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Flask api deployment #1

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11 changes: 11 additions & 0 deletions .gitignore
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.idea/*
.ebextensions
.ebextensions/*
__pycache__
__pycache__/*

# Elastic Beanstalk Files
.elasticbeanstalk/*
!.elasticbeanstalk/*.cfg.yml
!.elasticbeanstalk/*.global.yml
91 changes: 91 additions & 0 deletions Depth decoder
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DepthDecoder(
(decoder): ModuleList(
(0): ConvBlock(
(conv): Conv3x3(
(pad): ReflectionPad2d((1, 1, 1, 1))
(conv): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1))
)
(nonlin): ELU(alpha=1.0, inplace)
)
(1): ConvBlock(
(conv): Conv3x3(
(pad): ReflectionPad2d((1, 1, 1, 1))
(conv): Conv2d(512, 256, kernel_size=(3, 3), stride=(1, 1))
)
(nonlin): ELU(alpha=1.0, inplace)
)
(2): ConvBlock(
(conv): Conv3x3(
(pad): ReflectionPad2d((1, 1, 1, 1))
(conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1))
)
(nonlin): ELU(alpha=1.0, inplace)
)
(3): ConvBlock(
(conv): Conv3x3(
(pad): ReflectionPad2d((1, 1, 1, 1))
(conv): Conv2d(256, 128, kernel_size=(3, 3), stride=(1, 1))
)
(nonlin): ELU(alpha=1.0, inplace)
)
(4): ConvBlock(
(conv): Conv3x3(
(pad): ReflectionPad2d((1, 1, 1, 1))
(conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1))
)
(nonlin): ELU(alpha=1.0, inplace)
)
(5): ConvBlock(
(conv): Conv3x3(
(pad): ReflectionPad2d((1, 1, 1, 1))
(conv): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1))
)
(nonlin): ELU(alpha=1.0, inplace)
)
(6): ConvBlock(
(conv): Conv3x3(
(pad): ReflectionPad2d((1, 1, 1, 1))
(conv): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1))
)
(nonlin): ELU(alpha=1.0, inplace)
)
(7): ConvBlock(
(conv): Conv3x3(
(pad): ReflectionPad2d((1, 1, 1, 1))
(conv): Conv2d(96, 32, kernel_size=(3, 3), stride=(1, 1))
)
(nonlin): ELU(alpha=1.0, inplace)
)
(8): ConvBlock(
(conv): Conv3x3(
(pad): ReflectionPad2d((1, 1, 1, 1))
(conv): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1))
)
(nonlin): ELU(alpha=1.0, inplace)
)
(9): ConvBlock(
(conv): Conv3x3(
(pad): ReflectionPad2d((1, 1, 1, 1))
(conv): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1))
)
(nonlin): ELU(alpha=1.0, inplace)
)
(10): Conv3x3(
(pad): ReflectionPad2d((1, 1, 1, 1))
(conv): Conv2d(16, 1, kernel_size=(3, 3), stride=(1, 1))
)
(11): Conv3x3(
(pad): ReflectionPad2d((1, 1, 1, 1))
(conv): Conv2d(32, 1, kernel_size=(3, 3), stride=(1, 1))
)
(12): Conv3x3(
(pad): ReflectionPad2d((1, 1, 1, 1))
(conv): Conv2d(64, 1, kernel_size=(3, 3), stride=(1, 1))
)
(13): Conv3x3(
(pad): ReflectionPad2d((1, 1, 1, 1))
(conv): Conv2d(128, 1, kernel_size=(3, 3), stride=(1, 1))
)
)
(sigmoid): Sigmoid()
)
2 changes: 0 additions & 2 deletions README.md
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86 changes: 86 additions & 0 deletions Resnet Encoder
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ResnetEncoder(
(encoder): ResNet(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer2): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer4): Sequential(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(avgpool): AvgPool2d(kernel_size=7, stride=1, padding=0)
(fc): Linear(in_features=512, out_features=1000, bias=True)
)
)
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