lib/torch.rb in torch-rb-0.3.6 vs lib/torch.rb in torch-rb-0.3.7

@@ -172,10 +172,13 @@
 require "torch/nn/poisson_nll_loss"
 require "torch/nn/smooth_l1_loss"
 require "torch/nn/soft_margin_loss"
 require "torch/nn/triplet_margin_loss"
 
+# nn vision
+require "torch/nn/upsample"
+
 # nn other
 require "torch/nn/functional"
 require "torch/nn/init"
 
 # utils
@@ -194,10 +197,36 @@
     def message
       "This feature has not been implemented yet. Consider submitting a PR."
     end
   end
 
+  # legacy
+  # but may make it easier to port tutorials
+  module Autograd
+    class Variable
+      def self.new(x)
+        raise ArgumentError, "Variable data has to be a tensor, but got #{x.class.name}" unless x.is_a?(Tensor)
+        warn "[torch] The Variable API is deprecated. Use tensors with requires_grad: true instead."
+        x
+      end
+    end
+  end
+
+  # TODO move to C++
+  class ByteStorage
+    # private
+    attr_reader :bytes
+
+    def initialize(bytes)
+      @bytes = bytes
+    end
+
+    def self.from_buffer(bytes)
+      new(bytes)
+    end
+  end
+
   # keys: https://pytorch.org/docs/stable/tensor_attributes.html#torch.torch.dtype
   # values: https://github.com/pytorch/pytorch/blob/master/c10/core/ScalarType.h
   DTYPE_TO_ENUM = {
     uint8: 0,
     int8: 1,
@@ -222,21 +251,27 @@
     qint32: 14,
     bfloat16: 15
   }
   ENUM_TO_DTYPE = DTYPE_TO_ENUM.map(&:reverse).to_h
 
+  TENSOR_TYPE_CLASSES = []
+
   def self._make_tensor_class(dtype, cuda = false)
     cls = Class.new
     device = cuda ? "cuda" : "cpu"
     cls.define_singleton_method("new") do |*args|
       if args.size == 1 && args.first.is_a?(Tensor)
         args.first.send(dtype).to(device)
+      elsif args.size == 1 && args.first.is_a?(ByteStorage) && dtype == :uint8
+        bytes = args.first.bytes
+        Torch._from_blob(bytes, [bytes.bytesize], TensorOptions.new.dtype(DTYPE_TO_ENUM[dtype]))
       elsif args.size == 1 && args.first.is_a?(Array)
         Torch.tensor(args.first, dtype: dtype, device: device)
       else
         Torch.empty(*args, dtype: dtype, device: device)
       end
     end
+    TENSOR_TYPE_CLASSES << cls
     cls
   end
 
   DTYPE_TO_CLASS = {
     float32: "FloatTensor",