# 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 annotations import math from typing import ( TYPE_CHECKING, TypedDict, ) from typing_extensions import ( NotRequired, Unpack, ) import paddle from paddle import nn from paddle.base.param_attr import ParamAttr from paddle.nn import AdaptiveAvgPool2D, AvgPool2D, Dropout, Linear, MaxPool2D from paddle.nn.initializer import Uniform from paddle.utils.download import get_weights_path_from_url from ..ops import ConvNormActivation if TYPE_CHECKING: from paddle import Tensor class _InceptionV3Options(TypedDict): num_classes: NotRequired[int] with_pool: NotRequired[bool] __all__ = [] model_urls = { "inception_v3": ( "https://paddle-hapi.bj.bcebos.com/models/inception_v3.pdparams", "649a4547c3243e8b59c656f41fe330b8", ) } class InceptionStem(nn.Layer): def __init__(self) -> None: super().__init__() self.conv_1a_3x3 = ConvNormActivation( in_channels=3, out_channels=32, kernel_size=3, stride=2, padding=0, activation_layer=nn.ReLU, ) self.conv_2a_3x3 = ConvNormActivation( in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=0, activation_layer=nn.ReLU, ) self.conv_2b_3x3 = ConvNormActivation( in_channels=32, out_channels=64, kernel_size=3, padding=1, activation_layer=nn.ReLU, ) self.max_pool = MaxPool2D(kernel_size=3, stride=2, padding=0) self.conv_3b_1x1 = ConvNormActivation( in_channels=64, out_channels=80, kernel_size=1, padding=0, activation_layer=nn.ReLU, ) self.conv_4a_3x3 = ConvNormActivation( in_channels=80, out_channels=192, kernel_size=3, padding=0, activation_layer=nn.ReLU, ) def forward(self, x: Tensor) -> Tensor: x = self.conv_1a_3x3(x) x = self.conv_2a_3x3(x) x = self.conv_2b_3x3(x) x = self.max_pool(x) x = self.conv_3b_1x1(x) x = self.conv_4a_3x3(x) x = self.max_pool(x) return x class InceptionA(nn.Layer): def __init__(self, num_channels: int, pool_features: int) -> None: super().__init__() self.branch1x1 = ConvNormActivation( in_channels=num_channels, out_channels=64, kernel_size=1, padding=0, activation_layer=nn.ReLU, ) self.branch5x5_1 = ConvNormActivation( in_channels=num_channels, out_channels=48, kernel_size=1, padding=0, activation_layer=nn.ReLU, ) self.branch5x5_2 = ConvNormActivation( in_channels=48, out_channels=64, kernel_size=5, padding=2, activation_layer=nn.ReLU, ) self.branch3x3dbl_1 = ConvNormActivation( in_channels=num_channels, out_channels=64, kernel_size=1, padding=0, activation_layer=nn.ReLU, ) self.branch3x3dbl_2 = ConvNormActivation( in_channels=64, out_channels=96, kernel_size=3, padding=1, activation_layer=nn.ReLU, ) self.branch3x3dbl_3 = ConvNormActivation( in_channels=96, out_channels=96, kernel_size=3, padding=1, activation_layer=nn.ReLU, ) self.branch_pool = AvgPool2D( kernel_size=3, stride=1, padding=1, exclusive=False ) self.branch_pool_conv = ConvNormActivation( in_channels=num_channels, out_channels=pool_features, kernel_size=1, padding=0, activation_layer=nn.ReLU, ) def forward(self, x: Tensor) -> Tensor: branch1x1 = self.branch1x1(x) branch5x5 = self.branch5x5_1(x) branch5x5 = self.branch5x5_2(branch5x5) branch3x3dbl = self.branch3x3dbl_1(x) branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) branch_pool = self.branch_pool(x) branch_pool = self.branch_pool_conv(branch_pool) x = paddle.concat( [branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=1 ) return x class InceptionB(nn.Layer): def __init__(self, num_channels: int) -> None: super().__init__() self.branch3x3 = ConvNormActivation( in_channels=num_channels, out_channels=384, kernel_size=3, stride=2, padding=0, activation_layer=nn.ReLU, ) self.branch3x3dbl_1 = ConvNormActivation( in_channels=num_channels, out_channels=64, kernel_size=1, padding=0, activation_layer=nn.ReLU, ) self.branch3x3dbl_2 = ConvNormActivation( in_channels=64, out_channels=96, kernel_size=3, padding=1, activation_layer=nn.ReLU, ) self.branch3x3dbl_3 = ConvNormActivation( in_channels=96, out_channels=96, kernel_size=3, stride=2, padding=0, activation_layer=nn.ReLU, ) self.branch_pool = MaxPool2D(kernel_size=3, stride=2) def forward(self, x: Tensor) -> Tensor: branch3x3 = self.branch3x3(x) branch3x3dbl = self.branch3x3dbl_1(x) branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) branch_pool = self.branch_pool(x) x = paddle.concat([branch3x3, branch3x3dbl, branch_pool], axis=1) return x class InceptionC(nn.Layer): def __init__(self, num_channels: int, channels_7x7: int) -> None: super().__init__() self.branch1x1 = ConvNormActivation( in_channels=num_channels, out_channels=192, kernel_size=1, padding=0, activation_layer=nn.ReLU, ) self.branch7x7_1 = ConvNormActivation( in_channels=num_channels, out_channels=channels_7x7, kernel_size=1, stride=1, padding=0, activation_layer=nn.ReLU, ) self.branch7x7_2 = ConvNormActivation( in_channels=channels_7x7, out_channels=channels_7x7, kernel_size=(1, 7), stride=1, padding=(0, 3), activation_layer=nn.ReLU, ) self.branch7x7_3 = ConvNormActivation( in_channels=channels_7x7, out_channels=192, kernel_size=(7, 1), stride=1, padding=(3, 0), activation_layer=nn.ReLU, ) self.branch7x7dbl_1 = ConvNormActivation( in_channels=num_channels, out_channels=channels_7x7, kernel_size=1, padding=0, activation_layer=nn.ReLU, ) self.branch7x7dbl_2 = ConvNormActivation( in_channels=channels_7x7, out_channels=channels_7x7, kernel_size=(7, 1), padding=(3, 0), activation_layer=nn.ReLU, ) self.branch7x7dbl_3 = ConvNormActivation( in_channels=channels_7x7, out_channels=channels_7x7, kernel_size=(1, 7), padding=(0, 3), activation_layer=nn.ReLU, ) self.branch7x7dbl_4 = ConvNormActivation( in_channels=channels_7x7, out_channels=channels_7x7, kernel_size=(7, 1), padding=(3, 0), activation_layer=nn.ReLU, ) self.branch7x7dbl_5 = ConvNormActivation( in_channels=channels_7x7, out_channels=192, kernel_size=(1, 7), padding=(0, 3), activation_layer=nn.ReLU, ) self.branch_pool = AvgPool2D( kernel_size=3, stride=1, padding=1, exclusive=False ) self.branch_pool_conv = ConvNormActivation( in_channels=num_channels, out_channels=192, kernel_size=1, padding=0, activation_layer=nn.ReLU, ) def forward(self, x: Tensor) -> Tensor: branch1x1 = self.branch1x1(x) branch7x7 = self.branch7x7_1(x) branch7x7 = self.branch7x7_2(branch7x7) branch7x7 = self.branch7x7_3(branch7x7) branch7x7dbl = self.branch7x7dbl_1(x) branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl) branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl) branch_pool = self.branch_pool(x) branch_pool = self.branch_pool_conv(branch_pool) x = paddle.concat( [branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1 ) return x class InceptionD(nn.Layer): def __init__(self, num_channels: int) -> None: super().__init__() self.branch3x3_1 = ConvNormActivation( in_channels=num_channels, out_channels=192, kernel_size=1, padding=0, activation_layer=nn.ReLU, ) self.branch3x3_2 = ConvNormActivation( in_channels=192, out_channels=320, kernel_size=3, stride=2, padding=0, activation_layer=nn.ReLU, ) self.branch7x7x3_1 = ConvNormActivation( in_channels=num_channels, out_channels=192, kernel_size=1, padding=0, activation_layer=nn.ReLU, ) self.branch7x7x3_2 = ConvNormActivation( in_channels=192, out_channels=192, kernel_size=(1, 7), padding=(0, 3), activation_layer=nn.ReLU, ) self.branch7x7x3_3 = ConvNormActivation( in_channels=192, out_channels=192, kernel_size=(7, 1), padding=(3, 0), activation_layer=nn.ReLU, ) self.branch7x7x3_4 = ConvNormActivation( in_channels=192, out_channels=192, kernel_size=3, stride=2, padding=0, activation_layer=nn.ReLU, ) self.branch_pool = MaxPool2D(kernel_size=3, stride=2) def forward(self, x: Tensor) -> Tensor: branch3x3 = self.branch3x3_1(x) branch3x3 = self.branch3x3_2(branch3x3) branch7x7x3 = self.branch7x7x3_1(x) branch7x7x3 = self.branch7x7x3_2(branch7x7x3) branch7x7x3 = self.branch7x7x3_3(branch7x7x3) branch7x7x3 = self.branch7x7x3_4(branch7x7x3) branch_pool = self.branch_pool(x) x = paddle.concat([branch3x3, branch7x7x3, branch_pool], axis=1) return x class InceptionE(nn.Layer): def __init__(self, num_channels: int) -> None: super().__init__() self.branch1x1 = ConvNormActivation( in_channels=num_channels, out_channels=320, kernel_size=1, padding=0, activation_layer=nn.ReLU, ) self.branch3x3_1 = ConvNormActivation( in_channels=num_channels, out_channels=384, kernel_size=1, padding=0, activation_layer=nn.ReLU, ) self.branch3x3_2a = ConvNormActivation( in_channels=384, out_channels=384, kernel_size=(1, 3), padding=(0, 1), activation_layer=nn.ReLU, ) self.branch3x3_2b = ConvNormActivation( in_channels=384, out_channels=384, kernel_size=(3, 1), padding=(1, 0), activation_layer=nn.ReLU, ) self.branch3x3dbl_1 = ConvNormActivation( in_channels=num_channels, out_channels=448, kernel_size=1, padding=0, activation_layer=nn.ReLU, ) self.branch3x3dbl_2 = ConvNormActivation( in_channels=448, out_channels=384, kernel_size=3, padding=1, activation_layer=nn.ReLU, ) self.branch3x3dbl_3a = ConvNormActivation( in_channels=384, out_channels=384, kernel_size=(1, 3), padding=(0, 1), activation_layer=nn.ReLU, ) self.branch3x3dbl_3b = ConvNormActivation( in_channels=384, out_channels=384, kernel_size=(3, 1), padding=(1, 0), activation_layer=nn.ReLU, ) self.branch_pool = AvgPool2D( kernel_size=3, stride=1, padding=1, exclusive=False ) self.branch_pool_conv = ConvNormActivation( in_channels=num_channels, out_channels=192, kernel_size=1, padding=0, activation_layer=nn.ReLU, ) def forward(self, x: Tensor) -> Tensor: branch1x1 = self.branch1x1(x) branch3x3 = self.branch3x3_1(x) branch3x3 = [ self.branch3x3_2a(branch3x3), self.branch3x3_2b(branch3x3), ] branch3x3 = paddle.concat(branch3x3, axis=1) branch3x3dbl = self.branch3x3dbl_1(x) branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) branch3x3dbl = [ self.branch3x3dbl_3a(branch3x3dbl), self.branch3x3dbl_3b(branch3x3dbl), ] branch3x3dbl = paddle.concat(branch3x3dbl, axis=1) branch_pool = self.branch_pool(x) branch_pool = self.branch_pool_conv(branch_pool) x = paddle.concat( [branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1 ) return x class InceptionV3(nn.Layer): """Inception v3 model from `"Rethinking the Inception Architecture for Computer Vision" `_. 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 Inception v3 model. Examples: .. code-block:: python >>> import paddle >>> from paddle.vision.models import InceptionV3 >>> inception_v3 = InceptionV3() >>> x = paddle.rand([1, 3, 299, 299]) >>> out = inception_v3(x) >>> print(out.shape) [1, 1000] """ num_classes: int with_pool: bool def __init__(self, num_classes: int = 1000, with_pool: bool = True) -> None: super().__init__() self.num_classes = num_classes self.with_pool = with_pool self.layers_config = { "inception_a": [[192, 256, 288], [32, 64, 64]], "inception_b": [288], "inception_c": [[768, 768, 768, 768], [128, 160, 160, 192]], "inception_d": [768], "inception_e": [1280, 2048], } inception_a_list = self.layers_config["inception_a"] inception_c_list = self.layers_config["inception_c"] inception_b_list = self.layers_config["inception_b"] inception_d_list = self.layers_config["inception_d"] inception_e_list = self.layers_config["inception_e"] self.inception_stem = InceptionStem() self.inception_block_list = nn.LayerList() for i in range(len(inception_a_list[0])): inception_a = InceptionA( inception_a_list[0][i], inception_a_list[1][i] ) self.inception_block_list.append(inception_a) for i in range(len(inception_b_list)): inception_b = InceptionB(inception_b_list[i]) self.inception_block_list.append(inception_b) for i in range(len(inception_c_list[0])): inception_c = InceptionC( inception_c_list[0][i], inception_c_list[1][i] ) self.inception_block_list.append(inception_c) for i in range(len(inception_d_list)): inception_d = InceptionD(inception_d_list[i]) self.inception_block_list.append(inception_d) for i in range(len(inception_e_list)): inception_e = InceptionE(inception_e_list[i]) self.inception_block_list.append(inception_e) if with_pool: self.avg_pool = AdaptiveAvgPool2D(1) if num_classes > 0: self.dropout = Dropout(p=0.2, mode="downscale_in_infer") stdv = 1.0 / math.sqrt(2048 * 1.0) self.fc = Linear( 2048, num_classes, weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)), bias_attr=ParamAttr(), ) def forward(self, x: Tensor) -> Tensor: x = self.inception_stem(x) for inception_block in self.inception_block_list: x = inception_block(x) if self.with_pool: x = self.avg_pool(x) if self.num_classes > 0: x = paddle.reshape(x, shape=[-1, 2048]) x = self.dropout(x) x = self.fc(x) return x def inception_v3( pretrained: bool = False, **kwargs: Unpack[_InceptionV3Options] ) -> InceptionV3: """Inception v3 model from `"Rethinking the Inception Architecture for Computer Vision" `_. 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:`InceptionV3 `. Returns: :ref:`api_paddle_nn_Layer`. An instance of Inception v3 model. Examples: .. code-block:: python >>> import paddle >>> from paddle.vision.models import inception_v3 >>> # Build model >>> model = inception_v3() >>> # Build model and load imagenet pretrained weight >>> # model = inception_v3(pretrained=True) >>> x = paddle.rand([1, 3, 299, 299]) >>> out = model(x) >>> print(out.shape) [1, 1000] """ model = InceptionV3(**kwargs) arch = "inception_v3" if pretrained: assert arch in model_urls, ( f"{arch} model do not have a pretrained model now, you should set pretrained=False" ) 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