import paddle from paddle import ParamAttr import paddle.nn as nn import paddle.nn.functional as F from paddle.nn import Conv2D, BatchNorm, Linear, Dropout from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D from paddle.nn.initializer import Uniform import math from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url MODEL_URLS = {"GoogLeNet": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams", } __all__ = list(MODEL_URLS.keys()) def xavier(channels, filter_size, name): stdv = (3.0 / (filter_size**2 * channels))**0.5 param_attr = ParamAttr( initializer=Uniform(-stdv, stdv), name=name + "_weights") return param_attr class ConvLayer(nn.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( in_channels=num_channels, out_channels=num_filters, kernel_size=filter_size, stride=stride, padding=(filter_size - 1) // 2, groups=groups, weight_attr=ParamAttr(name=name + "_weights"), bias_attr=False) def forward(self, inputs): y = self._conv(inputs) return y class Inception(nn.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 = MaxPool2D(kernel_size=3, stride=1, 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 = paddle.concat([conv1, conv3, conv5, convprj], axis=1) cat = F.relu(cat) return cat class GoogLeNetDY(nn.Layer): def __init__(self, class_dim=1000): super(GoogLeNetDY, self).__init__() self._conv = ConvLayer(3, 64, 7, 2, name="conv1") self._pool = MaxPool2D(kernel_size=3, 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 = AvgPool2D(kernel_size=7, stride=7) self._drop = Dropout(p=0.4, mode="downscale_in_infer") self._fc_out = Linear( 1024, class_dim, weight_attr=xavier(1024, 1, "out"), bias_attr=ParamAttr(name="out_offset")) self._pool_o1 = AvgPool2D(kernel_size=5, stride=3) self._conv_o1 = ConvLayer(512, 128, 1, name="conv_o1") self._fc_o1 = Linear( 1152, 1024, weight_attr=xavier(2048, 1, "fc_o1"), bias_attr=ParamAttr(name="fc_o1_offset")) self._drop_o1 = Dropout(p=0.7, mode="downscale_in_infer") self._out1 = Linear( 1024, class_dim, weight_attr=xavier(1024, 1, "out1"), bias_attr=ParamAttr(name="out1_offset")) self._pool_o2 = AvgPool2D(kernel_size=5, stride=3) self._conv_o2 = ConvLayer(528, 128, 1, name="conv_o2") self._fc_o2 = Linear( 1152, 1024, weight_attr=xavier(2048, 1, "fc_o2"), bias_attr=ParamAttr(name="fc_o2_offset")) self._drop_o2 = Dropout(p=0.7, mode="downscale_in_infer") self._out2 = Linear( 1024, class_dim, weight_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 = paddle.squeeze(x, axis=[2, 3]) out = self._fc_out(x) x = self._pool_o1(ince4a) x = self._conv_o1(x) x = paddle.flatten(x, start_axis=1, stop_axis=-1) x = self._fc_o1(x) x = F.relu(x) x = self._drop_o1(x) out1 = self._out1(x) x = self._pool_o2(ince4d) x = self._conv_o2(x) x = paddle.flatten(x, start_axis=1, stop_axis=-1) x = self._fc_o2(x) x = self._drop_o2(x) out2 = self._out2(x) return [out, out1, out2] def _load_pretrained(pretrained, model, model_url, use_ssld=False): if pretrained is False: pass elif pretrained is True: load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld) elif isinstance(pretrained, str): load_dygraph_pretrain(model, pretrained) else: raise RuntimeError( "pretrained type is not available. Please use `string` or `boolean` type." ) def GoogLeNet(pretrained=False, use_ssld=False, **kwargs): model = GoogLeNetDY(**kwargs) _load_pretrained(pretrained, model, MODEL_URLS["GoogLeNet"], use_ssld=use_ssld) return model