200 lines
7.0 KiB
Python
200 lines
7.0 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/1804.02767
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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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"DarkNet53":
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"https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DarkNet53_pretrained.pdparams"
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}
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__all__ = list(MODEL_URLS.keys())
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class ConvBNLayer(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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filter_size,
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stride,
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padding,
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name=None):
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super(ConvBNLayer, self).__init__()
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self._conv = Conv2D(
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in_channels=input_channels,
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out_channels=output_channels,
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kernel_size=filter_size,
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stride=stride,
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padding=padding,
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weight_attr=ParamAttr(name=name + ".conv.weights"),
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bias_attr=False)
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bn_name = name + ".bn"
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self._bn = BatchNorm(
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num_channels=output_channels,
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act="relu",
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param_attr=ParamAttr(name=bn_name + ".scale"),
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bias_attr=ParamAttr(name=bn_name + ".offset"),
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moving_mean_name=bn_name + ".mean",
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moving_variance_name=bn_name + ".var")
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def forward(self, inputs):
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x = self._conv(inputs)
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x = self._bn(x)
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return x
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class BasicBlock(nn.Layer):
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def __init__(self, input_channels, output_channels, name=None):
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super(BasicBlock, self).__init__()
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self._conv1 = ConvBNLayer(
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input_channels, output_channels, 1, 1, 0, name=name + ".0")
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self._conv2 = ConvBNLayer(
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output_channels, output_channels * 2, 3, 1, 1, name=name + ".1")
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def forward(self, inputs):
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x = self._conv1(inputs)
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x = self._conv2(x)
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return paddle.add(x=inputs, y=x)
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class DarkNet(nn.Layer):
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def __init__(self, class_num=1000):
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super(DarkNet, self).__init__()
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self.stages = [1, 2, 8, 8, 4]
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self._conv1 = ConvBNLayer(3, 32, 3, 1, 1, name="yolo_input")
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self._conv2 = ConvBNLayer(
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32, 64, 3, 2, 1, name="yolo_input.downsample")
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self._basic_block_01 = BasicBlock(64, 32, name="stage.0.0")
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self._downsample_0 = ConvBNLayer(
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64, 128, 3, 2, 1, name="stage.0.downsample")
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self._basic_block_11 = BasicBlock(128, 64, name="stage.1.0")
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self._basic_block_12 = BasicBlock(128, 64, name="stage.1.1")
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self._downsample_1 = ConvBNLayer(
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128, 256, 3, 2, 1, name="stage.1.downsample")
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self._basic_block_21 = BasicBlock(256, 128, name="stage.2.0")
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self._basic_block_22 = BasicBlock(256, 128, name="stage.2.1")
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self._basic_block_23 = BasicBlock(256, 128, name="stage.2.2")
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self._basic_block_24 = BasicBlock(256, 128, name="stage.2.3")
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self._basic_block_25 = BasicBlock(256, 128, name="stage.2.4")
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self._basic_block_26 = BasicBlock(256, 128, name="stage.2.5")
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self._basic_block_27 = BasicBlock(256, 128, name="stage.2.6")
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self._basic_block_28 = BasicBlock(256, 128, name="stage.2.7")
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self._downsample_2 = ConvBNLayer(
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256, 512, 3, 2, 1, name="stage.2.downsample")
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self._basic_block_31 = BasicBlock(512, 256, name="stage.3.0")
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self._basic_block_32 = BasicBlock(512, 256, name="stage.3.1")
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self._basic_block_33 = BasicBlock(512, 256, name="stage.3.2")
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self._basic_block_34 = BasicBlock(512, 256, name="stage.3.3")
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self._basic_block_35 = BasicBlock(512, 256, name="stage.3.4")
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self._basic_block_36 = BasicBlock(512, 256, name="stage.3.5")
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self._basic_block_37 = BasicBlock(512, 256, name="stage.3.6")
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self._basic_block_38 = BasicBlock(512, 256, name="stage.3.7")
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self._downsample_3 = ConvBNLayer(
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512, 1024, 3, 2, 1, name="stage.3.downsample")
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self._basic_block_41 = BasicBlock(1024, 512, name="stage.4.0")
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self._basic_block_42 = BasicBlock(1024, 512, name="stage.4.1")
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self._basic_block_43 = BasicBlock(1024, 512, name="stage.4.2")
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self._basic_block_44 = BasicBlock(1024, 512, name="stage.4.3")
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self._pool = AdaptiveAvgPool2D(1)
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stdv = 1.0 / math.sqrt(1024.0)
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self._out = Linear(
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1024,
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class_num,
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weight_attr=ParamAttr(
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name="fc_weights", initializer=Uniform(-stdv, stdv)),
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bias_attr=ParamAttr(name="fc_offset"))
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def forward(self, inputs):
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x = self._conv1(inputs)
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x = self._conv2(x)
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x = self._basic_block_01(x)
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x = self._downsample_0(x)
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x = self._basic_block_11(x)
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x = self._basic_block_12(x)
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x = self._downsample_1(x)
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x = self._basic_block_21(x)
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x = self._basic_block_22(x)
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x = self._basic_block_23(x)
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x = self._basic_block_24(x)
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x = self._basic_block_25(x)
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x = self._basic_block_26(x)
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x = self._basic_block_27(x)
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x = self._basic_block_28(x)
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x = self._downsample_2(x)
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x = self._basic_block_31(x)
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x = self._basic_block_32(x)
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x = self._basic_block_33(x)
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x = self._basic_block_34(x)
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x = self._basic_block_35(x)
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x = self._basic_block_36(x)
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x = self._basic_block_37(x)
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x = self._basic_block_38(x)
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x = self._downsample_3(x)
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x = self._basic_block_41(x)
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x = self._basic_block_42(x)
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x = self._basic_block_43(x)
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x = self._basic_block_44(x)
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x = self._pool(x)
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x = paddle.squeeze(x, axis=[2, 3])
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x = self._out(x)
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return x
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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 DarkNet53(pretrained=False, use_ssld=False, **kwargs):
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model = DarkNet(**kwargs)
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_load_pretrained(
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pretrained, model, MODEL_URLS["DarkNet53"], use_ssld=use_ssld)
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return model
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