diff --git a/examples/yolov3/darknet.py b/examples/yolov3/darknet.py new file mode 100644 index 0000000000000000000000000000000000000000..e03b1f705e0ba2d50e1a58a9689b5357150346f7 --- /dev/null +++ b/examples/yolov3/darknet.py @@ -0,0 +1,239 @@ +# 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 math +import paddle.fluid as fluid +from paddle.fluid.param_attr import ParamAttr +from paddle.fluid.regularizer import L2Decay + +from paddle.fluid.dygraph.nn import Conv2D, BatchNorm, Pool2D, Linear + +from paddle.incubate.hapi.model import Model +from paddle.incubate.hapi.download import get_weights_path_from_url + +__all__ = ['DarkNet', 'darknet53'] + +# {num_layers: (url, md5)} +model_urls = { + 'darknet53': + ('https://paddle-hapi.bj.bcebos.com/models/darknet53.pdparams', + 'ca506a90e2efecb9a2093f8ada808708') +} + + +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__() + + 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.))) + + self.act = act + + 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 + + +class DownSample(fluid.dygraph.Layer): + def __init__(self, ch_in, ch_out, filter_size=3, stride=2, padding=1): + + super(DownSample, self).__init__() + + 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 + + +class BasicBlock(fluid.dygraph.Layer): + def __init__(self, ch_in, ch_out): + super(BasicBlock, self).__init__() + + 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 + + +class LayerWarp(fluid.dygraph.Layer): + def __init__(self, ch_in, ch_out, count): + super(LayerWarp, self).__init__() + + 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 + + +DarkNet_cfg = {53: ([1, 2, 8, 8, 4])} + + +class DarkNet(Model): + """DarkNet model from + `"YOLOv3: An Incremental Improvement" `_ + + Args: + num_layers (int): layer number of DarkNet, only 53 supported currently, default: 53. + num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer + will not be defined. Default: 1000. + with_pool (bool): use pool before the last fc layer or not. Default: True. + classifier_activation (str): activation for the last fc layer. Default: 'softmax'. + + """ + + def __init__(self, + num_layers=53, + num_classes=1000, + with_pool=True, + classifier_activation='softmax'): + super(DarkNet, 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] + self.num_classes = num_classes + self.with_pool = True + ch_in = 3 + self.conv0 = ConvBNLayer( + ch_in=ch_in, ch_out=32, filter_size=3, stride=1, padding=1) + + 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) + + if self.with_pool: + self.global_pool = Pool2D( + pool_size=7, pool_type='avg', global_pooling=True) + + if self.num_classes > 0: + stdv = 1.0 / math.sqrt(32 * (2**(i + 2))) + self.fc_input_dim = 32 * (2**(i + 2)) + + self.fc = Linear( + self.fc_input_dim, + num_classes, + act='softmax', + param_attr=fluid.param_attr.ParamAttr( + initializer=fluid.initializer.Uniform(-stdv, stdv))) + + def forward(self, inputs): + + out = self.conv0(inputs) + out = self.downsample0(out) + + for i, conv_block_i in enumerate(self.darknet53_conv_block_list): + out = conv_block_i(out) + if i < len(self.stages) - 1: + out = self.downsample_list[i](out) + + if self.with_pool: + out = self.global_pool(out) + if self.num_classes > 0: + out = fluid.layers.reshape(out, shape=[-1, self.fc_input_dim]) + out = self.fc(out) + return out + + +def _darknet(arch, num_layers=53, pretrained=False, **kwargs): + model = DarkNet(num_layers, **kwargs) + if pretrained: + assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format( + arch) + weight_path = get_weights_path_from_url(*(model_urls[arch])) + assert weight_path.endswith('.pdparams'), \ + "suffix of weight must be .pdparams" + model.load(weight_path) + return model + + +def darknet53(pretrained=False, **kwargs): + """DarkNet 53-layer model + + Args: + input_channels (bool): channel number of input data, default 3. + pretrained (bool): If True, returns a model pre-trained on ImageNet, + default True. + + """ + return _darknet('darknet53', 53, pretrained, **kwargs)