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Merge pull request PaddlePaddle#23 from heavengate/yolov3
add YOLOv3
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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. | ||
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from . import resnet | ||
from . import darknet | ||
from . import yolov3 | ||
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from .resnet import * | ||
from .darknet import * | ||
from .yolov3 import * | ||
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__all__ = resnet.__all__ \ | ||
+ darknet.__all__ \ | ||
+ yolov3.__all__ |
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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. | ||
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import paddle.fluid as fluid | ||
from paddle.fluid.param_attr import ParamAttr | ||
from paddle.fluid.regularizer import L2Decay | ||
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from paddle.fluid.dygraph.nn import Conv2D, BatchNorm | ||
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from model import Model | ||
from .download import get_weights_path | ||
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__all__ = ['DarkNet53', 'ConvBNLayer', 'darknet53'] | ||
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# {num_layers: (url, md5)} | ||
pretrain_infos = { | ||
53: ('https://paddlemodels.bj.bcebos.com/hapi/darknet53.pdparams', | ||
'2506357a5c31e865785112fc614a487d') | ||
} | ||
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class ConvBNLayer(fluid.dygraph.Layer): | ||
def __init__(self, | ||
ch_in, | ||
ch_out, | ||
filter_size=3, | ||
stride=1, | ||
groups=1, | ||
padding=0, | ||
act="leaky"): | ||
super(ConvBNLayer, self).__init__() | ||
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self.conv = Conv2D( | ||
num_channels=ch_in, | ||
num_filters=ch_out, | ||
filter_size=filter_size, | ||
stride=stride, | ||
padding=padding, | ||
groups=groups, | ||
param_attr=ParamAttr( | ||
initializer=fluid.initializer.Normal(0., 0.02)), | ||
bias_attr=False, | ||
act=None) | ||
self.batch_norm = BatchNorm( | ||
num_channels=ch_out, | ||
param_attr=ParamAttr( | ||
initializer=fluid.initializer.Normal(0., 0.02), | ||
regularizer=L2Decay(0.)), | ||
bias_attr=ParamAttr( | ||
initializer=fluid.initializer.Constant(0.0), | ||
regularizer=L2Decay(0.))) | ||
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self.act = act | ||
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def forward(self, inputs): | ||
out = self.conv(inputs) | ||
out = self.batch_norm(out) | ||
if self.act == 'leaky': | ||
out = fluid.layers.leaky_relu(x=out, alpha=0.1) | ||
return out | ||
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class DownSample(fluid.dygraph.Layer): | ||
def __init__(self, | ||
ch_in, | ||
ch_out, | ||
filter_size=3, | ||
stride=2, | ||
padding=1): | ||
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super(DownSample, self).__init__() | ||
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self.conv_bn_layer = ConvBNLayer( | ||
ch_in=ch_in, | ||
ch_out=ch_out, | ||
filter_size=filter_size, | ||
stride=stride, | ||
padding=padding) | ||
self.ch_out = ch_out | ||
def forward(self, inputs): | ||
out = self.conv_bn_layer(inputs) | ||
return out | ||
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class BasicBlock(fluid.dygraph.Layer): | ||
def __init__(self, ch_in, ch_out): | ||
super(BasicBlock, self).__init__() | ||
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self.conv1 = ConvBNLayer( | ||
ch_in=ch_in, | ||
ch_out=ch_out, | ||
filter_size=1, | ||
stride=1, | ||
padding=0) | ||
self.conv2 = ConvBNLayer( | ||
ch_in=ch_out, | ||
ch_out=ch_out*2, | ||
filter_size=3, | ||
stride=1, | ||
padding=1) | ||
def forward(self, inputs): | ||
conv1 = self.conv1(inputs) | ||
conv2 = self.conv2(conv1) | ||
out = fluid.layers.elementwise_add(x=inputs, y=conv2, act=None) | ||
return out | ||
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class LayerWarp(fluid.dygraph.Layer): | ||
def __init__(self, ch_in, ch_out, count): | ||
super(LayerWarp,self).__init__() | ||
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self.basicblock0 = BasicBlock(ch_in, ch_out) | ||
self.res_out_list = [] | ||
for i in range(1,count): | ||
res_out = self.add_sublayer("basic_block_%d" % (i), | ||
BasicBlock( | ||
ch_out*2, | ||
ch_out)) | ||
self.res_out_list.append(res_out) | ||
self.ch_out = ch_out | ||
def forward(self,inputs): | ||
y = self.basicblock0(inputs) | ||
for basic_block_i in self.res_out_list: | ||
y = basic_block_i(y) | ||
return y | ||
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DarkNet_cfg = {53: ([1, 2, 8, 8, 4])} | ||
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class DarkNet53(Model): | ||
def __init__(self, num_layers=53, ch_in=3): | ||
super(DarkNet53, self).__init__() | ||
assert num_layers in DarkNet_cfg.keys(), \ | ||
"only support num_layers in {} currently" \ | ||
.format(DarkNet_cfg.keys()) | ||
self.stages = DarkNet_cfg[num_layers] | ||
self.stages = self.stages[0:5] | ||
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self.conv0 = ConvBNLayer( | ||
ch_in=ch_in, | ||
ch_out=32, | ||
filter_size=3, | ||
stride=1, | ||
padding=1) | ||
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self.downsample0 = DownSample( | ||
ch_in=32, | ||
ch_out=32 * 2) | ||
self.darknet53_conv_block_list = [] | ||
self.downsample_list = [] | ||
ch_in = [64,128,256,512,1024] | ||
for i, stage in enumerate(self.stages): | ||
conv_block = self.add_sublayer( | ||
"stage_%d" % (i), | ||
LayerWarp( | ||
int(ch_in[i]), | ||
32*(2**i), | ||
stage)) | ||
self.darknet53_conv_block_list.append(conv_block) | ||
for i in range(len(self.stages) - 1): | ||
downsample = self.add_sublayer( | ||
"stage_%d_downsample" % i, | ||
DownSample( | ||
ch_in = 32*(2**(i+1)), | ||
ch_out = 32*(2**(i+2)))) | ||
self.downsample_list.append(downsample) | ||
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def forward(self,inputs): | ||
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out = self.conv0(inputs) | ||
out = self.downsample0(out) | ||
blocks = [] | ||
for i, conv_block_i in enumerate(self.darknet53_conv_block_list): | ||
out = conv_block_i(out) | ||
blocks.append(out) | ||
if i < len(self.stages) - 1: | ||
out = self.downsample_list[i](out) | ||
return blocks[-1:-4:-1] | ||
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def _darknet(num_layers=53, input_channels=3, pretrained=True): | ||
model = DarkNet53(num_layers, input_channels) | ||
if pretrained: | ||
assert num_layers in pretrain_infos.keys(), \ | ||
"DarkNet{} do not have pretrained weights now, " \ | ||
"pretrained should be set as False".format(num_layers) | ||
weight_path = get_weights_path(*(pretrain_infos[num_layers])) | ||
assert weight_path.endswith('.pdparams'), \ | ||
"suffix of weight must be .pdparams" | ||
model.load(weight_path[:-9]) | ||
return model | ||
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def darknet53(input_channels=3, pretrained=True): | ||
return _darknet(53, input_channels, pretrained) |
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