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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
- 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 paddlex.ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
- MODEL_URLS = {
- "SqueezeNet1_0":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_0_pretrained.pdparams",
- "SqueezeNet1_1":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_1_pretrained.pdparams",
- }
- __all__ = list(MODEL_URLS.keys())
- class MakeFireConv(nn.Layer):
- def __init__(self,
- input_channels,
- output_channels,
- filter_size,
- padding=0,
- name=None):
- super(MakeFireConv, self).__init__()
- self._conv = Conv2D(
- input_channels,
- output_channels,
- filter_size,
- padding=padding,
- weight_attr=ParamAttr(name=name + "_weights"),
- bias_attr=ParamAttr(name=name + "_offset"))
- def forward(self, x):
- x = self._conv(x)
- x = F.relu(x)
- return x
- class MakeFire(nn.Layer):
- def __init__(self,
- input_channels,
- squeeze_channels,
- expand1x1_channels,
- expand3x3_channels,
- name=None):
- super(MakeFire, self).__init__()
- self._conv = MakeFireConv(
- input_channels, squeeze_channels, 1, name=name + "_squeeze1x1")
- self._conv_path1 = MakeFireConv(
- squeeze_channels, expand1x1_channels, 1, name=name + "_expand1x1")
- self._conv_path2 = MakeFireConv(
- squeeze_channels,
- expand3x3_channels,
- 3,
- padding=1,
- name=name + "_expand3x3")
- def forward(self, inputs):
- x = self._conv(inputs)
- x1 = self._conv_path1(x)
- x2 = self._conv_path2(x)
- return paddle.concat([x1, x2], axis=1)
- class SqueezeNet(nn.Layer):
- def __init__(self, version, class_num=1000):
- super(SqueezeNet, self).__init__()
- self.version = version
- if self.version == "1.0":
- self._conv = Conv2D(
- 3,
- 96,
- 7,
- stride=2,
- weight_attr=ParamAttr(name="conv1_weights"),
- bias_attr=ParamAttr(name="conv1_offset"))
- self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
- self._conv1 = MakeFire(96, 16, 64, 64, name="fire2")
- self._conv2 = MakeFire(128, 16, 64, 64, name="fire3")
- self._conv3 = MakeFire(128, 32, 128, 128, name="fire4")
- self._conv4 = MakeFire(256, 32, 128, 128, name="fire5")
- self._conv5 = MakeFire(256, 48, 192, 192, name="fire6")
- self._conv6 = MakeFire(384, 48, 192, 192, name="fire7")
- self._conv7 = MakeFire(384, 64, 256, 256, name="fire8")
- self._conv8 = MakeFire(512, 64, 256, 256, name="fire9")
- else:
- self._conv = Conv2D(
- 3,
- 64,
- 3,
- stride=2,
- padding=1,
- weight_attr=ParamAttr(name="conv1_weights"),
- bias_attr=ParamAttr(name="conv1_offset"))
- self._pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
- self._conv1 = MakeFire(64, 16, 64, 64, name="fire2")
- self._conv2 = MakeFire(128, 16, 64, 64, name="fire3")
- self._conv3 = MakeFire(128, 32, 128, 128, name="fire4")
- self._conv4 = MakeFire(256, 32, 128, 128, name="fire5")
- self._conv5 = MakeFire(256, 48, 192, 192, name="fire6")
- self._conv6 = MakeFire(384, 48, 192, 192, name="fire7")
- self._conv7 = MakeFire(384, 64, 256, 256, name="fire8")
- self._conv8 = MakeFire(512, 64, 256, 256, name="fire9")
- self._drop = Dropout(p=0.5, mode="downscale_in_infer")
- self._conv9 = Conv2D(
- 512,
- class_num,
- 1,
- weight_attr=ParamAttr(name="conv10_weights"),
- bias_attr=ParamAttr(name="conv10_offset"))
- self._avg_pool = AdaptiveAvgPool2D(1)
- def forward(self, inputs):
- x = self._conv(inputs)
- x = F.relu(x)
- x = self._pool(x)
- if self.version == "1.0":
- x = self._conv1(x)
- x = self._conv2(x)
- x = self._conv3(x)
- x = self._pool(x)
- x = self._conv4(x)
- x = self._conv5(x)
- x = self._conv6(x)
- x = self._conv7(x)
- x = self._pool(x)
- x = self._conv8(x)
- else:
- x = self._conv1(x)
- x = self._conv2(x)
- x = self._pool(x)
- x = self._conv3(x)
- x = self._conv4(x)
- x = self._pool(x)
- x = self._conv5(x)
- x = self._conv6(x)
- x = self._conv7(x)
- x = self._conv8(x)
- x = self._drop(x)
- x = self._conv9(x)
- x = F.relu(x)
- x = self._avg_pool(x)
- x = paddle.squeeze(x, axis=[2, 3])
- return x
- def _load_pretrained(pretrained, model, model_url, use_ssld=False):
- 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 SqueezeNet1_0(pretrained=False, use_ssld=False, **kwargs):
- model = SqueezeNet(version="1.0", **kwargs)
- _load_pretrained(
- pretrained, model, MODEL_URLS["SqueezeNet1_0"], use_ssld=use_ssld)
- return model
- def SqueezeNet1_1(pretrained=False, use_ssld=False, **kwargs):
- model = SqueezeNet(version="1.1", **kwargs)
- _load_pretrained(
- pretrained, model, MODEL_URLS["SqueezeNet1_1"], use_ssld=use_ssld)
- return model
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