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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.
-
- # reference: https://arxiv.org/abs/1905.02244
-
- from __future__ import absolute_import, division, print_function
-
- import paddle
- import paddle.nn as nn
- from paddle import ParamAttr
- from paddle.nn import AdaptiveAvgPool2D, BatchNorm, Conv2D, Dropout, Linear
- from paddle.regularizer import L2Decay
-
- from ..base.theseus_layer import TheseusLayer
- from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
-
- MODEL_URLS = {
- "MobileNetV3_small_x0_35":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_pretrained.pdparams",
- "MobileNetV3_small_x0_5":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_5_pretrained.pdparams",
- "MobileNetV3_small_x0_75":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_75_pretrained.pdparams",
- "MobileNetV3_small_x1_0":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_pretrained.pdparams",
- "MobileNetV3_small_x1_25":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_25_pretrained.pdparams",
- "MobileNetV3_large_x0_35":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_35_pretrained.pdparams",
- "MobileNetV3_large_x0_5":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_5_pretrained.pdparams",
- "MobileNetV3_large_x0_75":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_75_pretrained.pdparams",
- "MobileNetV3_large_x1_0":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_pretrained.pdparams",
- "MobileNetV3_large_x1_25":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_25_pretrained.pdparams",
- }
-
- 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]"]
- }
-
- __all__ = MODEL_URLS.keys()
-
- # "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
- 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],
- [5, 72, 40, True, "relu", 2],
- [5, 120, 40, True, "relu", 1],
- [5, 120, 40, True, "relu", 1],
- [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],
- [5, 672, 160, True, "hardswish", 2],
- [5, 960, 160, True, "hardswish", 1],
- [5, 960, 160, True, "hardswish", 1],
- ],
- "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],
- ]
- }
- # first conv output channel number in MobileNetV3
- STEM_CONV_NUMBER = 16
- # last second conv output channel for "small"
- LAST_SECOND_CONV_SMALL = 576
- # last second conv output channel for "large"
- LAST_SECOND_CONV_LARGE = 960
- # last conv output channel number for "large" and "small"
- LAST_CONV = 1280
-
-
- 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(TheseusLayer):
- """
- MobileNetV3
- Args:
- config: list. MobileNetV3 depthwise blocks config.
- scale: float=1.0. The coefficient that controls the size of network parameters.
- class_num: int=1000. The number of classes.
- inplanes: int=16. The output channel number of first convolution layer.
- class_squeeze: int=960. The output channel number of penultimate convolution layer.
- class_expand: int=1280. The output channel number of last convolution layer.
- dropout_prob: float=0.2. Probability of setting units to zero.
- Returns:
- model: nn.Layer. Specific MobileNetV3 model depends on args.
- """
-
- def __init__(self,
- config,
- stages_pattern,
- scale=1.0,
- class_num=1000,
- inplanes=STEM_CONV_NUMBER,
- class_squeeze=LAST_SECOND_CONV_LARGE,
- class_expand=LAST_CONV,
- dropout_prob=0.2,
- return_patterns=None,
- return_stages=None,
- **kwargs):
- super().__init__()
-
- self.cfg = config
- self.scale = scale
- self.inplanes = inplanes
- self.class_squeeze = class_squeeze
- self.class_expand = class_expand
- self.class_num = class_num
-
- self.conv = ConvBNLayer(
- in_c=3,
- out_c=_make_divisible(self.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(self.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) for i, (k, exp, c, se, act, s) in enumerate(self.cfg)
- ])
-
- self.last_second_conv = ConvBNLayer(
- in_c=_make_divisible(self.cfg[-1][2] * self.scale),
- out_c=_make_divisible(self.scale * self.class_squeeze),
- filter_size=1,
- stride=1,
- padding=0,
- num_groups=1,
- if_act=True,
- act="hardswish")
-
- self.avg_pool = AdaptiveAvgPool2D(1)
-
- self.last_conv = Conv2D(
- in_channels=_make_divisible(self.scale * self.class_squeeze),
- out_channels=self.class_expand,
- kernel_size=1,
- stride=1,
- padding=0,
- bias_attr=False)
-
- self.hardswish = nn.Hardswish()
- if dropout_prob is not None:
- self.dropout = Dropout(p=dropout_prob, mode="downscale_in_infer")
- else:
- self.dropout = None
- self.flatten = nn.Flatten(start_axis=1, stop_axis=-1)
-
- self.fc = Linear(self.class_expand, class_num)
-
- super().init_res(
- stages_pattern,
- return_patterns=return_patterns,
- return_stages=return_stages)
-
- def forward(self, x):
- x = self.conv(x)
- x = self.blocks(x)
- x = self.last_second_conv(x)
- x = self.avg_pool(x)
- x = self.last_conv(x)
- x = self.hardswish(x)
- if self.dropout is not None:
- x = self.dropout(x)
- x = self.flatten(x)
- x = self.fc(x)
-
- return x
-
-
- class ConvBNLayer(TheseusLayer):
- def __init__(self,
- in_c,
- out_c,
- filter_size,
- stride,
- padding,
- num_groups=1,
- if_act=True,
- act=None):
- 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)
- 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(TheseusLayer):
- def __init__(self,
- in_c,
- mid_c,
- out_c,
- filter_size,
- stride,
- use_se,
- act=None):
- 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),
- num_groups=mid_c,
- if_act=True,
- act=act)
- 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(TheseusLayer):
- 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(TheseusLayer):
- 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)
-
-
- def _load_pretrained(pretrained, model, model_url, use_ssld):
- if pretrained is False:
- pass
- elif pretrained is True:
- load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
- elif isinstance(pretrained, str):
- load_dygraph_pretrain(model, pretrained)
- else:
- raise RuntimeError(
- "pretrained type is not available. Please use `string` or `boolean` type."
- )
-
-
- def MobileNetV3_small_x0_35(pretrained=False, use_ssld=False, **kwargs):
- """
- MobileNetV3_small_x0_35
- Args:
- pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
- If str, means the path of the pretrained model.
- use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
- Returns:
- model: nn.Layer. Specific `MobileNetV3_small_x0_35` model depends on args.
- """
- model = MobileNetV3(
- config=NET_CONFIG["small"],
- scale=0.35,
- stages_pattern=MODEL_STAGES_PATTERN["MobileNetV3_small"],
- class_squeeze=LAST_SECOND_CONV_SMALL,
- **kwargs)
- _load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_small_x0_35"],
- use_ssld)
- return model
-
-
- def MobileNetV3_small_x0_5(pretrained=False, use_ssld=False, **kwargs):
- """
- MobileNetV3_small_x0_5
- Args:
- pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
- If str, means the path of the pretrained model.
- use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
- Returns:
- model: nn.Layer. Specific `MobileNetV3_small_x0_5` model depends on args.
- """
- model = MobileNetV3(
- config=NET_CONFIG["small"],
- scale=0.5,
- stages_pattern=MODEL_STAGES_PATTERN["MobileNetV3_small"],
- class_squeeze=LAST_SECOND_CONV_SMALL,
- **kwargs)
- _load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_small_x0_5"],
- use_ssld)
- return model
-
-
- def MobileNetV3_small_x0_75(pretrained=False, use_ssld=False, **kwargs):
- """
- MobileNetV3_small_x0_75
- Args:
- pretrained: bool=false or str. if `true` load pretrained parameters, `false` otherwise.
- if str, means the path of the pretrained model.
- use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
- Returns:
- model: nn.Layer. Specific `MobileNetV3_small_x0_75` model depends on args.
- """
- model = MobileNetV3(
- config=NET_CONFIG["small"],
- scale=0.75,
- stages_pattern=MODEL_STAGES_PATTERN["MobileNetV3_small"],
- class_squeeze=LAST_SECOND_CONV_SMALL,
- **kwargs)
- _load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_small_x0_75"],
- use_ssld)
- return model
-
-
- def MobileNetV3_small_x1_0(pretrained=False, use_ssld=False, **kwargs):
- """
- MobileNetV3_small_x1_0
- Args:
- pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
- If str, means the path of the pretrained model.
- use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
- Returns:
- model: nn.Layer. Specific `MobileNetV3_small_x1_0` model depends on args.
- """
- model = MobileNetV3(
- config=NET_CONFIG["small"],
- scale=1.0,
- stages_pattern=MODEL_STAGES_PATTERN["MobileNetV3_small"],
- class_squeeze=LAST_SECOND_CONV_SMALL,
- **kwargs)
- _load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_small_x1_0"],
- use_ssld)
- return model
-
-
- def MobileNetV3_small_x1_25(pretrained=False, use_ssld=False, **kwargs):
- """
- MobileNetV3_small_x1_25
- Args:
- pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
- If str, means the path of the pretrained model.
- use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
- Returns:
- model: nn.Layer. Specific `MobileNetV3_small_x1_25` model depends on args.
- """
- model = MobileNetV3(
- config=NET_CONFIG["small"],
- scale=1.25,
- stages_pattern=MODEL_STAGES_PATTERN["MobileNetV3_small"],
- class_squeeze=LAST_SECOND_CONV_SMALL,
- **kwargs)
- _load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_small_x1_25"],
- use_ssld)
- return model
-
-
- def MobileNetV3_large_x0_35(pretrained=False, use_ssld=False, **kwargs):
- """
- MobileNetV3_large_x0_35
- Args:
- pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
- If str, means the path of the pretrained model.
- use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
- Returns:
- model: nn.Layer. Specific `MobileNetV3_large_x0_35` model depends on args.
- """
- model = MobileNetV3(
- config=NET_CONFIG["large"],
- scale=0.35,
- stages_pattern=MODEL_STAGES_PATTERN["MobileNetV3_small"],
- class_squeeze=LAST_SECOND_CONV_LARGE,
- **kwargs)
- _load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_large_x0_35"],
- use_ssld)
- return model
-
-
- def MobileNetV3_large_x0_5(pretrained=False, use_ssld=False, **kwargs):
- """
- MobileNetV3_large_x0_5
- Args:
- pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
- If str, means the path of the pretrained model.
- use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
- Returns:
- model: nn.Layer. Specific `MobileNetV3_large_x0_5` model depends on args.
- """
- model = MobileNetV3(
- config=NET_CONFIG["large"],
- scale=0.5,
- stages_pattern=MODEL_STAGES_PATTERN["MobileNetV3_large"],
- class_squeeze=LAST_SECOND_CONV_LARGE,
- **kwargs)
- _load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_large_x0_5"],
- use_ssld)
- return model
-
-
- def MobileNetV3_large_x0_75(pretrained=False, use_ssld=False, **kwargs):
- """
- MobileNetV3_large_x0_75
- Args:
- pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
- If str, means the path of the pretrained model.
- use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
- Returns:
- model: nn.Layer. Specific `MobileNetV3_large_x0_75` model depends on args.
- """
- model = MobileNetV3(
- config=NET_CONFIG["large"],
- scale=0.75,
- stages_pattern=MODEL_STAGES_PATTERN["MobileNetV3_large"],
- class_squeeze=LAST_SECOND_CONV_LARGE,
- **kwargs)
- _load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_large_x0_75"],
- use_ssld)
- return model
-
-
- def MobileNetV3_large_x1_0(pretrained=False, use_ssld=False, **kwargs):
- """
- MobileNetV3_large_x1_0
- Args:
- pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
- If str, means the path of the pretrained model.
- use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
- Returns:
- model: nn.Layer. Specific `MobileNetV3_large_x1_0` model depends on args.
- """
- model = MobileNetV3(
- config=NET_CONFIG["large"],
- scale=1.0,
- stages_pattern=MODEL_STAGES_PATTERN["MobileNetV3_large"],
- class_squeeze=LAST_SECOND_CONV_LARGE,
- **kwargs)
- _load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_large_x1_0"],
- use_ssld)
- return model
-
-
- def MobileNetV3_large_x1_25(pretrained=False, use_ssld=False, **kwargs):
- """
- MobileNetV3_large_x1_25
- Args:
- pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
- If str, means the path of the pretrained model.
- use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
- Returns:
- model: nn.Layer. Specific `MobileNetV3_large_x1_25` model depends on args.
- """
- model = MobileNetV3(
- config=NET_CONFIG["large"],
- scale=1.25,
- stages_pattern=MODEL_STAGES_PATTERN["MobileNetV3_large"],
- class_squeeze=LAST_SECOND_CONV_LARGE,
- **kwargs)
- _load_pretrained(pretrained, model, MODEL_URLS["MobileNetV3_large_x1_25"],
- use_ssld)
- return model
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