From ff06df6d184ec3eaded3938fa19e10dc4d00e324 Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Fri, 22 Oct 2021 11:07:17 +0800 Subject: [PATCH] [PaddlePaddle Hackathon] add InceptionV3 (#36064) * add inceptionv3 Co-authored-by: Ainavo Co-authored-by: pithygit --- python/paddle/tests/test_pretrained_model.py | 2 +- python/paddle/tests/test_vision_models.py | 3 + python/paddle/vision/__init__.py | 2 + python/paddle/vision/models/__init__.py | 6 +- python/paddle/vision/models/inceptionv3.py | 560 +++++++++++++++++++ 5 files changed, 571 insertions(+), 2 deletions(-) create mode 100644 python/paddle/vision/models/inceptionv3.py diff --git a/python/paddle/tests/test_pretrained_model.py b/python/paddle/tests/test_pretrained_model.py index ac2b1194dd..f2b779e317 100644 --- a/python/paddle/tests/test_pretrained_model.py +++ b/python/paddle/tests/test_pretrained_model.py @@ -54,7 +54,7 @@ class TestPretrainedModel(unittest.TestCase): def test_models(self): arches = [ 'mobilenet_v1', 'mobilenet_v2', 'resnet18', 'vgg16', 'alexnet', - 'resnext50_32x4d' + 'resnext50_32x4d', 'inception_v3' ] for arch in arches: self.infer(arch) diff --git a/python/paddle/tests/test_vision_models.py b/python/paddle/tests/test_vision_models.py index 9ef8165508..9eb75826b7 100644 --- a/python/paddle/tests/test_vision_models.py +++ b/python/paddle/tests/test_vision_models.py @@ -91,6 +91,9 @@ class TestVisonModels(unittest.TestCase): def test_resnext152_64x4d(self): self.models_infer('resnext152_64x4d') + def test_inception_v3(self): + self.models_infer('inception_v3') + def test_vgg16_num_classes(self): vgg16 = models.__dict__['vgg16'](pretrained=False, num_classes=10) diff --git a/python/paddle/vision/__init__.py b/python/paddle/vision/__init__.py index 3ea4f5cd2d..e5db5f6c4f 100644 --- a/python/paddle/vision/__init__.py +++ b/python/paddle/vision/__init__.py @@ -53,6 +53,8 @@ from .models import resnext101_32x4d # noqa: F401 from .models import resnext101_64x4d # noqa: F401 from .models import resnext152_32x4d # noqa: F401 from .models import resnext152_64x4d # noqa: F401 +from .models import InceptionV3 # noqa: F401 +from .models import inception_v3 # noqa: F401 from .transforms import BaseTransform # noqa: F401 from .transforms import Compose # noqa: F401 from .transforms import Resize # noqa: F401 diff --git a/python/paddle/vision/models/__init__.py b/python/paddle/vision/models/__init__.py index 3f48b1475e..7d8cb58fad 100644 --- a/python/paddle/vision/models/__init__.py +++ b/python/paddle/vision/models/__init__.py @@ -37,6 +37,8 @@ from .resnext import resnext101_32x4d # noqa: F401 from .resnext import resnext101_64x4d # noqa: F401 from .resnext import resnext152_32x4d # noqa: F401 from .resnext import resnext152_64x4d # noqa: F401 +from .inceptionv3 import InceptionV3 # noqa: F401 +from .inceptionv3 import inception_v3 # noqa: F401 __all__ = [ #noqa 'ResNet', @@ -63,5 +65,7 @@ __all__ = [ #noqa 'resnext101_32x4d', 'resnext101_64x4d', 'resnext152_32x4d', - 'resnext152_64x4d' + 'resnext152_64x4d', + 'InceptionV3', + 'inception_v3' ] diff --git a/python/paddle/vision/models/inceptionv3.py b/python/paddle/vision/models/inceptionv3.py new file mode 100644 index 0000000000..9e8a8b8146 --- /dev/null +++ b/python/paddle/vision/models/inceptionv3.py @@ -0,0 +1,560 @@ +# 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 absolute_import +from __future__ import division +from __future__ import print_function + +import math +import paddle +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 paddle.fluid.param_attr import ParamAttr + +from paddle.utils.download import get_weights_path_from_url + +__all__ = [] + +model_urls = { + "inception_v3": + ("https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/InceptionV3_pretrained.pdparams", + "e4d0905a818f6bb7946e881777a8a935") +} + + +class ConvBNLayer(nn.Layer): + 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(nn.Layer): + 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(nn.Layer): + 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(nn.Layer): + 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(nn.Layer): + 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(nn.Layer): + 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(nn.Layer): + 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 InceptionV3(nn.Layer): + """ + InceptionV3 + 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. + + 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) + """ + + def __init__(self, num_classes=1000, with_pool=True): + 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): + 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=False, **kwargs): + """ + InceptionV3 model from + `"Rethinking the Inception Architecture for Computer Vision" `_ + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + + 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) + """ + model = InceptionV3(**kwargs) + arch = "inception_v3" + 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 -- GitLab