models/rexnetv1.py

import torch
import torch.nn
import torch.functional
import torch.nn.functional


class rexnetv1(torch.nn.Module):
    def __init__(self):
        super().__init__()

        self.features_0 = torch.nn.modules.conv.Conv2d(3, 32, (3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), bias=False)
        self.features_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self.features_3_out_0 = torch.nn.modules.conv.Conv2d(32, 32, (3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=32, bias=False)
        self.features_3_out_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self.features_3_out_2 = torch.nn.modules.activation.ReLU6()
        self.features_3_out_3 = torch.nn.modules.conv.Conv2d(32, 16, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_3_out_4 = torch.nn.modules.batchnorm.BatchNorm2d(16)
        self.features_4_out_0 = torch.nn.modules.conv.Conv2d(16, 96, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_4_out_1 = torch.nn.modules.batchnorm.BatchNorm2d(96)
        self.features_4_out_3 = torch.nn.modules.conv.Conv2d(96, 96, (3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=96, bias=False)
        self.features_4_out_4 = torch.nn.modules.batchnorm.BatchNorm2d(96)
        self.features_4_out_5 = torch.nn.modules.activation.ReLU6()
        self.features_4_out_6 = torch.nn.modules.conv.Conv2d(96, 27, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_4_out_7 = torch.nn.modules.batchnorm.BatchNorm2d(27)
        self.features_5_out_0 = torch.nn.modules.conv.Conv2d(27, 162, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_5_out_1 = torch.nn.modules.batchnorm.BatchNorm2d(162)
        self.features_5_out_3 = torch.nn.modules.conv.Conv2d(162, 162, (3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=162, bias=False)
        self.features_5_out_4 = torch.nn.modules.batchnorm.BatchNorm2d(162)
        self.features_5_out_5 = torch.nn.modules.activation.ReLU6()
        self.features_5_out_6 = torch.nn.modules.conv.Conv2d(162, 38, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_5_out_7 = torch.nn.modules.batchnorm.BatchNorm2d(38)
        self.features_6_out_0 = torch.nn.modules.conv.Conv2d(38, 228, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_6_out_1 = torch.nn.modules.batchnorm.BatchNorm2d(228)
        self.features_6_out_3 = torch.nn.modules.conv.Conv2d(228, 228, (3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=228, bias=False)
        self.features_6_out_4 = torch.nn.modules.batchnorm.BatchNorm2d(228)
        self.features_6_out_5_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
        self.features_6_out_5_fc_0 = torch.nn.modules.conv.Conv2d(228, 19, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_6_out_5_fc_1 = torch.nn.modules.batchnorm.BatchNorm2d(19)
        self.features_6_out_5_fc_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.features_6_out_5_fc_3 = torch.nn.modules.conv.Conv2d(19, 228, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_6_out_5_fc_4 = torch.nn.modules.activation.Sigmoid()
        self.features_6_out_6 = torch.nn.modules.activation.ReLU6()
        self.features_6_out_7 = torch.nn.modules.conv.Conv2d(228, 50, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_6_out_8 = torch.nn.modules.batchnorm.BatchNorm2d(50)
        self.features_7_out_0 = torch.nn.modules.conv.Conv2d(50, 300, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_7_out_1 = torch.nn.modules.batchnorm.BatchNorm2d(300)
        self.features_7_out_3 = torch.nn.modules.conv.Conv2d(300, 300, (3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=300, bias=False)
        self.features_7_out_4 = torch.nn.modules.batchnorm.BatchNorm2d(300)
        self.features_7_out_5_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
        self.features_7_out_5_fc_0 = torch.nn.modules.conv.Conv2d(300, 25, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_7_out_5_fc_1 = torch.nn.modules.batchnorm.BatchNorm2d(25)
        self.features_7_out_5_fc_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.features_7_out_5_fc_3 = torch.nn.modules.conv.Conv2d(25, 300, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_7_out_5_fc_4 = torch.nn.modules.activation.Sigmoid()
        self.features_7_out_6 = torch.nn.modules.activation.ReLU6()
        self.features_7_out_7 = torch.nn.modules.conv.Conv2d(300, 61, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_7_out_8 = torch.nn.modules.batchnorm.BatchNorm2d(61)
        self.features_8_out_0 = torch.nn.modules.conv.Conv2d(61, 366, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_8_out_1 = torch.nn.modules.batchnorm.BatchNorm2d(366)
        self.features_8_out_3 = torch.nn.modules.conv.Conv2d(366, 366, (3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=366, bias=False)
        self.features_8_out_4 = torch.nn.modules.batchnorm.BatchNorm2d(366)
        self.features_8_out_5_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
        self.features_8_out_5_fc_0 = torch.nn.modules.conv.Conv2d(366, 30, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_8_out_5_fc_1 = torch.nn.modules.batchnorm.BatchNorm2d(30)
        self.features_8_out_5_fc_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.features_8_out_5_fc_3 = torch.nn.modules.conv.Conv2d(30, 366, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_8_out_5_fc_4 = torch.nn.modules.activation.Sigmoid()
        self.features_8_out_6 = torch.nn.modules.activation.ReLU6()
        self.features_8_out_7 = torch.nn.modules.conv.Conv2d(366, 72, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_8_out_8 = torch.nn.modules.batchnorm.BatchNorm2d(72)
        self.features_9_out_0 = torch.nn.modules.conv.Conv2d(72, 432, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_9_out_1 = torch.nn.modules.batchnorm.BatchNorm2d(432)
        self.features_9_out_3 = torch.nn.modules.conv.Conv2d(432, 432, (3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=432, bias=False)
        self.features_9_out_4 = torch.nn.modules.batchnorm.BatchNorm2d(432)
        self.features_9_out_5_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
        self.features_9_out_5_fc_0 = torch.nn.modules.conv.Conv2d(432, 36, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_9_out_5_fc_1 = torch.nn.modules.batchnorm.BatchNorm2d(36)
        self.features_9_out_5_fc_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.features_9_out_5_fc_3 = torch.nn.modules.conv.Conv2d(36, 432, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_9_out_5_fc_4 = torch.nn.modules.activation.Sigmoid()
        self.features_9_out_6 = torch.nn.modules.activation.ReLU6()
        self.features_9_out_7 = torch.nn.modules.conv.Conv2d(432, 84, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_9_out_8 = torch.nn.modules.batchnorm.BatchNorm2d(84)
        self.features_10_out_0 = torch.nn.modules.conv.Conv2d(84, 504, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_10_out_1 = torch.nn.modules.batchnorm.BatchNorm2d(504)
        self.features_10_out_3 = torch.nn.modules.conv.Conv2d(504, 504, (3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=504, bias=False)
        self.features_10_out_4 = torch.nn.modules.batchnorm.BatchNorm2d(504)
        self.features_10_out_5_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
        self.features_10_out_5_fc_0 = torch.nn.modules.conv.Conv2d(504, 42, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_10_out_5_fc_1 = torch.nn.modules.batchnorm.BatchNorm2d(42)
        self.features_10_out_5_fc_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.features_10_out_5_fc_3 = torch.nn.modules.conv.Conv2d(42, 504, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_10_out_5_fc_4 = torch.nn.modules.activation.Sigmoid()
        self.features_10_out_6 = torch.nn.modules.activation.ReLU6()
        self.features_10_out_7 = torch.nn.modules.conv.Conv2d(504, 95, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_10_out_8 = torch.nn.modules.batchnorm.BatchNorm2d(95)
        self.features_11_out_0 = torch.nn.modules.conv.Conv2d(95, 570, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_11_out_1 = torch.nn.modules.batchnorm.BatchNorm2d(570)
        self.features_11_out_3 = torch.nn.modules.conv.Conv2d(570, 570, (3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=570, bias=False)
        self.features_11_out_4 = torch.nn.modules.batchnorm.BatchNorm2d(570)
        self.features_11_out_5_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
        self.features_11_out_5_fc_0 = torch.nn.modules.conv.Conv2d(570, 47, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_11_out_5_fc_1 = torch.nn.modules.batchnorm.BatchNorm2d(47)
        self.features_11_out_5_fc_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.features_11_out_5_fc_3 = torch.nn.modules.conv.Conv2d(47, 570, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_11_out_5_fc_4 = torch.nn.modules.activation.Sigmoid()
        self.features_11_out_6 = torch.nn.modules.activation.ReLU6()
        self.features_11_out_7 = torch.nn.modules.conv.Conv2d(570, 106, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_11_out_8 = torch.nn.modules.batchnorm.BatchNorm2d(106)
        self.features_12_out_0 = torch.nn.modules.conv.Conv2d(106, 636, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_12_out_1 = torch.nn.modules.batchnorm.BatchNorm2d(636)
        self.features_12_out_3 = torch.nn.modules.conv.Conv2d(636, 636, (3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=636, bias=False)
        self.features_12_out_4 = torch.nn.modules.batchnorm.BatchNorm2d(636)
        self.features_12_out_5_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
        self.features_12_out_5_fc_0 = torch.nn.modules.conv.Conv2d(636, 53, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_12_out_5_fc_1 = torch.nn.modules.batchnorm.BatchNorm2d(53)
        self.features_12_out_5_fc_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.features_12_out_5_fc_3 = torch.nn.modules.conv.Conv2d(53, 636, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_12_out_5_fc_4 = torch.nn.modules.activation.Sigmoid()
        self.features_12_out_6 = torch.nn.modules.activation.ReLU6()
        self.features_12_out_7 = torch.nn.modules.conv.Conv2d(636, 117, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_12_out_8 = torch.nn.modules.batchnorm.BatchNorm2d(117)
        self.features_13_out_0 = torch.nn.modules.conv.Conv2d(117, 702, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_13_out_1 = torch.nn.modules.batchnorm.BatchNorm2d(702)
        self.features_13_out_3 = torch.nn.modules.conv.Conv2d(702, 702, (3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=702, bias=False)
        self.features_13_out_4 = torch.nn.modules.batchnorm.BatchNorm2d(702)
        self.features_13_out_5_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
        self.features_13_out_5_fc_0 = torch.nn.modules.conv.Conv2d(702, 58, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_13_out_5_fc_1 = torch.nn.modules.batchnorm.BatchNorm2d(58)
        self.features_13_out_5_fc_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.features_13_out_5_fc_3 = torch.nn.modules.conv.Conv2d(58, 702, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_13_out_5_fc_4 = torch.nn.modules.activation.Sigmoid()
        self.features_13_out_6 = torch.nn.modules.activation.ReLU6()
        self.features_13_out_7 = torch.nn.modules.conv.Conv2d(702, 128, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_13_out_8 = torch.nn.modules.batchnorm.BatchNorm2d(128)
        self.features_14_out_0 = torch.nn.modules.conv.Conv2d(128, 768, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_14_out_1 = torch.nn.modules.batchnorm.BatchNorm2d(768)
        self.features_14_out_3 = torch.nn.modules.conv.Conv2d(768, 768, (3, 3), stride=(2, 2), padding=(1, 1), dilation=(1, 1), groups=768, bias=False)
        self.features_14_out_4 = torch.nn.modules.batchnorm.BatchNorm2d(768)
        self.features_14_out_5_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
        self.features_14_out_5_fc_0 = torch.nn.modules.conv.Conv2d(768, 64, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_14_out_5_fc_1 = torch.nn.modules.batchnorm.BatchNorm2d(64)
        self.features_14_out_5_fc_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.features_14_out_5_fc_3 = torch.nn.modules.conv.Conv2d(64, 768, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_14_out_5_fc_4 = torch.nn.modules.activation.Sigmoid()
        self.features_14_out_6 = torch.nn.modules.activation.ReLU6()
        self.features_14_out_7 = torch.nn.modules.conv.Conv2d(768, 140, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_14_out_8 = torch.nn.modules.batchnorm.BatchNorm2d(140)
        self.features_15_out_0 = torch.nn.modules.conv.Conv2d(140, 840, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_15_out_1 = torch.nn.modules.batchnorm.BatchNorm2d(840)
        self.features_15_out_3 = torch.nn.modules.conv.Conv2d(840, 840, (3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=840, bias=False)
        self.features_15_out_4 = torch.nn.modules.batchnorm.BatchNorm2d(840)
        self.features_15_out_5_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
        self.features_15_out_5_fc_0 = torch.nn.modules.conv.Conv2d(840, 70, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_15_out_5_fc_1 = torch.nn.modules.batchnorm.BatchNorm2d(70)
        self.features_15_out_5_fc_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.features_15_out_5_fc_3 = torch.nn.modules.conv.Conv2d(70, 840, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_15_out_5_fc_4 = torch.nn.modules.activation.Sigmoid()
        self.features_15_out_6 = torch.nn.modules.activation.ReLU6()
        self.features_15_out_7 = torch.nn.modules.conv.Conv2d(840, 151, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_15_out_8 = torch.nn.modules.batchnorm.BatchNorm2d(151)
        self.features_16_out_0 = torch.nn.modules.conv.Conv2d(151, 906, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_16_out_1 = torch.nn.modules.batchnorm.BatchNorm2d(906)
        self.features_16_out_3 = torch.nn.modules.conv.Conv2d(906, 906, (3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=906, bias=False)
        self.features_16_out_4 = torch.nn.modules.batchnorm.BatchNorm2d(906)
        self.features_16_out_5_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
        self.features_16_out_5_fc_0 = torch.nn.modules.conv.Conv2d(906, 75, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_16_out_5_fc_1 = torch.nn.modules.batchnorm.BatchNorm2d(75)
        self.features_16_out_5_fc_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.features_16_out_5_fc_3 = torch.nn.modules.conv.Conv2d(75, 906, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_16_out_5_fc_4 = torch.nn.modules.activation.Sigmoid()
        self.features_16_out_6 = torch.nn.modules.activation.ReLU6()
        self.features_16_out_7 = torch.nn.modules.conv.Conv2d(906, 162, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_16_out_8 = torch.nn.modules.batchnorm.BatchNorm2d(162)
        self.features_17_out_0 = torch.nn.modules.conv.Conv2d(162, 972, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_17_out_1 = torch.nn.modules.batchnorm.BatchNorm2d(972)
        self.features_17_out_3 = torch.nn.modules.conv.Conv2d(972, 972, (3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=972, bias=False)
        self.features_17_out_4 = torch.nn.modules.batchnorm.BatchNorm2d(972)
        self.features_17_out_5_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
        self.features_17_out_5_fc_0 = torch.nn.modules.conv.Conv2d(972, 81, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_17_out_5_fc_1 = torch.nn.modules.batchnorm.BatchNorm2d(81)
        self.features_17_out_5_fc_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.features_17_out_5_fc_3 = torch.nn.modules.conv.Conv2d(81, 972, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_17_out_5_fc_4 = torch.nn.modules.activation.Sigmoid()
        self.features_17_out_6 = torch.nn.modules.activation.ReLU6()
        self.features_17_out_7 = torch.nn.modules.conv.Conv2d(972, 174, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_17_out_8 = torch.nn.modules.batchnorm.BatchNorm2d(174)
        self.features_18_out_0 = torch.nn.modules.conv.Conv2d(174, 1044, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_18_out_1 = torch.nn.modules.batchnorm.BatchNorm2d(1044)
        self.features_18_out_3 = torch.nn.modules.conv.Conv2d(1044, 1044, (3, 3), stride=(1, 1), padding=(1, 1), dilation=(1, 1), groups=1044, bias=False)
        self.features_18_out_4 = torch.nn.modules.batchnorm.BatchNorm2d(1044)
        self.features_18_out_5_avg_pool = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
        self.features_18_out_5_fc_0 = torch.nn.modules.conv.Conv2d(1044, 87, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_18_out_5_fc_1 = torch.nn.modules.batchnorm.BatchNorm2d(87)
        self.features_18_out_5_fc_2 = torch.nn.modules.activation.ReLU(inplace=True)
        self.features_18_out_5_fc_3 = torch.nn.modules.conv.Conv2d(87, 1044, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))
        self.features_18_out_5_fc_4 = torch.nn.modules.activation.Sigmoid()
        self.features_18_out_6 = torch.nn.modules.activation.ReLU6()
        self.features_18_out_7 = torch.nn.modules.conv.Conv2d(1044, 185, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_18_out_8 = torch.nn.modules.batchnorm.BatchNorm2d(185)
        self.features_19 = torch.nn.modules.conv.Conv2d(185, 1280, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1), bias=False)
        self.features_20 = torch.nn.modules.batchnorm.BatchNorm2d(1280)
        self.features_22 = torch.nn.modules.pooling.AdaptiveAvgPool2d(1)
        self.output_0 = torch.nn.modules.dropout.Dropout(p=0.2)
        self.output_1 = torch.nn.modules.conv.Conv2d(1280, 1000, (1, 1), stride=(1, 1), padding=(0, 0), dilation=(1, 1))

    def forward(self, input_1):
        features_0 = self.features_0(input_1)
        features_1 = self.features_1(features_0)
        sigmoid_1 = features_1.sigmoid()
        mul_1 = features_1.mul_(sigmoid_1)
        features_3_out_0 = self.features_3_out_0(mul_1)
        features_3_out_1 = self.features_3_out_1(features_3_out_0)
        features_3_out_2 = self.features_3_out_2(features_3_out_1)
        features_3_out_3 = self.features_3_out_3(features_3_out_2)
        features_3_out_4 = self.features_3_out_4(features_3_out_3)
        features_4_out_0 = self.features_4_out_0(features_3_out_4)
        features_4_out_1 = self.features_4_out_1(features_4_out_0)
        sigmoid_2 = features_4_out_1.sigmoid()
        mul_2 = features_4_out_1.mul_(sigmoid_2)
        features_4_out_3 = self.features_4_out_3(mul_2)
        features_4_out_4 = self.features_4_out_4(features_4_out_3)
        features_4_out_5 = self.features_4_out_5(features_4_out_4)
        features_4_out_6 = self.features_4_out_6(features_4_out_5)
        features_4_out_7 = self.features_4_out_7(features_4_out_6)
        features_5_out_0 = self.features_5_out_0(features_4_out_7)
        features_5_out_1 = self.features_5_out_1(features_5_out_0)
        sigmoid_3 = features_5_out_1.sigmoid()
        mul_3 = features_5_out_1.mul_(sigmoid_3)
        features_5_out_3 = self.features_5_out_3(mul_3)
        features_5_out_4 = self.features_5_out_4(features_5_out_3)
        features_5_out_5 = self.features_5_out_5(features_5_out_4)
        features_5_out_6 = self.features_5_out_6(features_5_out_5)
        features_5_out_7 = self.features_5_out_7(features_5_out_6)
        getitem_1 = features_5_out_7[:, 0:27]
        add_1 = getitem_1.__iadd__(features_4_out_7)
        features_6_out_0 = self.features_6_out_0(features_5_out_7)
        features_6_out_1 = self.features_6_out_1(features_6_out_0)
        sigmoid_4 = features_6_out_1.sigmoid()
        mul_4 = features_6_out_1.mul_(sigmoid_4)
        features_6_out_3 = self.features_6_out_3(mul_4)
        features_6_out_4 = self.features_6_out_4(features_6_out_3)
        features_6_out_5_avg_pool = self.features_6_out_5_avg_pool(features_6_out_4)
        features_6_out_5_fc_0 = self.features_6_out_5_fc_0(features_6_out_5_avg_pool)
        features_6_out_5_fc_1 = self.features_6_out_5_fc_1(features_6_out_5_fc_0)
        features_6_out_5_fc_2 = self.features_6_out_5_fc_2(features_6_out_5_fc_1)
        features_6_out_5_fc_3 = self.features_6_out_5_fc_3(features_6_out_5_fc_2)
        features_6_out_5_fc_4 = self.features_6_out_5_fc_4(features_6_out_5_fc_3)
        mul_5 = features_6_out_4.__mul__(features_6_out_5_fc_4)
        features_6_out_6 = self.features_6_out_6(mul_5)
        features_6_out_7 = self.features_6_out_7(features_6_out_6)
        features_6_out_8 = self.features_6_out_8(features_6_out_7)
        features_7_out_0 = self.features_7_out_0(features_6_out_8)
        features_7_out_1 = self.features_7_out_1(features_7_out_0)
        sigmoid_5 = features_7_out_1.sigmoid()
        mul_6 = features_7_out_1.mul_(sigmoid_5)
        features_7_out_3 = self.features_7_out_3(mul_6)
        features_7_out_4 = self.features_7_out_4(features_7_out_3)
        features_7_out_5_avg_pool = self.features_7_out_5_avg_pool(features_7_out_4)
        features_7_out_5_fc_0 = self.features_7_out_5_fc_0(features_7_out_5_avg_pool)
        features_7_out_5_fc_1 = self.features_7_out_5_fc_1(features_7_out_5_fc_0)
        features_7_out_5_fc_2 = self.features_7_out_5_fc_2(features_7_out_5_fc_1)
        features_7_out_5_fc_3 = self.features_7_out_5_fc_3(features_7_out_5_fc_2)
        features_7_out_5_fc_4 = self.features_7_out_5_fc_4(features_7_out_5_fc_3)
        mul_7 = features_7_out_4.__mul__(features_7_out_5_fc_4)
        features_7_out_6 = self.features_7_out_6(mul_7)
        features_7_out_7 = self.features_7_out_7(features_7_out_6)
        features_7_out_8 = self.features_7_out_8(features_7_out_7)
        getitem_2 = features_7_out_8[:, 0:50]
        add_2 = getitem_2.__iadd__(features_6_out_8)
        features_8_out_0 = self.features_8_out_0(features_7_out_8)
        features_8_out_1 = self.features_8_out_1(features_8_out_0)
        sigmoid_6 = features_8_out_1.sigmoid()
        mul_8 = features_8_out_1.mul_(sigmoid_6)
        features_8_out_3 = self.features_8_out_3(mul_8)
        features_8_out_4 = self.features_8_out_4(features_8_out_3)
        features_8_out_5_avg_pool = self.features_8_out_5_avg_pool(features_8_out_4)
        features_8_out_5_fc_0 = self.features_8_out_5_fc_0(features_8_out_5_avg_pool)
        features_8_out_5_fc_1 = self.features_8_out_5_fc_1(features_8_out_5_fc_0)
        features_8_out_5_fc_2 = self.features_8_out_5_fc_2(features_8_out_5_fc_1)
        features_8_out_5_fc_3 = self.features_8_out_5_fc_3(features_8_out_5_fc_2)
        features_8_out_5_fc_4 = self.features_8_out_5_fc_4(features_8_out_5_fc_3)
        mul_9 = features_8_out_4.__mul__(features_8_out_5_fc_4)
        features_8_out_6 = self.features_8_out_6(mul_9)
        features_8_out_7 = self.features_8_out_7(features_8_out_6)
        features_8_out_8 = self.features_8_out_8(features_8_out_7)
        features_9_out_0 = self.features_9_out_0(features_8_out_8)
        features_9_out_1 = self.features_9_out_1(features_9_out_0)
        sigmoid_7 = features_9_out_1.sigmoid()
        mul_10 = features_9_out_1.mul_(sigmoid_7)
        features_9_out_3 = self.features_9_out_3(mul_10)
        features_9_out_4 = self.features_9_out_4(features_9_out_3)
        features_9_out_5_avg_pool = self.features_9_out_5_avg_pool(features_9_out_4)
        features_9_out_5_fc_0 = self.features_9_out_5_fc_0(features_9_out_5_avg_pool)
        features_9_out_5_fc_1 = self.features_9_out_5_fc_1(features_9_out_5_fc_0)
        features_9_out_5_fc_2 = self.features_9_out_5_fc_2(features_9_out_5_fc_1)
        features_9_out_5_fc_3 = self.features_9_out_5_fc_3(features_9_out_5_fc_2)
        features_9_out_5_fc_4 = self.features_9_out_5_fc_4(features_9_out_5_fc_3)
        mul_11 = features_9_out_4.__mul__(features_9_out_5_fc_4)
        features_9_out_6 = self.features_9_out_6(mul_11)
        features_9_out_7 = self.features_9_out_7(features_9_out_6)
        features_9_out_8 = self.features_9_out_8(features_9_out_7)
        getitem_3 = features_9_out_8[:, 0:72]
        add_3 = getitem_3.__iadd__(features_8_out_8)
        features_10_out_0 = self.features_10_out_0(features_9_out_8)
        features_10_out_1 = self.features_10_out_1(features_10_out_0)
        sigmoid_8 = features_10_out_1.sigmoid()
        mul_12 = features_10_out_1.mul_(sigmoid_8)
        features_10_out_3 = self.features_10_out_3(mul_12)
        features_10_out_4 = self.features_10_out_4(features_10_out_3)
        features_10_out_5_avg_pool = self.features_10_out_5_avg_pool(features_10_out_4)
        features_10_out_5_fc_0 = self.features_10_out_5_fc_0(features_10_out_5_avg_pool)
        features_10_out_5_fc_1 = self.features_10_out_5_fc_1(features_10_out_5_fc_0)
        features_10_out_5_fc_2 = self.features_10_out_5_fc_2(features_10_out_5_fc_1)
        features_10_out_5_fc_3 = self.features_10_out_5_fc_3(features_10_out_5_fc_2)
        features_10_out_5_fc_4 = self.features_10_out_5_fc_4(features_10_out_5_fc_3)
        mul_13 = features_10_out_4.__mul__(features_10_out_5_fc_4)
        features_10_out_6 = self.features_10_out_6(mul_13)
        features_10_out_7 = self.features_10_out_7(features_10_out_6)
        features_10_out_8 = self.features_10_out_8(features_10_out_7)
        getitem_4 = features_10_out_8[:, 0:84]
        add_4 = getitem_4.__iadd__(features_9_out_8)
        features_11_out_0 = self.features_11_out_0(features_10_out_8)
        features_11_out_1 = self.features_11_out_1(features_11_out_0)
        sigmoid_9 = features_11_out_1.sigmoid()
        mul_14 = features_11_out_1.mul_(sigmoid_9)
        features_11_out_3 = self.features_11_out_3(mul_14)
        features_11_out_4 = self.features_11_out_4(features_11_out_3)
        features_11_out_5_avg_pool = self.features_11_out_5_avg_pool(features_11_out_4)
        features_11_out_5_fc_0 = self.features_11_out_5_fc_0(features_11_out_5_avg_pool)
        features_11_out_5_fc_1 = self.features_11_out_5_fc_1(features_11_out_5_fc_0)
        features_11_out_5_fc_2 = self.features_11_out_5_fc_2(features_11_out_5_fc_1)
        features_11_out_5_fc_3 = self.features_11_out_5_fc_3(features_11_out_5_fc_2)
        features_11_out_5_fc_4 = self.features_11_out_5_fc_4(features_11_out_5_fc_3)
        mul_15 = features_11_out_4.__mul__(features_11_out_5_fc_4)
        features_11_out_6 = self.features_11_out_6(mul_15)
        features_11_out_7 = self.features_11_out_7(features_11_out_6)
        features_11_out_8 = self.features_11_out_8(features_11_out_7)
        getitem_5 = features_11_out_8[:, 0:95]
        add_5 = getitem_5.__iadd__(features_10_out_8)
        features_12_out_0 = self.features_12_out_0(features_11_out_8)
        features_12_out_1 = self.features_12_out_1(features_12_out_0)
        sigmoid_10 = features_12_out_1.sigmoid()
        mul_16 = features_12_out_1.mul_(sigmoid_10)
        features_12_out_3 = self.features_12_out_3(mul_16)
        features_12_out_4 = self.features_12_out_4(features_12_out_3)
        features_12_out_5_avg_pool = self.features_12_out_5_avg_pool(features_12_out_4)
        features_12_out_5_fc_0 = self.features_12_out_5_fc_0(features_12_out_5_avg_pool)
        features_12_out_5_fc_1 = self.features_12_out_5_fc_1(features_12_out_5_fc_0)
        features_12_out_5_fc_2 = self.features_12_out_5_fc_2(features_12_out_5_fc_1)
        features_12_out_5_fc_3 = self.features_12_out_5_fc_3(features_12_out_5_fc_2)
        features_12_out_5_fc_4 = self.features_12_out_5_fc_4(features_12_out_5_fc_3)
        mul_17 = features_12_out_4.__mul__(features_12_out_5_fc_4)
        features_12_out_6 = self.features_12_out_6(mul_17)
        features_12_out_7 = self.features_12_out_7(features_12_out_6)
        features_12_out_8 = self.features_12_out_8(features_12_out_7)
        getitem_6 = features_12_out_8[:, 0:106]
        add_6 = getitem_6.__iadd__(features_11_out_8)
        features_13_out_0 = self.features_13_out_0(features_12_out_8)
        features_13_out_1 = self.features_13_out_1(features_13_out_0)
        sigmoid_11 = features_13_out_1.sigmoid()
        mul_18 = features_13_out_1.mul_(sigmoid_11)
        features_13_out_3 = self.features_13_out_3(mul_18)
        features_13_out_4 = self.features_13_out_4(features_13_out_3)
        features_13_out_5_avg_pool = self.features_13_out_5_avg_pool(features_13_out_4)
        features_13_out_5_fc_0 = self.features_13_out_5_fc_0(features_13_out_5_avg_pool)
        features_13_out_5_fc_1 = self.features_13_out_5_fc_1(features_13_out_5_fc_0)
        features_13_out_5_fc_2 = self.features_13_out_5_fc_2(features_13_out_5_fc_1)
        features_13_out_5_fc_3 = self.features_13_out_5_fc_3(features_13_out_5_fc_2)
        features_13_out_5_fc_4 = self.features_13_out_5_fc_4(features_13_out_5_fc_3)
        mul_19 = features_13_out_4.__mul__(features_13_out_5_fc_4)
        features_13_out_6 = self.features_13_out_6(mul_19)
        features_13_out_7 = self.features_13_out_7(features_13_out_6)
        features_13_out_8 = self.features_13_out_8(features_13_out_7)
        getitem_7 = features_13_out_8[:, 0:117]
        add_7 = getitem_7.__iadd__(features_12_out_8)
        features_14_out_0 = self.features_14_out_0(features_13_out_8)
        features_14_out_1 = self.features_14_out_1(features_14_out_0)
        sigmoid_12 = features_14_out_1.sigmoid()
        mul_20 = features_14_out_1.mul_(sigmoid_12)
        features_14_out_3 = self.features_14_out_3(mul_20)
        features_14_out_4 = self.features_14_out_4(features_14_out_3)
        features_14_out_5_avg_pool = self.features_14_out_5_avg_pool(features_14_out_4)
        features_14_out_5_fc_0 = self.features_14_out_5_fc_0(features_14_out_5_avg_pool)
        features_14_out_5_fc_1 = self.features_14_out_5_fc_1(features_14_out_5_fc_0)
        features_14_out_5_fc_2 = self.features_14_out_5_fc_2(features_14_out_5_fc_1)
        features_14_out_5_fc_3 = self.features_14_out_5_fc_3(features_14_out_5_fc_2)
        features_14_out_5_fc_4 = self.features_14_out_5_fc_4(features_14_out_5_fc_3)
        mul_21 = features_14_out_4.__mul__(features_14_out_5_fc_4)
        features_14_out_6 = self.features_14_out_6(mul_21)
        features_14_out_7 = self.features_14_out_7(features_14_out_6)
        features_14_out_8 = self.features_14_out_8(features_14_out_7)
        features_15_out_0 = self.features_15_out_0(features_14_out_8)
        features_15_out_1 = self.features_15_out_1(features_15_out_0)
        sigmoid_13 = features_15_out_1.sigmoid()
        mul_22 = features_15_out_1.mul_(sigmoid_13)
        features_15_out_3 = self.features_15_out_3(mul_22)
        features_15_out_4 = self.features_15_out_4(features_15_out_3)
        features_15_out_5_avg_pool = self.features_15_out_5_avg_pool(features_15_out_4)
        features_15_out_5_fc_0 = self.features_15_out_5_fc_0(features_15_out_5_avg_pool)
        features_15_out_5_fc_1 = self.features_15_out_5_fc_1(features_15_out_5_fc_0)
        features_15_out_5_fc_2 = self.features_15_out_5_fc_2(features_15_out_5_fc_1)
        features_15_out_5_fc_3 = self.features_15_out_5_fc_3(features_15_out_5_fc_2)
        features_15_out_5_fc_4 = self.features_15_out_5_fc_4(features_15_out_5_fc_3)
        mul_23 = features_15_out_4.__mul__(features_15_out_5_fc_4)
        features_15_out_6 = self.features_15_out_6(mul_23)
        features_15_out_7 = self.features_15_out_7(features_15_out_6)
        features_15_out_8 = self.features_15_out_8(features_15_out_7)
        getitem_8 = features_15_out_8[:, 0:140]
        add_8 = getitem_8.__iadd__(features_14_out_8)
        features_16_out_0 = self.features_16_out_0(features_15_out_8)
        features_16_out_1 = self.features_16_out_1(features_16_out_0)
        sigmoid_14 = features_16_out_1.sigmoid()
        mul_24 = features_16_out_1.mul_(sigmoid_14)
        features_16_out_3 = self.features_16_out_3(mul_24)
        features_16_out_4 = self.features_16_out_4(features_16_out_3)
        features_16_out_5_avg_pool = self.features_16_out_5_avg_pool(features_16_out_4)
        features_16_out_5_fc_0 = self.features_16_out_5_fc_0(features_16_out_5_avg_pool)
        features_16_out_5_fc_1 = self.features_16_out_5_fc_1(features_16_out_5_fc_0)
        features_16_out_5_fc_2 = self.features_16_out_5_fc_2(features_16_out_5_fc_1)
        features_16_out_5_fc_3 = self.features_16_out_5_fc_3(features_16_out_5_fc_2)
        features_16_out_5_fc_4 = self.features_16_out_5_fc_4(features_16_out_5_fc_3)
        mul_25 = features_16_out_4.__mul__(features_16_out_5_fc_4)
        features_16_out_6 = self.features_16_out_6(mul_25)
        features_16_out_7 = self.features_16_out_7(features_16_out_6)
        features_16_out_8 = self.features_16_out_8(features_16_out_7)
        getitem_9 = features_16_out_8[:, 0:151]
        add_9 = getitem_9.__iadd__(features_15_out_8)
        features_17_out_0 = self.features_17_out_0(features_16_out_8)
        features_17_out_1 = self.features_17_out_1(features_17_out_0)
        sigmoid_15 = features_17_out_1.sigmoid()
        mul_26 = features_17_out_1.mul_(sigmoid_15)
        features_17_out_3 = self.features_17_out_3(mul_26)
        features_17_out_4 = self.features_17_out_4(features_17_out_3)
        features_17_out_5_avg_pool = self.features_17_out_5_avg_pool(features_17_out_4)
        features_17_out_5_fc_0 = self.features_17_out_5_fc_0(features_17_out_5_avg_pool)
        features_17_out_5_fc_1 = self.features_17_out_5_fc_1(features_17_out_5_fc_0)
        features_17_out_5_fc_2 = self.features_17_out_5_fc_2(features_17_out_5_fc_1)
        features_17_out_5_fc_3 = self.features_17_out_5_fc_3(features_17_out_5_fc_2)
        features_17_out_5_fc_4 = self.features_17_out_5_fc_4(features_17_out_5_fc_3)
        mul_27 = features_17_out_4.__mul__(features_17_out_5_fc_4)
        features_17_out_6 = self.features_17_out_6(mul_27)
        features_17_out_7 = self.features_17_out_7(features_17_out_6)
        features_17_out_8 = self.features_17_out_8(features_17_out_7)
        getitem_10 = features_17_out_8[:, 0:162]
        add_10 = getitem_10.__iadd__(features_16_out_8)
        features_18_out_0 = self.features_18_out_0(features_17_out_8)
        features_18_out_1 = self.features_18_out_1(features_18_out_0)
        sigmoid_16 = features_18_out_1.sigmoid()
        mul_28 = features_18_out_1.mul_(sigmoid_16)
        features_18_out_3 = self.features_18_out_3(mul_28)
        features_18_out_4 = self.features_18_out_4(features_18_out_3)
        features_18_out_5_avg_pool = self.features_18_out_5_avg_pool(features_18_out_4)
        features_18_out_5_fc_0 = self.features_18_out_5_fc_0(features_18_out_5_avg_pool)
        features_18_out_5_fc_1 = self.features_18_out_5_fc_1(features_18_out_5_fc_0)
        features_18_out_5_fc_2 = self.features_18_out_5_fc_2(features_18_out_5_fc_1)
        features_18_out_5_fc_3 = self.features_18_out_5_fc_3(features_18_out_5_fc_2)
        features_18_out_5_fc_4 = self.features_18_out_5_fc_4(features_18_out_5_fc_3)
        mul_29 = features_18_out_4.__mul__(features_18_out_5_fc_4)
        features_18_out_6 = self.features_18_out_6(mul_29)
        features_18_out_7 = self.features_18_out_7(features_18_out_6)
        features_18_out_8 = self.features_18_out_8(features_18_out_7)
        getitem_11 = features_18_out_8[:, 0:174]
        add_11 = getitem_11.__iadd__(features_17_out_8)
        features_19 = self.features_19(features_18_out_8)
        features_20 = self.features_20(features_19)
        sigmoid_17 = features_20.sigmoid()
        mul_30 = features_20.mul_(sigmoid_17)
        features_22 = self.features_22(mul_30)
        output_0 = self.output_0(features_22)
        output_1 = self.output_1(output_0)
        flatten_1 = output_1.flatten(1)
        return flatten_1


if __name__ == "__main__":
    model = rexnetv1()

    model.eval()
    model.cpu()

    dummy_input_0 = torch.ones((2, 3, 224, 224), dtype=torch.float32)

    output = model(dummy_input_0)
    print(output)