wehub-resource-sync
655 lines
20 KiB
Python
655 lines
20 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import math
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from typing import (
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TYPE_CHECKING,
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TypedDict,
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)
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from typing_extensions import (
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NotRequired,
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Unpack,
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)
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import paddle
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from paddle import nn
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from paddle.base.param_attr import ParamAttr
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from paddle.nn import AdaptiveAvgPool2D, AvgPool2D, Dropout, Linear, MaxPool2D
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from paddle.nn.initializer import Uniform
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from paddle.utils.download import get_weights_path_from_url
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from ..ops import ConvNormActivation
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if TYPE_CHECKING:
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from paddle import Tensor
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class _InceptionV3Options(TypedDict):
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num_classes: NotRequired[int]
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with_pool: NotRequired[bool]
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__all__ = []
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model_urls = {
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"inception_v3": (
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"https://paddle-hapi.bj.bcebos.com/models/inception_v3.pdparams",
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"649a4547c3243e8b59c656f41fe330b8",
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)
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}
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class InceptionStem(nn.Layer):
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def __init__(self) -> None:
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super().__init__()
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self.conv_1a_3x3 = ConvNormActivation(
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in_channels=3,
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out_channels=32,
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kernel_size=3,
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stride=2,
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padding=0,
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activation_layer=nn.ReLU,
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)
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self.conv_2a_3x3 = ConvNormActivation(
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in_channels=32,
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out_channels=32,
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kernel_size=3,
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stride=1,
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padding=0,
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activation_layer=nn.ReLU,
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)
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self.conv_2b_3x3 = ConvNormActivation(
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in_channels=32,
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out_channels=64,
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kernel_size=3,
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padding=1,
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activation_layer=nn.ReLU,
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)
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self.max_pool = MaxPool2D(kernel_size=3, stride=2, padding=0)
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self.conv_3b_1x1 = ConvNormActivation(
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in_channels=64,
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out_channels=80,
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kernel_size=1,
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padding=0,
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activation_layer=nn.ReLU,
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)
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self.conv_4a_3x3 = ConvNormActivation(
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in_channels=80,
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out_channels=192,
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kernel_size=3,
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padding=0,
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activation_layer=nn.ReLU,
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)
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def forward(self, x: Tensor) -> Tensor:
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x = self.conv_1a_3x3(x)
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x = self.conv_2a_3x3(x)
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x = self.conv_2b_3x3(x)
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x = self.max_pool(x)
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x = self.conv_3b_1x1(x)
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x = self.conv_4a_3x3(x)
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x = self.max_pool(x)
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return x
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class InceptionA(nn.Layer):
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def __init__(self, num_channels: int, pool_features: int) -> None:
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super().__init__()
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self.branch1x1 = ConvNormActivation(
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in_channels=num_channels,
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out_channels=64,
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kernel_size=1,
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padding=0,
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activation_layer=nn.ReLU,
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)
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self.branch5x5_1 = ConvNormActivation(
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in_channels=num_channels,
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out_channels=48,
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kernel_size=1,
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padding=0,
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activation_layer=nn.ReLU,
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)
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self.branch5x5_2 = ConvNormActivation(
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in_channels=48,
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out_channels=64,
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kernel_size=5,
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padding=2,
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activation_layer=nn.ReLU,
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)
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self.branch3x3dbl_1 = ConvNormActivation(
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in_channels=num_channels,
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out_channels=64,
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kernel_size=1,
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padding=0,
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activation_layer=nn.ReLU,
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)
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self.branch3x3dbl_2 = ConvNormActivation(
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in_channels=64,
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out_channels=96,
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kernel_size=3,
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padding=1,
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activation_layer=nn.ReLU,
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)
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self.branch3x3dbl_3 = ConvNormActivation(
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in_channels=96,
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out_channels=96,
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kernel_size=3,
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padding=1,
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activation_layer=nn.ReLU,
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)
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self.branch_pool = AvgPool2D(
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kernel_size=3, stride=1, padding=1, exclusive=False
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)
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self.branch_pool_conv = ConvNormActivation(
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in_channels=num_channels,
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out_channels=pool_features,
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kernel_size=1,
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padding=0,
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activation_layer=nn.ReLU,
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)
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def forward(self, x: Tensor) -> Tensor:
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branch1x1 = self.branch1x1(x)
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branch5x5 = self.branch5x5_1(x)
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branch5x5 = self.branch5x5_2(branch5x5)
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branch3x3dbl = self.branch3x3dbl_1(x)
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branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
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branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
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branch_pool = self.branch_pool(x)
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branch_pool = self.branch_pool_conv(branch_pool)
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x = paddle.concat(
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[branch1x1, branch5x5, branch3x3dbl, branch_pool], axis=1
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)
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return x
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class InceptionB(nn.Layer):
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def __init__(self, num_channels: int) -> None:
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super().__init__()
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self.branch3x3 = ConvNormActivation(
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in_channels=num_channels,
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out_channels=384,
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kernel_size=3,
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stride=2,
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padding=0,
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activation_layer=nn.ReLU,
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)
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self.branch3x3dbl_1 = ConvNormActivation(
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in_channels=num_channels,
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out_channels=64,
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kernel_size=1,
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padding=0,
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activation_layer=nn.ReLU,
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)
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self.branch3x3dbl_2 = ConvNormActivation(
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in_channels=64,
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out_channels=96,
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kernel_size=3,
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padding=1,
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activation_layer=nn.ReLU,
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)
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self.branch3x3dbl_3 = ConvNormActivation(
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in_channels=96,
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out_channels=96,
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kernel_size=3,
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stride=2,
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padding=0,
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activation_layer=nn.ReLU,
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)
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self.branch_pool = MaxPool2D(kernel_size=3, stride=2)
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def forward(self, x: Tensor) -> Tensor:
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branch3x3 = self.branch3x3(x)
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branch3x3dbl = self.branch3x3dbl_1(x)
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branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
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branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
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branch_pool = self.branch_pool(x)
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x = paddle.concat([branch3x3, branch3x3dbl, branch_pool], axis=1)
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return x
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class InceptionC(nn.Layer):
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def __init__(self, num_channels: int, channels_7x7: int) -> None:
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super().__init__()
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self.branch1x1 = ConvNormActivation(
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in_channels=num_channels,
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out_channels=192,
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kernel_size=1,
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padding=0,
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activation_layer=nn.ReLU,
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)
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self.branch7x7_1 = ConvNormActivation(
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in_channels=num_channels,
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out_channels=channels_7x7,
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kernel_size=1,
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stride=1,
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padding=0,
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activation_layer=nn.ReLU,
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)
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self.branch7x7_2 = ConvNormActivation(
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in_channels=channels_7x7,
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out_channels=channels_7x7,
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kernel_size=(1, 7),
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stride=1,
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padding=(0, 3),
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activation_layer=nn.ReLU,
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)
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self.branch7x7_3 = ConvNormActivation(
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in_channels=channels_7x7,
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out_channels=192,
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kernel_size=(7, 1),
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stride=1,
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padding=(3, 0),
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activation_layer=nn.ReLU,
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)
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self.branch7x7dbl_1 = ConvNormActivation(
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in_channels=num_channels,
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out_channels=channels_7x7,
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kernel_size=1,
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padding=0,
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activation_layer=nn.ReLU,
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)
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self.branch7x7dbl_2 = ConvNormActivation(
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in_channels=channels_7x7,
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out_channels=channels_7x7,
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kernel_size=(7, 1),
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padding=(3, 0),
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activation_layer=nn.ReLU,
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)
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self.branch7x7dbl_3 = ConvNormActivation(
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in_channels=channels_7x7,
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out_channels=channels_7x7,
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kernel_size=(1, 7),
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padding=(0, 3),
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activation_layer=nn.ReLU,
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)
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self.branch7x7dbl_4 = ConvNormActivation(
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in_channels=channels_7x7,
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out_channels=channels_7x7,
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kernel_size=(7, 1),
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padding=(3, 0),
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activation_layer=nn.ReLU,
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)
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self.branch7x7dbl_5 = ConvNormActivation(
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in_channels=channels_7x7,
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out_channels=192,
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kernel_size=(1, 7),
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padding=(0, 3),
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activation_layer=nn.ReLU,
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)
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self.branch_pool = AvgPool2D(
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kernel_size=3, stride=1, padding=1, exclusive=False
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)
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self.branch_pool_conv = ConvNormActivation(
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in_channels=num_channels,
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out_channels=192,
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kernel_size=1,
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padding=0,
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activation_layer=nn.ReLU,
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)
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def forward(self, x: Tensor) -> Tensor:
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branch1x1 = self.branch1x1(x)
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branch7x7 = self.branch7x7_1(x)
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branch7x7 = self.branch7x7_2(branch7x7)
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branch7x7 = self.branch7x7_3(branch7x7)
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branch7x7dbl = self.branch7x7dbl_1(x)
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branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl)
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branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl)
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branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl)
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branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl)
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branch_pool = self.branch_pool(x)
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branch_pool = self.branch_pool_conv(branch_pool)
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x = paddle.concat(
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[branch1x1, branch7x7, branch7x7dbl, branch_pool], axis=1
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)
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return x
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class InceptionD(nn.Layer):
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def __init__(self, num_channels: int) -> None:
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super().__init__()
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self.branch3x3_1 = ConvNormActivation(
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in_channels=num_channels,
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out_channels=192,
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kernel_size=1,
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padding=0,
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activation_layer=nn.ReLU,
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)
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self.branch3x3_2 = ConvNormActivation(
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in_channels=192,
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out_channels=320,
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kernel_size=3,
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stride=2,
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padding=0,
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activation_layer=nn.ReLU,
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)
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self.branch7x7x3_1 = ConvNormActivation(
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in_channels=num_channels,
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out_channels=192,
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kernel_size=1,
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padding=0,
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activation_layer=nn.ReLU,
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)
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self.branch7x7x3_2 = ConvNormActivation(
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in_channels=192,
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out_channels=192,
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kernel_size=(1, 7),
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padding=(0, 3),
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activation_layer=nn.ReLU,
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)
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self.branch7x7x3_3 = ConvNormActivation(
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in_channels=192,
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out_channels=192,
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kernel_size=(7, 1),
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padding=(3, 0),
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activation_layer=nn.ReLU,
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)
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self.branch7x7x3_4 = ConvNormActivation(
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in_channels=192,
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out_channels=192,
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kernel_size=3,
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stride=2,
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padding=0,
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activation_layer=nn.ReLU,
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)
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self.branch_pool = MaxPool2D(kernel_size=3, stride=2)
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def forward(self, x: Tensor) -> Tensor:
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branch3x3 = self.branch3x3_1(x)
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branch3x3 = self.branch3x3_2(branch3x3)
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branch7x7x3 = self.branch7x7x3_1(x)
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branch7x7x3 = self.branch7x7x3_2(branch7x7x3)
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branch7x7x3 = self.branch7x7x3_3(branch7x7x3)
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branch7x7x3 = self.branch7x7x3_4(branch7x7x3)
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branch_pool = self.branch_pool(x)
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x = paddle.concat([branch3x3, branch7x7x3, branch_pool], axis=1)
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return x
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class InceptionE(nn.Layer):
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def __init__(self, num_channels: int) -> None:
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super().__init__()
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self.branch1x1 = ConvNormActivation(
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in_channels=num_channels,
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out_channels=320,
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kernel_size=1,
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padding=0,
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activation_layer=nn.ReLU,
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)
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self.branch3x3_1 = ConvNormActivation(
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in_channels=num_channels,
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out_channels=384,
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kernel_size=1,
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padding=0,
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activation_layer=nn.ReLU,
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)
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self.branch3x3_2a = ConvNormActivation(
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in_channels=384,
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out_channels=384,
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kernel_size=(1, 3),
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padding=(0, 1),
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activation_layer=nn.ReLU,
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)
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self.branch3x3_2b = ConvNormActivation(
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in_channels=384,
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out_channels=384,
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kernel_size=(3, 1),
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padding=(1, 0),
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activation_layer=nn.ReLU,
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)
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self.branch3x3dbl_1 = ConvNormActivation(
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in_channels=num_channels,
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out_channels=448,
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kernel_size=1,
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padding=0,
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activation_layer=nn.ReLU,
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)
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self.branch3x3dbl_2 = ConvNormActivation(
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in_channels=448,
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out_channels=384,
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kernel_size=3,
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padding=1,
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activation_layer=nn.ReLU,
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)
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self.branch3x3dbl_3a = ConvNormActivation(
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in_channels=384,
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out_channels=384,
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kernel_size=(1, 3),
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padding=(0, 1),
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activation_layer=nn.ReLU,
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)
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self.branch3x3dbl_3b = ConvNormActivation(
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in_channels=384,
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out_channels=384,
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kernel_size=(3, 1),
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padding=(1, 0),
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activation_layer=nn.ReLU,
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)
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self.branch_pool = AvgPool2D(
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kernel_size=3, stride=1, padding=1, exclusive=False
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)
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self.branch_pool_conv = ConvNormActivation(
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in_channels=num_channels,
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out_channels=192,
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kernel_size=1,
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padding=0,
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activation_layer=nn.ReLU,
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)
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def forward(self, x: Tensor) -> Tensor:
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branch1x1 = self.branch1x1(x)
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branch3x3 = self.branch3x3_1(x)
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branch3x3 = [
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self.branch3x3_2a(branch3x3),
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self.branch3x3_2b(branch3x3),
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]
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branch3x3 = paddle.concat(branch3x3, axis=1)
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branch3x3dbl = self.branch3x3dbl_1(x)
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branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
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branch3x3dbl = [
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self.branch3x3dbl_3a(branch3x3dbl),
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self.branch3x3dbl_3b(branch3x3dbl),
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]
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branch3x3dbl = paddle.concat(branch3x3dbl, axis=1)
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branch_pool = self.branch_pool(x)
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branch_pool = self.branch_pool_conv(branch_pool)
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x = paddle.concat(
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[branch1x1, branch3x3, branch3x3dbl, branch_pool], axis=1
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)
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return x
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class InceptionV3(nn.Layer):
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"""Inception v3 model from
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`"Rethinking the Inception Architecture for Computer Vision" <https://arxiv.org/pdf/1512.00567.pdf>`_.
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Args:
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num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer
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will not be defined. Default: 1000.
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with_pool (bool, optional): Use pool before the last fc layer or not. Default: True.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of Inception v3 model.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> from paddle.vision.models import InceptionV3
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>>> inception_v3 = InceptionV3()
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>>> x = paddle.rand([1, 3, 299, 299])
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>>> out = inception_v3(x)
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>>> print(out.shape)
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paddle.Size([1, 1000])
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"""
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num_classes: int
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with_pool: bool
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def __init__(self, num_classes: int = 1000, with_pool: bool = True) -> None:
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super().__init__()
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self.num_classes = num_classes
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self.with_pool = with_pool
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self.layers_config = {
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"inception_a": [[192, 256, 288], [32, 64, 64]],
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"inception_b": [288],
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"inception_c": [[768, 768, 768, 768], [128, 160, 160, 192]],
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"inception_d": [768],
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"inception_e": [1280, 2048],
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}
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inception_a_list = self.layers_config["inception_a"]
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inception_c_list = self.layers_config["inception_c"]
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inception_b_list = self.layers_config["inception_b"]
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inception_d_list = self.layers_config["inception_d"]
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inception_e_list = self.layers_config["inception_e"]
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self.inception_stem = InceptionStem()
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self.inception_block_list = nn.LayerList()
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for i in range(len(inception_a_list[0])):
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inception_a = InceptionA(
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inception_a_list[0][i], inception_a_list[1][i]
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)
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self.inception_block_list.append(inception_a)
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for i in range(len(inception_b_list)):
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inception_b = InceptionB(inception_b_list[i])
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self.inception_block_list.append(inception_b)
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for i in range(len(inception_c_list[0])):
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inception_c = InceptionC(
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inception_c_list[0][i], inception_c_list[1][i]
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)
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self.inception_block_list.append(inception_c)
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for i in range(len(inception_d_list)):
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inception_d = InceptionD(inception_d_list[i])
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self.inception_block_list.append(inception_d)
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for i in range(len(inception_e_list)):
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inception_e = InceptionE(inception_e_list[i])
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self.inception_block_list.append(inception_e)
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if with_pool:
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self.avg_pool = AdaptiveAvgPool2D(1)
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if num_classes > 0:
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self.dropout = Dropout(p=0.2, mode="downscale_in_infer")
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stdv = 1.0 / math.sqrt(2048 * 1.0)
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self.fc = Linear(
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2048,
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num_classes,
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weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)),
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bias_attr=ParamAttr(),
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)
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def forward(self, x: Tensor) -> Tensor:
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x = self.inception_stem(x)
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for inception_block in self.inception_block_list:
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x = inception_block(x)
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if self.with_pool:
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x = self.avg_pool(x)
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if self.num_classes > 0:
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x = paddle.reshape(x, shape=[-1, 2048])
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x = self.dropout(x)
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x = self.fc(x)
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return x
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def inception_v3(
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pretrained: bool = False, **kwargs: Unpack[_InceptionV3Options]
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) -> InceptionV3:
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"""Inception v3 model from
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`"Rethinking the Inception Architecture for Computer Vision" <https://arxiv.org/pdf/1512.00567.pdf>`_.
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Args:
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pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
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on ImageNet. Default: False.
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**kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`InceptionV3 <api_paddle_vision_models_InceptionV3>`.
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Returns:
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:ref:`api_paddle_nn_Layer`. An instance of Inception v3 model.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> from paddle.vision.models import inception_v3
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>>> # Build model
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>>> model = inception_v3()
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>>> # Build model and load imagenet pretrained weight
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>>> # model = inception_v3(pretrained=True)
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>>> x = paddle.rand([1, 3, 299, 299])
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>>> out = model(x)
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>>> print(out.shape)
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paddle.Size([1, 1000])
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"""
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model = InceptionV3(**kwargs)
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arch = "inception_v3"
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if pretrained:
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assert arch in model_urls, (
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f"{arch} model do not have a pretrained model now, you should set pretrained=False"
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)
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weight_path = get_weights_path_from_url(
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model_urls[arch][0], model_urls[arch][1]
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)
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param = paddle.load(weight_path)
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model.set_dict(param)
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return model
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