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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.
- from __future__ import absolute_import, division, print_function
- from paddle import ParamAttr
- import paddle.nn as nn
- from paddle.nn import Conv2D, BatchNorm, Linear, ReLU, Flatten
- from paddle.nn import AdaptiveAvgPool2D
- from paddle.nn.initializer import KaimingNormal
- from paddlex.ppcls.arch.backbone.base.theseus_layer import TheseusLayer
- from paddlex.ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
- MODEL_URLS = {
- "MobileNetV1_x0_25":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_25_pretrained.pdparams",
- "MobileNetV1_x0_5":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_5_pretrained.pdparams",
- "MobileNetV1_x0_75":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_75_pretrained.pdparams",
- "MobileNetV1":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_pretrained.pdparams"
- }
- __all__ = MODEL_URLS.keys()
- class ConvBNLayer(TheseusLayer):
- def __init__(self,
- num_channels,
- filter_size,
- num_filters,
- stride,
- padding,
- num_groups=1):
- super().__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(initializer=KaimingNormal()),
- bias_attr=False)
- self.bn = BatchNorm(num_filters)
- self.relu = ReLU()
- def forward(self, x):
- x = self.conv(x)
- x = self.bn(x)
- x = self.relu(x)
- return x
- class DepthwiseSeparable(TheseusLayer):
- def __init__(self, num_channels, num_filters1, num_filters2, num_groups,
- stride, scale):
- super().__init__()
- self.depthwise_conv = ConvBNLayer(
- num_channels=num_channels,
- num_filters=int(num_filters1 * scale),
- filter_size=3,
- stride=stride,
- padding=1,
- num_groups=int(num_groups * scale))
- self.pointwise_conv = ConvBNLayer(
- num_channels=int(num_filters1 * scale),
- filter_size=1,
- num_filters=int(num_filters2 * scale),
- stride=1,
- padding=0)
- def forward(self, x):
- x = self.depthwise_conv(x)
- x = self.pointwise_conv(x)
- return x
- class MobileNet(TheseusLayer):
- """
- MobileNet
- Args:
- scale: float=1.0. The coefficient that controls the size of network parameters.
- class_num: int=1000. The number of classes.
- Returns:
- model: nn.Layer. Specific MobileNet model depends on args.
- """
- def __init__(self, scale=1.0, class_num=1000, return_patterns=None):
- super().__init__()
- self.scale = scale
- self.conv = ConvBNLayer(
- num_channels=3,
- filter_size=3,
- num_filters=int(32 * scale),
- stride=2,
- padding=1)
- #num_channels, num_filters1, num_filters2, num_groups, stride
- self.cfg = [[int(32 * scale), 32, 64, 32, 1],
- [int(64 * scale), 64, 128, 64, 2],
- [int(128 * scale), 128, 128, 128, 1],
- [int(128 * scale), 128, 256, 128, 2],
- [int(256 * scale), 256, 256, 256, 1],
- [int(256 * scale), 256, 512, 256, 2],
- [int(512 * scale), 512, 512, 512, 1],
- [int(512 * scale), 512, 512, 512, 1],
- [int(512 * scale), 512, 512, 512, 1],
- [int(512 * scale), 512, 512, 512, 1],
- [int(512 * scale), 512, 512, 512, 1],
- [int(512 * scale), 512, 1024, 512, 2],
- [int(1024 * scale), 1024, 1024, 1024, 1]]
- self.blocks = nn.Sequential(*[
- DepthwiseSeparable(
- num_channels=params[0],
- num_filters1=params[1],
- num_filters2=params[2],
- num_groups=params[3],
- stride=params[4],
- scale=scale) for params in self.cfg
- ])
- self.avg_pool = AdaptiveAvgPool2D(1)
- self.flatten = Flatten(start_axis=1, stop_axis=-1)
- self.fc = Linear(
- int(1024 * scale),
- class_num,
- weight_attr=ParamAttr(initializer=KaimingNormal()))
- if return_patterns is not None:
- self.update_res(return_patterns)
- self.register_forward_post_hook(self._return_dict_hook)
- def forward(self, x):
- x = self.conv(x)
- x = self.blocks(x)
- x = self.avg_pool(x)
- x = self.flatten(x)
- x = self.fc(x)
- return 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 MobileNetV1_x0_25(pretrained=False, use_ssld=False, **kwargs):
- """
- MobileNetV1_x0_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 `MobileNetV1_x0_25` model depends on args.
- """
- model = MobileNet(scale=0.25, **kwargs)
- _load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1_x0_25"],
- use_ssld)
- return model
- def MobileNetV1_x0_5(pretrained=False, use_ssld=False, **kwargs):
- """
- MobileNetV1_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 `MobileNetV1_x0_5` model depends on args.
- """
- model = MobileNet(scale=0.5, **kwargs)
- _load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1_x0_5"],
- use_ssld)
- return model
- def MobileNetV1_x0_75(pretrained=False, use_ssld=False, **kwargs):
- """
- MobileNetV1_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 `MobileNetV1_x0_75` model depends on args.
- """
- model = MobileNet(scale=0.75, **kwargs)
- _load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1_x0_75"],
- use_ssld)
- return model
- def MobileNetV1(pretrained=False, use_ssld=False, **kwargs):
- """
- MobileNetV1
- 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 `MobileNetV1` model depends on args.
- """
- model = MobileNet(scale=1.0, **kwargs)
- _load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1"], use_ssld)
- return model
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