1) // 2) * dilation,
- num_groups=mid_c,
- if_act=True,
- act=act,
- dilation=dilation)
- if self.if_se:
- self.mid_se = SEModule(mid_c)
- self.linear_conv = ConvBNLayer(
- in_c=mid_c,
- out_c=out_c,
- filter_size=1,
- stride=1,
- padding=0,
- if_act=False,
- act=None)
-
- def forward(self, x):
- identity = x
- x = self.expand_conv(x)
- x = self.bottleneck_conv(x)
- if self.if_se:
- x = self.mid_se(x)
- x = self.linear_conv(x)
- if self.if_shortcut:
- x = paddle.add(identity, x)
- return x
-
-
- # nn.Hardsigmoid can't transfer "slope" and "offset" in nn.functional.hardsigmoid
- class Hardsigmoid(nn.Layer):
- def __init__(self, slope=0.2, offset=0.5):
- super().__init__()
- self.slope = slope
- self.offset = offset
-
- def forward(self, x):
- return nn.functional.hardsigmoid(
- x, slope=self.slope, offset=self.offset)
-
-
- class SEModule(nn.Layer):
- def __init__(self, channel, reduction=4):
- super().__init__()
- self.avg_pool = AdaptiveAvgPool2D(1)
- self.conv1 = Conv2D(
- in_channels=channel,
- out_channels=channel // reduction,
- kernel_size=1,
- stride=1,
- padding=0)
- self.relu = nn.ReLU()
- self.conv2 = Conv2D(
- in_channels=channel // reduction,
- out_channels=channel,
- kernel_size=1,
- stride=1,
- padding=0)
- self.hardsigmoid = Hardsigmoid(slope=0.2, offset=0.5)
-
- def forward(self, x):
- identity = x
- x = self.avg_pool(x)
- x = self.conv1(x)
- x = self.relu(x)
- x = self.conv2(x)
- x = self.hardsigmoid(x)
- return paddle.multiply(x=identity, y=x)
-
-
- @manager.BACKBONES.add_component
- def MobileNetV3_small_x0_35(**kwargs):
- model = MobileNetV3(
- config=NET_CONFIG["small"],
- scale=0.35,
- stages_pattern=MODEL_STAGES_PATTERN["MobileNetV3_small"],
- out_index=OUT_INDEX["small"],
- **kwargs)
- return model
-
-
- @manager.BACKBONES.add_component
- def MobileNetV3_small_x0_5(**kwargs):
- model = MobileNetV3(
- config=NET_CONFIG["small"],
- scale=0.5,
- stages_pattern=MODEL_STAGES_PATTERN["MobileNetV3_small"],
- out_index=OUT_INDEX["small"],
- **kwargs)
- return model
-
-
- @manager.BACKBONES.add_component
- def MobileNetV3_small_x0_75(**kwargs):
- model = MobileNetV3(
- config=NET_CONFIG["small"],
- scale=0.75,
- stages_pattern=MODEL_STAGES_PATTERN["MobileNetV3_small"],
- out_index=OUT_INDEX["small"],
- **kwargs)
- return model
-
-
- @manager.BACKBONES.add_component
- def MobileNetV3_small_x1_0(**kwargs):
- model = MobileNetV3(
- config=NET_CONFIG["small"],
- scale=1.0,
- stages_pattern=MODEL_STAGES_PATTERN["MobileNetV3_small"],
- out_index=OUT_INDEX["small"],
- **kwargs)
- return model
-
-
- @manager.BACKBONES.add_component
- def MobileNetV3_small_x1_25(**kwargs):
- model = MobileNetV3(
- config=NET_CONFIG["small"],
- scale=1.25,
- stages_pattern=MODEL_STAGES_PATTERN["MobileNetV3_small"],
- out_index=OUT_INDEX["small"],
- **kwargs)
- return model
-
-
- @manager.BACKBONES.add_component
- def MobileNetV3_large_x0_35(**kwargs):
- model = MobileNetV3(
- config=NET_CONFIG["large"],
- scale=0.35,
- stages_pattern=MODEL_STAGES_PATTERN["MobileNetV3_small"],
- out_index=OUT_INDEX["large"],
- **kwargs)
- return model
-
-
- @manager.BACKBONES.add_component
- def MobileNetV3_large_x0_5(**kwargs):
- model = MobileNetV3(
- config=NET_CONFIG["large"],
- scale=0.5,
- stages_pattern=MODEL_STAGES_PATTERN["MobileNetV3_large"],
- out_index=OUT_INDEX["large"],
- **kwargs)
- return model
-
-
- @manager.BACKBONES.add_component
- def MobileNetV3_large_x0_75(**kwargs):
- model = MobileNetV3(
- config=NET_CONFIG["large"],
- scale=0.75,
- stages_pattern=MODEL_STAGES_PATTERN["MobileNetV3_large"],
- out_index=OUT_INDEX["large"],
- **kwargs)
- return model
-
-
- @manager.BACKBONES.add_component
- def MobileNetV3_large_x1_0(**kwargs):
- model = MobileNetV3(
- config=NET_CONFIG["large"],
- scale=1.0,
- stages_pattern=MODEL_STAGES_PATTERN["MobileNetV3_large"],
- out_index=OUT_INDEX["large"],
- **kwargs)
- return model
-
-
- @manager.BACKBONES.add_component
- def MobileNetV3_large_x1_25(**kwargs):
- model = MobileNetV3(
- config=NET_CONFIG["large"],
- scale=1.25,
- stages_pattern=MODEL_STAGES_PATTERN["MobileNetV3_large"],
- out_index=OUT_INDEX["large"],
- **kwargs)
- return model
-
-
- @manager.BACKBONES.add_component
- def MobileNetV3_large_x1_0_os8(**kwargs):
- model = MobileNetV3(
- config=NET_CONFIG["large_os8"],
- scale=1.0,
- stages_pattern=MODEL_STAGES_PATTERN["MobileNetV3_large"],
- out_index=OUT_INDEX["large"],
- **kwargs)
- return model
-
-
- @manager.BACKBONES.add_component
- def MobileNetV3_small_x1_0_os8(**kwargs):
- model = MobileNetV3(
- config=NET_CONFIG["small_os8"],
- scale=1.0,
- stages_pattern=MODEL_STAGES_PATTERN["MobileNetV3_small"],
- out_index=OUT_INDEX["small"],
- **kwargs)
- return model
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