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- # copyright (c) 2021 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.
- from __future__ import absolute_import, division, print_function
- import math
- import paddle
- from paddle import ParamAttr
- import paddle.nn as nn
- from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
- from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
- from paddle.nn.initializer import Uniform
- from paddlex.ppcls.arch.backbone.base.theseus_layer import TheseusLayer
- from paddlex.ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
- MODEL_URLS = {
- "InceptionV3":
- "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/InceptionV3_pretrained.pdparams"
- }
- __all__ = MODEL_URLS.keys()
- '''
- InceptionV3 config: dict.
- key: inception blocks of InceptionV3.
- values: conv num in different blocks.
- '''
- NET_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]
- }
- class ConvBNLayer(TheseusLayer):
- def __init__(self,
- num_channels,
- num_filters,
- filter_size,
- stride=1,
- padding=0,
- groups=1,
- act="relu"):
- super().__init__()
- self.act = act
- self.conv = Conv2D(
- in_channels=num_channels,
- out_channels=num_filters,
- kernel_size=filter_size,
- stride=stride,
- padding=padding,
- groups=groups,
- bias_attr=False)
- self.bn = BatchNorm(num_filters)
- self.relu = nn.ReLU()
- def forward(self, x):
- x = self.conv(x)
- x = self.bn(x)
- if self.act:
- x = self.relu(x)
- return x
- class InceptionStem(TheseusLayer):
- def __init__(self):
- super().__init__()
- self.conv_1a_3x3 = ConvBNLayer(
- num_channels=3,
- num_filters=32,
- filter_size=3,
- stride=2,
- act="relu")
- self.conv_2a_3x3 = ConvBNLayer(
- num_channels=32,
- num_filters=32,
- filter_size=3,
- stride=1,
- act="relu")
- self.conv_2b_3x3 = ConvBNLayer(
- num_channels=32,
- num_filters=64,
- filter_size=3,
- padding=1,
- act="relu")
- self.max_pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
- self.conv_3b_1x1 = ConvBNLayer(
- num_channels=64, num_filters=80, filter_size=1, act="relu")
- self.conv_4a_3x3 = ConvBNLayer(
- num_channels=80, num_filters=192, filter_size=3, act="relu")
- def forward(self, x):
- 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(TheseusLayer):
- def __init__(self, num_channels, pool_features):
- super().__init__()
- self.branch1x1 = ConvBNLayer(
- num_channels=num_channels,
- num_filters=64,
- filter_size=1,
- act="relu")
- self.branch5x5_1 = ConvBNLayer(
- num_channels=num_channels,
- num_filters=48,
- filter_size=1,
- act="relu")
- self.branch5x5_2 = ConvBNLayer(
- num_channels=48,
- num_filters=64,
- filter_size=5,
- padding=2,
- act="relu")
- self.branch3x3dbl_1 = ConvBNLayer(
- num_channels=num_channels,
- num_filters=64,
- filter_size=1,
- act="relu")
- self.branch3x3dbl_2 = ConvBNLayer(
- num_channels=64,
- num_filters=96,
- filter_size=3,
- padding=1,
- act="relu")
- self.branch3x3dbl_3 = ConvBNLayer(
- num_channels=96,
- num_filters=96,
- filter_size=3,
- padding=1,
- act="relu")
- self.branch_pool = AvgPool2D(
- kernel_size=3, stride=1, padding=1, exclusive=False)
- self.branch_pool_conv = ConvBNLayer(
- num_channels=num_channels,
- num_filters=pool_features,
- filter_size=1,
- act="relu")
- def forward(self, x):
- 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(TheseusLayer):
- def __init__(self, num_channels):
- super().__init__()
- self.branch3x3 = ConvBNLayer(
- num_channels=num_channels,
- num_filters=384,
- filter_size=3,
- stride=2,
- act="relu")
- self.branch3x3dbl_1 = ConvBNLayer(
- num_channels=num_channels,
- num_filters=64,
- filter_size=1,
- act="relu")
- self.branch3x3dbl_2 = ConvBNLayer(
- num_channels=64,
- num_filters=96,
- filter_size=3,
- padding=1,
- act="relu")
- self.branch3x3dbl_3 = ConvBNLayer(
- num_channels=96,
- num_filters=96,
- filter_size=3,
- stride=2,
- act="relu")
- self.branch_pool = MaxPool2D(kernel_size=3, stride=2)
- def forward(self, x):
- 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(TheseusLayer):
- def __init__(self, num_channels, channels_7x7):
- super().__init__()
- self.branch1x1 = ConvBNLayer(
- num_channels=num_channels,
- num_filters=192,
- filter_size=1,
- act="relu")
- self.branch7x7_1 = ConvBNLayer(
- num_channels=num_channels,
- num_filters=channels_7x7,
- filter_size=1,
- stride=1,
- act="relu")
- self.branch7x7_2 = ConvBNLayer(
- num_channels=channels_7x7,
- num_filters=channels_7x7,
- filter_size=(1, 7),
- stride=1,
- padding=(0, 3),
- act="relu")
- self.branch7x7_3 = ConvBNLayer(
- num_channels=channels_7x7,
- num_filters=192,
- filter_size=(7, 1),
- stride=1,
- padding=(3, 0),
- act="relu")
- self.branch7x7dbl_1 = ConvBNLayer(
- num_channels=num_channels,
- num_filters=channels_7x7,
- filter_size=1,
- act="relu")
- self.branch7x7dbl_2 = ConvBNLayer(
- num_channels=channels_7x7,
- num_filters=channels_7x7,
- filter_size=(7, 1),
- padding=(3, 0),
- act="relu")
- self.branch7x7dbl_3 = ConvBNLayer(
- num_channels=channels_7x7,
- num_filters=channels_7x7,
- filter_size=(1, 7),
- padding=(0, 3),
- act="relu")
- self.branch7x7dbl_4 = ConvBNLayer(
- num_channels=channels_7x7,
- num_filters=channels_7x7,
- filter_size=(7, 1),
- padding=(3, 0),
- act="relu")
- self.branch7x7dbl_5 = ConvBNLayer(
- num_channels=channels_7x7,
- num_filters=192,
- filter_size=(1, 7),
- padding=(0, 3),
- act="relu")
- self.branch_pool = AvgPool2D(
- kernel_size=3, stride=1, padding=1, exclusive=False)
- self.branch_pool_conv = ConvBNLayer(
- num_channels=num_channels,
- num_filters=192,
- filter_size=1,
- act="relu")
- def forward(self, x):
- 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(TheseusLayer):
- def __init__(self, num_channels):
- super().__init__()
- self.branch3x3_1 = ConvBNLayer(
- num_channels=num_channels,
- num_filters=192,
- filter_size=1,
- act="relu")
- self.branch3x3_2 = ConvBNLayer(
- num_channels=192,
- num_filters=320,
- filter_size=3,
- stride=2,
- act="relu")
- self.branch7x7x3_1 = ConvBNLayer(
- num_channels=num_channels,
- num_filters=192,
- filter_size=1,
- act="relu")
- self.branch7x7x3_2 = ConvBNLayer(
- num_channels=192,
- num_filters=192,
- filter_size=(1, 7),
- padding=(0, 3),
- act="relu")
- self.branch7x7x3_3 = ConvBNLayer(
- num_channels=192,
- num_filters=192,
- filter_size=(7, 1),
- padding=(3, 0),
- act="relu")
- self.branch7x7x3_4 = ConvBNLayer(
- num_channels=192,
- num_filters=192,
- filter_size=3,
- stride=2,
- act="relu")
- self.branch_pool = MaxPool2D(kernel_size=3, stride=2)
- def forward(self, x):
- 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(TheseusLayer):
- def __init__(self, num_channels):
- super().__init__()
- self.branch1x1 = ConvBNLayer(
- num_channels=num_channels,
- num_filters=320,
- filter_size=1,
- act="relu")
- self.branch3x3_1 = ConvBNLayer(
- num_channels=num_channels,
- num_filters=384,
- filter_size=1,
- act="relu")
- self.branch3x3_2a = ConvBNLayer(
- num_channels=384,
- num_filters=384,
- filter_size=(1, 3),
- padding=(0, 1),
- act="relu")
- self.branch3x3_2b = ConvBNLayer(
- num_channels=384,
- num_filters=384,
- filter_size=(3, 1),
- padding=(1, 0),
- act="relu")
- self.branch3x3dbl_1 = ConvBNLayer(
- num_channels=num_channels,
- num_filters=448,
- filter_size=1,
- act="relu")
- self.branch3x3dbl_2 = ConvBNLayer(
- num_channels=448,
- num_filters=384,
- filter_size=3,
- padding=1,
- act="relu")
- self.branch3x3dbl_3a = ConvBNLayer(
- num_channels=384,
- num_filters=384,
- filter_size=(1, 3),
- padding=(0, 1),
- act="relu")
- self.branch3x3dbl_3b = ConvBNLayer(
- num_channels=384,
- num_filters=384,
- filter_size=(3, 1),
- padding=(1, 0),
- act="relu")
- self.branch_pool = AvgPool2D(
- kernel_size=3, stride=1, padding=1, exclusive=False)
- self.branch_pool_conv = ConvBNLayer(
- num_channels=num_channels,
- num_filters=192,
- filter_size=1,
- act="relu")
- def forward(self, x):
- 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 Inception_V3(TheseusLayer):
- """
- Inception_V3
- Args:
- config: dict. config of Inception_V3.
- class_num: int=1000. The number of classes.
- pretrained: (True or False) or path of pretrained_model. Whether to load the pretrained model.
- Returns:
- model: nn.Layer. Specific Inception_V3 model depends on args.
- """
- def __init__(self, config, class_num=1000, return_patterns=None):
- super().__init__()
- self.inception_a_list = config["inception_a"]
- self.inception_c_list = config["inception_c"]
- self.inception_b_list = config["inception_b"]
- self.inception_d_list = config["inception_d"]
- self.inception_e_list = config["inception_e"]
- self.inception_stem = InceptionStem()
- self.inception_block_list = nn.LayerList()
- for i in range(len(self.inception_a_list[0])):
- inception_a = InceptionA(self.inception_a_list[0][i],
- self.inception_a_list[1][i])
- self.inception_block_list.append(inception_a)
- for i in range(len(self.inception_b_list)):
- inception_b = InceptionB(self.inception_b_list[i])
- self.inception_block_list.append(inception_b)
- for i in range(len(self.inception_c_list[0])):
- inception_c = InceptionC(self.inception_c_list[0][i],
- self.inception_c_list[1][i])
- self.inception_block_list.append(inception_c)
- for i in range(len(self.inception_d_list)):
- inception_d = InceptionD(self.inception_d_list[i])
- self.inception_block_list.append(inception_d)
- for i in range(len(self.inception_e_list)):
- inception_e = InceptionE(self.inception_e_list[i])
- self.inception_block_list.append(inception_e)
- self.avg_pool = AdaptiveAvgPool2D(1)
- self.dropout = Dropout(p=0.2, mode="downscale_in_infer")
- stdv = 1.0 / math.sqrt(2048 * 1.0)
- self.fc = Linear(
- 2048,
- class_num,
- weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)),
- bias_attr=ParamAttr())
- if return_patterns is not None:
- self.update_res(return_patterns)
- self.register_forward_post_hook(self._return_dict_hook)
- def forward(self, x):
- x = self.inception_stem(x)
- for inception_block in self.inception_block_list:
- x = inception_block(x)
- x = self.avg_pool(x)
- x = paddle.reshape(x, shape=[-1, 2048])
- x = self.dropout(x)
- x = self.fc(x)
- return x
- def _load_pretrained(pretrained, model, model_url, use_ssld):
- 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 InceptionV3(pretrained=False, use_ssld=False, **kwargs):
- """
- InceptionV3
- Args:
- pretrained: bool=false or str. if `true` load pretrained parameters, `false` otherwise.
- if str, means the path of the pretrained model.
- use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
- Returns:
- model: nn.Layer. Specific `InceptionV3` model
- """
- model = Inception_V3(NET_CONFIG, **kwargs)
- _load_pretrained(pretrained, model, MODEL_URLS["InceptionV3"], use_ssld)
- return model
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