1 files changed, 71 insertions, 5 deletions
diff --git a/nerv/nn/layer_dag.lua b/nerv/nn/layer_dag.lua
index 8e30216..73bb77d 100644
--- a/nerv/nn/layer_dag.lua
+++ b/nerv/nn/layer_dag.lua
@@ -79,7 +79,7 @@ function DAGLayer:__init(id, global_conf, layer_conf)
 end
 
 table.insert(parsed_conn,
- {{ref_from, port_from}, {ref_to, port_to}})
+ {{ref_from, port_from}, {ref_to, port_to}})
 table.insert(ref_from.next_layers, ref_to) -- add edge
 ref_to.in_deg = ref_to.in_deg + 1 -- increase the in-degree of the target layer
 end
@@ -112,7 +112,7 @@ function DAGLayer:__init(id, global_conf, layer_conf)
 end
 end
 for i = 1, #queue do
- nerv.info("enqueued layer: %s", queue[i].layer.id)
+ nerv.info("enqueued layer: %s %s", queue[i].layer, queue[i].layer.id)
 end
 
 for id, ref in pairs(layers) do
@@ -125,6 +125,7 @@ function DAGLayer:__init(id, global_conf, layer_conf)
 self.layers = layers
 self.inputs = inputs
 self.outputs = outputs
+ self.id = id
 self.dim_in = dim_in
 self.dim_out = dim_out
 self.parsed_conn = parsed_conn
@@ -139,8 +140,11 @@ function DAGLayer:init(batch_size)
 ref_from, port_from = unpack(conn[1])
 ref_to, port_to = unpack(conn[2])
 _, output_dim = ref_from.layer:get_dim()
- local mid = self.gconf.cumat_type(batch_size,
- output_dim[port_from])
+ local dim = 1
+ if output_dim[port_from] > 0 then
+ dim = output_dim[port_from]
+ end
+ local mid = self.gconf.cumat_type(batch_size, dim)
 local err_mid = mid:create()
 
 ref_from.outputs[port_from] = mid
@@ -175,8 +179,38 @@ function DAGLayer:init(batch_size)
 end
 end
 
+function DAGLayer:batch_resize(batch_size)
+ self.gconf.batch_size = batch_size
+
+ for i, conn in ipairs(self.parsed_conn) do
+ local _, output_dim
+ local ref_from, port_from, ref_to, port_to
+ ref_from, port_from = unpack(conn[1])
+ ref_to, port_to = unpack(conn[2])
+ _, output_dim = ref_from.layer:get_dim()
+
+ if ref_from.outputs[port_from]:nrow() ~= batch_size and output_dim[port_from] > 0 then
+ local mid = self.gconf.cumat_type(batch_size, output_dim[port_from])
+ local err_mid = mid:create()
+
+ ref_from.outputs[port_from] = mid
+ ref_to.inputs[port_to] = mid
+
+ ref_from.err_inputs[port_from] = err_mid
+ ref_to.err_outputs[port_to] = err_mid
+ end
+ end
+ for id, ref in pairs(self.layers) do
+ ref.layer:batch_resize(batch_size)
+ end
+ collectgarbage("collect")
+end
+
 function DAGLayer:set_inputs(input)
 for i = 1, #self.dim_in do
+ if input[i] == nil then
+ nerv.error("some input is not provided");
+ end
 local layer = self.inputs[i][1]
 local port = self.inputs[i][2]
 layer.inputs[port] = input[i]
@@ -185,6 +219,9 @@ end
 
 function DAGLayer:set_outputs(output)
 for i = 1, #self.dim_out do
+ if output[i] == nil then
+ nerv.error("some output is not provided");
+ end
 local layer = self.outputs[i][1]
 local port = self.outputs[i][2]
 layer.outputs[port] = output[i]
@@ -221,11 +258,13 @@ end
 function DAGLayer:propagate(input, output)
 self:set_inputs(input)
 self:set_outputs(output)
+ local ret = false
 for i = 1, #self.queue do
 local ref = self.queue[i]
 -- print(ref.layer.id)
- ref.layer:propagate(ref.inputs, ref.outputs)
+ ret = ref.layer:propagate(ref.inputs, ref.outputs)
 end
+ return ret
 end
 
 function DAGLayer:back_propagate(bp_err, next_bp_err, input, output)
@@ -247,3 +286,30 @@ function DAGLayer:get_params()
 end
 return nerv.ParamRepo.merge(param_repos)
 end
+
+DAGLayer.PORT_TYPES = {
+ INPUT = {},
+ OUTPUT = {},
+ ERR_INPUT = {},
+ ERR_OUTPUT = {}
+}
+
+function DAGLayer:get_intermediate(id, port_type)
+ if id == "<input>" or id == "<output>" then
+ nerv.error("an actual real layer id is expected")
+ end
+ local layer = self.layers[id]
+ if layer == nil then
+ nerv.error("layer id %s not found", id)
+ end
+ if port_type == DAGLayer.PORT_TYPES.INPUT then
+ return layer.inputs
+ elseif port_type == DAGLayer.PORT_TYPES.OUTPUT then
+ return layer.outputs
+ elseif port_type == DAGLayer.PORT_TYPES.ERR_INPUT then
+ return layer.err_inputs
+ elseif port_type == DAGLayer.PORT_TYPES.ERR_OUTPUT then
+ return layer.err_outputs
+ end
+ nerv.error("unrecognized port type")
+end