260 lines
8.8 KiB
Python
260 lines
8.8 KiB
Python
# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# reference: https://arxiv.org/abs/1704.04861
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from __future__ import absolute_import, division, print_function
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from paddle import ParamAttr
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import paddle.nn as nn
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from paddle.nn import Conv2D, BatchNorm, Linear, ReLU, Flatten
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from paddle.nn import AdaptiveAvgPool2D
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from paddle.nn.initializer import KaimingNormal
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from ..base.theseus_layer import TheseusLayer
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from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
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MODEL_URLS = {
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"MobileNetV1_x0_25":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_25_pretrained.pdparams",
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"MobileNetV1_x0_5":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_5_pretrained.pdparams",
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"MobileNetV1_x0_75":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_75_pretrained.pdparams",
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"MobileNetV1":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_pretrained.pdparams"
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}
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MODEL_STAGES_PATTERN = {
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"MobileNetV1": ["blocks[0]", "blocks[2]", "blocks[4]", "blocks[10]"]
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}
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__all__ = MODEL_URLS.keys()
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class ConvBNLayer(TheseusLayer):
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def __init__(self,
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num_channels,
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filter_size,
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num_filters,
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stride,
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padding,
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num_groups=1):
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super().__init__()
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self.conv = Conv2D(
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in_channels=num_channels,
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out_channels=num_filters,
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kernel_size=filter_size,
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stride=stride,
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padding=padding,
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groups=num_groups,
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weight_attr=ParamAttr(initializer=KaimingNormal()),
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bias_attr=False)
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self.bn = BatchNorm(num_filters)
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self.relu = ReLU()
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def forward(self, x):
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x = self.conv(x)
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x = self.bn(x)
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x = self.relu(x)
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return x
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class DepthwiseSeparable(TheseusLayer):
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def __init__(self, num_channels, num_filters1, num_filters2, num_groups,
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stride, scale):
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super().__init__()
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self.depthwise_conv = ConvBNLayer(
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num_channels=num_channels,
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num_filters=int(num_filters1 * scale),
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filter_size=3,
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stride=stride,
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padding=1,
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num_groups=int(num_groups * scale))
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self.pointwise_conv = ConvBNLayer(
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num_channels=int(num_filters1 * scale),
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filter_size=1,
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num_filters=int(num_filters2 * scale),
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stride=1,
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padding=0)
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def forward(self, x):
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x = self.depthwise_conv(x)
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x = self.pointwise_conv(x)
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return x
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class MobileNet(TheseusLayer):
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"""
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MobileNet
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Args:
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scale: float=1.0. The coefficient that controls the size of network parameters.
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class_num: int=1000. The number of classes.
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Returns:
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model: nn.Layer. Specific MobileNet model depends on args.
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"""
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def __init__(self,
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stages_pattern,
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scale=1.0,
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class_num=1000,
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return_patterns=None,
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return_stages=None):
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super().__init__()
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self.scale = scale
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self.conv = ConvBNLayer(
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num_channels=3,
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filter_size=3,
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num_filters=int(32 * scale),
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stride=2,
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padding=1)
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#num_channels, num_filters1, num_filters2, num_groups, stride
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self.cfg = [[int(32 * scale), 32, 64, 32, 1],
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[int(64 * scale), 64, 128, 64, 2],
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[int(128 * scale), 128, 128, 128, 1],
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[int(128 * scale), 128, 256, 128, 2],
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[int(256 * scale), 256, 256, 256, 1],
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[int(256 * scale), 256, 512, 256, 2],
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[int(512 * scale), 512, 512, 512, 1],
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[int(512 * scale), 512, 512, 512, 1],
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[int(512 * scale), 512, 512, 512, 1],
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[int(512 * scale), 512, 512, 512, 1],
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[int(512 * scale), 512, 512, 512, 1],
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[int(512 * scale), 512, 1024, 512, 2],
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[int(1024 * scale), 1024, 1024, 1024, 1]]
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self.blocks = nn.Sequential(* [
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DepthwiseSeparable(
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num_channels=params[0],
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num_filters1=params[1],
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num_filters2=params[2],
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num_groups=params[3],
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stride=params[4],
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scale=scale) for params in self.cfg
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])
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self.avg_pool = AdaptiveAvgPool2D(1)
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self.flatten = Flatten(start_axis=1, stop_axis=-1)
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self.fc = Linear(
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int(1024 * scale),
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class_num,
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weight_attr=ParamAttr(initializer=KaimingNormal()))
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super().init_res(
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stages_pattern,
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return_patterns=return_patterns,
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return_stages=return_stages)
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def forward(self, x):
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x = self.conv(x)
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x = self.blocks(x)
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x = self.avg_pool(x)
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x = self.flatten(x)
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x = self.fc(x)
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return x
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def _load_pretrained(pretrained, model, model_url, use_ssld):
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if pretrained is False:
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pass
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elif pretrained is True:
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load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
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elif isinstance(pretrained, str):
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load_dygraph_pretrain(model, pretrained)
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else:
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raise RuntimeError(
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"pretrained type is not available. Please use `string` or `boolean` type."
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)
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def MobileNetV1_x0_25(pretrained=False, use_ssld=False, **kwargs):
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"""
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MobileNetV1_x0_25
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Args:
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pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
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If str, means the path of the pretrained model.
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use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
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Returns:
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model: nn.Layer. Specific `MobileNetV1_x0_25` model depends on args.
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"""
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model = MobileNet(
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scale=0.25,
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stages_pattern=MODEL_STAGES_PATTERN["MobileNetV1"],
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1_x0_25"],
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use_ssld)
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return model
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def MobileNetV1_x0_5(pretrained=False, use_ssld=False, **kwargs):
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"""
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MobileNetV1_x0_5
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Args:
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pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
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If str, means the path of the pretrained model.
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use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
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Returns:
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model: nn.Layer. Specific `MobileNetV1_x0_5` model depends on args.
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"""
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model = MobileNet(
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scale=0.5,
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stages_pattern=MODEL_STAGES_PATTERN["MobileNetV1"],
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1_x0_5"],
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use_ssld)
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return model
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def MobileNetV1_x0_75(pretrained=False, use_ssld=False, **kwargs):
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"""
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MobileNetV1_x0_75
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Args:
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pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
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If str, means the path of the pretrained model.
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use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
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Returns:
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model: nn.Layer. Specific `MobileNetV1_x0_75` model depends on args.
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"""
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model = MobileNet(
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scale=0.75,
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stages_pattern=MODEL_STAGES_PATTERN["MobileNetV1"],
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1_x0_75"],
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use_ssld)
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return model
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def MobileNetV1(pretrained=False, use_ssld=False, **kwargs):
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"""
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MobileNetV1
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Args:
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pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
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If str, means the path of the pretrained model.
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use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
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Returns:
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model: nn.Layer. Specific `MobileNetV1` model depends on args.
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"""
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model = MobileNet(
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scale=1.0,
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stages_pattern=MODEL_STAGES_PATTERN["MobileNetV1"],
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**kwargs)
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_load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1"], use_ssld)
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return model
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