# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # # 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. from __future__ import division from __future__ import print_function import paddle import paddle.nn as nn import paddle.nn.functional as F from paddle.nn import Conv2D, Linear, Dropout from paddle.nn import MaxPool2D, AvgPool2D, AdaptiveAvgPool2D from paddle.nn.initializer import Uniform from paddle.fluid.param_attr import ParamAttr from paddle.utils.download import get_weights_path_from_url __all__ = [] model_urls = { "googlenet": ("https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams", "80c06f038e905c53ab32c40eca6e26ae") } def xavier(channels, filter_size): stdv = (3.0 / (filter_size**2 * channels))**0.5 param_attr = ParamAttr(initializer=Uniform(-stdv, stdv)) return param_attr class ConvLayer(nn.Layer): def __init__(self, num_channels, num_filters, filter_size, stride=1, groups=1): 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, 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): super(Inception, self).__init__() self._conv1 = ConvLayer(input_channels, filter1, 1) self._conv3r = ConvLayer(input_channels, filter3R, 1) self._conv3 = ConvLayer(filter3R, filter3, 3) self._conv5r = ConvLayer(input_channels, filter5R, 1) self._conv5 = ConvLayer(filter5R, filter5, 5) self._pool = MaxPool2D(kernel_size=3, stride=1, padding=1) self._convprj = ConvLayer(input_channels, proj, 1) 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 GoogLeNet(nn.Layer): """GoogLeNet (Inception v1) model architecture from `"Going Deeper with Convolutions" `_. Args: num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer will not be defined. Default: 1000. with_pool (bool, optional): Use pool before the last fc layer or not. Default: True. Returns: :ref:`api_paddle_nn_Layer`. An instance of GoogLeNet (Inception v1) model. Examples: .. code-block:: python import paddle from paddle.vision.models import GoogLeNet # build model model = GoogLeNet() x = paddle.rand([1, 3, 224, 224]) out, out1, out2 = model(x) print(out.shape, out1.shape, out2.shape) # [1, 1000] [1, 1000] [1, 1000] """ def __init__(self, num_classes=1000, with_pool=True): super(GoogLeNet, self).__init__() self.num_classes = num_classes self.with_pool = with_pool self._conv = ConvLayer(3, 64, 7, 2) self._pool = MaxPool2D(kernel_size=3, stride=2) self._conv_1 = ConvLayer(64, 64, 1) self._conv_2 = ConvLayer(64, 192, 3) self._ince3a = Inception(192, 192, 64, 96, 128, 16, 32, 32) self._ince3b = Inception(256, 256, 128, 128, 192, 32, 96, 64) self._ince4a = Inception(480, 480, 192, 96, 208, 16, 48, 64) self._ince4b = Inception(512, 512, 160, 112, 224, 24, 64, 64) self._ince4c = Inception(512, 512, 128, 128, 256, 24, 64, 64) self._ince4d = Inception(512, 512, 112, 144, 288, 32, 64, 64) self._ince4e = Inception(528, 528, 256, 160, 320, 32, 128, 128) self._ince5a = Inception(832, 832, 256, 160, 320, 32, 128, 128) self._ince5b = Inception(832, 832, 384, 192, 384, 48, 128, 128) if with_pool: # out self._pool_5 = AdaptiveAvgPool2D(1) # out1 self._pool_o1 = AvgPool2D(kernel_size=5, stride=3) # out2 self._pool_o2 = AvgPool2D(kernel_size=5, stride=3) if num_classes > 0: # out self._drop = Dropout(p=0.4, mode="downscale_in_infer") self._fc_out = Linear(1024, num_classes, weight_attr=xavier(1024, 1)) # out1 self._conv_o1 = ConvLayer(512, 128, 1) self._fc_o1 = Linear(1152, 1024, weight_attr=xavier(2048, 1)) self._drop_o1 = Dropout(p=0.7, mode="downscale_in_infer") self._out1 = Linear(1024, num_classes, weight_attr=xavier(1024, 1)) # out2 self._conv_o2 = ConvLayer(528, 128, 1) self._fc_o2 = Linear(1152, 1024, weight_attr=xavier(2048, 1)) self._drop_o2 = Dropout(p=0.7, mode="downscale_in_infer") self._out2 = Linear(1024, num_classes, weight_attr=xavier(1024, 1)) 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) out, out1, out2 = ince5b, ince4a, ince4d if self.with_pool: out = self._pool_5(out) out1 = self._pool_o1(out1) out2 = self._pool_o2(out2) if self.num_classes > 0: out = self._drop(out) out = paddle.squeeze(out, axis=[2, 3]) out = self._fc_out(out) out1 = self._conv_o1(out1) out1 = paddle.flatten(out1, start_axis=1, stop_axis=-1) out1 = self._fc_o1(out1) out1 = F.relu(out1) out1 = self._drop_o1(out1) out1 = self._out1(out1) out2 = self._conv_o2(out2) out2 = paddle.flatten(out2, start_axis=1, stop_axis=-1) out2 = self._fc_o2(out2) out2 = self._drop_o2(out2) out2 = self._out2(out2) return [out, out1, out2] def googlenet(pretrained=False, **kwargs): """GoogLeNet (Inception v1) model architecture from `"Going Deeper with Convolutions" `_. Args: pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained on ImageNet. Default: False. **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`GoogLeNet `. Returns: :ref:`api_paddle_nn_Layer`. An instance of GoogLeNet (Inception v1) model. Examples: .. code-block:: python import paddle from paddle.vision.models import googlenet # build model model = googlenet() # build model and load imagenet pretrained weight # model = googlenet(pretrained=True) x = paddle.rand([1, 3, 224, 224]) out, out1, out2 = model(x) print(out.shape, out1.shape, out2.shape) # [1, 1000] [1, 1000] [1, 1000] """ model = GoogLeNet(**kwargs) arch = "googlenet" 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][0], model_urls[arch][1]) param = paddle.load(weight_path) model.set_dict(param) return model