import paddle import paddle.fluid as fluid from paddle.fluid.param_attr import ParamAttr from paddle.fluid.layer_helper import LayerHelper from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear import math __all__ = ['GoogLeNet_DY'] def xavier(channels, filter_size, name): stdv = (3.0 / (filter_size**2 * channels))**0.5 param_attr = ParamAttr( initializer=fluid.initializer.Uniform(-stdv, stdv), name=name + "_weights") return param_attr class ConvLayer(fluid.dygraph.Layer): def __init__(self, num_channels, num_filters, filter_size, stride=1, groups=1, act=None, name=None): super(ConvLayer, self).__init__() self._conv = Conv2D( num_channels=num_channels, num_filters=num_filters, filter_size=filter_size, stride=stride, padding=(filter_size - 1) // 2, groups=groups, act=None, param_attr=ParamAttr(name=name + "_weights"), bias_attr=False) def forward(self, inputs): y = self._conv(inputs) return y class Inception(fluid.dygraph.Layer): def __init__(self, input_channels, output_channels, filter1, filter3R, filter3, filter5R, filter5, proj, name=None): super(Inception, self).__init__() self._conv1 = ConvLayer( input_channels, filter1, 1, name="inception_" + name + "_1x1") self._conv3r = ConvLayer( input_channels, filter3R, 1, name="inception_" + name + "_3x3_reduce") self._conv3 = ConvLayer( filter3R, filter3, 3, name="inception_" + name + "_3x3") self._conv5r = ConvLayer( input_channels, filter5R, 1, name="inception_" + name + "_5x5_reduce") self._conv5 = ConvLayer( filter5R, filter5, 5, name="inception_" + name + "_5x5") self._pool = Pool2D( pool_size=3, pool_type="max", pool_stride=1, pool_padding=1) self._convprj = ConvLayer( input_channels, proj, 1, name="inception_" + name + "_3x3_proj") def forward(self, inputs): conv1 = self._conv1(inputs) conv3r = self._conv3r(inputs) conv3 = self._conv3(conv3r) conv5r = self._conv5r(inputs) conv5 = self._conv5(conv5r) pool = self._pool(inputs) convprj = self._convprj(pool) cat = fluid.layers.concat([conv1, conv3, conv5, convprj], axis=1) layer_helper = LayerHelper(self.full_name(), act="relu") return layer_helper.append_activation(cat) class GoogleNetDY(fluid.dygraph.Layer): def __init__(self, class_dim=1000): super(GoogleNetDY, self).__init__() self._conv = ConvLayer(3, 64, 7, 2, name="conv1") self._pool = Pool2D(pool_size=3, pool_type="max", pool_stride=2) self._conv_1 = ConvLayer(64, 64, 1, name="conv2_1x1") self._conv_2 = ConvLayer(64, 192, 3, name="conv2_3x3") self._ince3a = Inception( 192, 192, 64, 96, 128, 16, 32, 32, name="ince3a") self._ince3b = Inception( 256, 256, 128, 128, 192, 32, 96, 64, name="ince3b") self._ince4a = Inception( 480, 480, 192, 96, 208, 16, 48, 64, name="ince4a") self._ince4b = Inception( 512, 512, 160, 112, 224, 24, 64, 64, name="ince4b") self._ince4c = Inception( 512, 512, 128, 128, 256, 24, 64, 64, name="ince4c") self._ince4d = Inception( 512, 512, 112, 144, 288, 32, 64, 64, name="ince4d") self._ince4e = Inception( 528, 528, 256, 160, 320, 32, 128, 128, name="ince4e") self._ince5a = Inception( 832, 832, 256, 160, 320, 32, 128, 128, name="ince5a") self._ince5b = Inception( 832, 832, 384, 192, 384, 48, 128, 128, name="ince5b") self._pool_5 = Pool2D(pool_size=7, pool_type='avg', pool_stride=7) self._drop = fluid.dygraph.Dropout(p=0.4) self._fc_out = Linear( 1024, class_dim, param_attr=xavier(1024, 1, "out"), bias_attr=ParamAttr(name="out_offset"), act="softmax") self._pool_o1 = Pool2D(pool_size=5, pool_stride=3, pool_type="avg") self._conv_o1 = ConvLayer(512, 128, 1, name="conv_o1") self._fc_o1 = Linear( 1152, 1024, param_attr=xavier(2048, 1, "fc_o1"), bias_attr=ParamAttr(name="fc_o1_offset"), act="relu") self._drop_o1 = fluid.dygraph.Dropout(p=0.7) self._out1 = Linear( 1024, class_dim, param_attr=xavier(1024, 1, "out1"), bias_attr=ParamAttr(name="out1_offset"), act="softmax") self._pool_o2 = Pool2D(pool_size=5, pool_stride=3, pool_type='avg') self._conv_o2 = ConvLayer(528, 128, 1, name="conv_o2") self._fc_o2 = Linear( 1152, 1024, param_attr=xavier(2048, 1, "fc_o2"), bias_attr=ParamAttr(name="fc_o2_offset")) self._drop_o2 = fluid.dygraph.Dropout(p=0.7) self._out2 = Linear( 1024, class_dim, param_attr=xavier(1024, 1, "out2"), bias_attr=ParamAttr(name="out2_offset")) def forward(self, inputs): x = self._conv(inputs) x = self._pool(x) x = self._conv_1(x) x = self._conv_2(x) x = self._pool(x) x = self._ince3a(x) x = self._ince3b(x) x = self._pool(x) ince4a = self._ince4a(x) x = self._ince4b(ince4a) x = self._ince4c(x) ince4d = self._ince4d(x) x = self._ince4e(ince4d) x = self._pool(x) x = self._ince5a(x) ince5b = self._ince5b(x) x = self._pool_5(ince5b) x = self._drop(x) x = fluid.layers.squeeze(x, axes=[2, 3]) out = self._fc_out(x) x = self._pool_o1(ince4a) x = self._conv_o1(x) x = fluid.layers.flatten(x) x = self._fc_o1(x) x = self._drop_o1(x) out1 = self._out1(x) x = self._pool_o2(ince4d) x = self._conv_o2(x) x = fluid.layers.flatten(x) x = self._fc_o2(x) x = self._drop_o2(x) out2 = self._out2(x) return [out, out1, out2] def GoogLeNet(**args): model = GoogleNetDY(**args) return model