mirror of https://github.com/alibaba/EasyCV.git
825 lines
46 KiB
Python
825 lines
46 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.mobilenet.py on 31th Aug, 2019
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"""
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import torch
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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 import nn
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from torch.nn.modules.batchnorm import _BatchNorm
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from ..modelzoo import bninception as model_urls
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from ..registry import BACKBONES
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__all__ = ['BNInception']
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@BACKBONES.register_module
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class BNInception(nn.Module):
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def __init__(self, num_classes=0):
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super(BNInception, self).__init__()
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inplace = True
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self.conv1_7x7_s2 = nn.Conv2d(
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3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3))
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self.conv1_7x7_s2_bn = nn.BatchNorm2d(64, affine=True)
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self.conv1_relu_7x7 = nn.ReLU(inplace)
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self.pool1_3x3_s2 = nn.MaxPool2d((3, 3),
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stride=(2, 2),
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dilation=(1, 1),
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ceil_mode=True)
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self.conv2_3x3_reduce = nn.Conv2d(
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64, 64, kernel_size=(1, 1), stride=(1, 1))
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self.conv2_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True)
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self.conv2_relu_3x3_reduce = nn.ReLU(inplace)
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self.conv2_3x3 = nn.Conv2d(
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64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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self.conv2_3x3_bn = nn.BatchNorm2d(192, affine=True)
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self.conv2_relu_3x3 = nn.ReLU(inplace)
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self.pool2_3x3_s2 = nn.MaxPool2d((3, 3),
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stride=(2, 2),
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dilation=(1, 1),
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ceil_mode=True)
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self.inception_3a_1x1 = nn.Conv2d(
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192, 64, kernel_size=(1, 1), stride=(1, 1))
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self.inception_3a_1x1_bn = nn.BatchNorm2d(64, affine=True)
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self.inception_3a_relu_1x1 = nn.ReLU(inplace)
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self.inception_3a_3x3_reduce = nn.Conv2d(
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192, 64, kernel_size=(1, 1), stride=(1, 1))
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self.inception_3a_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True)
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self.inception_3a_relu_3x3_reduce = nn.ReLU(inplace)
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self.inception_3a_3x3 = nn.Conv2d(
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64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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self.inception_3a_3x3_bn = nn.BatchNorm2d(64, affine=True)
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self.inception_3a_relu_3x3 = nn.ReLU(inplace)
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self.inception_3a_double_3x3_reduce = nn.Conv2d(
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192, 64, kernel_size=(1, 1), stride=(1, 1))
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self.inception_3a_double_3x3_reduce_bn = nn.BatchNorm2d(
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64, affine=True)
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self.inception_3a_relu_double_3x3_reduce = nn.ReLU(inplace)
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self.inception_3a_double_3x3_1 = nn.Conv2d(
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64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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self.inception_3a_double_3x3_1_bn = nn.BatchNorm2d(96, affine=True)
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self.inception_3a_relu_double_3x3_1 = nn.ReLU(inplace)
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self.inception_3a_double_3x3_2 = nn.Conv2d(
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96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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self.inception_3a_double_3x3_2_bn = nn.BatchNorm2d(96, affine=True)
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self.inception_3a_relu_double_3x3_2 = nn.ReLU(inplace)
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self.inception_3a_pool = nn.AvgPool2d(
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3, stride=1, padding=1, ceil_mode=True, count_include_pad=True)
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self.inception_3a_pool_proj = nn.Conv2d(
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192, 32, kernel_size=(1, 1), stride=(1, 1))
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self.inception_3a_pool_proj_bn = nn.BatchNorm2d(32, affine=True)
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self.inception_3a_relu_pool_proj = nn.ReLU(inplace)
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self.inception_3b_1x1 = nn.Conv2d(
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256, 64, kernel_size=(1, 1), stride=(1, 1))
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self.inception_3b_1x1_bn = nn.BatchNorm2d(64, affine=True)
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self.inception_3b_relu_1x1 = nn.ReLU(inplace)
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self.inception_3b_3x3_reduce = nn.Conv2d(
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256, 64, kernel_size=(1, 1), stride=(1, 1))
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self.inception_3b_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True)
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self.inception_3b_relu_3x3_reduce = nn.ReLU(inplace)
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self.inception_3b_3x3 = nn.Conv2d(
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64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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self.inception_3b_3x3_bn = nn.BatchNorm2d(96, affine=True)
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self.inception_3b_relu_3x3 = nn.ReLU(inplace)
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self.inception_3b_double_3x3_reduce = nn.Conv2d(
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256, 64, kernel_size=(1, 1), stride=(1, 1))
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self.inception_3b_double_3x3_reduce_bn = nn.BatchNorm2d(
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64, affine=True)
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self.inception_3b_relu_double_3x3_reduce = nn.ReLU(inplace)
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self.inception_3b_double_3x3_1 = nn.Conv2d(
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64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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self.inception_3b_double_3x3_1_bn = nn.BatchNorm2d(96, affine=True)
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self.inception_3b_relu_double_3x3_1 = nn.ReLU(inplace)
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self.inception_3b_double_3x3_2 = nn.Conv2d(
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96, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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self.inception_3b_double_3x3_2_bn = nn.BatchNorm2d(96, affine=True)
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self.inception_3b_relu_double_3x3_2 = nn.ReLU(inplace)
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self.inception_3b_pool = nn.AvgPool2d(
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3, stride=1, padding=1, ceil_mode=True, count_include_pad=True)
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self.inception_3b_pool_proj = nn.Conv2d(
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256, 64, kernel_size=(1, 1), stride=(1, 1))
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self.inception_3b_pool_proj_bn = nn.BatchNorm2d(64, affine=True)
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self.inception_3b_relu_pool_proj = nn.ReLU(inplace)
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self.inception_3c_3x3_reduce = nn.Conv2d(
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320, 128, kernel_size=(1, 1), stride=(1, 1))
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self.inception_3c_3x3_reduce_bn = nn.BatchNorm2d(128, affine=True)
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self.inception_3c_relu_3x3_reduce = nn.ReLU(inplace)
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self.inception_3c_3x3 = nn.Conv2d(
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128, 160, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
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self.inception_3c_3x3_bn = nn.BatchNorm2d(160, affine=True)
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self.inception_3c_relu_3x3 = nn.ReLU(inplace)
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self.inception_3c_double_3x3_reduce = nn.Conv2d(
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320, 64, kernel_size=(1, 1), stride=(1, 1))
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self.inception_3c_double_3x3_reduce_bn = nn.BatchNorm2d(
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64, affine=True)
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self.inception_3c_relu_double_3x3_reduce = nn.ReLU(inplace)
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self.inception_3c_double_3x3_1 = nn.Conv2d(
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64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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self.inception_3c_double_3x3_1_bn = nn.BatchNorm2d(96, affine=True)
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self.inception_3c_relu_double_3x3_1 = nn.ReLU(inplace)
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self.inception_3c_double_3x3_2 = nn.Conv2d(
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96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
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self.inception_3c_double_3x3_2_bn = nn.BatchNorm2d(96, affine=True)
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self.inception_3c_relu_double_3x3_2 = nn.ReLU(inplace)
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self.inception_3c_pool = nn.MaxPool2d((3, 3),
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stride=(2, 2),
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dilation=(1, 1),
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ceil_mode=True)
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self.inception_4a_1x1 = nn.Conv2d(
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576, 224, kernel_size=(1, 1), stride=(1, 1))
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self.inception_4a_1x1_bn = nn.BatchNorm2d(224, affine=True)
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self.inception_4a_relu_1x1 = nn.ReLU(inplace)
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self.inception_4a_3x3_reduce = nn.Conv2d(
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576, 64, kernel_size=(1, 1), stride=(1, 1))
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self.inception_4a_3x3_reduce_bn = nn.BatchNorm2d(64, affine=True)
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self.inception_4a_relu_3x3_reduce = nn.ReLU(inplace)
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self.inception_4a_3x3 = nn.Conv2d(
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64, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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self.inception_4a_3x3_bn = nn.BatchNorm2d(96, affine=True)
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self.inception_4a_relu_3x3 = nn.ReLU(inplace)
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self.inception_4a_double_3x3_reduce = nn.Conv2d(
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576, 96, kernel_size=(1, 1), stride=(1, 1))
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self.inception_4a_double_3x3_reduce_bn = nn.BatchNorm2d(
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96, affine=True)
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self.inception_4a_relu_double_3x3_reduce = nn.ReLU(inplace)
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self.inception_4a_double_3x3_1 = nn.Conv2d(
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96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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self.inception_4a_double_3x3_1_bn = nn.BatchNorm2d(128, affine=True)
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self.inception_4a_relu_double_3x3_1 = nn.ReLU(inplace)
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self.inception_4a_double_3x3_2 = nn.Conv2d(
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128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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self.inception_4a_double_3x3_2_bn = nn.BatchNorm2d(128, affine=True)
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self.inception_4a_relu_double_3x3_2 = nn.ReLU(inplace)
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self.inception_4a_pool = nn.AvgPool2d(
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3, stride=1, padding=1, ceil_mode=True, count_include_pad=True)
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self.inception_4a_pool_proj = nn.Conv2d(
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576, 128, kernel_size=(1, 1), stride=(1, 1))
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self.inception_4a_pool_proj_bn = nn.BatchNorm2d(128, affine=True)
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self.inception_4a_relu_pool_proj = nn.ReLU(inplace)
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self.inception_4b_1x1 = nn.Conv2d(
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576, 192, kernel_size=(1, 1), stride=(1, 1))
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self.inception_4b_1x1_bn = nn.BatchNorm2d(192, affine=True)
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self.inception_4b_relu_1x1 = nn.ReLU(inplace)
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self.inception_4b_3x3_reduce = nn.Conv2d(
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576, 96, kernel_size=(1, 1), stride=(1, 1))
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self.inception_4b_3x3_reduce_bn = nn.BatchNorm2d(96, affine=True)
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self.inception_4b_relu_3x3_reduce = nn.ReLU(inplace)
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self.inception_4b_3x3 = nn.Conv2d(
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96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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self.inception_4b_3x3_bn = nn.BatchNorm2d(128, affine=True)
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self.inception_4b_relu_3x3 = nn.ReLU(inplace)
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self.inception_4b_double_3x3_reduce = nn.Conv2d(
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576, 96, kernel_size=(1, 1), stride=(1, 1))
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self.inception_4b_double_3x3_reduce_bn = nn.BatchNorm2d(
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96, affine=True)
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self.inception_4b_relu_double_3x3_reduce = nn.ReLU(inplace)
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self.inception_4b_double_3x3_1 = nn.Conv2d(
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96, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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self.inception_4b_double_3x3_1_bn = nn.BatchNorm2d(128, affine=True)
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self.inception_4b_relu_double_3x3_1 = nn.ReLU(inplace)
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self.inception_4b_double_3x3_2 = nn.Conv2d(
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128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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self.inception_4b_double_3x3_2_bn = nn.BatchNorm2d(128, affine=True)
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self.inception_4b_relu_double_3x3_2 = nn.ReLU(inplace)
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self.inception_4b_pool = nn.AvgPool2d(
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3, stride=1, padding=1, ceil_mode=True, count_include_pad=True)
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self.inception_4b_pool_proj = nn.Conv2d(
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576, 128, kernel_size=(1, 1), stride=(1, 1))
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self.inception_4b_pool_proj_bn = nn.BatchNorm2d(128, affine=True)
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self.inception_4b_relu_pool_proj = nn.ReLU(inplace)
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self.inception_4c_1x1 = nn.Conv2d(
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576, 160, kernel_size=(1, 1), stride=(1, 1))
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self.inception_4c_1x1_bn = nn.BatchNorm2d(160, affine=True)
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self.inception_4c_relu_1x1 = nn.ReLU(inplace)
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self.inception_4c_3x3_reduce = nn.Conv2d(
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576, 128, kernel_size=(1, 1), stride=(1, 1))
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self.inception_4c_3x3_reduce_bn = nn.BatchNorm2d(128, affine=True)
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self.inception_4c_relu_3x3_reduce = nn.ReLU(inplace)
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self.inception_4c_3x3 = nn.Conv2d(
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128, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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self.inception_4c_3x3_bn = nn.BatchNorm2d(160, affine=True)
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self.inception_4c_relu_3x3 = nn.ReLU(inplace)
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self.inception_4c_double_3x3_reduce = nn.Conv2d(
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576, 128, kernel_size=(1, 1), stride=(1, 1))
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self.inception_4c_double_3x3_reduce_bn = nn.BatchNorm2d(
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128, affine=True)
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self.inception_4c_relu_double_3x3_reduce = nn.ReLU(inplace)
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self.inception_4c_double_3x3_1 = nn.Conv2d(
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128, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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self.inception_4c_double_3x3_1_bn = nn.BatchNorm2d(160, affine=True)
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self.inception_4c_relu_double_3x3_1 = nn.ReLU(inplace)
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self.inception_4c_double_3x3_2 = nn.Conv2d(
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160, 160, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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self.inception_4c_double_3x3_2_bn = nn.BatchNorm2d(160, affine=True)
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self.inception_4c_relu_double_3x3_2 = nn.ReLU(inplace)
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self.inception_4c_pool = nn.AvgPool2d(
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3, stride=1, padding=1, ceil_mode=True, count_include_pad=True)
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self.inception_4c_pool_proj = nn.Conv2d(
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576, 128, kernel_size=(1, 1), stride=(1, 1))
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self.inception_4c_pool_proj_bn = nn.BatchNorm2d(128, affine=True)
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self.inception_4c_relu_pool_proj = nn.ReLU(inplace)
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self.inception_4d_1x1 = nn.Conv2d(
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608, 96, kernel_size=(1, 1), stride=(1, 1))
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self.inception_4d_1x1_bn = nn.BatchNorm2d(96, affine=True)
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self.inception_4d_relu_1x1 = nn.ReLU(inplace)
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self.inception_4d_3x3_reduce = nn.Conv2d(
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608, 128, kernel_size=(1, 1), stride=(1, 1))
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self.inception_4d_3x3_reduce_bn = nn.BatchNorm2d(128, affine=True)
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self.inception_4d_relu_3x3_reduce = nn.ReLU(inplace)
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self.inception_4d_3x3 = nn.Conv2d(
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128, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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self.inception_4d_3x3_bn = nn.BatchNorm2d(192, affine=True)
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self.inception_4d_relu_3x3 = nn.ReLU(inplace)
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self.inception_4d_double_3x3_reduce = nn.Conv2d(
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608, 160, kernel_size=(1, 1), stride=(1, 1))
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self.inception_4d_double_3x3_reduce_bn = nn.BatchNorm2d(
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160, affine=True)
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self.inception_4d_relu_double_3x3_reduce = nn.ReLU(inplace)
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self.inception_4d_double_3x3_1 = nn.Conv2d(
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160, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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self.inception_4d_double_3x3_1_bn = nn.BatchNorm2d(192, affine=True)
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self.inception_4d_relu_double_3x3_1 = nn.ReLU(inplace)
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self.inception_4d_double_3x3_2 = nn.Conv2d(
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192, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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self.inception_4d_double_3x3_2_bn = nn.BatchNorm2d(192, affine=True)
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self.inception_4d_relu_double_3x3_2 = nn.ReLU(inplace)
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self.inception_4d_pool = nn.AvgPool2d(
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3, stride=1, padding=1, ceil_mode=True, count_include_pad=True)
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self.inception_4d_pool_proj = nn.Conv2d(
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608, 128, kernel_size=(1, 1), stride=(1, 1))
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self.inception_4d_pool_proj_bn = nn.BatchNorm2d(128, affine=True)
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self.inception_4d_relu_pool_proj = nn.ReLU(inplace)
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self.inception_4e_3x3_reduce = nn.Conv2d(
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608, 128, kernel_size=(1, 1), stride=(1, 1))
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self.inception_4e_3x3_reduce_bn = nn.BatchNorm2d(128, affine=True)
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self.inception_4e_relu_3x3_reduce = nn.ReLU(inplace)
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self.inception_4e_3x3 = nn.Conv2d(
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128, 192, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
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self.inception_4e_3x3_bn = nn.BatchNorm2d(192, affine=True)
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self.inception_4e_relu_3x3 = nn.ReLU(inplace)
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self.inception_4e_double_3x3_reduce = nn.Conv2d(
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608, 192, kernel_size=(1, 1), stride=(1, 1))
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self.inception_4e_double_3x3_reduce_bn = nn.BatchNorm2d(
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192, affine=True)
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self.inception_4e_relu_double_3x3_reduce = nn.ReLU(inplace)
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self.inception_4e_double_3x3_1 = nn.Conv2d(
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192, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
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self.inception_4e_double_3x3_1_bn = nn.BatchNorm2d(256, affine=True)
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self.inception_4e_relu_double_3x3_1 = nn.ReLU(inplace)
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self.inception_4e_double_3x3_2 = nn.Conv2d(
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256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
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self.inception_4e_double_3x3_2_bn = nn.BatchNorm2d(256, affine=True)
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self.inception_4e_relu_double_3x3_2 = nn.ReLU(inplace)
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self.inception_4e_pool = nn.MaxPool2d((3, 3),
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stride=(2, 2),
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dilation=(1, 1),
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ceil_mode=True)
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self.inception_5a_1x1 = nn.Conv2d(
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1056, 352, kernel_size=(1, 1), stride=(1, 1))
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self.inception_5a_1x1_bn = nn.BatchNorm2d(352, affine=True)
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self.inception_5a_relu_1x1 = nn.ReLU(inplace)
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self.inception_5a_3x3_reduce = nn.Conv2d(
|
|
1056, 192, kernel_size=(1, 1), stride=(1, 1))
|
|
self.inception_5a_3x3_reduce_bn = nn.BatchNorm2d(192, affine=True)
|
|
self.inception_5a_relu_3x3_reduce = nn.ReLU(inplace)
|
|
self.inception_5a_3x3 = nn.Conv2d(
|
|
192, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
|
self.inception_5a_3x3_bn = nn.BatchNorm2d(320, affine=True)
|
|
self.inception_5a_relu_3x3 = nn.ReLU(inplace)
|
|
self.inception_5a_double_3x3_reduce = nn.Conv2d(
|
|
1056, 160, kernel_size=(1, 1), stride=(1, 1))
|
|
self.inception_5a_double_3x3_reduce_bn = nn.BatchNorm2d(
|
|
160, affine=True)
|
|
self.inception_5a_relu_double_3x3_reduce = nn.ReLU(inplace)
|
|
self.inception_5a_double_3x3_1 = nn.Conv2d(
|
|
160, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
|
self.inception_5a_double_3x3_1_bn = nn.BatchNorm2d(224, affine=True)
|
|
self.inception_5a_relu_double_3x3_1 = nn.ReLU(inplace)
|
|
self.inception_5a_double_3x3_2 = nn.Conv2d(
|
|
224, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
|
self.inception_5a_double_3x3_2_bn = nn.BatchNorm2d(224, affine=True)
|
|
self.inception_5a_relu_double_3x3_2 = nn.ReLU(inplace)
|
|
self.inception_5a_pool = nn.AvgPool2d(
|
|
3, stride=1, padding=1, ceil_mode=True, count_include_pad=True)
|
|
self.inception_5a_pool_proj = nn.Conv2d(
|
|
1056, 128, kernel_size=(1, 1), stride=(1, 1))
|
|
self.inception_5a_pool_proj_bn = nn.BatchNorm2d(128, affine=True)
|
|
self.inception_5a_relu_pool_proj = nn.ReLU(inplace)
|
|
self.inception_5b_1x1 = nn.Conv2d(
|
|
1024, 352, kernel_size=(1, 1), stride=(1, 1))
|
|
self.inception_5b_1x1_bn = nn.BatchNorm2d(352, affine=True)
|
|
self.inception_5b_relu_1x1 = nn.ReLU(inplace)
|
|
self.inception_5b_3x3_reduce = nn.Conv2d(
|
|
1024, 192, kernel_size=(1, 1), stride=(1, 1))
|
|
self.inception_5b_3x3_reduce_bn = nn.BatchNorm2d(192, affine=True)
|
|
self.inception_5b_relu_3x3_reduce = nn.ReLU(inplace)
|
|
self.inception_5b_3x3 = nn.Conv2d(
|
|
192, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
|
self.inception_5b_3x3_bn = nn.BatchNorm2d(320, affine=True)
|
|
self.inception_5b_relu_3x3 = nn.ReLU(inplace)
|
|
self.inception_5b_double_3x3_reduce = nn.Conv2d(
|
|
1024, 192, kernel_size=(1, 1), stride=(1, 1))
|
|
self.inception_5b_double_3x3_reduce_bn = nn.BatchNorm2d(
|
|
192, affine=True)
|
|
self.inception_5b_relu_double_3x3_reduce = nn.ReLU(inplace)
|
|
self.inception_5b_double_3x3_1 = nn.Conv2d(
|
|
192, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
|
self.inception_5b_double_3x3_1_bn = nn.BatchNorm2d(224, affine=True)
|
|
self.inception_5b_relu_double_3x3_1 = nn.ReLU(inplace)
|
|
self.inception_5b_double_3x3_2 = nn.Conv2d(
|
|
224, 224, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
|
|
self.inception_5b_double_3x3_2_bn = nn.BatchNorm2d(224, affine=True)
|
|
self.inception_5b_relu_double_3x3_2 = nn.ReLU(inplace)
|
|
self.inception_5b_pool = nn.MaxPool2d((3, 3),
|
|
stride=(1, 1),
|
|
padding=(1, 1),
|
|
dilation=(1, 1),
|
|
ceil_mode=True)
|
|
self.inception_5b_pool_proj = nn.Conv2d(
|
|
1024, 128, kernel_size=(1, 1), stride=(1, 1))
|
|
self.inception_5b_pool_proj_bn = nn.BatchNorm2d(128, affine=True)
|
|
self.inception_5b_relu_pool_proj = nn.ReLU(inplace)
|
|
self.num_classes = num_classes
|
|
if num_classes > 0:
|
|
self.last_linear = nn.Linear(1024, num_classes)
|
|
|
|
self.default_pretrained_model_path = model_urls[
|
|
self.__class__.__name__]
|
|
|
|
def init_weights(self):
|
|
for m in self.modules():
|
|
if isinstance(m, nn.Conv2d):
|
|
kaiming_init(m, mode='fan_in', nonlinearity='relu')
|
|
elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
|
|
constant_init(m, 1)
|
|
|
|
def features(self, input):
|
|
conv1_7x7_s2_out = self.conv1_7x7_s2(input)
|
|
conv1_7x7_s2_bn_out = self.conv1_7x7_s2_bn(conv1_7x7_s2_out)
|
|
conv1_relu_7x7_out = self.conv1_relu_7x7(conv1_7x7_s2_bn_out)
|
|
pool1_3x3_s2_out = self.pool1_3x3_s2(conv1_relu_7x7_out)
|
|
conv2_3x3_reduce_out = self.conv2_3x3_reduce(pool1_3x3_s2_out)
|
|
conv2_3x3_reduce_bn_out = self.conv2_3x3_reduce_bn(
|
|
conv2_3x3_reduce_out)
|
|
conv2_relu_3x3_reduce_out = self.conv2_relu_3x3_reduce(
|
|
conv2_3x3_reduce_bn_out)
|
|
conv2_3x3_out = self.conv2_3x3(conv2_relu_3x3_reduce_out)
|
|
conv2_3x3_bn_out = self.conv2_3x3_bn(conv2_3x3_out)
|
|
conv2_relu_3x3_out = self.conv2_relu_3x3(conv2_3x3_bn_out)
|
|
pool2_3x3_s2_out = self.pool2_3x3_s2(conv2_relu_3x3_out)
|
|
inception_3a_1x1_out = self.inception_3a_1x1(pool2_3x3_s2_out)
|
|
inception_3a_1x1_bn_out = self.inception_3a_1x1_bn(
|
|
inception_3a_1x1_out)
|
|
inception_3a_relu_1x1_out = self.inception_3a_relu_1x1(
|
|
inception_3a_1x1_bn_out)
|
|
inception_3a_3x3_reduce_out = self.inception_3a_3x3_reduce(
|
|
pool2_3x3_s2_out)
|
|
inception_3a_3x3_reduce_bn_out = self.inception_3a_3x3_reduce_bn(
|
|
inception_3a_3x3_reduce_out)
|
|
inception_3a_relu_3x3_reduce_out = self.inception_3a_relu_3x3_reduce(
|
|
inception_3a_3x3_reduce_bn_out)
|
|
inception_3a_3x3_out = self.inception_3a_3x3(
|
|
inception_3a_relu_3x3_reduce_out)
|
|
inception_3a_3x3_bn_out = self.inception_3a_3x3_bn(
|
|
inception_3a_3x3_out)
|
|
inception_3a_relu_3x3_out = self.inception_3a_relu_3x3(
|
|
inception_3a_3x3_bn_out)
|
|
inception_3a_double_3x3_reduce_out = self.inception_3a_double_3x3_reduce(
|
|
pool2_3x3_s2_out)
|
|
inception_3a_double_3x3_reduce_bn_out = self.inception_3a_double_3x3_reduce_bn(
|
|
inception_3a_double_3x3_reduce_out)
|
|
inception_3a_relu_double_3x3_reduce_out = self.inception_3a_relu_double_3x3_reduce(
|
|
inception_3a_double_3x3_reduce_bn_out)
|
|
inception_3a_double_3x3_1_out = self.inception_3a_double_3x3_1(
|
|
inception_3a_relu_double_3x3_reduce_out)
|
|
inception_3a_double_3x3_1_bn_out = self.inception_3a_double_3x3_1_bn(
|
|
inception_3a_double_3x3_1_out)
|
|
inception_3a_relu_double_3x3_1_out = self.inception_3a_relu_double_3x3_1(
|
|
inception_3a_double_3x3_1_bn_out)
|
|
inception_3a_double_3x3_2_out = self.inception_3a_double_3x3_2(
|
|
inception_3a_relu_double_3x3_1_out)
|
|
inception_3a_double_3x3_2_bn_out = self.inception_3a_double_3x3_2_bn(
|
|
inception_3a_double_3x3_2_out)
|
|
inception_3a_relu_double_3x3_2_out = self.inception_3a_relu_double_3x3_2(
|
|
inception_3a_double_3x3_2_bn_out)
|
|
inception_3a_pool_out = self.inception_3a_pool(pool2_3x3_s2_out)
|
|
inception_3a_pool_proj_out = self.inception_3a_pool_proj(
|
|
inception_3a_pool_out)
|
|
inception_3a_pool_proj_bn_out = self.inception_3a_pool_proj_bn(
|
|
inception_3a_pool_proj_out)
|
|
inception_3a_relu_pool_proj_out = self.inception_3a_relu_pool_proj(
|
|
inception_3a_pool_proj_bn_out)
|
|
inception_3a_output_out = torch.cat([
|
|
inception_3a_relu_1x1_out, inception_3a_relu_3x3_out,
|
|
inception_3a_relu_double_3x3_2_out, inception_3a_relu_pool_proj_out
|
|
], 1)
|
|
inception_3b_1x1_out = self.inception_3b_1x1(inception_3a_output_out)
|
|
inception_3b_1x1_bn_out = self.inception_3b_1x1_bn(
|
|
inception_3b_1x1_out)
|
|
inception_3b_relu_1x1_out = self.inception_3b_relu_1x1(
|
|
inception_3b_1x1_bn_out)
|
|
inception_3b_3x3_reduce_out = self.inception_3b_3x3_reduce(
|
|
inception_3a_output_out)
|
|
inception_3b_3x3_reduce_bn_out = self.inception_3b_3x3_reduce_bn(
|
|
inception_3b_3x3_reduce_out)
|
|
inception_3b_relu_3x3_reduce_out = self.inception_3b_relu_3x3_reduce(
|
|
inception_3b_3x3_reduce_bn_out)
|
|
inception_3b_3x3_out = self.inception_3b_3x3(
|
|
inception_3b_relu_3x3_reduce_out)
|
|
inception_3b_3x3_bn_out = self.inception_3b_3x3_bn(
|
|
inception_3b_3x3_out)
|
|
inception_3b_relu_3x3_out = self.inception_3b_relu_3x3(
|
|
inception_3b_3x3_bn_out)
|
|
inception_3b_double_3x3_reduce_out = self.inception_3b_double_3x3_reduce(
|
|
inception_3a_output_out)
|
|
inception_3b_double_3x3_reduce_bn_out = self.inception_3b_double_3x3_reduce_bn(
|
|
inception_3b_double_3x3_reduce_out)
|
|
inception_3b_relu_double_3x3_reduce_out = self.inception_3b_relu_double_3x3_reduce(
|
|
inception_3b_double_3x3_reduce_bn_out)
|
|
inception_3b_double_3x3_1_out = self.inception_3b_double_3x3_1(
|
|
inception_3b_relu_double_3x3_reduce_out)
|
|
inception_3b_double_3x3_1_bn_out = self.inception_3b_double_3x3_1_bn(
|
|
inception_3b_double_3x3_1_out)
|
|
inception_3b_relu_double_3x3_1_out = self.inception_3b_relu_double_3x3_1(
|
|
inception_3b_double_3x3_1_bn_out)
|
|
inception_3b_double_3x3_2_out = self.inception_3b_double_3x3_2(
|
|
inception_3b_relu_double_3x3_1_out)
|
|
inception_3b_double_3x3_2_bn_out = self.inception_3b_double_3x3_2_bn(
|
|
inception_3b_double_3x3_2_out)
|
|
inception_3b_relu_double_3x3_2_out = self.inception_3b_relu_double_3x3_2(
|
|
inception_3b_double_3x3_2_bn_out)
|
|
inception_3b_pool_out = self.inception_3b_pool(inception_3a_output_out)
|
|
inception_3b_pool_proj_out = self.inception_3b_pool_proj(
|
|
inception_3b_pool_out)
|
|
inception_3b_pool_proj_bn_out = self.inception_3b_pool_proj_bn(
|
|
inception_3b_pool_proj_out)
|
|
inception_3b_relu_pool_proj_out = self.inception_3b_relu_pool_proj(
|
|
inception_3b_pool_proj_bn_out)
|
|
inception_3b_output_out = torch.cat([
|
|
inception_3b_relu_1x1_out, inception_3b_relu_3x3_out,
|
|
inception_3b_relu_double_3x3_2_out, inception_3b_relu_pool_proj_out
|
|
], 1)
|
|
inception_3c_3x3_reduce_out = self.inception_3c_3x3_reduce(
|
|
inception_3b_output_out)
|
|
inception_3c_3x3_reduce_bn_out = self.inception_3c_3x3_reduce_bn(
|
|
inception_3c_3x3_reduce_out)
|
|
inception_3c_relu_3x3_reduce_out = self.inception_3c_relu_3x3_reduce(
|
|
inception_3c_3x3_reduce_bn_out)
|
|
inception_3c_3x3_out = self.inception_3c_3x3(
|
|
inception_3c_relu_3x3_reduce_out)
|
|
inception_3c_3x3_bn_out = self.inception_3c_3x3_bn(
|
|
inception_3c_3x3_out)
|
|
inception_3c_relu_3x3_out = self.inception_3c_relu_3x3(
|
|
inception_3c_3x3_bn_out)
|
|
inception_3c_double_3x3_reduce_out = self.inception_3c_double_3x3_reduce(
|
|
inception_3b_output_out)
|
|
inception_3c_double_3x3_reduce_bn_out = self.inception_3c_double_3x3_reduce_bn(
|
|
inception_3c_double_3x3_reduce_out)
|
|
inception_3c_relu_double_3x3_reduce_out = self.inception_3c_relu_double_3x3_reduce(
|
|
inception_3c_double_3x3_reduce_bn_out)
|
|
inception_3c_double_3x3_1_out = self.inception_3c_double_3x3_1(
|
|
inception_3c_relu_double_3x3_reduce_out)
|
|
inception_3c_double_3x3_1_bn_out = self.inception_3c_double_3x3_1_bn(
|
|
inception_3c_double_3x3_1_out)
|
|
inception_3c_relu_double_3x3_1_out = self.inception_3c_relu_double_3x3_1(
|
|
inception_3c_double_3x3_1_bn_out)
|
|
inception_3c_double_3x3_2_out = self.inception_3c_double_3x3_2(
|
|
inception_3c_relu_double_3x3_1_out)
|
|
inception_3c_double_3x3_2_bn_out = self.inception_3c_double_3x3_2_bn(
|
|
inception_3c_double_3x3_2_out)
|
|
inception_3c_relu_double_3x3_2_out = self.inception_3c_relu_double_3x3_2(
|
|
inception_3c_double_3x3_2_bn_out)
|
|
inception_3c_pool_out = self.inception_3c_pool(inception_3b_output_out)
|
|
inception_3c_output_out = torch.cat([
|
|
inception_3c_relu_3x3_out, inception_3c_relu_double_3x3_2_out,
|
|
inception_3c_pool_out
|
|
], 1)
|
|
inception_4a_1x1_out = self.inception_4a_1x1(inception_3c_output_out)
|
|
inception_4a_1x1_bn_out = self.inception_4a_1x1_bn(
|
|
inception_4a_1x1_out)
|
|
inception_4a_relu_1x1_out = self.inception_4a_relu_1x1(
|
|
inception_4a_1x1_bn_out)
|
|
inception_4a_3x3_reduce_out = self.inception_4a_3x3_reduce(
|
|
inception_3c_output_out)
|
|
inception_4a_3x3_reduce_bn_out = self.inception_4a_3x3_reduce_bn(
|
|
inception_4a_3x3_reduce_out)
|
|
inception_4a_relu_3x3_reduce_out = self.inception_4a_relu_3x3_reduce(
|
|
inception_4a_3x3_reduce_bn_out)
|
|
inception_4a_3x3_out = self.inception_4a_3x3(
|
|
inception_4a_relu_3x3_reduce_out)
|
|
inception_4a_3x3_bn_out = self.inception_4a_3x3_bn(
|
|
inception_4a_3x3_out)
|
|
inception_4a_relu_3x3_out = self.inception_4a_relu_3x3(
|
|
inception_4a_3x3_bn_out)
|
|
inception_4a_double_3x3_reduce_out = self.inception_4a_double_3x3_reduce(
|
|
inception_3c_output_out)
|
|
inception_4a_double_3x3_reduce_bn_out = self.inception_4a_double_3x3_reduce_bn(
|
|
inception_4a_double_3x3_reduce_out)
|
|
inception_4a_relu_double_3x3_reduce_out = self.inception_4a_relu_double_3x3_reduce(
|
|
inception_4a_double_3x3_reduce_bn_out)
|
|
inception_4a_double_3x3_1_out = self.inception_4a_double_3x3_1(
|
|
inception_4a_relu_double_3x3_reduce_out)
|
|
inception_4a_double_3x3_1_bn_out = self.inception_4a_double_3x3_1_bn(
|
|
inception_4a_double_3x3_1_out)
|
|
inception_4a_relu_double_3x3_1_out = self.inception_4a_relu_double_3x3_1(
|
|
inception_4a_double_3x3_1_bn_out)
|
|
inception_4a_double_3x3_2_out = self.inception_4a_double_3x3_2(
|
|
inception_4a_relu_double_3x3_1_out)
|
|
inception_4a_double_3x3_2_bn_out = self.inception_4a_double_3x3_2_bn(
|
|
inception_4a_double_3x3_2_out)
|
|
inception_4a_relu_double_3x3_2_out = self.inception_4a_relu_double_3x3_2(
|
|
inception_4a_double_3x3_2_bn_out)
|
|
inception_4a_pool_out = self.inception_4a_pool(inception_3c_output_out)
|
|
inception_4a_pool_proj_out = self.inception_4a_pool_proj(
|
|
inception_4a_pool_out)
|
|
inception_4a_pool_proj_bn_out = self.inception_4a_pool_proj_bn(
|
|
inception_4a_pool_proj_out)
|
|
inception_4a_relu_pool_proj_out = self.inception_4a_relu_pool_proj(
|
|
inception_4a_pool_proj_bn_out)
|
|
inception_4a_output_out = torch.cat([
|
|
inception_4a_relu_1x1_out, inception_4a_relu_3x3_out,
|
|
inception_4a_relu_double_3x3_2_out, inception_4a_relu_pool_proj_out
|
|
], 1)
|
|
inception_4b_1x1_out = self.inception_4b_1x1(inception_4a_output_out)
|
|
inception_4b_1x1_bn_out = self.inception_4b_1x1_bn(
|
|
inception_4b_1x1_out)
|
|
inception_4b_relu_1x1_out = self.inception_4b_relu_1x1(
|
|
inception_4b_1x1_bn_out)
|
|
inception_4b_3x3_reduce_out = self.inception_4b_3x3_reduce(
|
|
inception_4a_output_out)
|
|
inception_4b_3x3_reduce_bn_out = self.inception_4b_3x3_reduce_bn(
|
|
inception_4b_3x3_reduce_out)
|
|
inception_4b_relu_3x3_reduce_out = self.inception_4b_relu_3x3_reduce(
|
|
inception_4b_3x3_reduce_bn_out)
|
|
inception_4b_3x3_out = self.inception_4b_3x3(
|
|
inception_4b_relu_3x3_reduce_out)
|
|
inception_4b_3x3_bn_out = self.inception_4b_3x3_bn(
|
|
inception_4b_3x3_out)
|
|
inception_4b_relu_3x3_out = self.inception_4b_relu_3x3(
|
|
inception_4b_3x3_bn_out)
|
|
inception_4b_double_3x3_reduce_out = self.inception_4b_double_3x3_reduce(
|
|
inception_4a_output_out)
|
|
inception_4b_double_3x3_reduce_bn_out = self.inception_4b_double_3x3_reduce_bn(
|
|
inception_4b_double_3x3_reduce_out)
|
|
inception_4b_relu_double_3x3_reduce_out = self.inception_4b_relu_double_3x3_reduce(
|
|
inception_4b_double_3x3_reduce_bn_out)
|
|
inception_4b_double_3x3_1_out = self.inception_4b_double_3x3_1(
|
|
inception_4b_relu_double_3x3_reduce_out)
|
|
inception_4b_double_3x3_1_bn_out = self.inception_4b_double_3x3_1_bn(
|
|
inception_4b_double_3x3_1_out)
|
|
inception_4b_relu_double_3x3_1_out = self.inception_4b_relu_double_3x3_1(
|
|
inception_4b_double_3x3_1_bn_out)
|
|
inception_4b_double_3x3_2_out = self.inception_4b_double_3x3_2(
|
|
inception_4b_relu_double_3x3_1_out)
|
|
inception_4b_double_3x3_2_bn_out = self.inception_4b_double_3x3_2_bn(
|
|
inception_4b_double_3x3_2_out)
|
|
inception_4b_relu_double_3x3_2_out = self.inception_4b_relu_double_3x3_2(
|
|
inception_4b_double_3x3_2_bn_out)
|
|
inception_4b_pool_out = self.inception_4b_pool(inception_4a_output_out)
|
|
inception_4b_pool_proj_out = self.inception_4b_pool_proj(
|
|
inception_4b_pool_out)
|
|
inception_4b_pool_proj_bn_out = self.inception_4b_pool_proj_bn(
|
|
inception_4b_pool_proj_out)
|
|
inception_4b_relu_pool_proj_out = self.inception_4b_relu_pool_proj(
|
|
inception_4b_pool_proj_bn_out)
|
|
inception_4b_output_out = torch.cat([
|
|
inception_4b_relu_1x1_out, inception_4b_relu_3x3_out,
|
|
inception_4b_relu_double_3x3_2_out, inception_4b_relu_pool_proj_out
|
|
], 1)
|
|
inception_4c_1x1_out = self.inception_4c_1x1(inception_4b_output_out)
|
|
inception_4c_1x1_bn_out = self.inception_4c_1x1_bn(
|
|
inception_4c_1x1_out)
|
|
inception_4c_relu_1x1_out = self.inception_4c_relu_1x1(
|
|
inception_4c_1x1_bn_out)
|
|
inception_4c_3x3_reduce_out = self.inception_4c_3x3_reduce(
|
|
inception_4b_output_out)
|
|
inception_4c_3x3_reduce_bn_out = self.inception_4c_3x3_reduce_bn(
|
|
inception_4c_3x3_reduce_out)
|
|
inception_4c_relu_3x3_reduce_out = self.inception_4c_relu_3x3_reduce(
|
|
inception_4c_3x3_reduce_bn_out)
|
|
inception_4c_3x3_out = self.inception_4c_3x3(
|
|
inception_4c_relu_3x3_reduce_out)
|
|
inception_4c_3x3_bn_out = self.inception_4c_3x3_bn(
|
|
inception_4c_3x3_out)
|
|
inception_4c_relu_3x3_out = self.inception_4c_relu_3x3(
|
|
inception_4c_3x3_bn_out)
|
|
inception_4c_double_3x3_reduce_out = self.inception_4c_double_3x3_reduce(
|
|
inception_4b_output_out)
|
|
inception_4c_double_3x3_reduce_bn_out = self.inception_4c_double_3x3_reduce_bn(
|
|
inception_4c_double_3x3_reduce_out)
|
|
inception_4c_relu_double_3x3_reduce_out = self.inception_4c_relu_double_3x3_reduce(
|
|
inception_4c_double_3x3_reduce_bn_out)
|
|
inception_4c_double_3x3_1_out = self.inception_4c_double_3x3_1(
|
|
inception_4c_relu_double_3x3_reduce_out)
|
|
inception_4c_double_3x3_1_bn_out = self.inception_4c_double_3x3_1_bn(
|
|
inception_4c_double_3x3_1_out)
|
|
inception_4c_relu_double_3x3_1_out = self.inception_4c_relu_double_3x3_1(
|
|
inception_4c_double_3x3_1_bn_out)
|
|
inception_4c_double_3x3_2_out = self.inception_4c_double_3x3_2(
|
|
inception_4c_relu_double_3x3_1_out)
|
|
inception_4c_double_3x3_2_bn_out = self.inception_4c_double_3x3_2_bn(
|
|
inception_4c_double_3x3_2_out)
|
|
inception_4c_relu_double_3x3_2_out = self.inception_4c_relu_double_3x3_2(
|
|
inception_4c_double_3x3_2_bn_out)
|
|
inception_4c_pool_out = self.inception_4c_pool(inception_4b_output_out)
|
|
inception_4c_pool_proj_out = self.inception_4c_pool_proj(
|
|
inception_4c_pool_out)
|
|
inception_4c_pool_proj_bn_out = self.inception_4c_pool_proj_bn(
|
|
inception_4c_pool_proj_out)
|
|
inception_4c_relu_pool_proj_out = self.inception_4c_relu_pool_proj(
|
|
inception_4c_pool_proj_bn_out)
|
|
inception_4c_output_out = torch.cat([
|
|
inception_4c_relu_1x1_out, inception_4c_relu_3x3_out,
|
|
inception_4c_relu_double_3x3_2_out, inception_4c_relu_pool_proj_out
|
|
], 1)
|
|
inception_4d_1x1_out = self.inception_4d_1x1(inception_4c_output_out)
|
|
inception_4d_1x1_bn_out = self.inception_4d_1x1_bn(
|
|
inception_4d_1x1_out)
|
|
inception_4d_relu_1x1_out = self.inception_4d_relu_1x1(
|
|
inception_4d_1x1_bn_out)
|
|
inception_4d_3x3_reduce_out = self.inception_4d_3x3_reduce(
|
|
inception_4c_output_out)
|
|
inception_4d_3x3_reduce_bn_out = self.inception_4d_3x3_reduce_bn(
|
|
inception_4d_3x3_reduce_out)
|
|
inception_4d_relu_3x3_reduce_out = self.inception_4d_relu_3x3_reduce(
|
|
inception_4d_3x3_reduce_bn_out)
|
|
inception_4d_3x3_out = self.inception_4d_3x3(
|
|
inception_4d_relu_3x3_reduce_out)
|
|
inception_4d_3x3_bn_out = self.inception_4d_3x3_bn(
|
|
inception_4d_3x3_out)
|
|
inception_4d_relu_3x3_out = self.inception_4d_relu_3x3(
|
|
inception_4d_3x3_bn_out)
|
|
inception_4d_double_3x3_reduce_out = self.inception_4d_double_3x3_reduce(
|
|
inception_4c_output_out)
|
|
inception_4d_double_3x3_reduce_bn_out = self.inception_4d_double_3x3_reduce_bn(
|
|
inception_4d_double_3x3_reduce_out)
|
|
inception_4d_relu_double_3x3_reduce_out = self.inception_4d_relu_double_3x3_reduce(
|
|
inception_4d_double_3x3_reduce_bn_out)
|
|
inception_4d_double_3x3_1_out = self.inception_4d_double_3x3_1(
|
|
inception_4d_relu_double_3x3_reduce_out)
|
|
inception_4d_double_3x3_1_bn_out = self.inception_4d_double_3x3_1_bn(
|
|
inception_4d_double_3x3_1_out)
|
|
inception_4d_relu_double_3x3_1_out = self.inception_4d_relu_double_3x3_1(
|
|
inception_4d_double_3x3_1_bn_out)
|
|
inception_4d_double_3x3_2_out = self.inception_4d_double_3x3_2(
|
|
inception_4d_relu_double_3x3_1_out)
|
|
inception_4d_double_3x3_2_bn_out = self.inception_4d_double_3x3_2_bn(
|
|
inception_4d_double_3x3_2_out)
|
|
inception_4d_relu_double_3x3_2_out = self.inception_4d_relu_double_3x3_2(
|
|
inception_4d_double_3x3_2_bn_out)
|
|
inception_4d_pool_out = self.inception_4d_pool(inception_4c_output_out)
|
|
inception_4d_pool_proj_out = self.inception_4d_pool_proj(
|
|
inception_4d_pool_out)
|
|
inception_4d_pool_proj_bn_out = self.inception_4d_pool_proj_bn(
|
|
inception_4d_pool_proj_out)
|
|
inception_4d_relu_pool_proj_out = self.inception_4d_relu_pool_proj(
|
|
inception_4d_pool_proj_bn_out)
|
|
inception_4d_output_out = torch.cat([
|
|
inception_4d_relu_1x1_out, inception_4d_relu_3x3_out,
|
|
inception_4d_relu_double_3x3_2_out, inception_4d_relu_pool_proj_out
|
|
], 1)
|
|
inception_4e_3x3_reduce_out = self.inception_4e_3x3_reduce(
|
|
inception_4d_output_out)
|
|
inception_4e_3x3_reduce_bn_out = self.inception_4e_3x3_reduce_bn(
|
|
inception_4e_3x3_reduce_out)
|
|
inception_4e_relu_3x3_reduce_out = self.inception_4e_relu_3x3_reduce(
|
|
inception_4e_3x3_reduce_bn_out)
|
|
inception_4e_3x3_out = self.inception_4e_3x3(
|
|
inception_4e_relu_3x3_reduce_out)
|
|
inception_4e_3x3_bn_out = self.inception_4e_3x3_bn(
|
|
inception_4e_3x3_out)
|
|
inception_4e_relu_3x3_out = self.inception_4e_relu_3x3(
|
|
inception_4e_3x3_bn_out)
|
|
inception_4e_double_3x3_reduce_out = self.inception_4e_double_3x3_reduce(
|
|
inception_4d_output_out)
|
|
inception_4e_double_3x3_reduce_bn_out = self.inception_4e_double_3x3_reduce_bn(
|
|
inception_4e_double_3x3_reduce_out)
|
|
inception_4e_relu_double_3x3_reduce_out = self.inception_4e_relu_double_3x3_reduce(
|
|
inception_4e_double_3x3_reduce_bn_out)
|
|
inception_4e_double_3x3_1_out = self.inception_4e_double_3x3_1(
|
|
inception_4e_relu_double_3x3_reduce_out)
|
|
inception_4e_double_3x3_1_bn_out = self.inception_4e_double_3x3_1_bn(
|
|
inception_4e_double_3x3_1_out)
|
|
inception_4e_relu_double_3x3_1_out = self.inception_4e_relu_double_3x3_1(
|
|
inception_4e_double_3x3_1_bn_out)
|
|
inception_4e_double_3x3_2_out = self.inception_4e_double_3x3_2(
|
|
inception_4e_relu_double_3x3_1_out)
|
|
inception_4e_double_3x3_2_bn_out = self.inception_4e_double_3x3_2_bn(
|
|
inception_4e_double_3x3_2_out)
|
|
inception_4e_relu_double_3x3_2_out = self.inception_4e_relu_double_3x3_2(
|
|
inception_4e_double_3x3_2_bn_out)
|
|
inception_4e_pool_out = self.inception_4e_pool(inception_4d_output_out)
|
|
inception_4e_output_out = torch.cat([
|
|
inception_4e_relu_3x3_out, inception_4e_relu_double_3x3_2_out,
|
|
inception_4e_pool_out
|
|
], 1)
|
|
inception_5a_1x1_out = self.inception_5a_1x1(inception_4e_output_out)
|
|
inception_5a_1x1_bn_out = self.inception_5a_1x1_bn(
|
|
inception_5a_1x1_out)
|
|
inception_5a_relu_1x1_out = self.inception_5a_relu_1x1(
|
|
inception_5a_1x1_bn_out)
|
|
inception_5a_3x3_reduce_out = self.inception_5a_3x3_reduce(
|
|
inception_4e_output_out)
|
|
inception_5a_3x3_reduce_bn_out = self.inception_5a_3x3_reduce_bn(
|
|
inception_5a_3x3_reduce_out)
|
|
inception_5a_relu_3x3_reduce_out = self.inception_5a_relu_3x3_reduce(
|
|
inception_5a_3x3_reduce_bn_out)
|
|
inception_5a_3x3_out = self.inception_5a_3x3(
|
|
inception_5a_relu_3x3_reduce_out)
|
|
inception_5a_3x3_bn_out = self.inception_5a_3x3_bn(
|
|
inception_5a_3x3_out)
|
|
inception_5a_relu_3x3_out = self.inception_5a_relu_3x3(
|
|
inception_5a_3x3_bn_out)
|
|
inception_5a_double_3x3_reduce_out = self.inception_5a_double_3x3_reduce(
|
|
inception_4e_output_out)
|
|
inception_5a_double_3x3_reduce_bn_out = self.inception_5a_double_3x3_reduce_bn(
|
|
inception_5a_double_3x3_reduce_out)
|
|
inception_5a_relu_double_3x3_reduce_out = self.inception_5a_relu_double_3x3_reduce(
|
|
inception_5a_double_3x3_reduce_bn_out)
|
|
inception_5a_double_3x3_1_out = self.inception_5a_double_3x3_1(
|
|
inception_5a_relu_double_3x3_reduce_out)
|
|
inception_5a_double_3x3_1_bn_out = self.inception_5a_double_3x3_1_bn(
|
|
inception_5a_double_3x3_1_out)
|
|
inception_5a_relu_double_3x3_1_out = self.inception_5a_relu_double_3x3_1(
|
|
inception_5a_double_3x3_1_bn_out)
|
|
inception_5a_double_3x3_2_out = self.inception_5a_double_3x3_2(
|
|
inception_5a_relu_double_3x3_1_out)
|
|
inception_5a_double_3x3_2_bn_out = self.inception_5a_double_3x3_2_bn(
|
|
inception_5a_double_3x3_2_out)
|
|
inception_5a_relu_double_3x3_2_out = self.inception_5a_relu_double_3x3_2(
|
|
inception_5a_double_3x3_2_bn_out)
|
|
inception_5a_pool_out = self.inception_5a_pool(inception_4e_output_out)
|
|
inception_5a_pool_proj_out = self.inception_5a_pool_proj(
|
|
inception_5a_pool_out)
|
|
inception_5a_pool_proj_bn_out = self.inception_5a_pool_proj_bn(
|
|
inception_5a_pool_proj_out)
|
|
inception_5a_relu_pool_proj_out = self.inception_5a_relu_pool_proj(
|
|
inception_5a_pool_proj_bn_out)
|
|
inception_5a_output_out = torch.cat([
|
|
inception_5a_relu_1x1_out, inception_5a_relu_3x3_out,
|
|
inception_5a_relu_double_3x3_2_out, inception_5a_relu_pool_proj_out
|
|
], 1)
|
|
inception_5b_1x1_out = self.inception_5b_1x1(inception_5a_output_out)
|
|
inception_5b_1x1_bn_out = self.inception_5b_1x1_bn(
|
|
inception_5b_1x1_out)
|
|
inception_5b_relu_1x1_out = self.inception_5b_relu_1x1(
|
|
inception_5b_1x1_bn_out)
|
|
inception_5b_3x3_reduce_out = self.inception_5b_3x3_reduce(
|
|
inception_5a_output_out)
|
|
inception_5b_3x3_reduce_bn_out = self.inception_5b_3x3_reduce_bn(
|
|
inception_5b_3x3_reduce_out)
|
|
inception_5b_relu_3x3_reduce_out = self.inception_5b_relu_3x3_reduce(
|
|
inception_5b_3x3_reduce_bn_out)
|
|
inception_5b_3x3_out = self.inception_5b_3x3(
|
|
inception_5b_relu_3x3_reduce_out)
|
|
inception_5b_3x3_bn_out = self.inception_5b_3x3_bn(
|
|
inception_5b_3x3_out)
|
|
inception_5b_relu_3x3_out = self.inception_5b_relu_3x3(
|
|
inception_5b_3x3_bn_out)
|
|
inception_5b_double_3x3_reduce_out = self.inception_5b_double_3x3_reduce(
|
|
inception_5a_output_out)
|
|
inception_5b_double_3x3_reduce_bn_out = self.inception_5b_double_3x3_reduce_bn(
|
|
inception_5b_double_3x3_reduce_out)
|
|
inception_5b_relu_double_3x3_reduce_out = self.inception_5b_relu_double_3x3_reduce(
|
|
inception_5b_double_3x3_reduce_bn_out)
|
|
inception_5b_double_3x3_1_out = self.inception_5b_double_3x3_1(
|
|
inception_5b_relu_double_3x3_reduce_out)
|
|
inception_5b_double_3x3_1_bn_out = self.inception_5b_double_3x3_1_bn(
|
|
inception_5b_double_3x3_1_out)
|
|
inception_5b_relu_double_3x3_1_out = self.inception_5b_relu_double_3x3_1(
|
|
inception_5b_double_3x3_1_bn_out)
|
|
inception_5b_double_3x3_2_out = self.inception_5b_double_3x3_2(
|
|
inception_5b_relu_double_3x3_1_out)
|
|
inception_5b_double_3x3_2_bn_out = self.inception_5b_double_3x3_2_bn(
|
|
inception_5b_double_3x3_2_out)
|
|
inception_5b_relu_double_3x3_2_out = self.inception_5b_relu_double_3x3_2(
|
|
inception_5b_double_3x3_2_bn_out)
|
|
inception_5b_pool_out = self.inception_5b_pool(inception_5a_output_out)
|
|
inception_5b_pool_proj_out = self.inception_5b_pool_proj(
|
|
inception_5b_pool_out)
|
|
inception_5b_pool_proj_bn_out = self.inception_5b_pool_proj_bn(
|
|
inception_5b_pool_proj_out)
|
|
inception_5b_relu_pool_proj_out = self.inception_5b_relu_pool_proj(
|
|
inception_5b_pool_proj_bn_out)
|
|
inception_5b_output_out = torch.cat([
|
|
inception_5b_relu_1x1_out, inception_5b_relu_3x3_out,
|
|
inception_5b_relu_double_3x3_2_out, inception_5b_relu_pool_proj_out
|
|
], 1)
|
|
return inception_5b_output_out
|
|
|
|
def logits(self, features):
|
|
adaptiveAvgPoolWidth = features.shape[2]
|
|
x = F.avg_pool2d(features, kernel_size=adaptiveAvgPoolWidth)
|
|
x = x.view(x.size(0), -1)
|
|
x = self.last_linear(x)
|
|
return x
|
|
|
|
def forward(self, input):
|
|
x = self.features(input)
|
|
if self.num_classes > 0:
|
|
x = self.logits(x)
|
|
return [x]
|