mirror of https://github.com/alibaba/EasyCV.git
355 lines
13 KiB
Python
355 lines
13 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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r""" This model is taken from the official PyTorch model zoo.
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- torchvision.models.inception.py on 31th Aug, 2019
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"""
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from collections import namedtuple
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from mmcv.cnn import constant_init, kaiming_init
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from torch.nn.modules.batchnorm import _BatchNorm
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from ..modelzoo import inceptionv3 as model_urls
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from ..registry import BACKBONES
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__all__ = ['Inception3']
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_InceptionOutputs = namedtuple('InceptionOutputs', ['logits', 'aux_logits'])
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@BACKBONES.register_module
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class Inception3(nn.Module):
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def __init__(self,
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num_classes: int = 0,
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aux_logits: bool = True,
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transform_input: bool = False) -> None:
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r"""
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:param num_classes: number of classes based on dataset.
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:param aux_logits: If True, adds two auxiliary branches that can improve training.
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Default: *False* when pretrained is True otherwise *True*
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:param transform_input: If True, preprocesses the input according to the method with which it
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was trained on ImageNet. Default: *False*
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"""
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super(Inception3, self).__init__()
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self.aux_logits = aux_logits
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self.transform_input = transform_input
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self.Conv2d_1a_3x3 = BasicConv2d(3, 32, kernel_size=3, stride=2)
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self.Conv2d_2a_3x3 = BasicConv2d(32, 32, kernel_size=3)
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self.Conv2d_2b_3x3 = BasicConv2d(32, 64, kernel_size=3, padding=1)
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self.Conv2d_3b_1x1 = BasicConv2d(64, 80, kernel_size=1)
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self.Conv2d_4a_3x3 = BasicConv2d(80, 192, kernel_size=3)
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self.Mixed_5b = InceptionA(192, pool_features=32)
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self.Mixed_5c = InceptionA(256, pool_features=64)
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self.Mixed_5d = InceptionA(288, pool_features=64)
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self.Mixed_6a = InceptionB(288)
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self.Mixed_6b = InceptionC(768, channels_7x7=128)
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self.Mixed_6c = InceptionC(768, channels_7x7=160)
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self.Mixed_6d = InceptionC(768, channels_7x7=160)
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self.Mixed_6e = InceptionC(768, channels_7x7=192)
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if aux_logits:
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self.AuxLogits = InceptionAux(768, num_classes)
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self.Mixed_7a = InceptionD(768)
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self.Mixed_7b = InceptionE(1280)
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self.Mixed_7c = InceptionE(2048)
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if num_classes > 0:
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self.fc = nn.Linear(2048, num_classes)
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self.default_pretrained_model_path = model_urls[
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self.__class__.__name__]
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def init_weights(self):
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for m in self.modules():
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if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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kaiming_init(m, mode='fan_in', nonlinearity='relu')
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elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
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constant_init(m, 1)
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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def forward(self, x):
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if self.transform_input:
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x_ch0 = torch.unsqueeze(x[:, 0],
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1) * (0.229 / 0.5) + (0.485 - 0.5) / 0.5
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x_ch1 = torch.unsqueeze(x[:, 1],
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1) * (0.224 / 0.5) + (0.456 - 0.5) / 0.5
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x_ch2 = torch.unsqueeze(x[:, 2],
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1) * (0.225 / 0.5) + (0.406 - 0.5) / 0.5
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x = torch.cat((x_ch0, x_ch1, x_ch2), 1)
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# N x 3 x 299 x 299
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x = self.Conv2d_1a_3x3(x)
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# N x 32 x 149 x 149
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x = self.Conv2d_2a_3x3(x)
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# N x 32 x 147 x 147
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x = self.Conv2d_2b_3x3(x)
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# N x 64 x 147 x 147
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x = F.max_pool2d(x, kernel_size=3, stride=2)
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# N x 64 x 73 x 73
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x = self.Conv2d_3b_1x1(x)
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# N x 80 x 73 x 73
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x = self.Conv2d_4a_3x3(x)
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# N x 192 x 71 x 71
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x = F.max_pool2d(x, kernel_size=3, stride=2)
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# N x 192 x 35 x 35
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x = self.Mixed_5b(x)
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# N x 256 x 35 x 35
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x = self.Mixed_5c(x)
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# N x 288 x 35 x 35
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x = self.Mixed_5d(x)
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# N x 288 x 35 x 35
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x = self.Mixed_6a(x)
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# N x 768 x 17 x 17
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x = self.Mixed_6b(x)
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# N x 768 x 17 x 17
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x = self.Mixed_6c(x)
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# N x 768 x 17 x 17
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x = self.Mixed_6d(x)
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# N x 768 x 17 x 17
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x = self.Mixed_6e(x)
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# N x 768 x 17 x 17
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if self.training and self.aux_logits:
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aux = self.AuxLogits(x)
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# N x 768 x 17 x 17
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x = self.Mixed_7a(x)
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# N x 1280 x 8 x 8
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x = self.Mixed_7b(x)
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# N x 2048 x 8 x 8
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x = self.Mixed_7c(x)
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# N x 2048 x 8 x 8
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# Adaptive average pooling
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x = F.adaptive_avg_pool2d(x, (1, 1))
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# N x 2048 x 1 x 1
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x = F.dropout(x, training=self.training)
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# N x 2048 x 1 x 1
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x = torch.flatten(x, 1)
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# N x 2048
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if hasattr(self, 'fc'):
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x = self.fc(x)
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# N x 1000 (num_classes)
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if self.training and self.aux_logits and hasattr(self, 'fc'):
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return [_InceptionOutputs(x, aux)]
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return [x]
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class InceptionA(nn.Module):
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def __init__(self, in_channels, pool_features):
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super(InceptionA, self).__init__()
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self.branch1x1 = BasicConv2d(in_channels, 64, kernel_size=1)
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self.branch5x5_1 = BasicConv2d(in_channels, 48, kernel_size=1)
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self.branch5x5_2 = BasicConv2d(48, 64, kernel_size=5, padding=2)
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self.branch3x3dbl_1 = BasicConv2d(in_channels, 64, kernel_size=1)
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self.branch3x3dbl_2 = BasicConv2d(64, 96, kernel_size=3, padding=1)
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self.branch3x3dbl_3 = BasicConv2d(96, 96, kernel_size=3, padding=1)
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self.branch_pool = BasicConv2d(
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in_channels, pool_features, kernel_size=1)
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def forward(self, x):
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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 = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
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branch_pool = self.branch_pool(branch_pool)
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outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
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return torch.cat(outputs, 1)
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class InceptionB(nn.Module):
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def __init__(self, in_channels):
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super(InceptionB, self).__init__()
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self.branch3x3 = BasicConv2d(in_channels, 384, kernel_size=3, stride=2)
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self.branch3x3dbl_1 = BasicConv2d(in_channels, 64, kernel_size=1)
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self.branch3x3dbl_2 = BasicConv2d(64, 96, kernel_size=3, padding=1)
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self.branch3x3dbl_3 = BasicConv2d(96, 96, kernel_size=3, stride=2)
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def forward(self, x):
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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 = F.max_pool2d(x, kernel_size=3, stride=2)
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outputs = [branch3x3, branch3x3dbl, branch_pool]
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return torch.cat(outputs, 1)
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class InceptionC(nn.Module):
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def __init__(self, in_channels, channels_7x7):
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super(InceptionC, self).__init__()
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self.branch1x1 = BasicConv2d(in_channels, 192, kernel_size=1)
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c7 = channels_7x7
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self.branch7x7_1 = BasicConv2d(in_channels, c7, kernel_size=1)
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self.branch7x7_2 = BasicConv2d(
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c7, c7, kernel_size=(1, 7), padding=(0, 3))
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self.branch7x7_3 = BasicConv2d(
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c7, 192, kernel_size=(7, 1), padding=(3, 0))
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self.branch7x7dbl_1 = BasicConv2d(in_channels, c7, kernel_size=1)
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self.branch7x7dbl_2 = BasicConv2d(
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c7, c7, kernel_size=(7, 1), padding=(3, 0))
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self.branch7x7dbl_3 = BasicConv2d(
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c7, c7, kernel_size=(1, 7), padding=(0, 3))
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self.branch7x7dbl_4 = BasicConv2d(
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c7, c7, kernel_size=(7, 1), padding=(3, 0))
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self.branch7x7dbl_5 = BasicConv2d(
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c7, 192, kernel_size=(1, 7), padding=(0, 3))
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self.branch_pool = BasicConv2d(in_channels, 192, kernel_size=1)
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def forward(self, x):
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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 = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
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branch_pool = self.branch_pool(branch_pool)
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outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool]
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return torch.cat(outputs, 1)
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class InceptionD(nn.Module):
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def __init__(self, in_channels):
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super(InceptionD, self).__init__()
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self.branch3x3_1 = BasicConv2d(in_channels, 192, kernel_size=1)
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self.branch3x3_2 = BasicConv2d(192, 320, kernel_size=3, stride=2)
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self.branch7x7x3_1 = BasicConv2d(in_channels, 192, kernel_size=1)
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self.branch7x7x3_2 = BasicConv2d(
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192, 192, kernel_size=(1, 7), padding=(0, 3))
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self.branch7x7x3_3 = BasicConv2d(
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192, 192, kernel_size=(7, 1), padding=(3, 0))
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self.branch7x7x3_4 = BasicConv2d(192, 192, kernel_size=3, stride=2)
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def forward(self, x):
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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 = F.max_pool2d(x, kernel_size=3, stride=2)
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outputs = [branch3x3, branch7x7x3, branch_pool]
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return torch.cat(outputs, 1)
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class InceptionE(nn.Module):
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def __init__(self, in_channels):
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super(InceptionE, self).__init__()
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self.branch1x1 = BasicConv2d(in_channels, 320, kernel_size=1)
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self.branch3x3_1 = BasicConv2d(in_channels, 384, kernel_size=1)
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self.branch3x3_2a = BasicConv2d(
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384, 384, kernel_size=(1, 3), padding=(0, 1))
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self.branch3x3_2b = BasicConv2d(
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384, 384, kernel_size=(3, 1), padding=(1, 0))
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self.branch3x3dbl_1 = BasicConv2d(in_channels, 448, kernel_size=1)
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self.branch3x3dbl_2 = BasicConv2d(448, 384, kernel_size=3, padding=1)
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self.branch3x3dbl_3a = BasicConv2d(
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384, 384, kernel_size=(1, 3), padding=(0, 1))
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self.branch3x3dbl_3b = BasicConv2d(
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384, 384, kernel_size=(3, 1), padding=(1, 0))
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self.branch_pool = BasicConv2d(in_channels, 192, kernel_size=1)
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def forward(self, x):
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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 = torch.cat(branch3x3, 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 = torch.cat(branch3x3dbl, 1)
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branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
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branch_pool = self.branch_pool(branch_pool)
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outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool]
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return torch.cat(outputs, 1)
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class InceptionAux(nn.Module):
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def __init__(self, in_channels, num_classes):
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super(InceptionAux, self).__init__()
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self.conv0 = BasicConv2d(in_channels, 128, kernel_size=1)
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self.conv1 = BasicConv2d(128, 768, kernel_size=5)
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self.conv1.stddev = 0.01
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self.fc = nn.Linear(768, num_classes)
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self.fc.stddev = 0.001
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def forward(self, x):
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# N x 768 x 17 x 17
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x = F.avg_pool2d(x, kernel_size=5, stride=3)
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# N x 768 x 5 x 5
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x = self.conv0(x)
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# N x 128 x 5 x 5
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x = self.conv1(x)
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# N x 768 x 1 x 1
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# Adaptive average pooling
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x = F.adaptive_avg_pool2d(x, (1, 1))
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# N x 768 x 1 x 1
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x = torch.flatten(x, 1)
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# N x 768
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x = self.fc(x)
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# N x 1000
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return x
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class BasicConv2d(nn.Module):
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def __init__(self, in_channels, out_channels, **kwargs):
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super(BasicConv2d, self).__init__()
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self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
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self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
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def forward(self, x):
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x = self.conv(x)
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x = self.bn(x)
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return F.relu(x, inplace=True)
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