Class: Transformers::Vit::ViTForImageClassification

Inherits:
ViTPreTrainedModel show all
Defined in:
lib/transformers/models/vit/modeling_vit.rb

Instance Attribute Summary

Attributes inherited from PreTrainedModel

#config

Instance Method Summary collapse

Methods inherited from ViTPreTrainedModel

#_init_weights

Methods inherited from PreTrainedModel

#_backward_compatibility_gradient_checkpointing, #_init_weights, #_initialize_weights, #base_model, #can_generate, #dequantize, #dummy_inputs, #framework, from_pretrained, #get_input_embeddings, #get_output_embeddings, #init_weights, #post_init, #prune_heads, #set_input_embeddings, #tie_weights, #warn_if_padding_and_no_attention_mask

Methods included from ClassAttribute

#class_attribute

Methods included from ModuleUtilsMixin

#device, #get_extended_attention_mask, #get_head_mask

Constructor Details

#initialize(config) ⇒ ViTForImageClassification

Returns a new instance of ViTForImageClassification.



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# File 'lib/transformers/models/vit/modeling_vit.rb', line 449

def initialize(config)
  super(config)

  @num_labels = config.num_labels
  @vit = ViTModel.new(config, add_pooling_layer: false)

  # Classifier head
  @classifier = config.num_labels > 0 ? Torch::NN::Linear.new(config.hidden_size, config.num_labels) : Torch::NN::Identity.new

  # Initialize weights and apply final processing
  post_init
end

Instance Method Details

#forward(pixel_values: nil, head_mask: nil, labels: nil, output_attentions: nil, output_hidden_states: nil, interpolate_pos_encoding: nil, return_dict: nil) ⇒ Object 



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# File 'lib/transformers/models/vit/modeling_vit.rb', line 462

def forward(
  pixel_values: nil,
  head_mask: nil,
  labels: nil,
  output_attentions: nil,
  output_hidden_states: nil,
  interpolate_pos_encoding: nil,
  return_dict: nil
)
  return_dict = !return_dict.nil? ? return_dict : @config.use_return_dict

  outputs = @vit.(
    pixel_values: pixel_values,
    head_mask: head_mask,
    output_attentions: output_attentions,
    output_hidden_states: output_hidden_states,
    interpolate_pos_encoding: interpolate_pos_encoding,
    return_dict: return_dict
  )

  sequence_output = outputs[0]

  logits = @classifier.(sequence_output[0.., 0, 0..])

  loss = nil
  if !labels.nil?
    raise Todo
  end

  if !return_dict
    raise Todo
  end

  ImageClassifierOutput.new(
    loss: loss,
    logits: logits,
    hidden_states: outputs.hidden_states,
    attentions: outputs.attentions
  )
end