Class: Torch::NN::Module
- Inherits:
-
Object
show all
- Includes:
- Utils
- Defined in:
- lib/torch/nn/module.rb
Direct Known Subclasses
AdaptiveAvgPoolNd, AdaptiveMaxPoolNd, AvgPoolNd, BatchNorm, Bilinear, ConstantPadNd, ConvNd, CosineSimilarity, DropoutNd, Embedding, EmbeddingBag, Fold, GroupNorm, Hardshrink, Identity, LPPoolNd, LayerNorm, LeakyReLU, Linear, LocalResponseNorm, LogSigmoid, LogSoftmax, Loss, MaxPoolNd, MaxUnpoolNd, ModuleList, MultiheadAttention, PReLU, PairwiseDistance, ParameterList, RNNBase, ReLU, ReflectionPadNd, ReplicationPadNd, Sequential, Sigmoid, Softmax, Softmax2d, Softmin, Softplus, Softshrink, Softsign, Tanh, Tanhshrink, Transformer, TransformerDecoder, TransformerDecoderLayer, TransformerEncoder, TransformerEncoderLayer, Unfold, Upsample
Instance Attribute Summary collapse
Instance Method Summary
collapse
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#_apply(fn) ⇒ Object
-
#add_module(name, mod) ⇒ Object
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#apply(fn) ⇒ Object
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#buffers ⇒ Object
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#call(*input, **kwargs) ⇒ Object
-
#children ⇒ Object
-
#cpu ⇒ Object
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#cuda ⇒ Object
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#deep_dup ⇒ Object
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#double ⇒ Object
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#eval ⇒ Object
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#float ⇒ Object
-
#forward ⇒ Object
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#half ⇒ Object
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#initialize ⇒ Module
constructor
A new instance of Module.
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#inspect ⇒ Object
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#load_state_dict(state_dict, strict: true) ⇒ Object
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#method_missing(method, *args, &block) ⇒ Object
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#modules ⇒ Object
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#named_buffers ⇒ Object
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#named_children ⇒ Object
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#named_modules(memo: nil, prefix: "") ⇒ Object
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#named_parameters(prefix: "", recurse: true) ⇒ Object
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#parameters ⇒ Object
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#register_buffer(name, tensor) ⇒ Object
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#register_parameter(name, param) ⇒ Object
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#requires_grad!(requires_grad: true) ⇒ Object
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#respond_to?(method, include_private = false) ⇒ Boolean
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#share_memory ⇒ Object
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#state_dict(destination: nil, prefix: "") ⇒ Object
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#to(device) ⇒ Object
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#train(mode = true) ⇒ Object
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#type(dst_type) ⇒ Object
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#zero_grad ⇒ Object
Methods included from Utils
#_activation_fn, #_clones, #_ntuple, #_pair, #_quadrupal, #_single, #_triple
Constructor Details
#initialize ⇒ Module
Returns a new instance of Module.
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# File 'lib/torch/nn/module.rb', line 8
def initialize
@training = true
@parameters = {}
@buffers = {}
@modules = {}
end
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Dynamic Method Handling
This class handles dynamic methods through the method_missing method
#method_missing(method, *args, &block) ⇒ Object
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# File 'lib/torch/nn/module.rb', line 288
def method_missing(method, *args, &block)
name = method.to_s
if named_parameters.key?(name)
named_parameters[name]
elsif named_buffers.key?(name)
named_buffers[name]
elsif named_modules.key?(name)
named_modules[name]
elsif method.end_with?("=") && named_modules.key?(method[0..-2])
if instance_variable_defined?("@#{method[0..-2]}")
instance_variable_set("@#{method[0..-2]}", *args)
else
raise NotImplementedYet
end
else
super
end
end
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Instance Attribute Details
#training ⇒ Object
Returns the value of attribute training.
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# File 'lib/torch/nn/module.rb', line 6
def training
@training
end
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Instance Method Details
#_apply(fn) ⇒ Object
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# File 'lib/torch/nn/module.rb', line 35
def _apply(fn)
children.each do |mod|
mod._apply(fn)
end
instance_variables.each do |key|
param = instance_variable_get(key)
if param.is_a?(Parameter)
param_applied = nil
Torch.no_grad do
param_applied = fn.call(param)
end
instance_variable_set(key, Parameter.new(param_applied, requires_grad: param.requires_grad))
if param.grad
grad_applied = nil
Torch.no_grad do
grad_applied = fn.call(param.grad)
end
instance_variable_get(key).grad = grad_applied.requires_grad!(param.grad.requires_grad)
end
end
end
@buffers.each_key do |k|
buf = @buffers[k]
unless buf.nil?
@buffers[k] = fn.call(buf)
instance_variable_set("@#{k}", @buffers[k])
end
end
self
end
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#add_module(name, mod) ⇒ Object
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# File 'lib/torch/nn/module.rb', line 30
def add_module(name, mod)
@modules[name] = mod
end
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#apply(fn) ⇒ Object
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# File 'lib/torch/nn/module.rb', line 72
def apply(fn)
children.each do |mod|
mod.apply(fn)
end
fn.call(self)
self
end
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#buffers ⇒ Object
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# File 'lib/torch/nn/module.rb', line 189
def buffers
named_buffers.values
end
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#call(*input, **kwargs) ⇒ Object
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# File 'lib/torch/nn/module.rb', line 114
def call(*input, **kwargs)
forward(*input, **kwargs)
end
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#children ⇒ Object
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# File 'lib/torch/nn/module.rb', line 197
def children
named_children.values
end
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#cpu ⇒ Object
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# File 'lib/torch/nn/module.rb', line 85
def cpu
_apply ->(t) { t.cpu }
end
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#cuda ⇒ Object
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# File 'lib/torch/nn/module.rb', line 81
def cuda
_apply ->(t) { t.cuda }
end
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#deep_dup ⇒ Object
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# File 'lib/torch/nn/module.rb', line 283
def deep_dup
memo = {}
dup_value(self, memo)
end
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#double ⇒ Object
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# File 'lib/torch/nn/module.rb', line 97
def double
_apply ->(t) { t.floating_point? ? t.double : t }
end
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#eval ⇒ Object
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# File 'lib/torch/nn/module.rb', line 243
def eval
train(false)
end
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#float ⇒ Object
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# File 'lib/torch/nn/module.rb', line 93
def float
_apply ->(t) { t.floating_point? ? t.float : t }
end
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#forward ⇒ Object
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# File 'lib/torch/nn/module.rb', line 15
def forward
raise NotImplementedError
end
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#half ⇒ Object
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# File 'lib/torch/nn/module.rb', line 101
def half
_apply ->(t) { t.floating_point? ? t.half : t }
end
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#inspect ⇒ Object
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# File 'lib/torch/nn/module.rb', line 267
def inspect
name = self.class.name.split("::").last
if named_children.empty?
"#{name}(#{})"
else
str = String.new
str << "#{name}(\n"
named_children.each do |name, mod|
mod_str = mod.inspect
mod_str = mod_str.lines.join(" ")
str << " (#{name}): #{mod_str}\n"
end
str << ")"
end
end
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#load_state_dict(state_dict, strict: true) ⇒ Object
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# File 'lib/torch/nn/module.rb', line 129
def load_state_dict(state_dict, strict: true)
raise "strict: false not implemented yet" unless strict
missing_keys = []
unexpected_keys = []
error_msgs = []
_load = lambda do |mod, prefix = ""|
local_metadata = {}
mod.send(:load_from_state_dict, state_dict, prefix, local_metadata, true, missing_keys, unexpected_keys, error_msgs)
mod.named_children.each do |name, child|
_load.call(child, prefix + name + ".") unless child.nil?
end
end
_load.call(self)
if strict
if unexpected_keys.any?
error_msgs << "Unexpected key(s) in state_dict: #{unexpected_keys.join(", ")}"
end
if missing_keys.any?
error_msgs << "Missing key(s) in state_dict: #{missing_keys.join(", ")}"
end
end
if error_msgs.any?
raise Error, error_msgs[0]
end
nil
end
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#modules ⇒ Object
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# File 'lib/torch/nn/module.rb', line 213
def modules
named_modules.values
end
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#named_buffers ⇒ Object
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# File 'lib/torch/nn/module.rb', line 193
def named_buffers
@buffers || {}
end
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#named_children ⇒ Object
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# File 'lib/torch/nn/module.rb', line 201
def named_children
modules = {}
instance_variables.each do |name|
mod = instance_variable_get(name)
modules[name[1..-1]] = mod if mod.is_a?(Module)
end
@modules.each do |name, mod|
modules[name] = mod
end
modules
end
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#named_modules(memo: nil, prefix: "") ⇒ Object
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# File 'lib/torch/nn/module.rb', line 218
def named_modules(memo: nil, prefix: "")
ret = {}
memo ||= Set.new
unless memo.include?(self)
memo << self
ret[prefix] = self
named_children.each do |name, mod|
next unless mod.is_a?(Module)
submodule_prefix = prefix + (!prefix.empty? ? "." : "") + name
mod.named_modules(memo: memo, prefix: submodule_prefix).each do |m|
ret[m[0]] = m[1]
end
end
end
ret
end
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#named_parameters(prefix: "", recurse: true) ⇒ Object
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# File 'lib/torch/nn/module.rb', line 172
def named_parameters(prefix: "", recurse: true)
params = {}
if recurse
named_children.each do |name, mod|
params.merge!(mod.named_parameters(prefix: "#{prefix}#{name}.", recurse: recurse))
end
end
instance_variables.each do |name|
param = instance_variable_get(name)
params[[prefix, name[1..-1]].join] = param if param.is_a?(Parameter)
end
@parameters.each do |name, param|
params[[prefix, name].join] = param if param
end
params
end
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#parameters ⇒ Object
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# File 'lib/torch/nn/module.rb', line 168
def parameters
named_parameters.values
end
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#register_buffer(name, tensor) ⇒ Object
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# File 'lib/torch/nn/module.rb', line 19
def register_buffer(name, tensor)
@buffers[name] = tensor
instance_variable_set("@#{name}", tensor)
end
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#register_parameter(name, param) ⇒ Object
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# File 'lib/torch/nn/module.rb', line 25
def register_parameter(name, param)
@parameters[name] = param
end
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#requires_grad!(requires_grad: true) ⇒ Object
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# File 'lib/torch/nn/module.rb', line 247
def requires_grad!(requires_grad: true)
parameters.each do |p|
p.requires_grad!(requires_grad)
end
self
end
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#respond_to?(method, include_private = false) ⇒ Boolean
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# File 'lib/torch/nn/module.rb', line 307
def respond_to?(method, include_private = false)
name = method.to_s
named_parameters.key?(name) || named_buffers.key?(name) || named_modules.key?(name) || super
end
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#share_memory ⇒ Object
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# File 'lib/torch/nn/module.rb', line 263
def share_memory
_apply ->(t) { t.share_memory! }
end
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#state_dict(destination: nil, prefix: "") ⇒ Object
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# File 'lib/torch/nn/module.rb', line 118
def state_dict(destination: nil, prefix: "")
destination ||= {}
save_to_state_dict(destination, prefix: prefix)
named_children.each do |name, mod|
next unless mod
mod.state_dict(destination: destination, prefix: prefix + name + ".")
end
destination
end
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#to(device) ⇒ Object
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# File 'lib/torch/nn/module.rb', line 106
def to(device)
convert = lambda do |t|
t.to(device)
end
_apply(convert)
end
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#train(mode = true) ⇒ Object
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# File 'lib/torch/nn/module.rb', line 235
def train(mode = true)
@training = mode
children.each do |mod|
mod.train(mode)
end
self
end
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#type(dst_type) ⇒ Object
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# File 'lib/torch/nn/module.rb', line 89
def type(dst_type)
_apply ->(t) { t.type(dst_type) }
end
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#zero_grad ⇒ Object
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# File 'lib/torch/nn/module.rb', line 254
def zero_grad
parameters.each do |param|
if param.grad
param.grad.detach!
param.grad.zero!
end
end
end
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