246 lines
7.8 KiB
Python
246 lines
7.8 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/1409.4842
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import paddle
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from paddle import ParamAttr
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
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from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
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from paddle.nn.initializer import Uniform
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import math
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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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"GoogLeNet":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams",
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}
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__all__ = list(MODEL_URLS.keys())
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def xavier(channels, filter_size, name):
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stdv = (3.0 / (filter_size**2 * channels))**0.5
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param_attr = ParamAttr(
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initializer=Uniform(-stdv, stdv), name=name + "_weights")
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return param_attr
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class ConvLayer(nn.Layer):
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def __init__(self,
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num_channels,
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num_filters,
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filter_size,
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stride=1,
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groups=1,
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act=None,
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name=None):
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super(ConvLayer, self).__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=(filter_size - 1) // 2,
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groups=groups,
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weight_attr=ParamAttr(name=name + "_weights"),
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bias_attr=False)
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def forward(self, inputs):
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y = self._conv(inputs)
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return y
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class Inception(nn.Layer):
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def __init__(self,
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input_channels,
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output_channels,
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filter1,
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filter3R,
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filter3,
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filter5R,
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filter5,
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proj,
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name=None):
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super(Inception, self).__init__()
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self._conv1 = ConvLayer(
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input_channels, filter1, 1, name="inception_" + name + "_1x1")
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self._conv3r = ConvLayer(
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input_channels,
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filter3R,
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1,
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name="inception_" + name + "_3x3_reduce")
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self._conv3 = ConvLayer(
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filter3R, filter3, 3, name="inception_" + name + "_3x3")
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self._conv5r = ConvLayer(
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input_channels,
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filter5R,
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1,
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name="inception_" + name + "_5x5_reduce")
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self._conv5 = ConvLayer(
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filter5R, filter5, 5, name="inception_" + name + "_5x5")
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self._pool = MaxPool2D(kernel_size=3, stride=1, padding=1)
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self._convprj = ConvLayer(
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input_channels, proj, 1, name="inception_" + name + "_3x3_proj")
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def forward(self, inputs):
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conv1 = self._conv1(inputs)
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conv3r = self._conv3r(inputs)
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conv3 = self._conv3(conv3r)
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conv5r = self._conv5r(inputs)
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conv5 = self._conv5(conv5r)
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pool = self._pool(inputs)
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convprj = self._convprj(pool)
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cat = paddle.concat([conv1, conv3, conv5, convprj], axis=1)
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cat = F.relu(cat)
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return cat
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class GoogLeNetDY(nn.Layer):
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def __init__(self, class_num=1000):
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super(GoogLeNetDY, self).__init__()
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self._conv = ConvLayer(3, 64, 7, 2, name="conv1")
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self._pool = MaxPool2D(kernel_size=3, stride=2)
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self._conv_1 = ConvLayer(64, 64, 1, name="conv2_1x1")
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self._conv_2 = ConvLayer(64, 192, 3, name="conv2_3x3")
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self._ince3a = Inception(
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192, 192, 64, 96, 128, 16, 32, 32, name="ince3a")
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self._ince3b = Inception(
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256, 256, 128, 128, 192, 32, 96, 64, name="ince3b")
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self._ince4a = Inception(
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480, 480, 192, 96, 208, 16, 48, 64, name="ince4a")
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self._ince4b = Inception(
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512, 512, 160, 112, 224, 24, 64, 64, name="ince4b")
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self._ince4c = Inception(
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512, 512, 128, 128, 256, 24, 64, 64, name="ince4c")
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self._ince4d = Inception(
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512, 512, 112, 144, 288, 32, 64, 64, name="ince4d")
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self._ince4e = Inception(
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528, 528, 256, 160, 320, 32, 128, 128, name="ince4e")
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self._ince5a = Inception(
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832, 832, 256, 160, 320, 32, 128, 128, name="ince5a")
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self._ince5b = Inception(
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832, 832, 384, 192, 384, 48, 128, 128, name="ince5b")
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self._pool_5 = AdaptiveAvgPool2D(1)
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self._drop = Dropout(p=0.4, mode="downscale_in_infer")
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self._fc_out = Linear(
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1024,
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class_num,
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weight_attr=xavier(1024, 1, "out"),
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bias_attr=ParamAttr(name="out_offset"))
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self._pool_o1 = AvgPool2D(kernel_size=5, stride=3)
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self._conv_o1 = ConvLayer(512, 128, 1, name="conv_o1")
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self._fc_o1 = Linear(
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1152,
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1024,
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weight_attr=xavier(2048, 1, "fc_o1"),
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bias_attr=ParamAttr(name="fc_o1_offset"))
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self._drop_o1 = Dropout(p=0.7, mode="downscale_in_infer")
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self._out1 = Linear(
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1024,
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class_num,
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weight_attr=xavier(1024, 1, "out1"),
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bias_attr=ParamAttr(name="out1_offset"))
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self._pool_o2 = AvgPool2D(kernel_size=5, stride=3)
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self._conv_o2 = ConvLayer(528, 128, 1, name="conv_o2")
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self._fc_o2 = Linear(
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1152,
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1024,
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weight_attr=xavier(2048, 1, "fc_o2"),
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bias_attr=ParamAttr(name="fc_o2_offset"))
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self._drop_o2 = Dropout(p=0.7, mode="downscale_in_infer")
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self._out2 = Linear(
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1024,
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class_num,
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weight_attr=xavier(1024, 1, "out2"),
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bias_attr=ParamAttr(name="out2_offset"))
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def forward(self, inputs):
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x = self._conv(inputs)
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x = self._pool(x)
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x = self._conv_1(x)
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x = self._conv_2(x)
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x = self._pool(x)
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x = self._ince3a(x)
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x = self._ince3b(x)
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x = self._pool(x)
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ince4a = self._ince4a(x)
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x = self._ince4b(ince4a)
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x = self._ince4c(x)
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ince4d = self._ince4d(x)
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x = self._ince4e(ince4d)
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x = self._pool(x)
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x = self._ince5a(x)
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ince5b = self._ince5b(x)
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x = self._pool_5(ince5b)
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x = self._drop(x)
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x = paddle.squeeze(x, axis=[2, 3])
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out = self._fc_out(x)
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x = self._pool_o1(ince4a)
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x = self._conv_o1(x)
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x = paddle.flatten(x, start_axis=1, stop_axis=-1)
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x = self._fc_o1(x)
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x = F.relu(x)
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x = self._drop_o1(x)
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out1 = self._out1(x)
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x = self._pool_o2(ince4d)
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x = self._conv_o2(x)
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x = paddle.flatten(x, start_axis=1, stop_axis=-1)
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x = self._fc_o2(x)
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x = self._drop_o2(x)
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out2 = self._out2(x)
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return [out, out1, out2]
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def _load_pretrained(pretrained, model, model_url, use_ssld=False):
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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 GoogLeNet(pretrained=False, use_ssld=False, **kwargs):
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model = GoogLeNetDY(**kwargs)
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_load_pretrained(
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pretrained, model, MODEL_URLS["GoogLeNet"], use_ssld=use_ssld)
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return model
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