Class: TorchVision::Models::VGG

Inherits:
Torch::NN::Module
  • Object
show all
Defined in:
lib/torchvision/models/vgg.rb

Constant Summary collapse

MODEL_URLS = 
{
  "vgg11" => "https://download.pytorch.org/models/vgg11-8a719046.pth",
  "vgg13" => "https://download.pytorch.org/models/vgg13-19584684.pth",
  "vgg16" => "https://download.pytorch.org/models/vgg16-397923af.pth",
  "vgg19" => "https://download.pytorch.org/models/vgg19-dcbb9e9d.pth",
  "vgg11_bn" => "https://download.pytorch.org/models/vgg11_bn-6002323d.pth",
  "vgg13_bn" => "https://download.pytorch.org/models/vgg13_bn-abd245e5.pth",
  "vgg16_bn" => "https://download.pytorch.org/models/vgg16_bn-6c64b313.pth",
  "vgg19_bn" => "https://download.pytorch.org/models/vgg19_bn-c79401a0.pth"
}
CFGS = 
{
  "A" => [64, "M", 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"],
  "B" => [64, 64, "M", 128, 128, "M", 256, 256, "M", 512, 512, "M", 512, 512, "M"],
  "D" => [64, 64, "M", 128, 128, "M", 256, 256, 256, "M", 512, 512, 512, "M", 512, 512, 512, "M"],
  "E" => [64, 64, "M", 128, 128, "M", 256, 256, 256, 256, "M", 512, 512, 512, 512, "M", 512, 512, 512, 512, "M"]
}

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(features, num_classes: 1000, init_weights: true) ⇒ VGG

Returns a new instance of VGG.



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# File 'lib/torchvision/models/vgg.rb', line 15

def initialize(features, num_classes: 1000, init_weights: true)
  super()
  @features = features
  @avgpool = Torch::NN::AdaptiveAvgPool2d.new([7, 7])
  @classifier = Torch::NN::Sequential.new(
    Torch::NN::Linear.new(512 * 7 * 7, 4096),
    Torch::NN::ReLU.new(inplace: true),
    Torch::NN::Dropout.new,
    Torch::NN::Linear.new(4096, 4096),
    Torch::NN::ReLU.new(inplace: true),
    Torch::NN::Dropout.new,
    Torch::NN::Linear.new(4096, num_classes)
  )
  _initialize_weights if init_weights
end

Class Method Details

.make_layers(cfg, batch_norm) ⇒ Object 



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# File 'lib/torchvision/models/vgg.rb', line 73

def self.make_layers(cfg, batch_norm)
  layers = []
  in_channels = 3
  cfg.each do |v|
    if v == "M"
      layers += [Torch::NN::MaxPool2d.new(2, stride: 2)]
    else
      conv2d = Torch::NN::Conv2d.new(in_channels, v, 3, padding: 1)
      if batch_norm
        layers += [conv2d, Torch::NN::BatchNorm2d.new(v), Torch::NN::ReLU.new(inplace: true)]
      else
        layers += [conv2d, Torch::NN::ReLU.new(inplace: true)]
      end
      in_channels = v
    end
  end
  Torch::NN::Sequential.new(*layers)
end

.make_model(arch, cfg, batch_norm, pretrained: false, **kwargs) ⇒ Object 



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# File 'lib/torchvision/models/vgg.rb', line 62

def self.make_model(arch, cfg, batch_norm, pretrained: false, **kwargs)
  kwargs[:init_weights] = false if pretrained
  model = VGG.new(make_layers(CFGS[cfg], batch_norm), **kwargs)
  if pretrained
    url = MODEL_URLS[arch]
    state_dict = Torch::Hub.load_state_dict_from_url(url)
    model.load_state_dict(state_dict)
  end
  model
end

Instance Method Details

#_initialize_weights ⇒ Object 



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# File 'lib/torchvision/models/vgg.rb', line 39

def _initialize_weights
  modules.each do |m|
    case m
    when Torch::NN::Conv2d
      Torch::NN::Init.kaiming_normal!(m.weight, mode: "fan_out", nonlinearity: "relu")
      Torch::NN::Init.constant!(m.bias, 0) if m.bias
    when Torch::NN::BatchNorm2d
      Torch::NN::Init.constant!(m.weight, 1)
      Torch::NN::Init.constant!(m.bias, 0)
    when Torch::NN::Linear
      Torch::NN::Init.normal!(m.weight, mean: 0, std: 0.01)
      Torch::NN::Init.constant!(m.bias, 0)
    end
  end
end

#forward(x) ⇒ Object 



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# File 'lib/torchvision/models/vgg.rb', line 31

def forward(x)
  x = @features.call(x)
  x = @avgpool.call(x)
  x = Torch.flatten(x, 1)
  x = @classifier.call(x)
  x
end