lib/torch.rb in torch-rb-0.1.5 vs lib/torch.rb in torch-rb-0.1.6

@@ -27,28 +27,73 @@
 require "torch/optim/lr_scheduler/lr_scheduler"
 require "torch/optim/lr_scheduler/step_lr"
 
 # nn parameters
 require "torch/nn/parameter"
+require "torch/nn/utils"
 
 # nn containers
 require "torch/nn/module"
 require "torch/nn/sequential"
 
 # nn convolution layers
 require "torch/nn/convnd"
+require "torch/nn/conv1d"
 require "torch/nn/conv2d"
+require "torch/nn/conv3d"
+require "torch/nn/unfold"
+require "torch/nn/fold"
 
 # nn pooling layers
 require "torch/nn/max_poolnd"
+require "torch/nn/max_pool1d"
 require "torch/nn/max_pool2d"
+require "torch/nn/max_pool3d"
+require "torch/nn/max_unpoolnd"
+require "torch/nn/max_unpool1d"
+require "torch/nn/max_unpool2d"
+require "torch/nn/max_unpool3d"
 require "torch/nn/avg_poolnd"
+require "torch/nn/avg_pool1d"
 require "torch/nn/avg_pool2d"
+require "torch/nn/avg_pool3d"
+require "torch/nn/lp_poolnd"
+require "torch/nn/lp_pool1d"
+require "torch/nn/lp_pool2d"
 
+# nn padding layers
+require "torch/nn/reflection_padnd"
+require "torch/nn/reflection_pad1d"
+require "torch/nn/reflection_pad2d"
+require "torch/nn/replication_padnd"
+require "torch/nn/replication_pad1d"
+require "torch/nn/replication_pad2d"
+require "torch/nn/replication_pad3d"
+require "torch/nn/constant_padnd"
+require "torch/nn/constant_pad1d"
+require "torch/nn/constant_pad2d"
+require "torch/nn/constant_pad3d"
+require "torch/nn/zero_pad2d"
+
+# nn normalization layers
+require "torch/nn/batch_norm"
+require "torch/nn/batch_norm1d"
+require "torch/nn/batch_norm2d"
+require "torch/nn/batch_norm3d"
+require "torch/nn/group_norm"
+require "torch/nn/instance_norm"
+require "torch/nn/instance_norm1d"
+require "torch/nn/instance_norm2d"
+require "torch/nn/instance_norm3d"
+require "torch/nn/layer_norm"
+require "torch/nn/local_response_norm"
+
 # nn recurrent layers
 require "torch/nn/rnn_base"
 require "torch/nn/rnn"
+require "torch/nn/lstm"
+require "torch/nn/gru"
 
 # nn linear layers
 require "torch/nn/bilinear"
 require "torch/nn/identity"
 require "torch/nn/linear"
@@ -60,15 +105,21 @@
 require "torch/nn/dropout2d"
 require "torch/nn/dropout3d"
 require "torch/nn/feature_alpha_dropout"
 
 # nn activations
+require "torch/nn/hardshrink"
 require "torch/nn/leaky_relu"
+require "torch/nn/log_sigmoid"
 require "torch/nn/prelu"
 require "torch/nn/relu"
 require "torch/nn/sigmoid"
 require "torch/nn/softplus"
+require "torch/nn/softshrink"
+require "torch/nn/softsign"
+require "torch/nn/tanh"
+require "torch/nn/tanhshrink"
 
 # nn activations other
 require "torch/nn/log_softmax"
 require "torch/nn/softmax"
 require "torch/nn/softmax2d"
@@ -360,51 +411,9 @@
       randn(input.size, like_options(input, options))
     end
 
     def zeros_like(input, **options)
       zeros(input.size, like_options(input, options))
-    end
-
-    # --- begin operations ---
-
-    # TODO support out
-    def mean(input, dim = nil, keepdim: false)
-      if dim
-        _mean_dim(input, dim, keepdim)
-      else
-        _mean(input)
-      end
-    end
-
-    # TODO support dtype
-    def sum(input, dim = nil, keepdim: false)
-      if dim
-        _sum_dim(input, dim, keepdim)
-      else
-        _sum(input)
-      end
-    end
-
-    def topk(input, k)
-      _topk(input, k)
-    end
-
-    def max(input, dim = nil, keepdim: false, out: nil)
-      if dim
-        raise NotImplementedYet unless out
-        _max_out(out[0], out[1], input, dim, keepdim)
-      else
-        _max(input)
-      end
-    end
-
-    # TODO make dim keyword argument
-    def log_softmax(input, dim)
-      _log_softmax(input, dim)
-    end
-
-    def softmax(input, dim: nil)
-      _softmax(input, dim)
     end
 
     private
 
     def tensor_size(size)