Class: TorchVision::Models::ResNet

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

Constant Summary collapse

MODEL_URLS = 
{
  "resnet18" => "https://download.pytorch.org/models/resnet18-f37072fd.pth",
  "resnet34" => "https://download.pytorch.org/models/resnet34-b627a593.pth",
  "resnet50" => "https://download.pytorch.org/models/resnet50-0676ba61.pth",
  "resnet101" => "https://download.pytorch.org/models/resnet101-63fe2227.pth",
  "resnet152" => "https://download.pytorch.org/models/resnet152-394f9c45.pth",
  "resnext50_32x4d" => "https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth",
  "resnext101_32x8d" => "https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth",
  "wide_resnet50_2" => "https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth",
  "wide_resnet101_2" => "https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth"
}

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(block, layers, num_classes = 1000, zero_init_residual: false, groups: 1, width_per_group: 64, replace_stride_with_dilation: nil, norm_layer: nil) ⇒ ResNet

Returns a new instance of ResNet.



16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
 
# File 'lib/torchvision/models/resnet.rb', line 16

def initialize(block, layers, num_classes = 1000, zero_init_residual: false,
  groups: 1, width_per_group: 64, replace_stride_with_dilation: nil, norm_layer: nil)

  super()
  norm_layer ||= Torch::NN::BatchNorm2d
  @norm_layer = norm_layer

  @inplanes = 64
  @dilation = 1
  if replace_stride_with_dilation.nil?
    # each element in the tuple indicates if we should replace
    # the 2x2 stride with a dilated convolution instead
    replace_stride_with_dilation = [false, false, false]
  end
  if replace_stride_with_dilation.length != 3
    raise ArgumentError, "replace_stride_with_dilation should be nil or a 3-element tuple, got #{replace_stride_with_dilation}"
  end
  @groups = groups
  @base_width = width_per_group
  @conv1 = Torch::NN::Conv2d.new(3, @inplanes, 7, stride: 2, padding: 3, bias: false)
  @bn1 = norm_layer.new(@inplanes)
  @relu = Torch::NN::ReLU.new(inplace: true)
  @maxpool = Torch::NN::MaxPool2d.new(3, stride: 2, padding: 1)
  @layer1 = _make_layer(block, 64, layers[0])
  @layer2 = _make_layer(block, 128, layers[1], stride: 2, dilate: replace_stride_with_dilation[0])
  @layer3 = _make_layer(block, 256, layers[2], stride: 2, dilate: replace_stride_with_dilation[1])
  @layer4 = _make_layer(block, 512, layers[3], stride: 2, dilate: replace_stride_with_dilation[2])
  @avgpool = Torch::NN::AdaptiveAvgPool2d.new([1, 1])
  @fc = Torch::NN::Linear.new(512 * block.expansion, num_classes)

  modules.each do |m|
    case m
    when Torch::NN::Conv2d
      Torch::NN::Init.kaiming_normal!(m.weight, mode: "fan_out", nonlinearity: "relu")
    when Torch::NN::BatchNorm2d, Torch::NN::GroupNorm
      Torch::NN::Init.constant!(m.weight, 1)
      Torch::NN::Init.constant!(m.bias, 0)
    end
  end

  # Zero-initialize the last BN in each residual branch,
  # so that the residual branch starts with zeros, and each residual block behaves like an identity.
  # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
  if zero_init_residual
    modules.each do |m|
      case m
      when Bottleneck
        Torch::NN::Init.constant!(m.bn3.weight, 0)
      when BasicBlock
        Torch::NN::Init.constant!(m.bn2.weight, 0)
      end
    end
  end
end

Class Method Details

.make_model(arch, block, layers, pretrained: false, **kwargs) ⇒ Object 



118
119
120
121
122
123
124
125
126
 
# File 'lib/torchvision/models/resnet.rb', line 118

def self.make_model(arch, block, layers, pretrained: false, **kwargs)
  model = ResNet.new(block, layers, **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

#_forward_impl(x) ⇒ Object 



96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
 
# File 'lib/torchvision/models/resnet.rb', line 96

def _forward_impl(x)
  x = @conv1.call(x)
  x = @bn1.call(x)
  x = @relu.call(x)
  x = @maxpool.call(x)

  x = @layer1.call(x)
  x = @layer2.call(x)
  x = @layer3.call(x)
  x = @layer4.call(x)

  x = @avgpool.call(x)
  x = Torch.flatten(x, 1)
  x = @fc.call(x)

  x
end

#_make_layer(block, planes, blocks, stride: 1, dilate: false) ⇒ Object 



71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
 
# File 'lib/torchvision/models/resnet.rb', line 71

def _make_layer(block, planes, blocks, stride: 1, dilate: false)
  norm_layer = @norm_layer
  downsample = nil
  previous_dilation = @dilation
  if dilate
    @dilation *= stride
    stride = 1
  end
  if stride != 1 || @inplanes != planes * block.expansion
    downsample = Torch::NN::Sequential.new(
      Torch::NN::Conv2d.new(@inplanes, planes * block.expansion, 1, stride: stride, bias: false),
      norm_layer.new(planes * block.expansion)
    )
  end

  layers = []
  layers << block.new(@inplanes, planes, stride: stride, downsample: downsample, groups: @groups, base_width: @base_width, dilation: previous_dilation, norm_layer: norm_layer)
  @inplanes = planes * block.expansion
  (blocks - 1).times do
    layers << block.new(@inplanes, planes, groups: @groups, base_width: @base_width, dilation: @dilation, norm_layer: norm_layer)
  end

  Torch::NN::Sequential.new(*layers)
end

#forward(x) ⇒ Object 



114
115
116
 
# File 'lib/torchvision/models/resnet.rb', line 114

def forward(x)
  _forward_impl(x)
end